Data Independent Acquisition Mass Spectrometry Can Identify Circulating Proteins That Predict Future Weight Loss with a Diet and Exercise Programme

Proteomics of the heart

Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.

The role of pathogens in diabetes pathogenesis and the potential of immunoproteomics as a diagnostic and prognostic tool

Diabetes mellitus (DM) is a group of metabolic diseases marked by hyperglycemia, which increases the risk of systemic infections. DM patients are at greater risk of hospitalization and mortality from bacterial, viral, and fungal infections. Poor glycemic control can result in skin, blood, bone, urinary, gastrointestinal, and respiratory tract infections and recurrent infections. Therefore, the evidence that infections play a critical role in DM progression and the hazard ratio for a person with DM dying from any infection is higher. Early diagnosis and better glycemic control can help prevent infections and improve treatment outcomes. Perhaps, half (49.7%) of the people living with DM are undiagnosed, resulting in a higher frequency of infections induced by the hyperglycemic milieu that favors immune dysfunction. Novel diagnostic and therapeutic markers for glycemic control and infection prevention are desirable. High-throughput blood-based immunoassays that screen infections and hyperglycemia are required to guide timely interventions and efficiently monitor treatment responses. The present review aims to collect information on the most common infections associated with DM, their origin, pathogenesis, and the potential of immunoproteomics assays in the early diagnosis of the infections. While infections are common in DM, their role in glycemic control and disease pathogenesis is poorly described. Nevertheless, more research is required to identify novel diagnostic and prognostic markers to understand DM pathogenesis and management of infections. Precise monitoring of diabetic infections by immunoproteomics may provide novel insights into disease pathogenesis and healthy prognosis.

Sensitive Detection of Pharmaceutical Drugs and Metabolites in Serum Using Data-Independent Acquisition Mass Spectrometry and Open-Access Data Acquisition Tools

Data-independent acquisition (DIA) based strategies have been explored in recent years for improving quantitative analysis of metabolites. However, the data analysis is challenging for DIA methods as the resulting spectra are highly multiplexed. Thus, the DIA mode requires advanced software analysis to facilitate the data deconvolution process. We proposed a pipeline for quantitative profiling of pharmaceutical drugs and serum metabolites in DIA mode after comparing the results obtained from full-scan, Data-dependent acquisition (DDA) and DIA modes. using open-access software. Pharmaceutical drugs (10) were pooled in healthy human serum and analysed by LC-ESI-QTOF-MS. MS1 full-scan and Data-dependent (MS2) results were used for identification using MS-DIAL software while deconvolution of MS1/MS2 spectra in DIA mode was achieved by using Skyline software. The results of acquisition methods for quantitative analysis validated the remarkable analytical performance of the constructed workflow, proving it to be a sensitive and reproducible pipeline for biological complex fluids.

Machine learning based prediction and the influence of complement - Coagulation pathway proteins on clinical outcome: Results from the NEURAPRO trial

BACKGROUND

Functional outcomes are important measures in the overall clinical course of psychosis and individuals at clinical high-risk (CHR), however, prediction of functional outcome remains difficult based on clinical information alone. In the first part of this study, we evaluated whether a combination of biological and clinical variables could predict future functional outcome in CHR individuals. The complement and coagulation pathways have previously been identified as being of relevance to the pathophysiology of psychosis and have been found to contribute to the prediction of clinical outcome in CHR participants. Hence, in the second part we extended the analysis to evaluate specifically the relationship of complement and coagulation proteins with psychotic symptoms and functional outcome in CHR.

MATERIALS AND METHODS

We carried out plasma proteomics and measured plasma cytokine levels, and erythrocyte membrane fatty acid levels in a sub-sample (n = 158) from the NEURAPRO clinical trial at baseline and 6 months follow up. Functional outcome was measured using Social and Occupational Functional assessment Score (SOFAS) scale. Firstly, we used support vector machine learning techniques to develop predictive models for functional outcome at 12 months. Secondly, we developed linear regression models to understand the association between 6-month follow-up levels of complement and coagulation proteins with 6-month follow-up measures of positive symptoms summary (PSS) scores and functional outcome.

RESULTS AND CONCLUSION

A prediction model based on clinical and biological data including the plasma proteome, erythrocyte fatty acids and cytokines, poorly predicted functional outcome at 12 months follow-up in CHR participants. In linear regression models, four complement and coagulation proteins (coagulation protein X, Complement C1r subcomponent like protein, Complement C4A & Complement C5) indicated a significant association with functional outcome; and two proteins (coagulation factor IX and complement C5) positively associated with the PSS score. Our study does not provide support for the utility of cytokines, proteomic or fatty acid data for prediction of functional outcomes in individuals at high-risk for psychosis. However, the association of complement protein levels with clinical outcome suggests a role for the complement system and the activity of its related pathway in the functional impairment and positive symptom severity of CHR patients.

