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

Plasma proteomics in children with new-onset type 1 diabetes identifies new potential biomarkers of partial remission

Partial remission (PR) occurs in only half of people with new-onset type 1 diabetes (T1D) and corresponds to a transient period characterized by low daily insulin needs, low glycemic fluctuations and increased endogenous insulin secretion. While identification of people with newly-onset T1D and significant residual beta-cell function may foster patient-specific interventions, reliable predictive biomarkers of PR occurrence currently lack. We analyzed the plasma of children with new-onset T1D to identify biomarkers present at diagnosis that predicted PR at 3 months post-diagnosis. We first performed an extensive shotgun proteomic analysis using Liquid Chromatography-Tandem-Mass-Spectrometry (LCMS/MS) on the plasma of 16 children with new-onset T1D and quantified 98 proteins significantly correlating with Insulin-Dose Adjusted glycated hemoglobin A1c score (IDAA1C). We next applied a series of both qualitative and statistical filters and selected protein candidates that were associated to pathophysiological mechanisms related to T1D. Finally, we translationally verified several of the candidates using single-shot targeted proteomic (PRM method) on raw plasma. Taken together, we identified plasma biomarkers present at diagnosis that may predict the occurrence of PR in a single mass-spectrometry run. We believe that the identification of new predictive biomarkers of PR and β-cell function is key to stratify people with new-onset T1D for β-cell preservation therapies.

Serum proteomics of mother-infant dyads carrying HLA-conferred type 1 diabetes risk

In-utero and dietary factors make important contributions toward health and development in early childhood. In this respect, serum proteomics of maturing infants can provide insights into studies of childhood diseases, which together with perinatal proteomes could reveal further biological perspectives. Accordingly, to determine differences between feeding groups and changes in infancy, serum proteomics analyses of mother-infant dyads with HLA-conferred susceptibility to type 1 diabetes (n = 22), weaned to either an extensively hydrolyzed or regular cow's milk formula, were made. The LC-MS/MS analyses included samples from the beginning of third trimester, the time of delivery, 3 months postpartum, cord blood, and samples from the infants at 3, 6, 9, and 12 months. Correlations between ranked protein intensities were detected within the dyads, together with perinatal and age-related changes. Comparison with intestinal permeability data revealed a number of significant correlations, which could merit further consideration in this context.

Molecular-Level Dysregulation of Insulin Pathways and Inflammatory Processes in Peripheral Blood Mononuclear Cells by Circadian Misalignment

Circadian misalignment due to night work has been associated with an elevated risk for chronic diseases. We investigated the effects of circadian misalignment using shotgun protein profiling of peripheral blood mononuclear cells taken from healthy humans during a constant routine protocol, which was conducted immediately after participants had been subjected to a 3-day simulated night shift schedule or a 3-day simulated day shift schedule. By comparing proteomic profiles between the simulated shift conditions, we identified proteins and pathways that are associated with the effects of circadian misalignment and observed that insulin regulation pathways and inflammation-related proteins displayed markedly different temporal patterns after simulated night shift. Further, by integrating the proteomic profiles with previously assessed metabolomic profiles in a network-based approach, we found key associations between circadian dysregulation of protein-level pathways and metabolites of interest in the context of chronic metabolic diseases. Endogenous circadian rhythms in circulating glucose and insulin differed between the simulated shift conditions. Overall, our results suggest that circadian misalignment is associated with a tug of war between central clock mechanisms controlling insulin secretion and peripheral clock mechanisms regulating insulin sensitivity, which may lead to adverse long-term outcomes such as diabetes and obesity. Our study provides a molecular-level mechanism linking circadian misalignment and adverse long-term health consequences of night work.

Decrease in multiple complement proteins associated with development of islet autoimmunity and type 1 diabetes

Type 1 diabetes (T1D) is a chronic condition caused by autoimmune destruction of the insulin-producing pancreatic β cells. While it is known that gene-environment interactions play a key role in triggering the autoimmune process leading to T1D, the pathogenic mechanism leading to the appearance of islet autoantibodies-biomarkers of autoimmunity-is poorly understood. Here we show that disruption of the complement system precedes the detection of islet autoantibodies and persists through disease onset. Our results suggest that children who exhibit islet autoimmunity and progress to clinical T1D have lower complement protein levels relative to those who do not progress within a similar time frame. Thus, the complement pathway, an understudied mechanistic and therapeutic target in T1D, merits increased attention for use as protein biomarkers of prediction and potentially prevention of T1D.

A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort

Background

Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the concept of integrated parallel multi-omics and employed a novel data augmentation approach which identified promising candidate biomarkers from a small cohort of high-risk T1D subjects. We now validate selected biomarkers to generate a potential composite signature of T1D risk.

