HMGB1 inhibition blocks ferroptosis and oxidative stress to ameliorate sepsis-induced acute lung injury by activating the Nrf2 pathway.

The proinflammatory properties of high-mobility group box protein 1 (HMGB1) in sepsis have been extensively studied. This study aimed to investigate the impact of HMGB1 on ferroptosis and its molecular mechanism in sepsis-induced acute lung injury (ALI). A septic mouse model was established using the cecal ligation and puncture method. Blocking HMGB1 resulted in improved survival rates, reduced lung injury, decreased levels of ferroptosis markers (reactive oxygen species, malondialdehyde, and Fe2+), and enhanced antioxidant enzyme activities (superoxide dismutase and catalase) in septic mice. In addition, knockdown of HMGB1 reduced cellular permeability, ferroptosis markers, and raised antioxidant enzyme levels in lipopolysaccharide (LPS)-stimulated MLE-12 cells. Silencing of HMGB1 led to elevations in the expressions of ferroptosis core-regulators in LPS-treated MLE-12 cells, such as solute carrier family 7 member 11 (SLC7A11), solute carrier family 3 member A2 (SLC3A2), and glutathione peroxidase 4. Furthermore, blocking HMGB1 did not alter ferroptosis, oxidative stress-related changes, and permeability in LPS-treated MLE-12 cells that were pretreated with ferrostatin-1 (a ferroptosis inhibitor). HMGB1 inhibition also led to elevated expressions of nuclear factor erythroid 2-related factor 2 (Nrf2) and its downstream targets, heme oxygenase-1 (HO-1) and NAD(P)H: quinone oxidoreductase 1 (NQO1) in LPS-treated MLE-12 cells and lung tissues from septic mice. The Nrf2-specific inhibitor ML385 reversed the effects of HMGB1 silencing on ferroptosis and cell permeability in LPS-treated MLE-12 cells. Our findings indicated that the inhibition of HMGB1 restrains ferroptosis and oxidative stress, thereby alleviating sepsis-induced ALI through the activation of Nrf2 signaling.

Automatic prediction of obstructive sleep apnea event using deep learning algorithm based on ECG and thoracic movement signals.

BACKGROUND: Obstructive sleep apnea (OSA) is a sleeping disorder that can cause multiple complications. AIMS/OBJECTIVE: Our aim is to build an automatic deep learning model for OSA event detection using combined signals from the electrocardiogram (ECG) and thoracic movement signals. MATERIALS AND METHODS: We retrospectively obtained 420 cases of PSG data and extracted the signals of ECG, as well as the thoracic movement signal. A deep learning algorithm named ResNeSt34 was used to construct the model using ECG with or without thoracic movement signal. The model performance was assessed by parameters such as accuracy, precision, recall, F1-score, receiver operating characteristic (ROC), and area under the ROC curve (AUC). RESULTS: The model using combined signals of ECG and thoracic movement signal performed much better than the model using ECG alone. The former had accuracy, precision, recall, F1-score, and AUC values of 89.0%, 88.8%, 89.0%, 88.2%, and 92.9%, respectively, while the latter had values of 84.1%, 83.1%, 84.1%, 83.3%, and 82.8%, respectively. CONCLUSIONS AND SIGNIFICANCE: The automatic OSA event detection model using combined signals of ECG and thoracic movement signal with the ResNeSt34 algorithm is reliable and can be used for OSA screening.

Facial Contour Features Measured on CT Reflects Upper Airway Morphology in Patients With OSA.