Methylation Status of Exon IV of the Brain-Derived Neurotrophic Factor (BDNF)-Encoding Gene in Patients with Non-Diabetic Hyperglycaemia (NDH) before and after a Lifestyle Intervention

BDNF signalling in hypothalamic neuronal circuits is thought to regulate mammalian food intake. In light of this, we investigated how a lifestyle intervention influenced serum levels and DNA methylation of BDNF gene in fat tissue and buffy coat of NDH individuals. In total, 20 participants underwent anthropometric measurements/fasting blood tests and adipose tissue biopsy pre-/post-lifestyle (6 months) intervention. DNA was extracted from adipose tissue and buffy coat, bisulphite converted, and pyrosequencing was used to determine methylation levels in exon IV of the BDNF gene. RNA was extracted from buffy coat for gene expression analysis and serum BDNF levels were measured by ELISA. No differences were found in BDNF serum levels, but buffy coat mean BDNF gene methylation decreased post-intervention. There were correlations between BDNF serum levels and/or methylation and cardiometabolic markers. (i) Pre-intervention: for BDNF methylation, we found positive correlations between mean methylation in fat tissue and waist-hip ratio, and negative correlations between mean methylation in buffy coat and weight. (ii) Post-intervention: we found correlations between BDNF mean methylation in buffy coat and HbA1c, BDNF methylation in buffy coat and circulating IGFBP-2, and BDNF serum and insulin. Higher BDNF % methylation levels are known to reduce BNDF expression. The fall in buffy coat mean BDNF methylation plus the association between lower BDNF methylation (so potentially higher BDNF) and higher HbA1c and serum IGFBP-2 (as a marker of insulin sensitivity) and between lower serum BDNF and higher circulating insulin are evidence for the degree of BDNF gene methylation being implicated in insulinisation and glucose homeostasis, particularly after lifestyle change in NDH individuals.

Changes in the Proteome Profile of People Achieving Remission of Type 2 Diabetes after Bariatric Surgery

Bariatric surgery (BS) results in metabolic pathway recalibration. We have identified potential biomarkers in plasma of people achieving type 2 diabetes mellitus (T2DM) remission after BS. Longitudinal analysis was performed on plasma from 10 individuals following Roux-en-Y gastric bypass (n = 7) or sleeve gastrectomy (n = 3). Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) was done on samples taken at 4 months before (baseline) and 6 and 12 months after BS. Four hundred sixty-seven proteins were quantified by SWATH-MS. Principal component analysis resolved samples from distinct time points after selection of key discriminatory proteins: 25 proteins were differentially expressed between baseline and 6 months post-surgery; 39 proteins between baseline and 12 months. Eight proteins (SHBG, TF, PRG4, APOA4, LRG1, HSPA4, EPHX2 and PGLYRP) were significantly different to baseline at both 6 and 12 months post-surgery. The panel of proteins identified as consistently different included peptides related to insulin sensitivity (SHBG increase), systemic inflammation (TF and HSPA4-both decreased) and lipid metabolism (APOA4 decreased). We found significant changes in the proteome for eight proteins at 6- and 12-months post-BS, and several of these are key components in metabolic and inflammatory pathways. These may represent potential biomarkers of remission of T2DM.

Discovery and Evaluation of Protein Biomarkers as a Signature of Wellness in Late-Stage Cancer Patients in Early Phase Clinical Trials

TARGET (tumour characterisation to guide experimental targeted therapy) is a cancer precision medicine programme focused on molecular characterisation of patients entering early phase clinical trials. Performance status (PS) measures a patient's ability to perform a variety of activities. However, the quality of present algorithms to assess PS is limited and based on qualitative clinician assessment. Plasma samples from patients enrolled into TARGET were analysed using the mass spectrometry (MS) technique: sequential window acquisition of all theoretical fragment ion spectra (SWATH)-MS. SWATH-MS was used on a discovery cohort of 55 patients to differentiate patients into either a good or poor prognosis by creation of a Wellness Score (WS) that showed stronger prediction of overall survival (p = 0.000551) compared to PS (p = 0.001). WS was then tested against a validation cohort of 77 patients showing significant (p = 0.000451) prediction of overall survival. WS in both sets had receiver operating characteristic curve area under the curve (AUC) values of 0.76 (p = 0.002) and 0.67 (p = 0.011): AUC of PS was 0.70 (p = 0.117) and 0.55 (p = 0.548). These signatures can now be evaluated further in larger patient populations to assess their utility in a clinical setting.

Circulating microRNA changes in patients with impaired glucose regulation

We analysed if levels of four miRNAs would change after a lifestyle intervention involving dietary and exercises in prediabetes. MiRNAs previously shown to be associated with diabetes (Let-7a, Let-7e, miR-144 and miR-92a) were extracted from serum pre- and post-intervention. mRNA was extracted from fat-tissue for gene expression analyses. The intervention resulted in increased Let-7a and miR-92a. We found correlations between miRNAs and clinical variables (triglycerides, cholesterol, insulin, weight and BMI). We also found correlations between miRNAs and target genes, revealing a link between miR-92a and IGF system. A lifestyle intervention resulted in marked changes in miRNAs. The association of miRNAs with insulin and the IGF system (both receptors and binding proteins) may represent a mechanism of regulating IGFs metabolic actions.

