IDENTIFICATION AND CLINICAL VALIDATION OF HYPOXIA-INDUCIBLE FACTOR 1α PROTEIN AS THE POTENTIAL BIOMARKER IN PATIENTS WITH SEPSIS.

ABSTRACT: Objective: Sepsis is a complex disease characterized by an inflammatory response and tissue hypoxia. Hypoxia-inducible factor 1α (HIF-1α) expression level is regulated by hypoxia and inflammation. This study aimed to explore the correlation between HIF-1α expression level and sepsis by bioinformatics analysis and clinical investigation. Methods: Bioinformatics tools were used to identify differentially expressed genes between sepsis and nonsepsis groups using the Gene Expression Omnibus data set. A clinical investigation was carried out to validate HIF-1α protein level in 54 nonseptic patients and 173 septic patients who were followed up for 28 days. Results: Bioinformatics analysis revealed that HIF-1α messenger RNA level was significantly different between septic and nonseptic patients ( P < 0.05). Consistent with the study hypothesis, higher HIF-1α levels in plasma were found in septic patients compared with those in nonseptic patients. The diagnostic accuracy for sepsis, as quantified by the area under the curve, was 0.926 (0.885-0.968) for HIF-1α expression level combined with oxygen saturation to fraction of inspired oxygen (SpO 2 /FiO 2 ), white blood cell, and blood urea nitrogen. The HIF-1α expression level was also significantly correlated with the severity of the disease. The results of the restricted cubic splines model indicated a U-shaped relationship between HIF-1α expression level and intensive care unit (ICU) mortality. Univariate and multivariate linear regression analyses indicated that septic patients with the elevated HIF-1α expression levels had shorter length of ICU stay versus those with the lower HIF-1α expression levels. Conclusion: Hypoxia-inducible factor 1α expression level can be used for diagnosing disease, assessing severity, and predicting length of ICU stay in septic patients.

Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study.

BACKGROUND: Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple heart abnormalities that covers a wide range of arrhythmias, with better-than-human accuracy, has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated multilabel diagnosis of heart rhythm or conduction abnormalities by real-time ECG analysis. METHODS: We used a dataset of ECGs (standard 10 s, 12-channel format) from adult patients (aged ≥18 years), with 21 distinct rhythm classes, including most types of heart rhythm or conduction abnormalities, for the diagnosis of arrhythmias at multilabel level. The ECGs were collected from three campuses of Tongji Hospital (Huazhong University of Science and Technology, Wuhan, China) and annotated by cardiologists. We used these datasets to develop a convolutional neural network approach to generate diagnoses of arrythmias. We collected a test dataset of ECGs from a new group of patients not included in the training dataset. The test dataset was annotated by consensus of a committee of board-certified, actively practicing cardiologists. To evaluate the performance of the model we assessed the F1 score and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, as well as quantifying sensitivity and specificity. To validate our results, findings for the test dataset were compared with diagnoses made by 53 ECG physicians working in cardiology departments who had a wide range of experience in ECG interpretation (range 0 to >12 years). An external public validation dataset of 962 ECGs from other hospitals was used to study generalisability of the diagnostic model. FINDINGS: Our training and validation dataset comprised 180 112 ECGs from 70 692 patients, collected between Jan 1, 2012, and Apr 30, 2019. The test dataset comprised 828 ECGs corresponding to 828 new patients, recorded between Sept 11, 2012, and Aug 30, 2019. At the multilabel level, our deep learning approach to diagnosing heart abnormalities resulted in an exact match in 658 (80%) of 828 ECGs, exceeding the mean performance of physicians (552 [67%] for physicians with 0-6 years of experience; 571 [69%] for physicians with 7-12 years of experience; 621 [75%] for physicians with more than 12 years of experience). Our model had an overall mean F1 score of 0·887 compared with 0·789 for physicians with 0-6 years of experience, 0·815 for physicians with 7-12 years of experience, and 0·831 for physicians with more than 12 years of experience. The model had a mean AUC ROC score of 0·983 (95% CI 0·980-0·986), sensitivity of 0·867 (0·849-0·885) and specificity of 0·995 (0·994-0·996). Promising F1 scores were also obtained from the external public database using our proposed model without any model modifications (mean F1 scores of 0·845 in multilabel and 0·852 in single-label ECGs). INTERPRETATION: Our model is more accurate than physicians working in cardiology departments at distinguishing a range of distinct arrhythmias in single-label and multilabel ECGs, laying a promising foundation for computational decision-support systems in clinical applications. FUNDING: National Natural Science Foundation of China and Hubei Science and Technology Project.

