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Gestational diabetes mellitus (GDM), described as glucose intolerance that is first diagnosed in pregnancy represents a hazard factor for long-term maternal and offspring cardiometabolic disease. Molecular chaperones play crucial parts in assisting polypeptide folding, chaperoning and cryoprotection inside the cells. However, novel information now associates these proteins as relevant participants in the extracellular surroundings, with involvement in vital stress-related mechanisms akin to inflammation and immunity. We hypothesize that a specific panel of chaperons could predict the onset of GDM. The project goal is to establish a panel of blood circulating chaperons that could quantitatively correlate with the pathogenesis and evolution of GDM. Our specific objectives are to set up of a dependent correlation between the expression pattern of circulating extracellular chaperones and GDM debut and to standardize a chaperone absolute quantification methodology that can be readily transferred to clinical practice. For this, we will use the “selected reaction monitoring” strategy, as a recognized technique for high-throughput, multiplexing analysis of proteins with a significantly increased value over the alternative antibody-based approaches. This technique assures the required sensitivity, selectivity and non-invasive measurement and will represent a cost-effective, valuable asset for the rapid transfer of basic biomedical research knowledge to clinical practice.

Objectives:

Objective 1: Correlation between the expression pattern of diabetic plasma circulating chaperones and GDM evolution;

Objective 2: Validation of the identified pattern of chaperones as potential markers of GDM evolution by an absolute quantitative profile of the chaperones.

The proposed workflow can be found below (Figure 1).

Figure 1: Graphical workflow of the project.

Report stage 2022:

Objective 1: Correlation between the expression pattern of diabetic plasma circulating chaperones and GDM evolution;

Activity 1.1: Patients’ professional enrollment and plasma sample collection
Activity 1.2: Plasma sample preparation and optimization for mass spectrometric analysis
Activity 1.3: Mass spectrometric analysis for the identification of circulating chaperones

At this stage, in collaboration with the National Institute of Diabetes, Nutrition and Metabolic Diseases "N. Paulescu", the process of recruitment of patients with GDM, control subjects (CG), clinical and paraclinical examination and collection of blood samples for plasma isolation was initiated. In this regard, the necessary approvals for the collection of human samples have been obtained, through the completion of patient informed consent and ethics documentation. Human samples started to be collected in the third trimester of gestation for the proposed proteomic analyses.

GDM was diagnosed by the 75-g oral glucose tolerance test (GTT) two hours after ingestion of aqueous glucose solution, as recommended by the National Institute for Health and Care Excellence (NIHCE), American Diabetes Association (ADA). GDM was diagnosed if one or more of the glucose level values exceeded the cut-off: fasting ≥92 mg/dL, 1 h ≥180 mg/dL, 2 h ≥153 mg/dL. Patient exclusion criteria were based on age (40 years) and the presence of hypertension, nephropathy, preeclampsia, retinopathy, and psychiatric treatment.

To reduce the dynamic domain of blood proteins, enrichment of samples in extracellular exosomal vesicles was preferred. These were concentrated from 500 µL of sample collected from the two experimental groups. Exosome size and stability were determined by dynamic light scattering (DLS) on a Zetasizer Nano ZS (ZEN 3600). The exosome samples were suitably prepared for nano-chromatography tandem mass spectrometric analysis.

The Easy nLC II nano-chromatographic system was coupled to the LTQ Orbitrap Velos Pro hybrid mass spectrometer (Thermo Scientific, San Jose, CA, USA) for peptide separation and mass spectra acquisition. For each sample, 1 µg of peptide in technological triplicate was separated in a 15 cm × 75 μm.d., C18, 3 μm, 120 Å analytical column (Thermo Fisher Scientific, Rockford, IL, USA), and separation was performed using a gradient of solvent B (acetonitrile with 0.1% (v/v) formic acid) over solvent A (water with 0.1% (v/v) formic acid) for 90 min at a flow rate of 300 nL/min.

The chromatographic method was optimized for optimal elution of exosomal peptides by varying the acetonitrile gradient. Thus, using a 2-step gradient, starting from 2% acetonitrile to 30% acetonitrile for 65 minutes, followed by another step of increasing the acetonitrile concentration to 50% for another 25 minutes, an optimal elution resolution of both hydrophilic and hydrophobic peptides was obtained (Figure 2).

Figure 2: Chromatographic elution of a representative exosomal peptide sample.

We chose to operate the mass spectrometer in a top 15 "Data Dependent" configuration at a resolution power of 60000 for full scanning, in the 350-1700 m/z range. The chosen fragmentation mode was collision induced dissociation with helium molecules for the mass spectral acquisition of the ion fragments.

The raw data were processed for protein identification using the Sequest HT search algorithm within Proteome Discoverer 2.4 software (Thermo Fisher Scientific, Rockford, IL, USA). The Uniprot/SwissProt Homo sapiens reference proteome (SwissProt TaxID = 9606, v 2019-10-04) was used, with methionine oxidation set as dynamic modification and carbamidomethylation of cysteine as the modification, while a maximum of two missed cleavages was allowed. A reverse database search was performed for strict False Discovery Rate (FDR) settings of proteins and peptides (<5%). Label-free relative quantification was performed using the precursor ion quantifier node and was based on the intensity of unique peptide precursors present in 90% of replicate features.

The effect of GDM pathology was revealed by multivariate static analyses, such as Principal Component Analysis and Hierarchical Clustering Heat-Map Analysis, which demonstrated exosome proteome modification in the GDM group compared to CG (Figure 3).

Figure 3: Multivariate statistical analyses: a) "Principal Component Analysis" and b) "Hierarchical Clustering Heat-Map Analysis" demonstrating an altered pathological state proteome (GDM) versus control (CG).