Integration of proteomics and metabolomics reveals energy and metabolic alterations induced by glucokinase (GCK) partial inactivation in hepatocytes.

AIMS: Glucokinase (GCK) acts as the glucose sensor in maintaining glucose homeostasis. The inactivating mutation of the GCK gene leads to glucokinase-maturity onset diabetes of the young (GCK-MODY). This study aims to gain further insights into the molecular alterations triggered by GCK partial inactivation in hepatocytes, potentially underlying the favorable prognosis of GCK-MODY. MAIN METHODS: A GCK knockdown HepG2 cell model was established, and the integration of proteomics and metabolomics was used to gain a comprehensive understanding of the molecular pathway changes caused by GCK inactivation in the liver. KEY FINDINGS: Proteomic analysis identified 257 differential proteins. KEGG pathway enrichment analysis showed that protein expression changes in the GCK knockdown group were significantly enriched in central carbon metabolism, the TCA cycle, amino acid metabolism and the oxidative phosphorylation pathway. Among them, enzymes in the TCA cycle (PC, IDH2, SDH) were significantly downregulated in GCK-knockdown group. Targeted metabolomics revealed that in the GCK knockdown hepatocytes, TCA cycle intermediates were significantly decreased, including pyruvate, oxaloacetate, citrate and succinic acid, and three metabolites increased including glycine, betaine and homocysteine. These metabolic alterations in turn reduced the accumulation of reactive oxygen species in GCK knockdown hepatocytes. Correlation analysis indicated that TCA cycle metabolites were positively correlated with proteins involved in the TCA cycle, carbon metabolism, glycolysis, Ras signaling, fibrosis and inflammation. SIGNIFICANCE: In conclusion, GCK knockdown reduced TCA cycle flux and oxidative stress in hepatocytes by influencing the levels of key transcription factors and enzymes, providing a comprehensive understanding of the effects of GCK partial inactivation on liver metabolism and molecular mechanisms.

Quantitative profiling and diagnostic potential of one-carbon and central metabolism pools in MODY2 and T1DM.

BACKGROUND: Maturity-onset diabetes of the young type 2 (MODY2) is a rare genetic disorder characterized as mild fasting hyperglycemia with low risk of vascular complications caused by glucokinase gene mutation. This study aims to investigate metabolites alteration associated with MODY2, exploring possible mechanism underlying characteristic clinical manifestations and low cardiovascular risks of MODY2 and providing serum metabolite biomarkers to facilitating MODY2 diagnosis. METHODS: Fasting serum samples from MODY2, type 1 diabetes (T1DM) and healthy individuals were collected. By using targeted metabolomics via liquid chromatography-tandem mass spectrometry platform, we quantified the metabolites involved in tricarboxylic acid (TCA) cycle and one-carbon metabolism. RESULTS: Metabolomic profiling revealed significant difference of intermediates from central metabolism cycle, methionine cycle and several amino acids between MODY2 and T1DM groups. Among these, serum citrate, α-ketoglutaric acid, serine, glycine, glutamine and homocysteine were significantly elevated in MODY2 patients compared with T1DM patients; and compared with healthy subjects, malate and methionine levels were significantly increased in the two groups of diabetic patients. The correlation analysis with clinical indexes showed that α- ketoglutarate, serine, glycine, and glutamine were negatively correlated with blood glucose indicators including fasting blood glucose, HbA1c, and GA, while citrate was positively correlated with C-peptide. And homocysteine displayed positive correlation with HDL and negative with C-reactive protein, which shed light on the mechanism of mild symptoms and low risk of cardiovascular complications in MODY2 patients. A panel of 4 metabolites differentiated MODY2 from T1DM with AUC of 0.924, and a combination of clinical indices and metabolite also gained good diagnostic value with AUC 0.948. CONCLUSION: In this research, we characterized the metabolite profiles of TCA cycle and one-carbon metabolism in MODY2 and T1DM and identified promising diagnostic biomarkers for MODY2. This study may provide novel insights into the pathogenesis and clinical manifestations of MODY2.

Single-cell transcriptomic analyses provide insights into the cellular origins and drivers of brain metastasis from lung adenocarcinoma.

