Gut butyrate-producers confer post-infarction cardiac protection.

The gut microbiome and its metabolites are increasingly implicated in several cardiovascular diseases, but their role in human myocardial infarction (MI) injury responses have yet to be established. To address this, we examined stool samples from 77 ST-elevation MI (STEMI) patients using 16 S V3-V4 next-generation sequencing, metagenomics and machine learning. Our analysis identified an enriched population of butyrate-producing bacteria. These findings were then validated using a controlled ischemia/reperfusion model using eight nonhuman primates. To elucidate mechanisms, we inoculated gnotobiotic mice with these bacteria and found that they can produce beta-hydroxybutyrate, supporting cardiac function post-MI. This was further confirmed using HMGCS2-deficient mice which lack endogenous ketogenesis and have poor outcomes after MI. Inoculation increased plasma ketone levels and provided significant improvements in cardiac function post-MI. Together, this demonstrates a previously unknown role of gut butyrate-producers in the post-MI response.

Identification of a damage-associated molecular pattern (DAMP) receptor and its cognate peptide ligand in sweet potato (Ipomoea batatas).

Sweet potato (Ipomoea batatas) is an important tuber crop, but also target of numerous insect pests. Intriguingly, the abundant storage protein in tubers, sporamin, has intrinsic trypsin protease inhibitory activity. In leaves, sporamin is induced by wounding or a volatile homoterpene and enhances insect resistance. While the signalling pathway leading to sporamin synthesis is partially established, the initial event, perception of a stress-related signal is still unknown. Here, we identified an IbLRR-RK1 that is induced upon wounding and herbivory, and related to peptide-elicitor receptors (PEPRs) from tomato and Arabidopsis. We also identified a gene encoding a precursor protein comprising a peptide ligand (IbPep1) for IbLRR-RK1. IbPep1 represents a distinct signal in sweet potato, which might work in a complementary and/or parallel pathway to the previously described hydroxyproline-rich systemin (HypSys) peptides to strengthen insect resistance. Notably, an interfamily compatibility in the Pep/PEPR system from Convolvulaceae and Solanaceae was identified.

A Noise-Tolerating Gene Association Network Uncovering an Oncogenic Regulatory Motif in Lymphoma Transcriptomics.

In cancer genomics research, gene expressions provide clues to gene regulations implicating patients' risk of survival. Gene expressions, however, fluctuate due to noises arising internally and externally, making their use to infer gene associations, hence regulation mechanisms, problematic. Here, we develop a new regression approach to model gene association networks while considering uncertain biological noises. In a series of simulation experiments accounting for varying levels of biological noises, the new method was shown to be robust and perform better than conventional regression methods, as judged by a number of statistical measures on unbiasedness, consistency and accuracy. Application to infer gene associations in germinal-center B cells led to the discovery of a three-by-two regulatory motif gene expression and a three-gene prognostic signature for diffuse large B-cell lymphoma.

Corrigendum: Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care.

[This corrects the article DOI: 10.3389/fcvm.2022.1001982.].

Implementation of an All-Day Artificial Intelligence-Based Triage System to Accelerate Door-to-Balloon Times.

