Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes

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.

Gene expression analysis reveals diabetes-related gene signatures

BACKGROUND

Diabetes is a spectrum of metabolic diseases affecting millions of people worldwide. The loss of pancreatic β-cell mass by either autoimmune destruction or apoptosis, in type 1-diabetes (T1D) and type 2-diabetes (T2D), respectively, represents a pathophysiological process leading to insulin deficiency. Therefore, therapeutic strategies focusing on restoring β-cell mass and β-cell insulin secretory capacity may impact disease management. This study took advantage of powerful integrative bioinformatic tools to scrutinize publicly available diabetes-associated gene expression data to unveil novel potential molecular targets associated with β-cell dysfunction.

METHODS

A comprehensive literature search for human studies on gene expression alterations in the pancreas associated with T1D and T2D was performed. A total of 6 studies were selected for data extraction and for bioinformatic analysis. Pathway enrichment analyses of differentially expressed genes (DEGs) were conducted, together with protein-protein interaction networks and the identification of potential transcription factors (TFs). For noncoding differentially expressed RNAs, microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), which exert regulatory activities associated with diabetes, identifying target genes and pathways regulated by these RNAs is fundamental for establishing a robust regulatory network.

RESULTS

Comparisons of DEGs among the 6 studies showed 59 genes in common among 4 or more studies. Besides alterations in mRNA, it was possible to identify differentially expressed miRNA and lncRNA. Among the top transcription factors (TFs), HIPK2, KLF5, STAT1 and STAT3 emerged as potential regulators of the altered gene expression. Integrated analysis of protein-coding genes, miRNAs, and lncRNAs pointed out several pathways involved in metabolism, cell signaling, the immune system, cell adhesion, and interactions. Interestingly, the GABAergic synapse pathway emerged as the only common pathway to all datasets.

CONCLUSIONS

This study demonstrated the power of bioinformatics tools in scrutinizing publicly available gene expression data, thereby revealing potential therapeutic targets like the GABAergic synapse pathway, which holds promise in modulating α-cells transdifferentiation into β-cells.

Metabolomics in diabetes mellitus: clinical insight

INTRODUCTION

Diabetes Mellitus (DM) is a chronic heterogeneous metabolic disorder characterized by hyperglycemia due to the destruction of insulin-producing pancreatic β cells and/or insulin resistance. It is now considered a global epidemic disease associated with serious threats to a patient's life. Understanding the metabolic pathways involved in disease pathogenesis and progression is important and would improve prevention and management strategies. Metabolomics is an emerging field of research that offers valuable insights into the metabolic perturbation associated with metabolic diseases, including DM.

AREA COVERED

Herein, we discussed the metabolomics in type 1 and 2 DM research, including its contribution to understanding disease pathogenesis and identifying potential novel biomarkers clinically useful for disease screening, monitoring, and prognosis. In addition, we highlighted the metabolic changes associated with treatment effects, including insulin and different anti-diabetic medications.

EXPERT OPINION

By analyzing the metabolome, the metabolic disturbances involved in T1DM and T2DM can be explored, enhancing our understanding of the disease progression and potentially leading to novel clinical diagnostic and effective new therapeutic approaches. In addition, identifying specific metabolites would be potential clinical biomarkers for predicting the disease and thus preventing and managing hyperglycemia and its complications.