BACKGROUND: Brain metastasis (BM) is the most common intracranial malignancy causing significant mortality, and lung cancer is the most common origin of BM. However, the cellular origins and drivers of BM from lung adenocarcinoma (LUAD) have yet to be defined. METHODS: The cellular constitutions were characterized by single-cell transcriptomic profiles of 11 LUAD primary tumor (PT) and 10 BM samples (GSE131907). Copy number variation (CNV) and clonality analysis were applied to illustrate the cellular origins of BM tumors. Brain metastasis-associated epithelial cells (BMAECs) were identified by pseudotime trajectory analysis. By using machine-learning algorithms, we developed the BM-index representing the relative abundance of BMAECs in the bulk RNA-seq data indicating a high risk of BM. Therapeutic drugs targeting BMAECs were predicted based on the drug sensitivity data of cancer cell lines. RESULTS: Differences in macrophages and T cells between PTs and BMs were investigated by single-cell RNA (scRNA) and immunohistochemistry and immunofluorescence data. CNV analysis demonstrated BM was derived from subclones of PT with a gain of chromosome 7. We then identified BMAECs and their biomarker, S100A9. Immunofluorescence indicated strong correlations of BMAECs with metastasis and prognosis evaluated by the paired PT and BM samples from Peking Union Medical College Hospital. We further evaluated the clinical significance of the BM-index and identified 7 drugs that potentially target BMAECs. CONCLUSIONS: This study clarified possible cellular origins and drivers of metastatic LUAD at the single-cell level and laid a foundation for early detection of LUAD patients with a high risk of BM.

Dynamic Plasma Lipidomic Analysis Revealed Cholesterol Ester and Amides Associated with Sepsis Development in Critically Ill Patients after Cardiovascular Surgery with Cardiopulmonary Bypass.

Background: Sepsis in patients after cardiovascular surgery with cardiopulmonary bypass (CPB) has a high rate of mortality. We sought to determine whether changes in lipidomics can predict sepsis after cardiac surgery. Methods: We used high-performance liquid chromatography coupled to tandem mass spectrometry to explore global lipidome changes in samples from a prospective case-control cohort (30 sepsis vs. 30 nonsepsis) hospitalized with cardiovascular surgery. All patients were sampled before and within 48−72 h after surgery. A bioinformatic pipeline was applied to acquire reliable features and MS/MS-driven identifications. Furthermore, a multiple-step machine learning framework was performed for signature discovery and performance evaluation. Results: Compared with preoperative samples, 94 features were upregulated and 282 features were downregulated in the postoperative samples of the sepsis group, and 73 features were upregulated and 265 features were downregulated in the postoperative samples of the nonsepsis group. "Autophagy", "pathogenic Escherichia coli infection" and "glycosylphosphatidylinositol-anchor biosynthesis" pathways were significantly enriched in the pathway enrichment analysis. A multistep machine learning framework further confirmed that two cholesterol esters, CE (18:0) and CE (16:0), were significantly decreased in the sepsis group (p < 0.05). In addition, oleamide and stearamide were increased significantly in the postoperative sepsis group (p < 0.001). Conclusions: This study revealed characteristic lipidomic changes in the plasma of septic patients before and after cardiac surgery with CPB. We discovered two cholesterol esters and two amides from peripheral blood that could be promising signatures for sepsis within a dynamic detection between the preoperative and postoperative groups.

Metabolomics Applied to Cord Serum in Preeclampsia Newborns: Implications for Neonatal Outcomes.

