Pyruvate is modified by tea/coffee metabolites and reversely correlated with multiple system atrophy and Parkinson's disease.

Introduction: Multiple system atrophy (MSA) is a rapidly progressing neurodegenerative disorder. Although diverse biomarkers have been established for Parkinson's disease (PD), no widely accepted markers have been identified in MSA. Pyruvate and lactate are the end-product of glycolysis and crucial for brain metabolism. However, their correlation with MSA remains unclear. Moreover, it is elusive how lifestyles modify these metabolites. Methods: To investigate the correlation and diagnostic value of plasma pyruvate and lactate levels in MSA and PD. Moreover, we explored how lifestyle-related metabolites interact with these metabolites in determining the disease risk. We assayed the 3 metabolites in pyruvate/lactate and 6 in the tea/coffee metabolic pathways by targeted mass spectrometry and evaluate their interactions and performance in diagnosis and differentiation between MSA and PD. Results: We found that 7 metabolites were significantly different between MSA, PD and healthy controls (HCs). Particularly, pyruvate was increased in PD while significantly decreased in MSA patients. Moreover, the tea/coffee metabolites were negatively associated with the pyruvate level in HCs, but not in MSA and PD patients. Using machine-learning models, we showed that the combination of pyruvate and tea/coffee metabolites diagnosed MSA (AUC = 0.878) and PD (AUC = 0.833) with good performance. Additionally, pyruvate had good performance in distinguishing MSA from PD (AUC = 0.860), and the differentiation increased (AUC = 0.922) when combined with theanine and 1,3-dimethyluric acid. Conclusions: This study demonstrates that pyruvate correlates reversely with MSA and PD, and may play distinct roles in their pathogenesis, which can be modified by lifestyle-related tea/coffee metabolites.

Risk factors prediction of 6-month mortality after noncardiac surgery of older patients in China: a multicentre retrospective cohort study.

BACKGROUND: Identifying the risk factors associated with perioperative mortality is crucial, particularly in older patients. Predicting 6-month mortality risk in older patients based on large datasets can assist patients and surgeons in perioperative clinical decision-making. This study aimed to develop a risk prediction model of mortality within 6 months after noncardiac surgery using the clinical data from 11 894 older patients in China. MATERIALS AND METHODS: A multicentre, retrospective cohort study was conducted in 20 tertiary hospitals. The authors retrospectively included 11 894 patients (aged ≥65 years) who underwent noncardiac surgery between April 2020 and April 2022. The least absolute shrinkage and selection operator model based on linear regression was used to analyse and select risk factors, and various machine learning methods were used to build predictive models of 6-month mortality. RESULTS: The authors predicted 12 preoperative risk factors associated with 6-month mortality in older patients after noncardiac surgery. Including laboratory-associated risk factors such as mononuclear cell ratio and total blood cholesterol level, etc. Also including medical history associated risk factors such as stroke, history of chronic diseases, etc. By using a random forest model, the authors constructed a predictive model with a satisfactory accuracy (area under the receiver operating characteristic curve=0.97). CONCLUSION: The authors identified 12 preoperative risk factors associated with 6-month mortality in noncardiac surgery older patients. These preoperative risk factors may provide evidence for a comprehensive preoperative anaesthesia assessment as well as necessary information for clinical decision-making by anaesthesiologists.

Multi-omics reveal neuroprotection of Acer truncatum Bunge Seed extract on hypoxic-ischemia encephalopathy rats under high-altitude.