Methods

Twelve candidate biomarkers, which were identified in the augmented data and selected based on their fold-change relative to healthy controls and cross-reference to proteomics data previously obtained in the expansive TEDDY and DAISY cohorts, were measured in the original samples by ELISA.

Results

All 12 biomarkers had established connections with lipid/lipoprotein metabolism, immune function, inflammation, and diabetes, but only 7 were found to be markedly changed in the high-risk subjects compared to the healthy controls: ApoC1 and PON1 were reduced while CETP, CD36, FGFR1, IGHM, PCSK9, SOD1, and VCAM1 were elevated.

Conclusions

Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.

Targeted serum proteomics of longitudinal samples from newly diagnosed youth with type 1 diabetes distinguishes markers of disease and C-peptide trajectory

AIMS/HYPOTHESIS

There is a growing need for markers that could help indicate the decline in beta cell function and recognise the need and efficacy of intervention in type 1 diabetes. Measurements of suitably selected serum markers could potentially provide a non-invasive and easily applicable solution to this challenge. Accordingly, we evaluated a broad panel of proteins previously associated with type 1 diabetes in serum from newly diagnosed individuals during the first year from diagnosis. To uncover associations with beta cell function, comparisons were made between these targeted proteomics measurements and changes in fasting C-peptide levels. To further distinguish proteins linked with the disease status, comparisons were made with measurements of the protein targets in age- and sex-matched autoantibody-negative unaffected family members (UFMs).

METHODS

Selected reaction monitoring (SRM) mass spectrometry analyses of serum, targeting 85 type 1 diabetes-associated proteins, were made. Sera from individuals diagnosed under 18 years (n=86) were drawn within 6 weeks of diagnosis and at 3, 6 and 12 months afterwards (288 samples in total). The SRM data were compared with fasting C-peptide/glucose data, which was interpreted as a measure of beta cell function. The protein data were further compared with cross-sectional SRM measurements from UFMs (n=194).

RESULTS

Eleven proteins had statistically significant associations with fasting C-peptide/glucose. Of these, apolipoprotein L1 and glutathione peroxidase 3 (GPX3) displayed the strongest positive and inverse associations, respectively. Changes in GPX3 levels during the first year after diagnosis indicated future fasting C-peptide/glucose levels. In addition, differences in the levels of 13 proteins were observed between the individuals with type 1 diabetes and the matched UFMs. These included GPX3, transthyretin, prothrombin, apolipoprotein C1 and members of the IGF family.

CONCLUSIONS/INTERPRETATION

The association of several targeted proteins with fasting C-peptide/glucose levels in the first year after diagnosis suggests their connection with the underlying changes accompanying alterations in beta cell function in type 1 diabetes. Moreover, the direction of change in GPX3 during the first year was indicative of subsequent fasting C-peptide/glucose levels, and supports further investigation of this and other serum protein measurements in future studies of beta cell function in type 1 diabetes.

Serum APOC1 levels are decreased in young autoantibody positive children who rapidly progress to type 1 diabetes

Better understanding of the early events in the development of type 1 diabetes is needed to improve prediction and monitoring of the disease progression during the substantially heterogeneous presymptomatic period of the beta cell damaging process. To address this concern, we used mass spectrometry-based proteomics to analyse longitudinal pre-onset plasma sample series from children positive for multiple islet autoantibodies who had rapidly progressed to type 1 diabetes before 4 years of age (n = 10) and compared these with similar measurements from matched children who were either positive for a single autoantibody (n = 10) or autoantibody negative (n = 10). Following statistical analysis of the longitudinal data, targeted serum proteomics was used to verify 11 proteins putatively associated with the disease development in a similar yet independent and larger cohort of children who progressed to the disease within 5 years of age (n = 31) and matched autoantibody negative children (n = 31). These data reiterated extensive age-related trends for protein levels in young children. Further, these analyses demonstrated that the serum levels of two peptides unique for apolipoprotein C1 (APOC1) were decreased after the appearance of the first islet autoantibody and remained relatively less abundant in children who progressed to type 1 diabetes, in comparison to autoantibody negative children.

Systematic review of type 1 diabetes biomarkers reveals regulation in circulating proteins related to complement, lipid metabolism, and immune response

BACKGROUND

Type 1 diabetes (T1D) results from an autoimmune attack of the pancreatic β cells that progresses to dysglycemia and symptomatic hyperglycemia. Current biomarkers to track this evolution are limited, with development of islet autoantibodies marking the onset of autoimmunity and metabolic tests used to detect dysglycemia. Therefore, additional biomarkers are needed to better track disease initiation and progression. Multiple clinical studies have used proteomics to identify biomarker candidates. However, most of the studies were limited to the initial candidate identification, which needs to be further validated and have assays developed for clinical use. Here we curate these studies to help prioritize biomarker candidates for validation studies and to obtain a broader view of processes regulated during disease development.