OBJECTIVE: To implement an all-day artificial intelligence (AI)-based system to facilitate chest pain triage in the emergency department. METHODS: The AI-based triage system encompasses an AI model combining a convolutional neural network and long short-term memory to detect ST-elevation myocardial infarction (STEMI) on electrocardiography (ECG) and a clinical risk score (ASAP) to prioritize patients for ECG examination. The AI model was developed on 2907 twelve-lead ECGs: 882 STEMI and 2025 non-STEMI ECGs. RESULTS: Between November 1, 2019, and October 31, 2020, we enrolled 154 consecutive patients with STEMI: 68 during the AI-based triage period and 86 during the conventional triage period. The mean ± SD door-to-balloon (D2B) time was significantly shortened from 64.5±35.3 minutes to 53.2±12.7 minutes (P=.007), with 98.5% vs 87.2% (P=.009) of D2B times being less than 90 minutes in the AI group vs the conventional group. Among patients with an ASAP score of 3 or higher, the median door-to-ECG time decreased from 30 minutes (interquartile range [IQR], 7-59 minutes) to 6 minutes (IQR, 4-30 minutes) (P<.001). The overall performances of the AI model in identifying STEMI from 21,035 ECGs assessed by accuracy, precision, recall, area under the receiver operating characteristic curve, F1 score, and specificity were 0.997, 0.802, 0.977, 0.999, 0.881, and 0.998, respectively. CONCLUSION: Implementation of an all-day AI-based triage system significantly reduced the D2B time, with a corresponding increase in the percentage of D2B times less than 90 minutes in the emergency department. This system may help minimize preventable delays in D2B times for patients with STEMI undergoing primary percutaneous coronary intervention.

Artificial Intelligence-Enabled Electrocardiogram Improves the Diagnosis and Prediction of Mortality in Patients With Pulmonary Hypertension.

Background: Pulmonary hypertension is a disabling and life-threatening cardiovascular disease. Early detection of elevated pulmonary artery pressure (ePAP) is needed for prompt diagnosis and treatment to avoid detrimental consequences of pulmonary hypertension. Objectives: This study sought to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify patients with ePAP and related prognostic implications. Methods: From a hospital-based ECG database, the authors extracted the first pairs of ECG and transthoracic echocardiography taken within 2 weeks of each other from 41,097 patients to develop an AI model for detecting ePAP (PAP > 50 mm Hg by transthoracic echocardiography). The model was evaluated on independent data sets, including an external cohort of patients from Japan. Results: Tests of 10-fold cross-validation neural-network deep learning showed that the area under the receiver-operating characteristic curve of the AI model was 0.88 (sensitivity 81.0%; specificity 79.6%) for detecting ePAP. The diagnostic performance was consistent across age, sex, and various comorbidities (diagnostic odds ratio >8 for most factors examined). At 6-year follow-up, the patients predicted by the AI model to have ePAP were independently associated with higher cardiovascular mortality (HR: 3.69). Similar diagnostic performance and prediction for cardiovascular mortality could be replicated in the external cohort. Conclusions: The ECG-based AI model identified patients with ePAP and predicted their future risk for cardiovascular mortality. This model could serve as a useful clinical test to identify patients with pulmonary hypertension so that treatment can be initiated early to improve their survival prognosis.

Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care.

Objective: To implement an all-day online artificial intelligence (AI)-assisted detection of ST-elevation myocardial infarction (STEMI) by prehospital 12-lead electrocardiograms (ECGs) to facilitate patient triage for timely reperfusion therapy. Methods: The proposed AI model combines a convolutional neural network and long short-term memory (CNN-LSTM) to predict STEMI on prehospital 12-lead ECGs obtained from mini-12-lead ECG devices equipped in ambulance vehicles in Central Taiwan. Emergency medical technicians (EMTs) from the 14 AI-implemented fire stations performed the on-site 12-lead ECG examinations using the mini portable device. The 12-lead ECG signals were transmitted to the AI center of China Medical University Hospital to classify the recordings as "STEMI" or "Not STEMI". In 11 non-AI fire stations, the ECG data were transmitted to a secure network and read by available on-line emergency physicians. The response time was defined as the time interval between the ECG transmission and ECG interpretation feedback. Results: Between July 17, 2021, and March 26, 2022, the AI model classified 362 prehospital 12-lead ECGs obtained from 275 consecutive patients who had called the 119 dispatch centers of fire stations in Central Taiwan for symptoms of chest pain or shortness of breath. The AI's response time to the EMTs in ambulance vehicles was 37.2 ± 11.3 s, which was shorter than the online physicians' response time from 11 other fire stations with no AI implementation (113.2 ± 369.4 s, P < 0.001) after analyzing another set of 335 prehospital 12-lead ECGs. The evaluation metrics including accuracy, precision, specificity, recall, area under the receiver operating characteristic curve, and F1 score to assess the overall AI performance in the remote detection of STEMI were 0.992, 0.889, 0.994, 0.941, 0.997, and 0.914, respectively. During the study period, the AI model promptly identified 10 STEMI patients who underwent primary percutaneous coronary intervention (PPCI) with a median contact-to-door time of 18.5 (IQR: 16-20.8) minutes. Conclusion: Implementation of an all-day real-time AI-assisted remote detection of STEMI on prehospital 12-lead ECGs in the field is feasible with a high diagnostic accuracy rate. This approach may help minimize preventable delays in contact-to-treatment times for STEMI patients who require PPCI.

