Next-generation Metabolomics in the Development of New Antidepressants: Using Albiflorin as an Example

Han, Jing; Xia, Yonghong; Lin, Lejun; Zhang, Zuoguang; Tian, Hai‐Yan; Li, Kefeng

doi:10.2174/1381612824666180727114134

Cited by 25 publications

(14 citation statements)

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“…Differentiating SCLC from NSCLC (large cell carcinoma or basaloid squamous cell carcinoma) using noninvasive methods is essential since the therapeutic strategies and clinical prognoses are significantly different. Several noninvasive blood metabolomic and dynamic multiphase computed tomography (CT) methods have been recently developed to discriminate SCLC and NSCLC [27][28][29][30]. However, blood metabolomic biomarker results that showed the differentiation of SCLC and NSCLC tumor types has not been confirmed with large cohort studies.…”

Section: Discussionmentioning

confidence: 99%

A Noninvasive Assessment of Tumor Proliferation in Lung cancer Patients using Intravoxel Incoherent Motion Magnetic Resonance Imaging

Zheng¹,

Huang²,

Zhang³

et al. 2021

J. Cancer

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Ki-67 is a nuclear antigen widely used in routine pathologic analyses as a tumor cell proliferation marker for lung cancer. However, Ki-67 expression analyses using immunohistochemistry (IHC) are invasive and frequently influenced by tissue sampling quality. In this study, we assessed the feasibility of noninvasive magnetic resonance imaging (MRI) in predicting the Ki-67 labeling indices (LIs). A total of 51 lung cancer patients, including 42 non-small cell lung cancer (NSCLC) cases and nine small cell lung cancer (SCLC) cases, were enrolled in this study. Quantitative MRI parameters from conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) were obtained, and their correlations with tumor tissue Ki-67 expression were analyzed. We found that the true diffusion coefficient (D value) from IVIM was negatively correlated with Ki-67 expression (Spearman r =-0.76, P < 0.001). The D values in the high Ki-67 group were significantly lower than those in the low Ki-67 group (0.90 ± 0.21 × 10-3 mm 2 /s vs. 1.22 ± 0.30 × 10-3 mm 2 /s). Among three MRI techniques used, D values from IVIM showed the best performance for distinguishing the high Ki-67 group from low Ki-67 group in receiver operating characteristic (ROC) analysis with an area under the ROC curve (AUROC) of 0.85 (95% CI: 0.73-0.97, P < 0.05). Moreover, D values performed well for differentiating SCLC from NSCLC with an AUROC of 0.82 (95% CI: 0.68-0.90), Youden index of 0.72, and F1 score of 0.81. In conclusion, D values were negatively correlated with Ki-67 expression in lung cancer tissues and can be used to distinguish high from low proliferation statuses, as well as SCLC from NSCLC.

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Section: Discussionmentioning

confidence: 99%

A Noninvasive Assessment of Tumor Proliferation in Lung cancer Patients using Intravoxel Incoherent Motion Magnetic Resonance Imaging

Zheng¹,

Huang²,

Zhang³

et al. 2021

J. Cancer

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“…Metabolomics aims to measure the small molecules (metabolites) within cells, biofluids, tissues, or organisms using various analytical techniques. Unlike genomics, transcriptomics, and proteomics, metabolomics represents the molecular phenotype of an organism because metabolites and their concentrations are the direct functional "readout" of cellular activity and the state of cells and tissues [11]. Therefore, clinical metabolomics offers a strategic advantage for the elucidation of the new roles of metabolism in disease, the identification of biomarkers, and the development of new therapeutics [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of sepsis mortality using metabolite biomarkers in the blood: a meta-analysis of death-related pathways and prospective validation