METHODS

This systematic review was registered with Open Science Framework ( https://doi.org/10.17605/OSF.IO/N8TSA ). Using PRISMA guidelines, we conducted a systematic search of proteomics studies of T1D in the PubMed to identify putative protein biomarkers of the disease. Studies that performed mass spectrometry-based untargeted/targeted proteomic analysis of human serum/plasma of control, pre-seroconversion, post-seroconversion, and/or T1D-diagnosed subjects were included. For unbiased screening, 3 reviewers screened all the articles independently using the pre-determined criteria.

RESULTS

A total of 13 studies met our inclusion criteria, resulting in the identification of 266 unique proteins, with 31 (11.6%) being identified across 3 or more studies. The circulating protein biomarkers were found to be enriched in complement, lipid metabolism, and immune response pathways, all of which are found to be dysregulated in different phases of T1D development. We found 2 subsets: 17 proteins (C3, C1R, C8G, C4B, IBP2, IBP3, ITIH1, ITIH2, BTD, APOE, TETN, C1S, C6A3, SAA4, ALS, SEPP1 and PI16) and 3 proteins (C3, CLUS and C4A) have consistent regulation in at least 2 independent studies at post-seroconversion and post-diagnosis compared to controls, respectively, making them strong candidates for clinical assay development.

CONCLUSIONS

Biomarkers analyzed in this systematic review highlight alterations in specific biological processes in T1D, including complement, lipid metabolism, and immune response pathways, and may have potential for further use in the clinic as prognostic or diagnostic assays.

Decrease in multiple complement protein levels is associated with the development of islet autoimmunity and type 1 diabetes

Type 1 diabetes (T1D) is a chronic condition caused by autoimmune destruction of the insulin-producing pancreatic β-cells. While it is known that gene-environment interactions play a key role in triggering the autoimmune process leading to T1D, the pathogenic mechanism leading to the appearance of islet autoantibodies - biomarkers of autoimmunity - is poorly understood. Here we show that disruption of the complement system precedes the detection of islet autoantibodies and persists through disease onset. Our results suggest that children who exhibit islet autoimmunity and progress to clinical T1D have lower complement protein levels relative to those who do not progress within a similar timeframe. Thus, the complement pathway, an understudied mechanistic and therapeutic target in T1D, merits increased attention for use as protein biomarkers of prediction and potentially prevention of T1D.

A Streamlined High-Throughput Plasma Proteomics Platform for Clinical Proteomics with Improved Proteome Coverage, Reproducibility, and Robustness

Mass spectrometry-based clinical proteomics requires high throughput, reproducibility, robustness, and comprehensive coverage to serve the needs of clinical diagnosis, prognosis, and personalized medicine. Oftentimes these requirements are contradictory to each other. We report the development of a streamlined High-Throughput Plasma Proteomics (sHTPP) platform for untargeted profiling of the blood plasma proteome, which includes 96-well plates and simplified procedures for sample preparation, disposable trap column for peptide loading, robust liquid chromatographic system for separation, data-independent acquisition in tandem mass spectrometry, and DIA-NN, FragPipe, and in-house peptide spectral library-based data analysis. Using the optimized platform at a throughput of 60 samples per day, over 600 protein groups including 57 FDA-approved biomarkers can be consistently identified from whole human plasma, and more than 85% of the detected proteins have 100% completeness in quantitative values across 300 samples. The balance achieved between proteome coverage, throughput, and reproducibility of this sHTPP platform makes it promising in clinical settings, where a large number of samples are to be measured quickly and reliably to support various needs of clinical medicine.

Systematic review of type 1 diabetes biomarkers reveals regulation in circulating proteins related to complement, lipid metabolism, and immune response

Aims

Type 1 diabetes (T1D) results from an autoimmune attack of the pancreatic β cells that progresses to dysglycemia and symptomatic hyperglycemia. Current biomarkers to track this evolution are limited, with development of islet autoantibodies marking the onset of autoimmunity and metabolic tests used to detect dysglycemia. Therefore, additional biomarkers are needed to better track disease initiation and progression. Multiple clinical studies have used proteomics to identify biomarker candidates. However, most of the studies were limited to the initial candidate identification, which needs to be further validated and have assays developed for clinical use. Here we curate these studies to help prioritize biomarker candidates for validation studies and to obtain a broader view of processes regulated during disease development.

Methods

This systematic review was registered with Open Science Framework (DOI 10.17605/OSF.IO/N8TSA). Using PRISMA guidelines, we conducted a systematic search of proteomics studies of T1D in the PubMed to identify putative protein biomarkers of the disease. Studies that performed mass spectrometry-based untargeted/targeted proteomic analysis of human serum/plasma of control, pre-seroconversion, post-seroconversion, and/or T1D-diagnosed subjects were included. For unbiased screening, 3 reviewers screened all the articles independently using the pre-determined criteria.

