Sora Mun

Major depressive disorder (MDD) is a multifactorial and highly recurrent illness. Patients are typically required to take antidepressants for 6–12 months following a single depressive episode, even if symptoms are alleviated. However, some individuals are reluctant to use these medications or adhere to prescribed dosing instructions. Therefore, this study aimed to identify biomarkers that can predict MDD recurrence, thus facilitating early detection of relapses and providing strong clinical justification for sustained antidepressant therapy. Serum samples were collected from participants, categorized into two groups: major depressive disorder with a single episode (MDD-S) and recurrent episodes (MDD-R) groups. Candidate biomarkers were identified via targeted tandem mass spectrometry, with features extracted using MassHunter Profinder v10.0 and Mass Profiler Professional software v 15.1 (p < 0.05, fold-change ≥ 1.2). After spectral matching, hypoxanthine was finally identified. Subsequently, a multiple reaction monitoring analysis, a targeted metabolomics technique, was performed to validate hypoxanthine. This analysis confirmed that hypoxanthine levels were significantly lower in the MDD-R group than in the MDD-S group. Validation results demonstrated that this difference was independent of sex and the presence or absence of antidepressant treatment. Moreover, receiver operating characteristic analysis confirmed that the MDD-S and MDD-R groups were significantly distinct (Area Under the Curve = 0.87, sensitivity = 80%, specificity = 80%). In conclusion, this study identified hypoxanthine as a biomarker for predicting MDD recurrence, confirming lower hypoxanthine levels in patients with recurrent MDD than in those with single-episode MDD. Hypoxanthine is a key intermediate in purine metabolism and is known to be involved in the regulation of neuronal energy homeostasis and oxidative stress; these processes are increasingly recognized as being closely linked to the pathophysiology of major depressive disorder. This biomarker may enable the identification of patients at a high risk of recurrence, thereby facilitating the development of personalized treatment strategies.

Objective: Spontaneous remission may influence the outcome of clinical trials and evaluation of antidepressant efficacy, as it is associated with placebo effects and false remission rates. However, the characteristics of spontaneous remission and its biological mechanisms remain poorly understood. This study aimed to explore the metabolic signatures and underlying biological mechanisms of spontaneous remission using metabolomics. Methods: This study conducted untargeted and targeted metabolomic analyses in a discovery cohort (n = 16) comprising patients with major depressive disorder (MDD) and those who were spontaneously remitted without medication. Findings were validated in an independent cohort (n = 185), comprising drug-treated patients and healthy controls. Results: Acetylcarnitine levels were significantly increased in spontaneous remission compared to depression, whereas glycerophosphocholine levels were decreased in spontaneous remission. Both metabolites showed the highest concentration in the control group, followed by the remission group, and the lowest concentration in the depression group, regardless of medication status. These changes suggest alterations in mitochondrial and membrane lipid metabolism. Conclusion: Altered levels in acetylcarnitine and glycerophosphocholine may reflect key pathogenic mechanisms of MDD. These findings offer new insight into spontaneous remission as a distinct clinical subtype of depression and may highlight the potential of these metabolites as biomarkers for treatment monitoring in MDD.Keywords: Depressive disorder, major; Remission, spontaneous; Metabolomics; Acetylcarnitine; Glycerophosphocholine

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

Purpose: Depressive mood is a major psychiatric symptom that causes serious disturbances in daily life. Unlike physical symptoms, psychiatric symptoms are more difficult to evaluate objectively. Therefore, we aimed to discover biomarkers that reflect changes in serum protein metabolism during a clinical depressive mood. Methods: Serum protein profiling was conducted in participants who were not experiencing a current depressive episode (healthy individuals and patients in remission). Serum proteins were identified and quantified using liquid chromatography–tandem mass spectrometry. Differentially expressed proteins with a p-value < 0.05 were selected, and candidate biomarkers were verified using multiple reaction monitoring analysis for absolute quantification. Results: Apolipoprotein A-IV levels were lower in the group with a current episode of depression than in the remission and healthy control groups. Further, fibronectin levels were also lower in the group with a current episode of depression than in the healthy control group but not in the remission group. Conclusion: We found that apolipoprotein A-IV-mediated inflammation is involved in clinical depressive moods, possibly by inducing neurological changes in the brain. Therefore, apolipoprotein A-IV and fibronectin levels may be explored as potentially novel biomarkers for detecting a current episode of depression.