Recent Developments in Islet Biology: A Review With Patient Perspectives

Navigating the coronavirus disease-2019 (COVID-19, now COVID) pandemic has required resilience and creativity worldwide. Despite early challenges to productivity, more than 2,000 peer-reviewed articles on islet biology were published in 2021. Herein, we highlight noteworthy advances in islet research between January 2021 and April 2022, focussing on 5 areas. First, we discuss new insights into the role of glucokinase, mitogen-activated protein kinase-kinase/extracellular signal-regulated kinase and mitochondrial function on insulin secretion from the pancreatic β cell, provided by new genetically modified mouse models and live imaging. We then discuss a new connection between lipid handling and improved insulin secretion in the context of glucotoxicity, focussing on fatty acid-binding protein 4 and fetuin-A. Advances in high-throughput "omic" analysis evolved to where one can generate more finely tuned genetic and molecular profiles within broad classifications of type 1 diabetes and type 2 diabetes. Next, we highlight breakthroughs in diabetes treatment using stem cell-derived β cells and innovative strategies to improve islet survival posttransplantation. Last, we update our understanding of the impact of severe acute respiratory syndrome-coronavirus-2 infection on pancreatic islet function and discuss current evidence regarding proposed links between COVID and new-onset diabetes. We address these breakthroughs in 2 settings: one for a scientific audience and the other for the public, particularly those living with or affected by diabetes. Bridging biomedical research in diabetes to the community living with or affected by diabetes, our partners living with type 1 diabetes or type 2 diabetes also provide their perspectives on these latest advances in islet biology.

Multi-omics of NET formation and correlations with CNDP1, PSPB, and L-cystine levels in severe and mild COVID-19 infections

The detailed mechanisms of COVID-19 infection pathology remain poorly understood. To improve our understanding of SARS-CoV-2 pathology, we performed a multi-omics and correlative analysis of an immunologically naïve SARS-CoV-2 clinical cohort from blood plasma of uninfected controls, mild, and severe infections. Consistent with previous observations, severe patient populations showed an elevation of pulmonary surfactant levels. Intriguingly, mild patients showed a statistically significant elevation in the carnosine dipeptidase modifying enzyme (CNDP1). Mild and severe patient populations showed a strong elevation in the metabolite L-cystine (oxidized form of the amino acid cysteine) and enzymes with roles in glutathione metabolism. Neutrophil extracellular traps (NETs) were observed in both mild and severe populations, and NET formation was higher in severe vs. mild samples. Our correlative analysis suggests a potential protective role for CNDP1 in suppressing PSPB release from the pulmonary space whereas NET formation correlates with increased PSPB levels and disease severity. In our discussion we put forward a possible model where NET formation drives pulmonary occlusions and CNDP1 promotes antioxidation, pleiotropic immune responses, and vasodilation by accelerating histamine synthesis.

Decreasing GDF15 Promotes Inflammatory Signals and Neutrophil Infiltration in Psoriasis Models

Psoriasis is driven by the interplay between hyperproliferative keratinocytes and infiltrating inflammatory cells. GDF15, a member of the TGF-β superfamily, has been implicated in cachexia, metabolic control, and cancer invasion. However, the expression and immunomodulatory role of GDF15 in inflammatory diseases has not been clarified. In this study, we report that GDF15 is decreased in the epidermis of patients with psoriasis and in an imiquimod-induced psoriasis-like mouse model. TNF-α suppresses GDF15 expression in keratinocytes by inhibiting the protein level of the transcription factor GATA2. GDF15 deficiency aggravates the development of psoriatic lesions, as evidenced by more severe skin inflammation in imiquimod-treated Gdf15-knockout (Gdf15^‒/‒) mice compared with that in wild-type mice. Importantly, GDF15 limited the synthesis of a panel of keratinocyte cytokines and chemokines by inhibiting TAK1/NF-κB activation and directly inhibited neutrophil adhesion and migration by inhibiting the activation of the small GTPase Rap1. Epidermal hyperplasia, infiltration of neutrophils, and transcripts of psoriasis-related markers in imiquimod-induced psoriasiform dermatitis were significantly alleviated by a topical supplement of recombinant murine GDF15. In summary, our study revealed an unexpected role of GDF15 in keratinocyte and neutrophil function in the skin of psoriasis, implying its therapeutic potential in treating psoriasis.

Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets

Background: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges. Methods: Here, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies. Results: Using proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using Ingenuity Pathway Analysis (IPA) software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the “golden ratio” of 38.2%:61.8%. Specifically, the Canonical Pathway and Diseases and Functions modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The Biomarker Prediction module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis. Conclusions: These preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited.