Preeclampsia (PE) is one of the leading causes of maternal and perinatal morbidity and mortality. However, it is still uncertain how PE affects neonate metabolism. We conducted an untargeted metabolomics analysis of cord blood to explore the metabolic changes in PE neonates. Umbilical cord serum samples from neonates with preeclampsia (n = 29) and non-preeclampsia (non-PE) (n = 32) pregnancies were analyzed using the UHPLC-QE-MS metabolomic platform. Different metabolites were screened, and pathway analysis was conducted. A subgroup analysis was performed among PE neonates to compare the metabolome between appropriate-for-gestational-age infants (n = 21) and small-for-gestational-age (SGA) infants (n = 8). A total of 159 different metabolites were detected in PE and non-PE neonates. Creatinine, N4-acetylcytidine, sphingomyelin (D18:1/16:0), pseudouridine, uric acid, and indolelactic acid were the most significant differential metabolites in the cord serum of PE neonates. Differential metabolite levels were elevated in PE neonates and were involved in the following metabolic pathways: glycine, serine, and threonine metabolism; sphingolipid, glyoxylate, and dicarboxylate metabolism; and arginine biosynthesis. In PE neonates, SGA neonates showed increased levels of hexacosanoyl carnitine and decreased abundance of 3-hydroxybutyric acid and 3-sulfinoalanine. Taurine-related metabolism and ketone body-related pathways were mainly affected. Based on the UHPLC-QE-MS metabolomics analysis, we identified the metabolic profiles of PE and SGA neonates. The abundance of metabolites related to certain amino acid, sphingolipid, and energy metabolism increased in the umbilical cord serum of PE neonates.

Maternal Dietary Betaine Prevents High-Fat Diet-Induced Metabolic Disorders and Gut Microbiota Alterations in Mouse Dams and Offspring From Young to Adult.

Early life is a critical window for preventing the intergenerational transmission of metabolic diseases. Betaine has been proven to play a role in improving glucose and lipid metabolism disorders in animal models. However, whether maternal betaine supplementation plays a role in regulating gut microbiota in both dams and offspring remains unclear. In this study, C57BL/6 female mice were fed with control diet (Ctr), high-fat diet (HF), and high-fat with betaine supplementation (0.3% betaine in the diet, HFB) from 3 weeks prior to mating and lasted throughout pregnancy and lactation. After weaning, the offspring got free access to normal chow diet until 20 weeks of age. We found that maternal dietary betaine supplementation significantly improved glucose and insulin resistance, as well as reduced free fatty acid (FFA) concentration in dams and offspring from young to adult. When compared to the HF group, Intestinimonas and Acetatifactor were reduced by betaine supplementation in dams; Desulfovibrio was reduced in 4-week-old offspring of the HFB group; and Lachnoclostridium was enriched in 20-week-old offspring of the HFB group. Moreover, the persistent elevated genus Romboutsia in both dams and offspring in the HFB group was reported for the first time. Overall, maternal betaine could dramatically alleviate the detrimental effects of maternal overnutrition on metabolism in both dams and offspring. The persistent alterations in gut microbiota might play critical roles in uncovering the intergenerational metabolic benefits of maternal betaine, which highlights evidence for combating generational metabolic diseases.

Ratiometric Mass Spectrometry Imaging for Stain-Free Delineation of Ischemic Tissue and Spatial Profiling of Ischemia-Related Molecular Signatures.

Mass spectrometry imaging (MSI) serves as an emerging tool for spatial profiling of metabolic dysfunction in ischemic tissue. Prior to MSI data analysis, commonly used staining methods, e.g., triphenyltetrazole chloride (TTC) staining, need to be implemented on the adjacent tissue for delineating lesion area and evaluating infarction, resulting in extra consumption of the tissue sample as well as morphological mismatch. Here, we propose an in situ ratiometric MSI method for simultaneous demarcation of lesion border and spatial annotation of metabolic and enzymatic signatures in ischemic tissue on identical tissue sections. In this method, the ion abundance ratio of a reactant pair in the TCA cycle, e.g., fumarate to malate, is extracted pixel-by-pixel from an ambient MSI dataset of ischemic tissue and used as a surrogate indicator for metabolic activity of mitochondria to delineate lesion area as if the tissue has been chemically stained. This method is shown to be precise and robust in identifying lesions in brain tissues and tissue samples from different ischemic models including heart, liver, and kidney. Furthermore, the proposed method allows screening and predicting metabolic and enzymatic alterations which are related to mitochondrial dysfunction. Being capable of concurrent lesion identification, in situ metabolomics analysis, and screening of enzymatic alterations, the ratiometric MSI method bears great potential to explore ischemic damages at both metabolic and enzymatic levels in biological research.