Lifestyle intervention in individuals with impaired glucose regulation affects Caveolin-1 expression and DNA methylation

Aims: We investigated whether a lifestyle intervention could influence expression and DNA methylation of diabetes-related genes in patients with impaired glucose regulation (IGR), the results were compared to bariatric surgery, considering it an intensive change. Methods: Twenty participants with IGR had adipose tissue biopsy and blood collected pre- and post-lifestyle (6 months) intervention; 12 obese patients had subcutaneous fat taken before and after bariatric surgery. RNA/DNA was extracted from all samples and underwent qPCR. DNA was bisulphite converted and 12 CpG sites of Caveolin-1 (CAV1) promoter were pyrosequenced. Results: lifestyle intervention resulted in opposite direction changes in fat tissue and blood for CAV1 expression and DNA methylation and these changes were correlated between tissues, while no significative differences were found in CAV1 expression after bariatric surgery. Conclusions: Our findings suggest a role for CAV1 in modulating adipocyte function as a consequence of lifestyle changes, as exercises and diet. These results may provide insights into new therapeutic targets for diabetes prevention.

Application of SWATH mass spectrometry in the identification of circulating proteins does not predict future weight gain in early psychosis

Weight gain is a common consequence of treatment with antipsychotic drugs in early psychosis, leading to further morbidity and poor treatment adherence. Identifying tools that can predict weight change in early psychosis may contribute to better-individualised treatment and adherence. Recently we showed that proteomic profiling with sequential window acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry (MS) can identify individuals with pre-diabetes more likely to experience weight change in relation to lifestyle change. We investigated whether baseline proteomic profiles predicted weight change over time using data from the BeneMin clinical trial of the anti-inflammatory antibiotic, minocycline, versus placebo. Expression levels for 844 proteins were determined by SWATH proteomics in 83 people (60 men and 23 women). Hierarchical clustering analysis and principal component analysis of baseline proteomics data did not reveal distinct separation between the proteome profiles of participants in different weight change categories. However, individuals with the highest weight loss had higher Positive and Negative Syndrome Scale (PANSS) scores. Our findings imply that mode of treatment i.e. the pharmacological intervention for psychosis may be the determining factor in weight change after diagnosis, rather than predisposing proteomic dynamics.

Paternal Resistance Training Modulates Calcaneal Tendon Proteome in the Offspring Exposed to High-Fat Diet

The increase in high-energy dietary intakes is a well-known risk factor for many diseases, and can also negatively impact the tendon. Ancestral lifestyle can mitigate the metabolic harmful effects of offspring exposed to high-fat diet (HF). However, the influence of paternal exercise on molecular pathways associated to offspring tendon remodeling remains to be determined. We investigated the effects of 8 weeks of paternal resistance training (RT) on offspring tendon proteome exposed to standard diet or HF diet. Wistar rats were randomly divided into two groups: sedentary fathers and trained fathers (8 weeks, three times per week, with 8–12 dynamic movements per climb in a stair climbing apparatus). The offspring were obtained by mating with sedentary females. Upon weaning, male offspring were divided into four groups (five animals per group): offspring from sedentary fathers were exposed either to control diet (SFO-C), or to high-fat diet (SFO-HF); offspring from trained fathers were exposed to control diet (TFO-C) or to a high-fat diet (TFO-HF). The Nano-LC-MS/MS analysis revealed 383 regulated proteins among offspring groups. HF diet induced a decrease of abundance in tendon proteins related to extracellular matrix organization, transport, immune response and translation. On the other hand, the changes in the offspring tendon proteome in response to paternal RT were more pronounced when the offspring were exposed to HF diet, resulting in positive regulation of proteins essential for the maintenance of tendon integrity. Most of the modulated proteins are associated to biological pathways related to tendon protection and damage recovery, such as extracellular matrix organization and transport. The present study demonstrated that the father’s lifestyle could be crucial for tendon homeostasis in the first generation. Our results provide important insights into the molecular mechanisms involved in paternal intergenerational effects and potential protective outcomes of paternal RT.

Advances in Research on Diabetes by Human Nutriomics

The incidence and prevalence of diabetes mellitus (DM) have increased rapidly worldwide over the last two decades. Because the pathogenic factors of DM are heterogeneous, determining clinically effective treatments for DM patients is difficult. Applying various nutrient analyses has yielded new insight and potential treatments for DM patients. In this review, we summarized the omics analysis methods, including nutrigenomics, nutritional-metabolomics, and foodomics. The list of the new targets of SNPs, genes, proteins, and gut microbiota associated with DM has been obtained by the analysis of nutrigenomics and microbiomics within last few years, which provides a reference for the diagnosis of DM. The use of nutrient metabolomics analysis can obtain new targets of amino acids, lipids, and metal elements, which provides a reference for the treatment of DM. Foodomics analysis can provide targeted dietary strategies for DM patients. This review summarizes the DM-associated molecular biomarkers in current applied omics analyses and may provide guidance for diagnosing and treating DM.