Results

A total of 13 studies met our inclusion criteria, resulting in the identification of 251 unique proteins, with 27 (11%) being identified across 3 or more studies. The circulating protein biomarkers were found to be enriched in complement, lipid metabolism, and immune response pathways, all of which are found to be dysregulated in different phases of T1D development. We found a subset of 3 proteins (C3, KNG1 & CFAH), 6 proteins (C3, C4A, APOA4, C4B, A2AP & BTD) and 7 proteins (C3, CLUS, APOA4, C6, A2AP, C1R & CFAI) have consistent regulation between multiple studies in samples from individuals at pre-seroconversion, post-seroconversion and post-diagnosis compared to controls, respectively, making them strong candidates for clinical assay development.

Conclusions

Biomarkers analyzed in this systematic review highlight alterations in specific biological processes in T1D, including complement, lipid metabolism, and immune response pathways, and may have potential for further use in the clinic as prognostic or diagnostic assays.

Autoantibody and T cell responses to oxidative post-translationally modified insulin neoantigenic peptides in type 1 diabetes

AIMS/HYPOTHESIS

Antibodies specific to oxidative post-translational modifications (oxPTM) of insulin (oxPTM-INS) are present in most individuals with type 1 diabetes, even before the clinical onset. However, the antigenic determinants of such response are still unknown. In this study, we investigated the antibody response to oxPTM-INS neoepitope peptides (oxPTM-INSPs) and evaluated their ability to stimulate humoral and T cell responses in type 1 diabetes. We also assessed the concordance between antibody and T cell responses to the oxPTM-INS neoantigenic peptides.

METHODS

oxPTM-INS was generated by exposing insulin to various reactive oxidants. The insulin fragments resulting from oxPTM were fractionated by size-exclusion chromatography further to ELISA and LC-MS/MS analysis to identify the oxidised peptide neoepitopes. Immunogenic peptide candidates were produced and then modified in house or designed to incorporate in silico-oxidised amino acids during synthesis. Autoantibodies to the oxPTM-INSPs were tested by ELISA using sera from 63 participants with new-onset type 1 diabetes and 30 control participants. An additional 18 fresh blood samples from participants with recently diagnosed type 1 diabetes, five with established disease, and from 11 control participants were used to evaluate, in parallel, CD4⁺ and CD8⁺ T cell activation by oxPTM-INSPs.

RESULTS

We observed antibody and T cell responses to three out of six LC-MS/MS-identified insulin peptide candidates: A:12-21 (SLYQLENYCN, native insulin peptide 3 [Nt-INSP-3]), B:11-30 (LVEALYLVCGERGFFYTPKT, Nt-INSP-4) and B:21-30 (ERGFFYTPKT, Nt-INSP-6). For Nt-INSP-4 and Nt-INSP-6, serum antibody binding was stronger in type 1 diabetes compared with healthy control participants (p≤0.02), with oxidised forms of ERGFFYTPKT, oxPTM-INSP-6 conferring the highest antibody binding (83% binders to peptide modified in house by hydroxyl radical [^●OH] and >88% to in silico-oxidised peptide; p≤0.001 vs control participants). Nt-INSP-4 induced the strongest T cell stimulation in type 1 diabetes compared with control participants for both CD4⁺ (p<0.001) and CD8⁺ (p=0.049). CD4⁺ response to oxPTM-INSP-6 was also commoner in type 1 diabetes than in control participants (66.7% vs 27.3%; p=0.039). Among individuals with type 1 diabetes, the CD4⁺ response to oxPTM-INSP-6 was more frequent than to Nt-INSP-6 (66.7% vs 27.8%; p=0.045). Overall, 44.4% of patients showed a concordant autoimmune response to oxPTM-INSP involving simultaneously CD4⁺ and CD8⁺ T cells and autoantibodies.

CONCLUSIONS/INTERPRETATION

Our findings support the concept that oxidative stress, and neoantigenic epitopes of insulin, may be involved in the immunopathogenesis of type 1 diabetes.

Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach

Quantitative proteomics has matured into an established tool and longitudinal proteomics experiments have begun to emerge. However, no effective, simple-to-use differential expression method for longitudinal proteomics data has been released. Typically, such data is noisy, contains missing values, and has only few time points and biological replicates. To address this need, we provide a comprehensive evaluation of several existing differential expression methods for high-throughput longitudinal omics data and introduce a Robust longitudinal Differential Expression (RolDE) approach. The methods are evaluated using over 3000 semi-simulated spike-in proteomics datasets and three large experimental datasets. In the comparisons, RolDE performs overall best; it is most tolerant to missing values, displays good reproducibility and is the top method in ranking the results in a biologically meaningful way. Furthermore, RolDE is suitable for different types of data with typically unknown patterns in longitudinal expression and can be applied by non-experienced users.