Hypoxic-ischemic encephalopathy (HIE) at high-altitudes leads to neonatal mortality and long-term neurological complications without effective treatment. Acer truncatum Bunge Seed extract (ASO) is reported to have effect on cognitive improvement, but its molecular mechanisms on HIE are unclear. In this study, ASO administration contributed to reduced neuronal cell edema and improved motor ability in HIE rats at a simulated 4500-meter altitude. Transcriptomics and WGCNA analysis showed genes associated with lipid biosynthesis, redox homeostasis, neuronal growth, and synaptic plasticity regulated in the ASO group. Targeted and untargeted-lipidomics revealed decreased free fatty acids and increased phospholipids with favorable ω-3/ω-6/ω-9 fatty acid ratios, as well as reduced oxidized glycerophospholipids (OxGPs) in the ASO group. Combining multi-omics analysis demonstrated FA to FA-CoA, phospholipids metabolism, and lipid peroxidation were regulated by ASO treatment. Our results illuminated preliminary metabolism mechanism of ASO ingesting in rats, implying ASO administration as potential intervention strategy for HIE under high-altitude.

Neuroprotective effects of Longxue Tongluo Capsule on ischemic stroke rats revealed by LC-MS/MS-based metabolomics approach.

Objective: The present study aimed to evaluate the therapeutic effect and explore the underlying mechanisms of Longxue Tongluo Capsule (LTC) on ischemic stroke rats. Methods: Twenty-six rats were randomly divided into four groups, including sham group, sham + LTC group, MCAO group, and MCAO + LTC group. Ischemic stroke rats were simulated by middle cerebral artery occlusion (MCAO), and LTC treatment group were orally administrated with 300 mg/kg of LTC once daily for seven consecutive days. LTC therapy was validated in terms of neurobehavioral abnormality evaluation, cerebral infarct area, and histological assessments. The plasma metabolome comparisons amongst different groups were conducted by UHPLC-Q Exactive MS in combination with subsequent multivariate statistical analysis, aiming to finding the molecules in respond to the surgery or LTC treatment. Results: Intragastric administration of LTC significantly decreased not only the neurobehavioral abnormality scores but also the cerebral infarct area of MCAO rats. The interstitial edema, atrophy, and pyknosis of glial and neuronal cells occurred in the infarcted area, core area, and marginal area of cerebral cortex were improved after LTC treatment. A total of 13 potential biomarkers were observed, and Youden index of 11 biomarkers such as LysoPC, SM, and PE were more than 0.7, which were involved in neuroprotective process. The correlation and pathway analysis showed that LTC was beneficial to ischemic stroke rats via regulating glycerophospholipid and sphingolipid metabolism, together with nicotinate and nicotinamide metabolism. Heatmap and ternary analysis indicated the synergistic effect of carbohydrates and lipids may be induced by flavonoid intake from LTC. Conclusion: The present study could provide evidence that metabolomics, as systematic approach, revealed its capacity to evaluate the holistic efficacy of TCM, and investigate the molecular mechanism underlying the clinical treatment of LTC on ischemic stroke.

Changes in Lipid biomarker related to trauma severity in intertrochanteric fracture patients.

This study aimed to identify whether there are elevations or declines in specific plasma lipids in intertrochanteric fracture (ITF) patients which might serve as potential biomarkers for assessing the severity of trauma, or therapeutic targets for controlling post-traumatic responses. Ten metal work removal patients were enrolled. Their preoperative blood samples served as the control group (C group). Their 24-hour postoperative blood samples served as the moderate trauma group (M group). The ITF group was composed of 12 intertrochanteric fracture patients. A total of 707 lipid species were identified from 32 plasma samples (10 controls, 10 moderate trauma and 12 ITF samples). We first identified 31 lipids that were elevated and 6 lipids that were decreased in the more severe trauma group in aged patients, with an especially strong relationship among 14 lipids that are candidates as markers for trauma severity evaluation. Fourteen lipids were identified as potential markers of bone trauma. The definition of important lipids in trauma may not only provide guidance for the formulation of optimum ITF operation time, but may also have importance in other traumatic models, and in further understanding the components of the systemic inflammatory response for new drug targets.

Lipidomics reveal the cognitive improvement effects of Acer truncatum Bunge seed oil on hypoxic-ischemic encephalopathy rats.