JAK2-CHK2 signaling safeguards the integrity of the mitotic spindle assembly checkpoint and genome stability.

Checkpoint kinase 2 (CHK2) plays an important role in safeguarding the mitotic progression, specifically the spindle assembly, though the mechanism of regulation remains poorly understood. Here, we identified a novel mitotic phosphorylation site on CHK2 Tyr156, and its responsible kinase JAK2. Expression of a phospho-deficient mutant CHK2 Y156F or treatment with JAK2 inhibitor IV compromised mitotic spindle assembly, leading to genome instability. In contrast, a phospho-mimicking mutant CHK2 Y156E restored mitotic normalcy in JAK2-inhibited cells. Mechanistically, we show that this phosphorylation is required for CHK2 interaction with and phosphorylation of the spindle assembly checkpoint (SAC) kinase Mps1, and failure of which results in impaired Mps1 kinetochore localization and defective SAC. Concordantly, analysis of clinical cancer datasets revealed that deletion of JAK2 is associated with increased genome alteration; and alteration in CHEK2 and JAK2 is linked to preferential deletion or amplification of cancer-related genes. Thus, our findings not only reveal a novel JAK2-CHK2 signaling axis that maintains genome integrity through SAC but also highlight the potential impact on genomic stability with clinical JAK2 inhibition.

Patient-Derived Organoid Serves as a Platform for Personalized Chemotherapy in Advanced Colorectal Cancer Patients.

Background: Addition of oxaliplatin to adjuvant 5-FU has significantly improved the disease-free survival and served as the first line adjuvant chemotherapy in advanced colorectal cancer (CRC) patients. However, a fraction of patients remains refractory to oxaliplatin-based treatment. It is urgent to establish a preclinical platform to predict the responsiveness toward oxaliplatin in CRC patients as well as to improve the efficacy in the resistant patients. Methods: A living biobank of organoid lines were established from advanced CRC patients. Oxaliplatin sensitivity was assessed in patient-derived tumor organoids (PDOs) in vitro and in PDO-xenografted tumors in mice. Based on in vitro oxaliplatin IC50 values, PDOs were classified into either oxaliplatin-resistant (OR) or oxaliplatin-sensitive (OS) PDOs. The outcomes of patients undergone oxaliplatin-based treatment was followed. RNA-sequencing and bioinformatics tools were performed for molecular profiling of OR and OS PDOs. Oxaliplatin response signatures were submitted to Connectivity Map algorithm to identify perturbagens that may antagonize oxaliplatin resistance. Results: Oxaliplatin sensitivity in PDOs was shown to correlate to oxaliplatin-mediated inhibition on PDO xenograft tumors in mice, and parallelled clinical outcomes of CRC patients who received FOLFOX treatment. Molecular profiling of transcriptomes revealed oxaliplatin-resistant and -sensitive PDOs as two separate entities, each being characterized with distinct hallmarks and gene sets. Using Leave-One-Out Cross Validation algorithm and Logistic Regression model, 18 gene signatures were identified as predictive biomarkers for oxaliplatin response. Candidate drugs identified by oxaliplatin response signature-based strategies, including inhibitors targeting c-ABL and Notch pathway, DNA/RNA synthesis inhibitors, and HDAC inhibitors, were demonstrated to potently and effectively increase oxaliplatin sensitivity in the resistant PDOs. Conclusions: PDOs are useful in informing decision-making on oxaliplatin-based chemotherapy and in designing personalized chemotherapy in CRC patients.