OBJECTIVE: To determine facial contour features, measured on computed tomography (CT), related to upper airway morphology in patients with obstructive sleep apnea (OSA); certain phenotype of facial abnormalities implying restriction of craniofacial skeleton and adipose tissue nimiety has predicted the value of the severity of OSA. MATERIALS AND METHOD: Sixty-four male patients with OSA [apnea-hypopnea index (AHI) ≥10/h] who had upper airway CT were randomly selected to quantitatively measure indicators of facial contour and upper airway structures. Pearson correlation analyses were performed. Partial correlation procedure was used to examine correlations while controlling body mass index (BMI). RESULTS: Upper airway anatomy can nearly all be reflected in the face, except retroglossal airway. Upper face width can be measured to assess the overall skeletal structures of the airway. Lower face width can be used to represent how much adipose tissue deposited. Hard palate, retropalatal, and hypopharyngeal airways have corresponding face indicators respectively. Midface width is a better predictor of AHI severity and minimum blood oxygen even than neck circumference because it contains the most anatomical information about the airway, including RP airway condition, soft palate length, tongue volume, etc. These correlations persisted even after correction for BMI. CONCLUSIONS: All anatomical features of the upper airway except retroglossal airway can be reflected in the face, and midface width is the best predictor of AHI severity and minimum blood oxygen, even better than neck circumference and BMI.

A New Cellular Interactome of SARS-CoV-2 Nucleocapsid Protein and Its Biological Implications.

There is still much to uncover regarding the molecular details of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. As the most abundant protein, coronavirus nucleocapsid (N) protein encapsidates viral RNAs, serving as the structural component of ribonucleoprotein and virion, and participates in transcription, replication, and host regulations. Virus-host interaction might give clues to better understand how the virus affects or is affected by its host during infection and identify promising therapeutic candidates. Considering the critical roles of N, we here established a new cellular interactome of SARS-CoV-2 N by using a high-specific affinity purification (S-pulldown) assay coupled with quantitative mass spectrometry and immunoblotting validations, uncovering many N-interacting host proteins unreported previously. Bioinformatics analysis revealed that these host factors are mainly involved in translation regulations, viral transcription, RNA processes, stress responses, protein folding and modification, and inflammatory/immune signaling pathways, in line with the supposed actions of N in viral infection. Existing pharmacological cellular targets and the directing drugs were then mined, generating a drug-host protein network. Accordingly, we experimentally identified several small-molecule compounds as novel inhibitors against SARS-CoV-2 replication. Furthermore, a newly identified host factor, DDX1, was verified to interact and colocalize with N mainly by binding to the N-terminal domain of the viral protein. Importantly, loss/gain/reconstitution-of-function experiments showed that DDX1 acts as a potent anti-SARS-CoV-2 host factor, inhibiting the viral replication and protein expression. The N-targeting and anti-SARS-CoV-2 abilities of DDX1 are consistently independent of its ATPase/helicase activity. Further mechanism studies revealed that DDX1 impedes multiple activities of N, including the N-N interaction, N oligomerization, and N-viral RNA binding, thus likely inhibiting viral propagation. These data provide new clues to better depiction of the N-cell interactions and SARS-CoV-2 infection and may help inform the development of new therapeutic candidates.

IMPContact: An Interhelical Residue Contact Prediction Method.

As an important category of proteins, alpha-helix transmembrane proteins (αTMPs) play an important role in various biological activities. Because the solved αTMP structures are inadequate, predicting the residue contacts among the transmembrane segments of an αTMP exhibits the basis of protein fold, which can be used to further discover more protein functions. A few efforts have been devoted to predict the interhelical residue contact using machine learning methods based on the prior knowledge of transmembrane protein structure. However, it is still a challenge to improve the prediction accuracy, while the deep learning method provides an opportunity to utilize the structural knowledge in a different insight. For this purpose, we proposed a novel αTMP residue-residue contact prediction method IMPContact, in which a convolutional neural network (CNN) was applied to recognize those interhelical contacts in a TMP using its specific structural features. There were four sequence-based TMP-specific features selected to descript a pair of residues, namely, evolutionary covariation, predicted topology structure, residue relative position, and evolutionary conservation. An up-to-date dataset was used to train and test the IMPContact; our method achieved better performance compared to peer methods. In the case studies, IHRCs in the regular transmembrane helixes were better predicted than in the irregular ones.