Hypoxic-ischemic encephalopathy (HIE) is one of the leading causes of acute neonatal death and chronic neurological damage, and severe HIE can have secondary sequelae such as cognitive impairment and cerebral palsy, for which effective interventions are lacking. In this study, we found that continuous 30-day intake of Acer truncatum Bunge seed oil (ASO) reduced brain damage and improved cognitive ability in HIE rats. Using lipidomic strategies, we observed that HIE rats had decreased unsaturated fatty acids and increased lysophospholipids in the brain. However, after 30 days of ASO treatment, phospholipids, plasmalogens, and unsaturated fatty acids increased, while lysophospholipids and oxidized glycerophospholipids decreased in both serum and the brain. Enrichment analysis showed that ASO intake mainly affected sphingolipid metabolism, fat digestion and absorption, glycerolipid metabolism and glycerophospholipid metabolic pathways in serum and the brain. Cluster, correlation, and confirmatory factor analyses showed that cognitive improvement after ASO administration was attributed to increased essential phospholipids and ω3/6/9 fatty acids, coupled with decreased oxidized glycerophospholipids in HIE rats. Our findings indicate that ASO has the potential to be developed as an effective food supplement for ischemic hypoxic newborns.

Thrombosis origin identification of cardioembolism and large artery atherosclerosis by distinct metabolites.

BACKGROUND: The diagnosis of cerebral thrombosis origin is challenging and remains unclear. This study aims to identify thrombosis due to cardioembolism (CE) and large artery atherosclerosis (LAA) from a new perspective of distinct metabolites. METHODS: Distinct metabolites between 26 CE and 22 LAA origin thrombi, which were extracted after successful mechanical thrombectomy in patients with acute ischemic stroke in the anterior circulation, were analyzed with a ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) system. Enriched metabolic pathways related to the metabolites were identified. Least absolute shrinkage selection operator regression analyses and a filtering method were used to select potential predictors. Furthermore, four machine learning classifiers, including decision tree, logistic regression, random forest (RF), and k means unsupervised classification model, were used to evaluate the predictive ability of the selected metabolites. RESULTS: UPLC-QTOF-MS analysis revealed that levels of 88 and 55 metabolites were elevated in LAA and CE thrombi, respectively. Kyoto Encyclopedia of Genes and Genomes analysis revealed a significant difference between the pathways enriched in the two types of thrombi. Six metabolites (diglyceride (DG, 18:3/24:0), DG (22:0/24:0), phytosphingosine, galabiosylceramide (18:1/24:1), triglyceride (15:0/16:1/o-18:0), and glucosylceramide (18:1/24:0)) were finally selected to build a predictive model. The predictive RF model was confirmed to be the best, with a satisfactory stability and prediction capacity (area under the curve=0.889). CONCLUSIONS: Six metabolites as potential predictors for distinguishing between cerebral thrombi of CE and LAA origin were identified. The results are useful for understanding the pathogenesis and for secondary stroke prevention.

Spatial metabolomics identifies lipid profiles of human carotid atherosclerosis.

BACKGROUND AND AIMS: Carotid atherosclerosis is an important cause of ischemic stroke. Lipids play a key role in the progression of atherosclerosis. To date, the spatial lipid profile of carotid atherosclerotic plaques related to histology has not been systematically investigated. METHODS: Carotid atherosclerosis samples from 12 patients were obtained and classified into four classical pathological stages (preatheroma, atheroma, fibroatheroma and complicated lesion) by histological staining. Desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) was used to investigate the lipid profile of carotid atherosclerosis, and correlated it with histological information. Bioinformatics technology was used to process MSI data among different pathological stages of atherosclerosis lesions. RESULTS: A total of 55 lipids (26 throughout cross-section regions [TCSRs], 13 in lipid-rich regions [LRRs], and 16 in collagen-rich regions [CRRs]) were initially identified in carotid plaque from one patient. Subsequently, 32 of 55 lipids (12 in TCSRs, eight in LRRs, and 12 in CRRs) were further screened in 11 patients. Pathway enrichment analysis showed that multiple metabolic pathways, such as fat digestion and absorption, cholesterol metabolism, lipid and atherosclerosis, were enriched in TCSRs; sphingolipid signaling pathway, necroptosis pathway were enriched in LRRs; and glycerophospholipid metabolism, ether lipid metabolism pathway were mainly enriched in CRRs. CONCLUSIONS: This study comprehensively showed the spatial lipid metabolism footprint in human carotid atherosclerotic plaques. The lipid profiles and related metabolism pathways in three regions of plaque with disease progression were different markedly, suggesting that the different metabolic mechanisms in these regions of carotid plaque may be critical in atherosclerosis progression.

