Identification of Novel Biomarkers for Pre-diabetic Diagnosis Using a Combinational Approach

Omics to Unveil Diabetes Mellitus Pathogenesis and Biomarkers: Focus on Proteomics, Lipidomics, and Metabolomics

Diabetes mellitus (DM) is a chronic metabolic disorder characterized by elevated blood sugar levels, resulting from either body's inability to produce or effectively utilize insulin. There are several types of DM, but the most common are type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes mellitus (GDM). DM is a complex disease and a global health concern, and the current clinical markers, such as fasting glucose, are helpful in the diagnosis of DM, but are not specific and sensitive, especially when measured on the beginning of the pathogenesis. Therefore, there is a pressing need to discover new early biomarkers that can provide an early diagnosis. Omics is an important field for the discovery of potential new biomarkers, especially proteomics, metabolomics, and lipidomics, where techniques such as liquid chromatography, mass spectrometry, and nuclear magnetic resonance are utilized to identify novel DM biomarkers and their pathways. In this review, we report papers that applied omics in the context of DM to identify new markers and their relationship with this disease, with the aim of elucidating new diagnostic techniques for the main types of DM.

Sex-specific transcriptional rewiring in the brain of Alzheimer’s disease patients

Sex-specific differences may contribute to Alzheimer’s disease (AD) development. AD is more prevalent in women worldwide, and female sex has been suggested as a disease risk factor. Nevertheless, the molecular mechanisms underlying sex-biased differences in AD remain poorly characterized. To this end, we analyzed the transcriptional changes in the entorhinal cortex of symptomatic and asymptomatic AD patients stratified by sex. Co-expression network analysis implemented by SWItchMiner software identified sex-specific signatures of switch genes responsible for drastic transcriptional changes in the brain of AD and asymptomatic AD individuals. Pathway analysis of the switch genes revealed that morphine addiction, retrograde endocannabinoid signaling, and autophagy are associated with both females with AD (F-AD) and males with (M-AD). In contrast, nicotine addiction, cell adhesion molecules, oxytocin signaling, adipocytokine signaling, prolactin signaling, and alcoholism are uniquely associated with M-AD. Similarly, some of the unique pathways associated with F-AD switch genes are viral myocarditis, Hippo signaling pathway, endometrial cancer, insulin signaling, and PI3K-AKT signaling. Together these results reveal that there are many sex-specific pathways that may lead to AD. Approximately 20–30% of the elderly have an accumulation of amyloid beta in the brain, but show no cognitive deficit. Asymptomatic females (F-asymAD) and males (M-asymAD) both shared dysregulation of endocytosis. In contrast, pathways uniquely associated with F-asymAD switch genes are insulin secretion, progesterone-mediated oocyte maturation, axon guidance, renal cell carcinoma, and ErbB signaling pathway. Similarly, pathways uniquely associated with M-asymAD switch genes are fluid shear stress and atherosclerosis, FcγR mediated phagocytosis, and proteoglycans in cancer. These results reveal for the first time unique pathways associated with either disease progression or cognitive resilience in asymptomatic individuals. Additionally, we identified numerous sex-specific transcription factors and potential neurotoxic chemicals that may be involved in the pathogenesis of AD. Together these results reveal likely molecular drivers of sex differences in the brain of AD patients. Future molecular studies dissecting the functional role of these switch genes in driving sex differences in AD are warranted.

Application of FTIR Spectroscopy to Detect Changes in Skeletal Muscle Composition Due to Obesity with Insulin Resistance and STZ-Induced Diabetes

Age, obesity, and diabetes mellitus are pathophysiologically interconnected factors that significantly contribute to the global burden of non-communicable diseases. These metabolic conditions are associated with impaired insulin function, which disrupts the metabolism of carbohydrates, lipids, and proteins and can lead to structural and functional changes in skeletal muscle. Therefore, the alterations in the macromolecular composition of skeletal muscle may provide an indication of the underlying mechanisms of insulin-related disorders. The aim of this study was to investigate the potential of Fourier transform infrared (FTIR) spectroscopy to reveal the changes in macromolecular composition in weight-bearing and non-weight-bearing muscles of old, obese, insulin-resistant, and young streptozotocin (STZ)-induced diabetic mice. The efficiency of FTIR spectroscopy was evaluated by comparison with the results of gold-standard histochemical techniques. The differences in biomolecular phenotypes and the alterations in muscle composition in relation to their functional properties observed from FTIR spectra suggest that FTIR spectroscopy can detect most of the changes observed in muscle tissue by histochemical analyses and more. Therefore, it could be used as an effective alternative because it allows for the complete characterization of macromolecular composition in a single, relatively simple experiment, avoiding some obvious drawbacks of histochemical methods.