CRM1-spike-mediated nuclear export of hepatitis B virus encapsidated viral RNA.

Hepatitis B virus (HBV) is a global pathogen. We report here that the cellular CRM1 machinery can mediate nuclear export of entire HBV core (HBc) particles containing encapsidated viral RNAs. Two CRM1-mediated nuclear export signals (NESCRM1) cluster at the conformationally flexible spike tips of HBc particles. Mutant NESCRM1 capsids exhibit strongly reduced associations with CRM1 and nucleoporin358 in vivo. CRM1 and NXF1 machineries mediate nuclear export of HBc particles independently. Inhibition of nuclear export has pleiotropic consequences, including nuclear accumulation of HBc particles, a significant reduction of encapsidated viral RNAs in the cytoplasm but not in the nucleus, and barely detectable viral DNA. We hypothesize an HBV life cycle where encapsidation of the RNA pregenome can initiate early in the nucleus, whereas DNA genome maturation occurs mainly in the cytoplasm. We identified a druggable target for HBV by blocking its intracellular trafficking.

ALDH2 deficiency induces atrial fibrillation through dysregulated cardiac sodium channel and mitochondrial bioenergetics: A multi-omics analysis.

Point mutation in alcohol dehydrogenase 2 (ALDH2), ALDH2*2 results in decreased catalytic enzyme activity and has been found to be associated with different human pathologies. Whether ALDH2*2 would induce cardiac remodeling and increase the attack of atrial fibrillation (AF) remains poorly understood. The present study evaluated the effect of ALDH2*2 mutation on AF susceptibility and unravelled the underlying mechanisms using a multi-omics approach including whole-genome gene expression and proteomics analysis. The in-vivo electrophysiological study showed an increase in the incidence and reduction in the threshold of AF for the mutant mice heterozygous for ALDH2*2 as compared to the wild type littermates. The microarray analysis revealed a reduction in the retinoic acid signals which was accompanied by a downstream reduction in the expression of voltage-gated Na+ channels (SCN5A). The treatment of an antagonist for retinoic acid receptor resulted in a decrease in SCN5A transcript levels. The integrated analysis of the transcriptome and proteome data showed a dysregulation of fatty acid ß-oxidation, adenosine triphosphate synthesis via electron transport chain, and activated oxidative responses in the mitochondria. Oral administration of Coenzyme Q10, an essential co-factor known to meliorate mitochondrial oxidative stress and preserve bioenergetics, conferred a protection against AF attack in the mutant ALDH2*2 mice. The multi-omics approach showed the unique pathophysiology mechanisms of concurrent dysregulated SCN5A channel and mitochondrial bioenergetics in AF. This inspired the development of a personalized therapeutic agent, Coenzyme Q10, to protect against AF attack in humans characterized by ALDH2*2 genotype.

Usefulness of multi-labelling artificial intelligence in detecting rhythm disorders and acute ST-elevation myocardial infarction on 12-lead electrocardiogram.