Serum metabolic profiling of coal worker's pneumoconiosis using untargeted lipidomics.

In this work, untargeted lipidomics was employed to analyze the effects of coal dust exposure on serum metabolite profiles. Furthermore, the potential of differential metabolites as novel biomarkers for diagnosis was investigated by binary logistic classification model. Nineteen differential metabolites were found among the three groups. The compounds were enriched in pathways associated with linoleic acid metabolism and pyrimidine metabolism. Fifty-three differential metabolites were found in coal dust-exposed people and CWP patients, and they were mainly enriched in glycerophospholipid metabolism. Three differential metabolites were correlated with lung function values. The diagnostic model, composed of lysoPI (16:0/0:0), bilirubin, and lysoPC (24:1/0:0), showed strong discrimination ability between dust-exposed people and CWP patients. The sensitivity, specificity, and AUC values of the model were 0.869, 0.600, and 0.750, respectively. The results suggest that coal worker's pneumoconiosis causes abnormal lipid metabolism in the body. A diagnostic model may aid current CWP diagnostic methods, and lysoPI (16:0/0:0), bilirubin, and lysoPC (24:1/0:0) can be used as potential CWP biomarkers. Further study is warranted to validate the findings in larger populations.

Integrating Choline and Specific Intestinal Microbiota to Classify Type 2 Diabetes in Adults: A Machine Learning Based Metagenomics Study.

Emerging evidence is examining the precise role of intestinal microbiota in the pathogenesis of type 2 diabetes. The aim of this study was to investigate the association of intestinal microbiota and microbiota-generated metabolites with glucose metabolism systematically in a large cross-sectional study in China. 1160 subjects were divided into three groups based on their glucose level: normal glucose group (n=504), prediabetes group (n=394), and diabetes group (n=262). Plasma concentrations of TMAO, choline, betaine, and carnitine were measured. Intestinal microbiota was measured in a subgroup of 161 controls, 144 prediabetes and 56 diabetes by using metagenomics sequencing. We identified that plasma choline [Per SD of log-transformed change: odds ratio 1.36 (95 confidence interval 1.16, 1.58)] was positively, while betaine [0.77 (0.66, 0.89)] was negatively associated with diabetes, independently of TMAO. Individuals with diabetes could be accurately distinguished from controls by integrating data on choline, and certain microbiota species, as well as traditional risk factors (AUC=0.971). KOs associated with the carbohydrate metabolism pathway were enhanced in individuals with high choline level. The functional shift in the carbohydrate metabolism pathway in high choline group was driven by species Ruminococcus lactaris, Coprococcus catus and Prevotella copri. We demonstrated the potential ability for classifying diabetic population by choline and specific species, and provided a novel insight of choline metabolism linking the microbiota to impaired glucose metabolism and diabetes.

Surfactant Therapy for Respiratory Distress Syndrome in High- and Ultra-High-Altitude Settings.