Inflammation-related citrullination of matrisome proteins in human cancer

INTRODUCTION

Protein arginine deiminases (PADs) are intracellular enzymes that may, especially in pathological conditions, also citrullinate extracellular substrates, including matrisome proteins such as structural proteins in extracellular matrix (ECM). PADs are abundantly expressed in human cancer cells. Citrullination of matrisome proteins has been reported in colon cancer but the phenomenon has never been systematically studied.

METHODS

To gain a broader view of citrullination of matrisome proteins in cancer, we analyzed cancer proteomics data sets in 3 public databases for citrullinated matrisome proteins. In addition, we used three-dimensional cell cocultures of fibroblasts and cancer cells and analyzed citrullination of ECM.

RESULTS AND DISCUSSION

Our new analysis indicate that citrullination of ECM occurs in human cancer, and there is a significant variation between tumors. Most frequently citrullinated proteins included fibrinogen and fibronectin, which are typically citrullinated in rheumatoid inflammation. We also detected correlation between immune cell marker proteins, matrix metalloproteinases and ECM citrullination, which suggests that in cancer, citrullination of matrisome proteins is predominantly an inflammation-related phenomenon. This was further supported by our analysis of three-dimensional spheroid co-cultures of nine human cancer cell lines and fibroblasts by mass spectrometry, which gave no evidence that cancer cells or fibroblasts could citrullinate matrisome proteins in tumor stroma. It also appears that in the spheroid cultures, matrisome proteins are protected from citrullination.

Inside the β Cell: Molecular Stress Response Pathways in Diabetes Pathogenesis

The pathogeneses of the 2 major forms of diabetes, type 1 and type 2, differ with respect to their major molecular insults (loss of immune tolerance and onset of tissue insulin resistance, respectively). However, evidence suggests that dysfunction and/or death of insulin-producing β-cells is common to virtually all forms of diabetes. Although the mechanisms underlying β-cell dysfunction remain incompletely characterized, recent years have witnessed major advances in our understanding of the molecular pathways that contribute to the demise of the β-cell. Cellular and environmental factors contribute to β-cell dysfunction/loss through the activation of molecular pathways that exacerbate endoplasmic reticulum stress, the integrated stress response, oxidative stress, and impaired autophagy. Whereas many of these stress responsive pathways are interconnected, their individual contributions to glucose homeostasis and β-cell health have been elucidated through the development and interrogation of animal models. In these studies, genetic models and pharmacological compounds have enabled the identification of genes and proteins specifically involved in β-cell dysfunction during diabetes pathogenesis. Here, we review the critical stress response pathways that are activated in β cells in the context of the animal models.

Biology of the Extracellular Proteasome

Proteasomes are traditionally considered intracellular complexes that play a critical role in maintaining proteostasis by degrading short-lived regulatory proteins and removing damaged proteins. Remarkably, in addition to these well-studied intracellular roles, accumulating data indicate that proteasomes are also present in extracellular body fluids. Not much is known about the origin, biological role, mode(s) of regulation or mechanisms of extracellular transport of these complexes. Nevertheless, emerging evidence indicates that the presence of proteasomes in the extracellular milieu is not a random phenomenon, but rather a regulated, coordinated physiological process. In this review, we provide an overview of the current understanding of extracellular proteasomes. To this end, we examine 143 proteomic datasets, leading us to the realization that 20S proteasome subunits are present in at least 25 different body fluids. Our analysis also indicates that while 19S subunits exist in some of those fluids, the dominant proteasome activator in these compartments is the PA28α/β complex. We also elaborate on the positive correlations that have been identified in plasma and extracellular vesicles, between 20S proteasome and activity levels to disease severity and treatment efficacy, suggesting the involvement of this understudied complex in pathophysiology. In addition, we address the considerations and practical experimental methods that should be taken when investigating extracellular proteasomes. Overall, we hope this review will stimulate new opportunities for investigation and thoughtful discussions on this exciting topic that will contribute to the maturation of the field.

The response of three-dimensional pancreatic alpha and beta cell co-cultures to oxidative stress

The pancreatic islets of Langerhans have low endogenous antioxidant levels and are thus especially sensitive to oxidative stress, which is known to influence cell survival and behaviour. As bioengineered islets are gaining interest for therapeutic purposes, it is important to understand how their composition can be optimized to diminish oxidative stress. We investigated how the ratio of the two main islet cell types (alpha and beta cells) and their culture in three-dimensional aggregates could protect against oxidative stress. Monolayer and aggregate cultures were established by seeding the alphaTC1 (alpha) and INS1E (beta) cell lines in varying ratios, and hydrogen peroxide was applied to induce oxidative stress. Viability, oxidative stress, and the level of the antioxidant glutathione were measured. Both aggregation and an increasing prevalence of INS1E cells in the co-cultures conferred greater resistance to cell death induced by oxidative stress. Increasing the prevalence of INS1E cells also decreased the number of alphaTC1 cells experiencing oxidative stress in the monolayer culture. In 3D aggregates, culturing the alphaTC1 and INS1E cells in a ratio of 50:50 prevented oxidative stress in both cell types. Together, the results of this study lead to new insight into how modulating the composition and dimensionality of a co-culture can influence the oxidative stress levels experienced by the cells.