Aims: To develop an artificial intelligence-based approach with multi-labelling capability to identify both ST-elevation myocardial infarction (STEMI) and 12 heart rhythms based on 12-lead electrocardiograms (ECGs). Methods and results: We trained, validated, and tested a long short-term memory (LSTM) model for the multi-label diagnosis of 13 ECG patterns (STEMI + 12 rhythm classes) using 60 537 clinical ECGs from 35 981 patients recorded between 15 January 2009 and 31 December 2018. In addition to the internal test above, we conducted a real-world external test, comparing the LSTM model with board-certified physicians of different specialties using a separate dataset of 308 ECGs covering all 13 ECG diagnoses. In the internal test, the area under the curves (AUCs) of the LSTM model in classifying the 13 ECG patterns ranged between 0.939 and 0.999. For the external test, the LSTM model for multi-labelling of the 13 ECG patterns evaluated by AUC was 0.987 ± 0.021, which was superior to those of cardiologists (0.898 ± 0.113, P < 0.001), emergency physicians (0.820 ± 0.134, P < 0.001), internists (0.765 ± 0.155, P < 0.001), and a commercial algorithm (0.845 ± 0.121, P < 0.001). Of note, the LSTM model achieved an accuracy of 0.987, AUC of 0.997, and precision, recall, and F 1 score of 0.952, 0.870, and 0.909, respectively, in detecting STEMI. Conclusions: We demonstrated the usefulness of an LSTM model in the multi-labelling detection of both rhythm classes and STEMI in competitive testing against board-certified physicians. This AI tool exceeding the cardiologist-level performance in detecting STEMI and rhythm classes on 12-lead ECG may be useful in prioritizing chest pain triage and expediting clinical decision-making in healthcare.

Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms.

BACKGROUND: Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify different types of cardiac arrhythmias with the use of a single-lead ECG input data set have been developed. It remains to be determined whether these algorithms can be generalized to 12-lead ECG-based rhythm classification. METHODS: We used a long short-term memory (LSTM) model to detect 12 heart rhythm classes with the use of 65,932 digital 12-lead ECG signals from 38,899 patients, using annotations obtained by consensus of 3 board-certified electrophysiologists as the criterion standard. RESULTS: The accuracy of the LSTM model for the classification of each of the 12 heart rhythms was ≥ 0.982 (range 0.982-1.0), with an area under the receiver operating characteristic curve of ≥ 0.987 (range 0.987-1.0). The precision and recall ranged from 0.692 to 1 and from 0.625 to 1, respectively, with an F1 score of ≥ 0.777 (range 0.777-1.0). The accuracy of the model (0.90) was superior to the mean accuracies of internists (0.55), emergency physicians (0.73), and cardiologists (0.83). CONCLUSIONS: We demonstrated the feasibility and effectiveness of the deep-learning LSTM model for interpreting 12 common heart rhythms according to 12-lead ECG signals. The findings may have clinical relevance for the early diagnosis of cardiac rhythm disorders.

PRAP1 is a novel lipid-binding protein that promotes lipid absorption by facilitating MTTP-mediated lipid transport.

Microsomal triglyceride transfer protein (MTTP) is an endoplasmic reticulum resident protein that is essential for the assembly and secretion of triglyceride (TG)-rich, apoB-containing lipoproteins. Although the function and structure of mammalian MTTP have been extensively studied, how exactly MTTP transfers lipids to lipid acceptors and whether there are other biomolecules involved in MTTP-mediated lipid transport remain elusive. Here we identify a role in this process for the poorly characterized protein PRAP1. We report that PRAP1 and MTTP are partially colocalized in the endoplasmic reticulum. We observe that PRAP1 directly binds to TG and facilitates MTTP-mediated lipid transfer. A single amino acid mutation at position 85 (E85V) impairs PRAP1's ability to form a ternary complex with TG and MTTP, as well as impairs its ability to facilitate MTTP-mediated apoB-containing lipoprotein assembly and secretion, suggesting that the ternary complex formation is required for PRAP1 to facilitate MTTP-mediated lipid transport. PRAP1 is detectable in chylomicron/VLDL-rich plasma fractions, suggesting that MTTP recognizes PRAP1-bound TG as a cargo and transfers TG along with PRAP1 to lipid acceptors. Both PRAP1-deficient and E85V knock-in mutant mice fed a chow diet manifested an increase in the length of their small intestines, likely to compensate for challenges in absorbing lipid. Interestingly, both genetically modified mice gained significantly less body weight and fat mass when on high-fat diets compared with littermate controls and were prevented from hepatosteatosis. Together, this study provides evidence that PRAP1 plays an important role in MTTP-mediated lipid transport and lipid absorption.