Syntaxin-17-Dependent Mitochondrial Dynamics is Essential for Protection Against Oxidative-Stress-Induced Apoptosis.

In this study, cell death induced by the oxidant tert-butylhydroperoxide (tBH) was observed in U2OS cells; this phenotype was rescued by Syntaxin 17 (STX17) knockout (KO) but the mechanism is unknown. STX17 plays dual roles in autophagosome-lysosome fusion and mitochondrial fission. However, the contribution of the two functions of STX17 to apoptosis has not been extensively studied. Here, we sought to dissect the dual roles of STX17 in oxidative-stress-induced apoptosis by taking advantage of STX17 knockout cells and an autophagosome-lysosome fusion defective mutant of STX17. We generated STX17 knockout U2OS cells using the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system and the STX17 knockout cells were reconstituted with wild-type STX17 and its autophagosome-lysosome fusion defective mutant. Autophagy was assessed by autophagic flux assay, Monomer red fluorescent protein (mRFP)-GFP-LC3 assay and protease protection assay. Golgi, endoplasmic reticulum (ER)/ER-Golgi intermediate compartment (ERGIC) and mitochondrial dynamics were examined by staining the different indicator proteins. Apoptosis was evaluated by caspase cleavage assay. The general reactive oxygen species (ROS) were detected by flow cytometry. In STX17 complete knockout cells, sealed autophagosomes were efficiently formed but their fusion with lysosomes was less defective. The fusion defect was rescued by wild-type STX17 but not the autophagosome-lysosome fusion defective mutant. No obvious defects in Golgi, ERGIC or ER dynamics were observed. Mitochondria were significantly elongated, supporting a role of STX17 in mitochondria fission and the elongation caused by STX17 KO was reversed by the autophagosome-lysosome fusion defective mutant. The clearance of protein aggregation was compromised, correlating with the autophagy defect but not with mitochondrial dynamics. This study revealed a mixed role of STX17 in autophagy, mitochondrial dynamics and oxidative stress response. STX17 knockout cells were highly resistant to oxidative stress, largely due to the function of STX17 in mitochondrial fission rather than autophagy.

Bisphenol A inhibits autophagosome-lysosome fusion and lipid droplet degradation.

Bisphenol A (BPA) is an artificial xenoestrogen widely used in consumer products containing polycarbonate plastics and epoxy resins. Exposure to BPA occurs through various channels, including ingestion of contaminated food and water. Autophagy is an important catabolic pathway that plays an important role in liver lipid metabolism. Evidence suggests that BPA exposure causes abnormal lipid droplet accumulation in liver, but the mechanism remains unknown. Here, we investigate the function of BPA in lipid metabolism and autophagy. BPA exposure increases lipid droplet and ROS accumulation which is accompanied by a defect in the fusion of the autophagosome to the lysosome. BPA exposure decreases the translocation of Stx17 to lysosome resulting in the autophagogome-lysosome fusion defect. There is no defect in the formation of the autophagosome indicated by increased LC3-II, p62 level, GFP/mRFP-LC3 ratios and decreased colocalization between LAMP2 with LC3. Mechanistically, BPA exposure reduces autophagy SNARE complex formation. Promoting autophagy by autophagy inducer (Torin2) partially reverses lipid droplet accumulation caused by BPA exposure. In summary, our results demonstrate BPA exposure inhibits autophagy resulting in decreased lipid droplet degradation and increased ROS levels. These results also provide a novel implication between autophagosome-lysosome fusion.

2-Amino-3,8-dimethylimidazo[4,5-f]quinoxaline Alters Autophagosome Maturation, Cellular Lipidomic Profiles, and Expression of Core Pluripotent Factors.

2-Amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx), one of the most abundant heterocyclic aromatic amines (HAAs) found in the human diet, is primarily produced during high-temperature meat or fish cooking. While MeIQx has been investigated as a potential carcinogen, the cytotoxicity and related molecular mechanisms remain unclear. Here, we demonstrate that autophagosome maturation is blocked by MeIQx. Mechanistically, MeIQx inhibits acidification of lysosomes rather than prevents autophagosome-lysosome fusion. Moreover, cellular lipid profiles are altered by MeIQx treatment. Notably, many phospholipids and sphingolipids are significantly upregulated after exposure to MeIQx. Furthermore, MeIQx decreases expression of pluripotency-associated proteins in mouse embryonic stem cells (ESCs). Together, MeIQx blocks autophagosome maturation through inhibiting acidification of lysosomes, alters lipid metabolism, and decreases expression of pluripotent factors. Our studies provide more cytotoxic evidence and elucidate related mechanisms on the risk of HAA exposure and are expected to promote supervision of food safety and human health.