Diabetes and Its Cardiovascular Complications: Comprehensive Network and Systematic Analyses

Diabetes mellitus is a worldwide health problem that usually comes with severe complications. There is no cure for diabetes yet and the threat of these complications is what keeps researchers investigating mechanisms and treatments for diabetes mellitus. Due to advancements in genomics, epigenomics, proteomics, and single-cell multiomics research, considerable progress has been made toward understanding the mechanisms of diabetes mellitus. In addition, investigation of the association between diabetes and other physiological systems revealed potentially novel pathways and targets involved in the initiation and progress of diabetes. This review focuses on current advancements in studying the mechanisms of diabetes by using genomic, epigenomic, proteomic, and single-cell multiomic analysis methods. It will also focus on recent findings pertaining to the relationship between diabetes and other biological processes, and new findings on the contribution of diabetes to several pathological conditions.

HBFP: a new repository for human body fluid proteome

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery.

Database URL:https://bmbl.bmi.osumc.edu/HBFP/

Controlled diabetes amends oxidative stress as mechanism related to severity of diabetic retinopathy

Oxidative stress is a well-accepted etiological mechanism that contributes to neuronal dysfunction. Role of oxidative stress as a mechanism of retinopathy in controlled type 2 diabetic patients was evaluated. Participants were divided into three groups: Group 1 as 30 normal eyes of 15 subjects, Group 2 comprised 24 eyes of 12 diabetic patients without retinopathy and Group 3 comprised 23 eyes of 12 diabetic patients with different grades of retinopathy (8 eyes with maculopathy). A complete ophthalmological examination was performed. Oxidative stress markers were measured in blood. Macular thickness was different in all quadrants among all groups and showed a tendency to increase in Group 3 due to diabetic retinopathy with insignificant changes in parapapillary retinal nerve fiber layer thickness although thinning was noted also with retinopathy. Non-significant differences in GST and lipid peroxide levels were observed between the three studied groups, whereas vitamin C and GSH levels were higher in diabetic patients when compared to those in controls. As oxidative stress, hyperglycemia and local inflammation are involved in the pathogenesis of DR, the present study proved that the progressive damage can be retarded in controlled type 2 diabetic patients using different treatment modalities that abated oxidative stress.

Oxidative Stress in Autoimmune Diseases: An Under Dealt Malice

Oxidative stress is the off-balance of antioxidants and free radicals. All kinds of diseases and disorders give rise to oxidative damage including autoimmune diseases. An autoimmune disorder is a pathological condition characterized by the breakdown of self-tolerance of the immune system in the body. Immunological processes against tissues and organs lead to enhanced oxidative stress and, in turn, misbalance of oxidative stress aggravates the pathobiology of the disease. Highly reactive nature of free radicals, for example hydroxyl and superoxide ions, alters DNA, protein, and lipids in the body which augment the pathologic processes of diseases. The damaged biomolecules are responsible for systemic complications and secondary disease co-morbidities. In this review, we discuss the role of oxidative stress in some incapacitating autoimmune diseases like Rheumatoid arthritis, Systemic Lupus Erythematosus, Type 1 Diabetes, and Multiple Sclerosis. Oxidative stress plays a central and course defining role in these diseases and it has become a necessity to study the pathological mechanism involved in oxidative stress to better understand and offer treatment holistically. Presently there are no clinically available parameters for measurement and treatment of pathological oxidative stress, therefore it requires intensive research. Probably, in the future, the discovery of easily detectable markers of oxidative stress can aid in the diagnosis, prognosis, and treatment of progressively destructive autoimmune diseases.

lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal Data

MOTIVATION

Longitudinal study designs are indispensable for studying disease progression. Inferring covariate effects from longitudinal data, however, requires interpretable methods that can model complicated covariance structures and detect nonlinear effects of both categorical and continuous covariates, as well as their interactions. Detecting disease effects is hindered by the fact that they often occur rapidly near the disease initiation time, and this time point cannot be exactly observed. An additional challenge is that the effect magnitude can be heterogeneous over the subjects.

RESULTS

We present lgpr, a widely applicable and interpretable method for nonparametric analysis of longitudinal data using additive Gaussian processes. We demonstrate that it outperforms previous approaches in identifying the relevant categorical and continuous covariates in various settings. Furthermore, it implements important novel features, including the ability to account for the heterogeneity of covariate effects, their temporal uncertainty, and appropriate observation models for different types of biomedical data. The lgpr tool is implemented as a comprehensive and user-friendly R-package.