SLC38A2 Overexpression Induces a Cancer-like Metabolic Profile and Cooperates with SLC1A5 in Pan-cancer Prognosis.

Cancer cells have dramatically increased demands for energy as well as biosynthetic precursors to fuel their restless growth. Enhanced glutaminolysis is a hallmark of cancer metabolism which fulfills these needs. Two glutamine transporters, SLC1A5 and SLC38A2, have been previously reported to promote glutaminolysis in cancer with controversial perspectives. In this study, we harnessed the proximity labeling reaction to map the protein interactome using mass spectrometry-based proteomics and discovered a potential protein-protein interaction between SLC1A5 and SLC38A2. The SLC1A5/SLC38A2 interaction was further confirmed by bimolecular fluorescence complementation assay. We further investigated the metabolic influence of SLC1A5 and SLC38A2 overexpression in human cells, respectively, and found that only SLC38A2, but not SLC1A5, resulted in a cancer-like metabolic profile, where the intracellular concentrations of essential amino acids and lactate were significantly increased as quantified by nuclear magnetic resonance spectroscopy. Finally, we analyzed the 5-year survival rates in a large pan-cancer cohort and found that the SLC1A5hi /SLC38A2lo group did not relate to a poor survival rate, whereas the SLC1A5lo /SLC38A2hi group significantly aggravated the lethality. Intriguingly, the SLC1A5hi /SLC38A2hi group resulted in an even worse prognosis, suggesting a cooperative effect between SLC1A5 and SCL38A2. Our data suggest that SLC38A2 plays a dominant role in reprogramming the cancer-like metabolism and promoting the cancer progression, whereas SLC1A5 may augment this effect when co-overexpressed with SLC38A2. We propose a model to explain the relationship between SLC1A5, SLC38A2 and SCL7A5, and discuss their impact on glutaminolysis and mTOR signaling.

Lumenal Galectin-9-Lamp2 interaction regulates lysosome and autophagy to prevent pathogenesis in the intestine and pancreas.

Intracellular galectins are carbohydrate-binding proteins capable of sensing and repairing damaged lysosomes. As in the physiological conditions glycosylated moieties are mostly in the lysosomal lumen but not cytosol, it is unclear whether galectins reside in lysosomes, bind to glycosylated proteins, and regulate lysosome functions. Here, we show in gut epithelial cells, galectin-9 is enriched in lysosomes and predominantly binds to lysosome-associated membrane protein 2 (Lamp2) in a Asn(N)-glycan dependent manner. At the steady state, galectin-9 binding to glycosylated Asn175 of Lamp2 is essential for functionality of lysosomes and autophagy. Loss of N-glycan-binding capability of galectin-9 causes its complete depletion from lysosomes and defective autophagy, leading to increased endoplasmic reticulum (ER) stress preferentially in autophagy-active Paneth cells and acinar cells. Unresolved ER stress consequently causes cell degeneration or apoptosis that associates with colitis and pancreatic disorders in mice. Therefore, lysosomal galectins maintain homeostatic function of lysosomes to prevent organ pathogenesis.

BGN/TLR4/NF-B Mediates Epigenetic Silencing of Immunosuppressive Siglec Ligands in Colon Cancer Cells.

Human Toll-like receptor (TLR) signaling plays a vital role in intestinal inflammation by activating the NF-B pathway. By querying GENT2 datasets, we identified the gene expression level of TLR2 and TLR4 as being substantially increased in colorectal cancer. Introduction of shRNAs for TLR4 but not TLR2 dramatically recovered disialyl Lewisa and sialyl 6-sulfo Lewisx glycans, which are preferentially expressed in non-malignant colonic epithelial cells and could serve as ligands for the immunosuppressive molecule Siglec-7. We screened several TLR4 ligands and found that among them BGN is highly expressed in cancers and is involved in the epigenetic silencing of Siglec-7 ligands. Suppression of BGN expression substantially downregulated NF-B activity and the marker H3K27me3 in the promoter regions of the SLC26A2 and ST6GalNAc6 genes, which are involved in the synthesis of those glycans, and restored expression of normal glycans as well as Siglec-7 binding activities. We show that in the presence of TLR4, inflammatory stimuli initiate a positive loop involving NF-B that activates BGN and further enhances TLR4 activity. Present findings indicate a putative mechanism for the promotion of carcinogenesis by loss of immunosuppressive ligands by the BGN/TLR4/ NF-B pathway.

Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model.

Electrocardiograms (ECGs) are widely used to clinically detect cardiac arrhythmias (CAs). They are also being used to develop computer-assisted methods for heart disease diagnosis. We have developed a convolution neural network model to detect and classify CAs, using a large 12-lead ECG dataset (6,877 recordings) provided by the China Physiological Signal Challenge (CPSC) 2018. Our model, which was ranked first in the challenge competition, achieved a median overall F1-score of 0.84 for the nine-type CA classification of CPSC2018's hidden test set of 2,954 ECG recordings. Further analysis showed that concurrent CAs were adequately predictive for 476 patients with multiple types of CA diagnoses in the dataset. Using only single-lead data yielded a performance that was only slightly worse than using the full 12-lead data, with leads aVR and V1 being the most prominent. We extensively consider these results in the context of their agreement with and relevance to clinical observations.

Novel Naturally Occurring Mutations of Enterovirus 71 Associated With Disease Severity.

Infection with the re-emerging enterovirus 71 (EV-A71) is associated with a wide range of disease severity, including herpangina, encephalitis, and cardiopulmonary failure. At present, there is no FDA-approved therapeutics for EV-A71. Early diagnosis for the high-risk children is the key to successful patient care. We examined viral genome sequences at the 5' untranslated region (UTR) and the capsid protein VP1 from 36 mild and 27 severe cases. We identified five EV-A71 mutations associated with severe diseases, including (1) the 5' UTR mutations C580U, A707G, C709U; (2) a VP1 alanine-to-threonine mutation at position 280 (280T), and (3) a VP1 glutamic acid-to-(non-glutamic acid) at position 145 [145(non-E)]. SCARB2 is a known entry receptor for EV-A71. Based on a recent cryoEM structure of the EV-A71-SCARB2 binding complex, VP1-280T is near the binding interface between the VP1-VP2 complex and its entry receptor SCARB2. A de novo created hydrogen bonding between the mutant VP1-280T and the VP2-139T, could help strengthen a web-like interaction structure of the VP1-VP2 complex. A stabilized loop turn of VP2, once in contact with SCARB2, can enhance interaction with the host SCARB2 receptor for viral entry. Our findings here could facilitate early detection of severe cases infected with EV-A71 in clinical medicine. In addition, it opens up the opportunity of functional studies via infectious cDNA cloning, site-directed mutagenesis, and animal models in the future.

Risk stratification for lung adenocarcinoma on EGFR and TP53 mutation status, chemotherapy, and PD-L1 immunotherapy.

The overall survival rates for lung cancer remain unsatisfactorily low, even for patients with biomarkers for which target therapies or immunotherapies are recommended. Better identification of at-risk patients is needed to achieve more effective personalized treatment. Here, we derived a risk-stratifying gene signature consisting of five genes that had the greatest differential expression by stage from lung adenocarcinoma (LUAD) transcriptomes. The new gene signature enabled survival prognosis for multiple LUAD datasets from different platforms of transcriptomics and risk stratification for patients with and without a mutation in TP53 or EGFR, with high and low levels of PD-L1, and with and without adjuvant chemotherapy treatment. Using these evaluations, it was also shown to be more robust compared to several other gene signatures. Functional analysis of the five genes and their protein-protein interaction partners indicated that they are functionally enriched in cell cycle, endocytosis, and EGFR regulation, which are biological processes associated with lung cancer and drug resistance. Extensive discussions on related experimental studies suggest that the five genes are novel and sensible targets for developing new drugs and/or tackling drug resistance problems for LUAD.