Objective: The objective of this study is to investigate the therapeutic effect of surfactant replacement therapy (SRT) on respiratory distress syndrome (RDS) in premature infants in the Qinghai-Tibet Plateau. Materials and Methods: This multi-center retrospective cohort study collected and screened reasonable clinical data of 337 premature infants with RDS from 10 hospitals in the Qinghai-Tibet Plateau from 2015 to 2017. We grouped the cases by rationally analyzing their baseline characteristics, using logistic analysis to evaluate each factor's effect on the prognosis of the infants, and comparing the short-term improvement in blood gas and mortality after SRT treatment at different altitudes, in high-altitude (1,500-3,500 m) and ultra-high-altitude (3,500-5,500 m) groups. Results: Independent of altitude, the mortality rate of children with RDS in the SRT group was significantly lower than that of children in the non-SRT group (both P < 0.05). The effect of SRT on preterm infants with RDS in the high-altitude group [odds ratio (OR) = 0.44, 95% confidence interval (CI) = 0.22-0.87, P = 0.02] was better than that in the infants in the ultra-high-altitude group (OR = 0.26, 95% CI = 0.13-0.58, P < 0.01), with death rates of 34.34 and 49.71%, respectively. Similarly, after SRT, the improvement of PaO2/FiO2 and pH of children at high altitude was significantly better than those of children at ultra-high altitude (all P < 0.01). Conclusions: SRT plays a prominent role in curing infants with RDS in both high- and ultra-high-altitude regions, although with better effects at high rather than ultra-high altitude. This study provides a basis for further large-scale studies on SRT for RDS treatment at high altitudes.

Cognitive improvement effect of nervonic acid and essential fatty acids on rats ingesting Acer truncatum Bunge seed oil revealed by lipidomics approach.

Acer truncatum Bunge seed oil (ASO) is rich in ω-9 (53.93%) and ω-6 (30.7%) fatty acids (FAs) and characterized by 3-7% nervonic acid (NA, C24:1ω-9). Evidence suggests that ω-9 FAs such as NA participate in processes of cognitive improvement; however, their mechanism remains ambiguous. In this study, we investigated the effect of ASO on rat memory and the change in lipid profiling and underlying metabolism. After ASO was administrated to rats for one, three and seven days, their capacity for learning and memory significantly increased via the MWM test. Lipid profiling showed alterations in a wide range of metabolic features after ASO was administrated to the rats, in which sphingolipids (SP) in the serum and glycerophospholipids (GP) in the brain were regulated significantly. The changes in the fatty acids in the serum and brain showed the synergetic effects of NA, EA, OA and DHA, where NA, EA and OA exhibited similar change trends. The enrichment analysis based on KEGG indicated that ASO supplementation evoked the pathways of neurotrophin signaling, glycerophospholipid metabolism and sphingolipid metabolism, which are related to memory and cognition improvement. Among the metabolites with different molecular forms, the biomarkers with C24:1ω-9 chains exhibited a positive correlation with others both in the serum SP and brain GP. These results suggest the synergistic effects of ω-9 FAs and that their conversion into each other may result in enhanced cognition in rats ingesting Acer truncatum Bunge seed oil.

Distinct lipid profiles of radiation-induced carotid plaques from atherosclerotic carotid plaques revealed by UPLC-QTOF-MS and DESI-MSI.

BACKGROUND AND PURPOSE: Radiotherapy is a standard treatment for head and neck tumors that significantly increases patients' long-term survival rates. However, late cerebrovascular complications, especially carotid artery stenosis (CAS), have gained increasing attention. Investigation of biomarkers of radiation-induced CAS may help to elucidate the mechanism by which radiation induces damage to blood vessels and identify possible preventive measures against such damage. MATERIALS AND METHODS: In this study, we used lipidomics strategy to characterize the lipids present in 8 radiation-induced carotid plaques (RICPs) and 12 atherosclerotic carotid plaques (ASCPs). We also used desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) to map the spatial distribution of the screened lipids from 2 RICPs samples and 2 ASCPs samples. RESULTS: The results showed that 31 metabolites in RICPs were significantly higher than that in ASCPs, 24 of which were triglycerides (TGs). We used four machine learning models to select potential indicators from the 31 metabolites. Six TGs [TG(17:2/17:2/18:0), TG(17:1/17:2/18:0), TG(17:0/17:2/18:0), TG(17:2/17:2/20:0), TG(17:1/17:2/20:0), TG(15:0/22:0/22:2)] were found to be the potential markers for distinguishing RICPs and ASCPs (AUC = 0.83). The DESI-MSI results suggested that the 6 TGs were localized in the collagen fiber regions and confirmed the differences of these TGs between the two kinds of plaques. CONCLUSIONS: The 6 TGs primarily localized in the collagen fiber regions of plaques are likely to be potential indicators for the differentiation of RICPs from ASCPs which may have implications in the mechanisms and possible preventive measures against RICPs.

Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques.

Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to efficiently predict outcomes of both the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in outpatients with epilepsy. Data from 441 outpatients with epilepsy were included; of these, 433 patients met the 12 clinical characteristic criteria and were divided into training (n = 304) and experimental (n = 129) groups. After descriptive statistics were analyzed, cross-validation was used to select the optimal model. The random forest (RF) algorithm was combined with the redundancy analysis (RDA) algorithm; then, optimal feature selection and resampling were carried out after removing linear redundancy information. The features that contributed more to multiple outcomes were selected. Finally, the external traceability of the model was evaluated using the follow-up data. The RF algorithm was the best prediction model for both MMSE and MoCA outcomes. Finally, seven markers were screened by overlapping the top ten important features for MMSE ranked by RF modeling, those ranked for MoCA ranked by RF modeling, and those for both assessments ranked by RDA. The optimal combination of features were namely, sex, age, age of onset, seizure frequency, brain MRI abnormalities, epileptiform discharge in EEG and usage of drugs. which was the most efficient in predicting outcomes of MMSE, MoCA, and both assessments.

A Diagnostic Analysis Workflow to Optimal Multiple Tumor Markers to Predict the Nonmetastatic Breast Cancer from Breast Lumps.

OBJECTIVE: To assess the diagnostic performance of clinically common single markers and combinations to distinguish nonmetastatic breast cancer and benign breast tumor. A predictive model with a better diagnostic ability for nonmetastatic breast cancer was established by using the diagnostic process. METHODS: A total of 222 patients with nonmetastatic breast cancer and 265 patients with benign breast disease were enrolled in this study. CEA, Ca 15-3, Ca 125, Ca 72-4, CYFRA 21-1, FERR, AFP, and NSE were measured by an electrochemiluminescent immunoenzymometric assay on the Elecsys system. There are four key steps for our diagnostic workflow, that is, feature selection, algorithm selection, parameter optimization, and outer test data was used to validate the optimal algorithm and markers. RESULTS: CEA, Ca 15-3, CYFRA 21-1, AFP, and FERR were selected using the t-test in our inner development set. The optimal algorithm among logical regression, decision tree, support vector machine, random forest, and gradient boost machine was selected by 10-fold cross-validation, and we found that random forest and logistic regression are the better classification. The outer test data was used to validate the best markers and classification. The random forest with CEA, Ca 15-3, CYFRA 21-1, AFP, and FERR showed the optimal combination for distinguishing breast cancer and benign breast disease. The AUC value was 0.888, the cut-off point was 0.484, and sensitivity and specificity were 78.9% and 90.1%. CONCLUSIONS: No single marker of these eight markers was good at identifying nonmetastatic breast cancer from benign tumors. But a diagnostic analysis workflow was established to develop a predictive model with better diagnostic capability for nonmetastatic breast cancer. This workflow is also applicable to the optimization of other disease markers and diagnostic models. The predictive model showed good diagnostic performance, and it could be gradually incorporated as a support method for the diagnosis of nonmetastatic breast cancer.

Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis.

The objective of this study was to identify potential biomarkers and possible metabolic pathways of malignant and benign thyroid nodules through lipidomics study. A total of 47 papillary thyroid carcinomas (PTC) and 33 control check (CK) were enrolled. Plasma samples were collected for UPLC-Q-TOF MS system detection, and then OPLS-DA model was used to identify differential metabolites. Based on classical statistical methods and machine learning, potential biomarkers were characterized and related metabolic pathways were identified. According to the metabolic spectrum, 13 metabolites were identified between PTC group and CK group, and a total of five metabolites were obtained after further screening. Its metabolic pathways were involved in glycerophospholipid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, Phosphatidylinositol signaling system and the metabolism of arachidonic acid metabolism. The metabolomics method based on PROTON nuclear magnetic resonance (NMR) had great potential for distinguishing normal subjects from PTC. GlcCer(d14:1/24:1), PE-NME (18:1/18:1), SM(d16:1/24:1), SM(d18:1/15:0), and SM(d18:1/16:1) can be used as potential serum markers for the diagnosis of PTC.

