2. Materials and methods
Monitoring of CCHF diagnosed patients and collection of the samples from the patients: Patients who were diagnosed with CCHF were selected from Çorum Hitit University Hospital. The first sample collection was conducted in April 2022, and blood samples were collected daily using Ethylenediaminetetraacetic acid (EDTA) tubes. In current metabolomics studies, opinions regarding the appropriate determination of sample size can considerably vary. However, numerous statistical analyses have underscored that a substantial sample size, for achieving meaningful results, around 30 samples (14). In this study, serum specimens were procured from 29 patients (n=29) diagnosed with CCHFV infection, as well as from 10 healthy control group. CCHF patients also admitted to the hospital with an infection diagnosis were subclassified into two categories based on their blood test values and symptoms: moderate (n=24) and severe (n=5). Four blood serum samples from patients each consecutive day (n=116 samples) and a single sample from the control group (n=10 samples) was taken. These blood samples were then subjected to centrifugation for 5 minutes at 3000g to separate the sample into plasma, white blood cell, and red blood cell phases. The plasma phase was extracted and subjected to metabolite extraction using multiple approaches: single methanol extraction, triple alcohol extraction, Methanol-chloroform, Acetone, Acetonitrile, and Ultrafiltration. Cold methanol-chloroform was chosen as the most effective extraction and used for extraction of all samples. Only the polar metabolites in the plasma were investigated, while proteins and apolar compounds were removed from the plasma samples. Overall in this study, we utilized a rigorous approach to sample collection, preparation, and analysis to investigate polar metabolites in the plasma of CCHF patients (15, 16, 17).
Ethical Statement: The study was approved by Koç University Ethics Committee and all the procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee ethical standards. A written informed consent was obtained from all patients.
NMR sample preparation: 4 mL ice-cold methanol-chloroform (1:1) mixture was added to 2 mL serum for methanol-chloroform extraction. The mixture was vortexed for 30 seconds and incubated for 10 minutes on ice. After incubation the mixture was centrifuged at 4500g at 4°C for 30 minutes. The methanol phase was collected and dried using a vacuum concentrator. The dried samples were dissolved in 550 uL D2O based NMR sample solution (50 mM PBS (pH 7.4), 20mM NaCl, 1 mM DSS) for standardized sample preparation.
NMR data collection, processing and statistical analysis: 500 MHz Bruker Ascend magnet with BBO paired resonance probe and Avance NEO console was used for NMR data collection. 1D NOESY-presat (noesygppr1d) pulse sequence was used for data collection. Each NMR data spectrum is composed of 4K screening and 32K complex data points. Spectrum widths were set to 9615.4 Hz. Bruker Topspin 4.2.0 software was used for NMR data processing. Data was divided into 0.02 ppm data packages along with their normalization coefficients. The dataset, which comprises data packets with a resolution of 0.02 ppm, was analyzed using the MetaboAnalyst 5.0 online metabolomics statistical analysis software. Henceforth the data will be referred to as (Bin.x.xx [ppm]) data packets and the day of when sample was taken. All data points were normalized using the average centering normalization method. Following this normalization, the dataset underwent statistical analysis using Partial Least Squares Discriminant Analysis (PLS-DA). PLS-DA is a classification and discrimination technique based on the Partial Least Squares (PLS) regression method. This method is widely utilized to determine the differences between classes, particularly in high-dimensional and multivariate datasets. PLS-DA is a commonly employed method in analyzing complex biological systems, such as metabolomics studies. The VIP Projection variable importance score plot is a graph that is utilized to assess the outcomes of PLS-DA and determine the most significant variables in the analysis. VIP scores quantify the importance of each variable (e.g., metabolites) in classification and aid in identifying the most critical features. VIP scores are computed based on the contribution of each variable to the components (latent variables) in the PLS-DA model. The values begin at 1, and higher VIP scores indicate that the variable is more important for classification. Variables with VIP scores greater than 1 are generally deemed significant, although this threshold may vary in practice. The VIP score plot displays the VIP scores of the variables on the vertical axis, while the variables themselves or their indices are shown on the horizontal axis. This graph facilitates the identification of important variables visually and helps focus on the variables that require prioritization in the analysis. In the VIP score graph, the peaks at the relevant ppm values that make up the data packages (Bin.x.xx) have been examined in more detail and the metabolites to which they belong have been identified. For this operation, NMR spectra have been reopened, and the metabolites to which the peak in the relevant ppm region belongs have been determined using the Chenomx software. It is thought that some peaks might belong to metabolites not found in the database, and the molecules these peaks belong to have not been identified. Further investigation and characterization may be required to fully understand these unidentified peaks and their role in the overall metabolic profile, ensuring that the final analysis provides an accurate reflection of the biological system under investigation.
Metabolomic pathway visualization : All metabolic pathways visualized using Metastate software Version BETA (https://metastate.bio) (Metastate Bio Inc.). Metastate algorithm employs the Kyoto Encyclopedia of Genes and Genomes (KEGG) database as its foundational input source. Software systematically retrieves details pertaining to biological pathways, chemical compounds, and molecular reactions of interest. Software curates and assembles a dynamic graphical representation of the data.