AVAILABILITY

lgpr is available at jtimonen.github.io/lgpr-usage with documentation, tutorials, test data, and code for reproducing the experiments of this paper.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Extending the Depth of Human Plasma Proteome Coverage Using Simple Fractionation Techniques

Human plasma is one of the most widely used tissues in clinical analysis, and plasma-based biomarkers are used for monitoring patient health status and/or response to medical treatment to avoid unnecessary invasive biopsy. Data-driven plasma proteomics has suffered from a lack of throughput and detection sensitivity, largely due to the complexity of the plasma proteome and in particular the enormous quantitative dynamic range, estimated to be between 9 and 13 orders of magnitude between the lowest and the highest abundance protein. A major challenge is to identify workflows that can achieve depth of plasma proteome coverage while minimizing the complexity of the sample workup and maximizing the sample throughput. In this study, we have performed intensive depletion of high-abundant plasma proteins or enrichment of low-abundant proteins using the Agilent multiple affinity removal liquid chromatography (LC) column-Human 6 (Hu6), the Agilent multiple affinity removal LC column-Human 14 (Hu14), and ProteoMiner followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS PAGE) and C18 prefractionation techniques. We compared the performance of each of these fractionation approaches to identify the method that satisfies requirements for analysis of clinical samples and to include good plasma proteome coverage in combination with reasonable sample output. In this study, we report that one-dimensional (1D) gel-based prefractionation allows parallel sample processing and no loss of proteome coverage, compared with serial chromatographic separation, and significantly accelerates analysis time, particularly important for large clinical projects. Furthermore, we show that a variety of methodologies can achieve similarly high plasma proteome coverage, allowing flexibility in method selection based on project-specific needs. These considerations are important in the effort to accelerate plasma proteomics research so as to provide efficient, reliable, and accurate diagnoses, population-based health screening, clinical research studies, and other clinical work.

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.

Predictive Modeling of Type 1 Diabetes Stages Using Disparate Data Sources

This study aims to model genetic, immunologic, metabolomics, and proteomic biomarkers for development of islet autoimmunity (IA) and progression to type 1 diabetes in a prospective high-risk cohort. We studied 67 children: 42 who developed IA (20 of 42 progressed to diabetes) and 25 control subjects matched for sex and age. Biomarkers were assessed at four time points: earliest available sample, just prior to IA, just after IA, and just prior to diabetes onset. Predictors of IA and progression to diabetes were identified across disparate sources using an integrative machine learning algorithm and optimization-based feature selection. Our integrative approach was predictive of IA (area under the receiver operating characteristic curve [AUC] 0.91) and progression to diabetes (AUC 0.92) based on standard cross-validation (CV). Among the strongest predictors of IA were change in serum ascorbate, 3-methyl-oxobutyrate, and the PTPN22 (rs2476601) polymorphism. Serum glucose, ADP fibrinogen, and mannose were among the strongest predictors of progression to diabetes. This proof-of-principle analysis is the first study to integrate large, diverse biomarker data sets into a limited number of features, highlighting differences in pathways leading to IA from those predicting progression to diabetes. Integrated models, if validated in independent populations, could provide novel clues concerning the pathways leading to IA and type 1 diabetes.

Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies

Plasma and serum are rich sources of information regarding an individual's health state, and protein tests inform medical decision making. Despite major investments, few new biomarkers have reached the clinic. Mass spectrometry (MS)-based proteomics now allows highly specific and quantitative readout of the plasma proteome. Here, we employ Plasma Proteome Profiling to define quality marker panels to assess plasma samples and the likelihood that suggested biomarkers are instead artifacts related to sample handling and processing. We acquire deep reference proteomes of erythrocytes, platelets, plasma, and whole blood of 20 individuals (> 6,000 proteins), and compare serum and plasma proteomes. Based on spike-in experiments, we determine sample quality-associated proteins, many of which have been reported as biomarker candidates as revealed by a comprehensive literature survey. We provide sample preparation guidelines and an online resource ( www.plasmaproteomeprofiling.org) to assess overall sample-related bias in clinical studies and to prevent costly miss-assignment of biomarker candidates.