Antigenic Evolution on a Global Scale Reveals the Potential Natural Selection of Severe Acute Respiratory Syndrome-Coronavirus 2 by Pre-existing Cross-Reactive T-Cell Immunity.

The mutation pattern of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has changed constantly during worldwide community transmission of this virus. However, the reasons for the changes in mutation patterns are still unclear. Accordingly, in this study, we present a comprehensive analysis of over 300 million peptides derived from 13,432 SARS-CoV-2 strains harboring 4,420 amino acid mutations to analyze the potential selective pressure of the host immune system and reveal the driver of mutations in circulating SARS-CoV-2 isolates. The results showed that the nonstructural protein ORF1ab and the structural protein Spike were most susceptible to mutations. Furthermore, mutations in cross-reactive T-cell epitopes between SARS-CoV-2 and seasonal human coronavirus may help SARS-CoV-2 to escape cellular immunity under long-term and large-scale community transmission. Additionally, through homology modeling and protein docking, mutations in Spike protein may enhance the ability of SARS-CoV-2 to invade host cells and escape antibody-mediated B-cell immunity. Our research provided insights into the potential mutation patterns of SARS-CoV-2 under natural selection, improved our understanding of the evolution of the virus, and established important guidance for potential vaccine design.

Japanese Encephalitis Virus Vaccination Elicits Cross-Reactive HLA-Class I-Restricted CD8 T Cell Response Against Zika Virus Infection.

Japanese encephalitis virus (JEV) exposure or vaccination could elicit cross-reactive CD8 T cell immunity against heterologous flaviviruses in humans. In addition, cross-reactive CD8 T cells induced by dengue virus (DENV) have been shown to play a protective role against Zika virus (ZIKV). However, how JEV exposure or vaccination affects ZIKV infection in humans remains unclear. In this report, epitope prediction algorithms were used to predict the cross-reactive CD8 T cell epitope restricted to human HLA between JEV and ZIKV. We found that these predicted CD8 T cell epitopes are immunogenic and cross-reactive in humanized HLA transgenic mice. Moreover, JEV vaccine immunization provided cross-protection against ZIKV infection. Furthermore, CD8 T cells were involved in the protection against ZKIV infection in vivo. Our results have an important clinical implication that vaccination with JEV SA14-14-2 may provide protection against ZIKV infection in humans.

Immunomodulatory and Antiviral Activity of Metformin and Its Potential Implications in Treating Coronavirus Disease 2019 and Lung Injury.

The pandemic of coronavirus disease 2019 (COVID-19), a disease which causes severe lung injury and multiple organ damage, presents an urgent need for new drugs. The case severity and fatality of COVID-19 are associated with excessive inflammation, namely, a cytokine storm. Metformin, a widely used drug to treat type 2 diabetes (T2D) mellitus and metabolic syndrome, has immunomodulatory activity that reduces the production of proinflammatory cytokines using macrophages and causes the formation of neutrophil extracellular traps (NETs). Metformin also inhibits the cytokine production of pathogenic Th1 and Th17 cells. Importantly, treatment with metformin alleviates various lung injuries in preclinical animal models. In addition, a recent proteomic study revealed that metformin has the potential to directly inhibit SARS-CoV-2 infection. Furthermore, retrospective clinical studies have revealed that metformin treatment reduces the mortality of T2D with COVID-19. Therefore, metformin has the potential to be repurposed to treat patients with COVID-19 at risk of developing severe illness. This review summarizes the immune pathogenesis of SARS-CoV-2 and addresses the effects of metformin on inhibiting cytokine storms and preventing SARS-CoV-2 infection, as well as its side effects.