Towards Multiplexed and Multimodal Biosensor Platforms in Real-Time Monitoring of Metabolic Disorders

Metabolic syndrome (MS) is a cluster of conditions that increases the probability of heart disease, stroke, and diabetes, and is very common worldwide. While the exact cause of MS has yet to be understood, there is evidence indicating the relationship between MS and the dysregulation of the immune system. The resultant biomarkers that are expressed in the process are gaining relevance in the early detection of related MS. However, sensing only a single analyte has its limitations because one analyte can be involved with various conditions. Thus, for MS, which generally results from the co-existence of multiple complications, a multi-analyte sensing platform is necessary for precise diagnosis. In this review, we summarize various types of biomarkers related to MS and the non-invasively accessible biofluids that are available for sensing. Then two types of widely used sensing platform, the electrochemical and optical, are discussed in terms of multimodal biosensing, figure-of-merit (FOM), sensitivity, and specificity for early diagnosis of MS. This provides a thorough insight into the current status of the available platforms and how the electrochemical and optical modalities can complement each other for a more reliable sensing platform for MS.

Predicting fragment intensities and retention time of iTRAQ- and TMTPro-labeled peptides with Prosit-TMT

Isobaric labeling increases the throughput of proteomics by enabling the parallel identification and quantification of peptides and proteins. Over the past decades, a variety of isobaric tags have been developed allowing the multiplexed analysis of up to 18 samples. However, experiments utilizing such tags often exhibit reduced identification rates and thus show decreased analytical depth. Re-scoring has been shown to rescue otherwise missed identifications but was not yet systematically applied on isobarically labeled data. Because iTRAQ 4/8-plex and the recently released TMTpro 16/18-plex share similar characteristics with TMT 6/10/11-plex, we hypothesized that Prosit-TMT, trained exclusively on 6/10/11-plex labeled peptides, may be applicable to these isobaric labeling strategies as well. To investigate this, we re-analyzed nine publicly available datasets covering iTRAQ and TMTpro labeling for samples with human and mouse origin. We highlight that Prosit-TMT shows remarkably good performance when comparing experimentally acquired and predicted fragmentation spectra (R of 0.84 - 0.9) and retention times (ΔRT95% of 3 - 10% gradient time) of peptides. Furthermore, re-scoring substantially increases the number of confidently identified spectra, peptides, and proteins. This article is protected by copyright. All rights reserved.

Identification of Novel Biomarkers for Pre-diabetic Diagnosis Using a Combinational Approach

Reliable protein markers for pre-diabetes in humans are not clinically available. In order to identify novel and reliable protein markers for pre-diabetes in humans, healthy volunteers and patients diagnosed with pre-diabetes and stroke were recruited for blood collection. Blood samples were collected from healthy and pre-diabetic subjects 12 h after fasting. BMI was calculated from body weight and height. Fasting blood glucose (FBG), glycated hemoglobin (HbA1C), triglyceride (TG), total cholesterol, high-density lipoprotein, low-density lipoprotein (LDL), insulin and albumin were assayed by automated clinical laboratory methods. We used a quantitative proteomics approach to identify 1074 proteins from the sera of pre-diabetic and healthy subjects. Among them, 500 proteins were then selected using Mascot analysis scores. Further, 70 out of 500 proteins were selected via volcano plot analysis according to their statistical significance and average relative protein ratio. Eventually, 7 serum proteins were singled out as candidate markers for pre-diabetes due to their diabetic relevance and statistical significance. Immunoblotting data demonstrated that laminin subunit alpha 2 (LAMA2), mixed-lineage leukemia 4 (MLL4), and plexin domain containing 2 (PLXDC2) were expressed in pre-diabetic patients but not healthy volunteers. Receiver operating characteristic curve analysis indicated that the combination of the three proteins has greater diagnostic efficacy than any individual protein. Thus, LAMA2, MLL4 and PLXDC2 are novel and reliable serum protein markers for pre-diabetic diagnosis in humans.