The role of proteomics in assessing beta-cell dysfunction and death in type 1 diabetes

Introduction: Type 1 diabetes (T1D) is characterized by autoimmune-induced dysfunction and destruction of the pancreatic beta cells. Unfortunately, this process is poorly understood, and the current best treatment for type 1 diabetes is the administration of exogenous insulin. To better understand these mechanisms and to develop new therapies, there is an urgent need for biomarkers that can reliably predict disease stage. Areas covered: Mass spectrometry (MS)-based proteomics and complementary techniques play an important role in understanding the autoimmune response, inflammation and beta-cell death. MS is also a leading technology for the identification of biomarkers. This, and the technical difficulties and new technologies that provide opportunities to characterize small amounts of sample in great depth and to analyze large sample cohorts will be discussed in this review. Expert opinion: Understanding disease mechanisms and the discovery of disease-associated biomarkers are highly interconnected goals. Ideal biomarkers would be molecules specific to the different stages of the disease process that are released from beta cells to the bloodstream. However, such molecules are likely to be present in trace amounts in the blood due to the small number of pancreatic beta cells in the human body and the heterogeneity of the target organ and disease process.

Plasma proteomics and the paediatric patient

INTRODUCTION

Plasma proteomics has been extensively utilized for studies that investigate various disease settings (e.g. cardiovascular disease), as well as to monitor the effect of pharmaceuticals on the plasma proteome (e.g. chemotherapy). However, plasma proteomic studies focusing on children represent a very small proportion of the plasma proteomic studies completed to date. Early disease detection and prevention is critical in pediatrics, as children must live with the disease outcomes for many years and often carry negative outcomes into adulthood. Pediatrics represents an area of plasma proteomics that is about to undergo a significant expansion. Areas covered: This review is based on a PubMed search focusing on five keywords that are plasma, biomarkers, pediatric, proteomics, and children. It is a comprehensive summary of plasma proteomic studies specific to the pediatric patient and discusses aspects such as the clinical setting, sample size, methodological approaches and outlines the significance of the findings. Expert commentary: Plasma proteomics is expanding significantly as a result of major advancements in proteomic technology. This is in synergy with the growing focus on true early disease detection and prevention in early life. We are about to see a new era of advanced medical science built from pediatric proteomics.

An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

Biomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models are the standard workhorse for statistical analysis of longitudinal data. However, analysis of longitudinal data can be complicated for reasons such as difficulties in modelling correlated outcome values, functional (time-varying) covariates, nonlinear and non-stationary effects, and model inference. We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We demonstrate LonGP's performance and accuracy by analysing various simulated and real longitudinal -omics datasets.

Immunological biomarkers for the development and progression of type 1 diabetes

Immune biomarkers of type 1 diabetes are many and diverse. Some of these, such as the autoantibodies, are well established but not discriminative enough to deal with the heterogeneity inherent to type 1 diabetes progression. As an alternative, high hopes are placed on T cell assays, which give insight into the cells that actually target the beta cell or play a crucial role in maintaining tolerance. These assays are approaching a level of robustness that may allow for solid conclusions on both disease progression and therapeutic efficacy of immune interventions. In addition, 'omics' approaches to biomarker discovery are rapidly progressing. The potential emergence of novel biomarkers creates a need for the introduction of bioinformatics and 'big data' analysis systems for the integration of the multitude of biomarker data that will be available, to translate these data into clinical tools. It is worth noting that it is unlikely that the same markers will apply to all individuals. Instead, individualised signatures of biomarkers, combining autoantibodies, T cell profiles and other biomarkers, will need to be used to classify at-risk patients into various categories, thus enabling personalised prediction, prevention and treatment approaches. To achieve this goal, the standardisation of assays for biomarker discovery, the integration of analyses and data from biomarker studies and, most importantly, the careful clinical characterisation of individuals providing samples for these studies are critical. Longitudinal sample-collection initiatives, like INNODIA, should lead to novel biomarker discovery, not only providing a better understanding of type 1 diabetes onset and progression, but also yielding biomarkers of therapeutic efficacy of interventions to prevent or arrest type 1 diabetes.

Serum biomarkers for diagnosis and prediction of type 1 diabetes

Type 1 diabetes (T1D) culminates in the autoimmune destruction of the pancreatic βcells, leading to insufficient production of insulin and development of hyperglycemia. Serum biomarkers including a combination of glucose, glycated molecules, C-peptide, and autoantibodies have been well established for the diagnosis of T1D. However, these molecules often mark a late stage of the disease when ∼90% of the pancreatic insulin-producing β-cells have already been lost. With the prevalence of T1D increasing worldwide and because of the physical and psychological burden induced by this disease, there is a great need for prognostic biomarkers to predict T1D development or progression. This would allow us to identify individuals at high risk for early prevention and intervention. Therefore, considerable efforts have been dedicated to the understanding of disease etiology and the discovery of novel biomarkers in the last few decades. The advent of high-throughput and sensitive "-omics" technologies for the study of proteins, nucleic acids, and metabolites have allowed large scale profiling of protein expression and gene changes in T1D patients relative to disease-free controls. In this review, we briefly discuss the classical diagnostic biomarkers of T1D but mainly focus on the novel biomarkers that are identified as markers of β-cell destruction and screened with the use of state-of-the-art "-omics" technologies.