Metabolomic and proteomic profiling in bipolar disorder patients revealed potential molecular signatures related to hemostasis

INTRODUCTION

Bipolar disorder (BD) is a mood disorder characterized by the occurrence of depressive episodes alternating with episodes of elevated mood (known as mania). There is also an increased risk of other medical comorbidities.

OBJECTIVES

This work uses a systems biology approach to compare BD treated patients with healthy controls (HCs), integrating proteomics and metabolomics data using partial correlation analysis in order to observe the interactions between altered proteins and metabolites, as well as proposing a potential metabolic signature panel for the disease.

METHODS

Data integration between proteomics and metabolomics was performed using GC-MS data and label-free proteomics from the same individuals (N = 13; 5 BD, 8 HC) using generalized canonical correlation analysis and partial correlation analysis, and then building a correlation network between metabolites and proteins. Ridge-logistic regression models were developed to stratify between BD and HC groups using an extended metabolomics dataset (N = 28; 14 BD, 14 HC), applying a recursive feature elimination for the optimal selection of the metabolites.

RESULTS

Network analysis demonstrated links between proteins and metabolites, pointing to possible alterations in hemostasis of BD patients. Ridge-logistic regression model indicated a molecular signature comprising 9 metabolites, with an area under the receiver operating characteristic curve (AUROC) of 0.833 (95% CI 0.817-0.914).

CONCLUSION

From our results, we conclude that several metabolic processes are related to BD, which can be considered as a multi-system disorder. We also demonstrate the feasibility of partial correlation analysis for integration of proteomics and metabolomics data in a case-control study setting.

Current and Novel Therapeutic Approaches for Treatment of Diabetic Macular Edema

Diabetic macular edema (DME) is a major ocular complication of diabetes mellitus (DM), leading to significant visual impairment. DME’s pathogenesis is multifactorial. Focal edema tends to occur when primary metabolic abnormalities lead to a persistent hyperglycemic state, causing the development of microaneurysms, often with extravascular lipoprotein in a circinate pattern around the focal leakage. On the other hand, diffusion edema is due to a generalized breakdown of the inner blood–retinal barrier, leading to profuse early leakage from the entire capillary bed of the posterior pole with the subsequent extravasation of fluid into the extracellular space. The pathogenesis of DME occurs through the interaction of multiple molecular mediators, including the overexpression of several growth factors, including vascular endothelial growth factor (VEGF), insulin-like growth factor-1, angiopoietin-1, and -2, stromal-derived factor-1, fibroblast growth factor-2, and tumor necrosis factor. Synergistically, these growth factors mediate angiogenesis, protease production, endothelial cell proliferation, and migration. Treatment for DME generally involves primary management of DM, laser photocoagulation, and pharmacotherapeutics targeting mediators, namely, the anti-VEGF pathway. The emergence of anti-VEGF therapies has resulted in significant clinical improvements compared to laser therapy alone. However, multiple factors influencing the visual outcome after anti-VEGF treatment and the presence of anti-VEGF non-responders have necessitated the development of new pharmacotherapies. In this review, we explore the pathophysiology of DME and current management strategies. In addition, we provide a comprehensive analysis of emerging therapeutic approaches to the treatment of DME.

Personalized Immunotherapies for Type 1 Diabetes: Who, What, When, and How?

Our understanding of the immunopathological features of type 1 diabetes (T1D) has greatly improved over the past two decades and has shed light on disease heterogeneity dictated by multiple immune, metabolic, and clinical parameters. This may explain the limited effects of immunotherapies tested so far to durably revert or prevent T1D, for which life-long insulin replacement remains the only therapeutic option. In the era of omics and precision medicine, offering personalized treatment could contribute to turning this tide. Here, we discuss how to structure the selection of the right patient at the right time for the right treatment. This individualized therapeutic approach involves enrolling patients at a defined disease stage depending on the target and mode of action of the selected drug, and better stratifying patients based on their T1D endotype, reflecting intrinsic disease aggressiveness and immune context. To this end, biomarker screening will be critical, not only to help stratify patients and disease stage, but also to select the best predicted responders ahead of treatment and at early time points during clinical trials. This strategy could contribute to increase therapeutic efficacy, notably through the selection of drugs with complementary effects, and to further develop precision multi-hit medicine.