Classifying myocardial infarction by subtype is crucial for appropriate patient management. Although troponin is currently the most commonly used biomarker, it is not a specific marker for myocardial infarction and cannot distinguish subtypes. Furthermore, previous studies have confirmed that proteins known as myocardial infarction markers could function to distinguish the type of myocardial infarction. Therefore, we identify a marker that can distinguish type 1 myocardial infarction from other diseases with elevated troponin. We used mass spectrometry to compare type 1 myocardial infarction with other conditions characterized by troponin elevation and identified new candidate markers for disease classification. We then verified these markers, along with those already known to be associated with cardiovascular disease and plaque rupture. We identified α-1 acid glycoprotein 2, corticosteroid-binding globulin, and serotransferrin as potential distinguishing markers. The presence of these markers and other parameters, such as chest pain, electrocardiogram, and troponin levels from the complementary diagnostic processes, could provide valuable information to specifically diagnose type 1 myocardial infarction.

Ergothioneine, which is a naturally occurring metabolite, generally accumulates in tissues and cells subjected to oxidative stress, owing to its structural stability at physiological pH; therefore, it has been attracting attention in various biomedical fields. Ergothioneine has also been suggested as a potential forensic marker, but its applicability has not yet been quantitatively validated. In this study, quantitative analysis of ergothioneine in bloodstains was conducted to estimate the age of bloodstains and that of bloodstain donors. Blood from youth and elderly participants was used to generate bloodstains. After extracting metabolites from the bloodstains under prevalent age conditions, ergothioneine levels were quantified by mass spectrometry via multiple reaction monitoring. The concentration of ergothioneine in day 0 bloodstains (fresh blood), was significantly higher in the elderly group than in the youth group, but it did not differ by sex. Statistically significant differences were observed between the samples from the two age groups on days 0, 5 and 7, and on days 2 and 3 compared with day 0. The findings suggest that ergothioneine can be used to estimate the age of bloodstains and of the donor; it could be useful as a potential marker in reconstructing crime scenes.

Major depressive disorder (MDD) is a common disorder involving depressive mood and decreased motivation. Due to its high heterogeneity, novel biomarkers are required to diagnose MDD. In this study, a proteomic method was used to identify a new MDD biomarker. Using sequential window acquisition of all theoretical mass spectra acquisitions and multiple reaction monitoring analysis via mass spectrometry, relative and absolute quantification of proteins in the sera was performed. The results of the relative quantitation by sequential window acquisition for all theoretical mass spectra data showed that seven proteins were significantly differently expressed between MDD patients and other patients with remission status. However, absolute quantification by multiple reaction monitoring analysis identified prothrombin as the only significantly upregulated protein in the depressive state compared to remission (p < 0.05) and was, thus, subsequently selected as an MDD biomarker. The area under the curve for prothrombin was 0.66. Additionally, increased prothrombin/thrombin induced hyper-activation of platelets via activating protease-activated receptors, a feature associated with MDD; specifically, activated platelets secrete various molecules related to MDD, including brain-derived neurotropic factors and serotonin. Therefore, prothrombin is a potential screening, prognostic, and diagnostic marker for MDD.

Acute coronary syndrome (ACS) is a condition in which the coronary artery supplying blood to the heart is infarcted via formation of a plaque and thrombus, resulting in abnormal blood supply and high mortality and morbidity. Therefore, the prompt and efficient diagnosis of ACS and the need for new ACS diagnostic biomarkers are important. In this study, we aimed to identify new ACS diagnostic biomarkers with high sensitivity and specificity using a proteomic approach. A discovery set with samples from 20 patients with ACS and 20 healthy controls was analyzed using mass spectrometry. Among the proteins identified, those showing a significant difference between each group were selected. Functional analysis of these proteins was conducted to confirm their association with functions in the diseased state. To determine ACS diagnostic biomarkers, standard peptides of the selected protein candidates from the discovery set were quantified, and these protein candidates were validated in a validation set consisting of the sera of 50 patients with ACS and 50 healthy controls. We showed that hemopexin, leucine-rich α-2-glycoprotein, and vitronectin levels were upregulated, whereas fibronectin level was downregulated, in patients with ACS. Thus, the use of these biomarkers may increase the accuracy of ACS diagnosis.

Ischemic stroke is caused by blood clot formation and consequent vessel blockage. Proteomic approaches provide a cost-effective alternative to current diagnostic methods, including computerized tomography (CT) scans and magnetic resonance imaging (MRI). To identify diagnostic biomarkers associated with ischemic stroke risk factors, we performed individual proteomic analysis of serum taken from 20 healthy controls and 20 ischemic stroke patients. We then performed SWATH analysis, a data-independent method, to assess quantitative changes in protein expression between the two experimental conditions. Our analysis identified several candidate protein biomarkers, 11 of which were validated by multiple reaction monitoring (MRM) analysis as novel diagnostic biomarkers associated with ischemic stroke risk factors. Our study identifies new biomarkers associated with the risk factors and pathogenesis of ischemic stroke which, to the best of our knowledge, were previously unknown. These markers may be effective in not only the diagnosis but also the prevention and management of ischemic stroke.