Wang

Sun

Teng

et al. 2020

BMC Med

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Background: Sepsis is a leading cause of death in intensive care units (ICUs), but outcomes of individual patients are difficult to predict. The recently developed clinical metabolomics has been recognized as a promising tool in the clinical practice of critical illness. The objective of this study was to identify the unique metabolic biomarkers and their pathways in the blood of sepsis nonsurvivors and to assess the prognostic value of these pathways. Methods: We searched PubMed, EMBASE, Cochrane, Web of Science, CNKI, Wangfang Data, and CQVIP from inception until July 2019. Eligible studies included the metabolomic analysis of blood samples from sepsis patients with the outcome. The metabolic pathway was assigned to each metabolite biomarker. The meta-analysis was performed using the pooled fold changes, area under the receiver operating characteristic curve (AUROC), and vote-counting of metabolic pathways. We also conducted a prospective cohort metabolomic study to validate the findings of our meta-analysis. Results: The meta-analysis included 21 cohorts reported in 16 studies with 2509 metabolite comparisons in the blood of 1287 individuals. We found highly limited overlap of the reported metabolite biomarkers across studies. However, these metabolites were enriched in several death-related metabolic pathways (DRMPs) including amino acids, mitochondrial metabolism, eicosanoids, and lysophospholipids. Prediction of sepsis death using DRMPs yielded a pooled AUROC of 0.81 (95% CI 0.76-0.87), which was similar to the combined metabolite biomarkers with a merged AUROC of 0.82 (95% CI 0.78-0.86) (P > 0.05). A prospective metabolomic analysis of 188 sepsis patients (134 survivors and 54 nonsurvivors) using the metabolites from DRMPs produced an AUROC of 0.88 (95% CI 0.78-0.97). The sensitivity and specificity for the prediction of sepsis death were 80.4% (95% CI 66.9-89.4%) and 78.8% (95% CI 62.3-89.3%), respectively. Conclusions: DRMP analysis minimizes the discrepancies of results obtained from different metabolomic methods and is more practical than blood metabolite biomarkers for sepsis mortality prediction. Trial registration: The meta-analysis was registered on OSF Registries, and the prospective cohort study was registered on the Chinese Clinical Trial Registry (ChiCTR1800015321).

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“…Antidepressants have become one of the most commonly prescribed drug classes in the United States 6,7 .…”

Section: Introductionmentioning

confidence: 99%

Long-term use of fluoxetine accelerates bone loss through the disruption of sphingolipids metabolism in bone marrow adipose tissue

Zhang

Zhao

et al. 2020

Transl Psychiatry

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Fluoxetine is a commonly prescribed antidepressant, and the mechanisms of increased bone fragility with its longterm use remain largely unknown. Here, we show that long-term administration of fluoxetine induces the disruption of sphingolipids metabolism in bone marrow adipose tissue (BMAT)through the inhibition of acid sphingomyelinase (ASM). Similarly, a significant reduction of the bone volume was observed in mice with ASM knockout (Smpd1 −/−). In detail, inhibition of ASM by fluoxetine reduces the sphingosine-1-phosphate (S1P) level in bone marrow adipocytes, leading to the increase of receptor activator of nuclear factor-kappa-Β ligand (RANKL) secretion, a key regulator for the activation of osteoclastogenesis and bone loss, through the upregulation of cyclooxygenase-2 and its enzymatic product prostaglandin E2 (COX-2/PGE2). In contrast, overexpression of ASM by cisplatin normalizes fluoxetine-induced RANKL overproduction. Furthermore, we conducted a clinical trial with L-serine, a precursor of sphingolipids biosynthesis. The results show that oral supplementation of L-serine (250 mg//kg/d) prevents the acceleration of bone loss caused by long-term fluoxetine (12 months) in postmenopausal women with major depressive disorder (mean total hip bone mineral density reduction: −2.0% vs −1.1%, P = 0.006). Our study provides new insights and potential treatment strategy on the bone loss caused by long-term use of fluoxetine.

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Next-generation Metabolomics in the Development of New Antidepressants: Using Albiflorin as an Example

Cited by 25 publications

References 0 publications

A Noninvasive Assessment of Tumor Proliferation in Lung cancer Patients using Intravoxel Incoherent Motion Magnetic Resonance Imaging

A Noninvasive Assessment of Tumor Proliferation in Lung cancer Patients using Intravoxel Incoherent Motion Magnetic Resonance Imaging

Prediction of sepsis mortality using metabolite biomarkers in the blood: a meta-analysis of death-related pathways and prospective validation

Long-term use of fluoxetine accelerates bone loss through the disruption of sphingolipids metabolism in bone marrow adipose tissue

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