An overview of technologies for MS-based proteomics-centric multi-omics

INTRODUCTION

Mass spectrometry-based proteomics reveals dynamic molecular signatures underlying phenotypes reflecting normal and perturbed conditions in living systems. Although valuable on its own, the proteome has only one level of moleclar information, with the genome, epigenome, transcriptome, and metabolome, all providing complementary information. Multi-omic analysis integrating information from one or more of these other domains with proteomic information provides a more complete picture of molecular contributors to dynamic biological systems.

AREAS COVERED

Here, we discuss the improvements to mass spectrometry-based technologies, focused on peptide-based, bottom-up approaches that have enabled deep, quantitative characterization of complex proteomes. These advances are facilitating the integration of proteomics data with other 'omic information, providing a more complete picture of living systems. We also describe the current state of bioinformatics software and approaches for integrating proteomics and other 'omics data, critical for enabling new discoveries driven by multi-omics.

EXPERT COMMENTARY

Multi-omics, centered on the integration of proteomics information with other 'omic information, has tremendous promise for biological and biomedical studies. Continued advances in approaches for generating deep, reliable proteomic data and bioinformatics tools aimed at integrating data across 'omic domains will ensure the discoveries offered by these multi-omic studies continue to increase.

Application of Metabolomics in Various Types of Diabetes

Metabolomics is the analysis of numerous small molecules known as metabolites. Over the past few years, with the continuous development in metabolomics, it has been widely used in the detection, diagnosis, and treatment of diabetes and has demonstrated great benefits. At the same time, studies on diabetes and its complications have discovered the metabolic markers that are characteristic of diabetes. However, the pathogenesis of diabetes has yet to be clarified, as well as no complete cure. The mechanism of diabetes has not been completely elucidated, and its eradication treatment is not available. Thus, prevention of the onset of the disease and its treatment have become very important. In this review, we focused on the recent progress in the use of metabolites in diabetes and their complications, as well as understanding the impact of diabetes metabolites.

Epigenomics and Lipidomics Integration in Alzheimer Disease: Pathways Involved in Early Stages

BACKGROUND

Alzheimer Disease (AD) is the most prevalent dementia. However, the physiopathological mechanisms involved in its development are unclear. In this sense, a multi-omics approach could provide some progress.

METHODS

Epigenomic and lipidomic analysis were carried out in plasma samples from patients with mild cognitive impairment (MCI) due to AD (n = 22), and healthy controls (n = 5). Then, omics integration between microRNAs (miRNAs) and lipids was performed by Sparse Partial Least Squares (s-PLS) regression and target genes for the selected miRNAs were identified.

RESULTS

25 miRNAs and 25 lipids with higher loadings in the sPLS regression were selected. Lipids from phosphatidylethanolamines (PE), lysophosphatidylcholines (LPC), ceramides, phosphatidylcholines (PC), triglycerides (TG) and several long chain fatty acids families were identified as differentially expressed in AD. Among them, several fatty acids showed strong positive correlations with miRNAs studied. In fact, these miRNAs regulated genes implied in fatty acids metabolism, as elongation of very long-chain fatty acids (ELOVL), and fatty acid desaturases (FADs).

CONCLUSIONS

The lipidomic-epigenomic integration showed that several lipids and miRNAs were differentially expressed in AD, being the fatty acids mechanisms potentially involved in the disease development. However, further work about targeted analysis should be carried out in a larger cohort, in order to validate these preliminary results and study the proposed pathways in detail.