Multimodal representation learning for predicting molecule-disease relations.

MOTIVATION: Predicting molecule-disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule-molecule, molecule-disease and disease-disease semantic dependencies can potentially improve prediction performance. METHODS: We introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common clinical concepts. To infer molecule-disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects. RESULTS: We extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/celehs/M2REMAP, and prediction results are provided at https://shiny.parse-health.org/drugs-diseases-dev/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Binding kinetics study of SARS-CoV-2 main protease and potential inhibitors via molecular dynamics simulations.

The pandemic COVID-19 was induced by the novel coronavirus SARS-CoV-2. The virus main protease (Mpro) cleaves the coronavirus polyprotein translated from the viral RNA in the host cells. Because of its crucial role in virus replication, Mpro is a potential drug target for COVID-19 treatment. Herein, we study the interactions between Mpro and three HIV-1 protease (HIV-1 PR) inhibitors, Lopinavir (LPV), Saquinavir (SQV), Ritonavir (RIT), and an inhibitor PF-07321332, by conventional and replica exchange molecular dynamics (MD) simulations. The association/dissociation rates and the affinities of the inhibitors were estimated. The three HIV-1 PR inhibitors exhibit low affinities, while PF-07321332 has the highest affinity among these four simulated inhibitors. Based on cluster analysis, the HIV-1 PR inhibitors bind to Mpro at multiple sites, while PF-07321332 specifically binds to the catalytically activated site of Mpro. The stable and specific binding is because PF-07321332 forms multiple H-bonds to His163 and Glu166 simultaneously. The simulations suggested PF-07321332 could serve as an effective inhibitor with high affinity and shed light on the strategy of drug design and drug repositioning.

A systematic review and meta-analysis of the seroconversion rates and adverse effects of COVID-19 mRNA vaccine and COVID-19 viral vector vaccine in kidney transplant recipient patients.

Patients received kidney transplantation (KTR) have a low seroconversion rate after vaccination. Our objective was to compare the seroconversion rates and adverse effects of additional different vaccinations in KTR patients in existing studies. Databases such as PubMed, Cochrane Library, Web of Science, Embase, ClinicalTrials.gov and others. Three high-quality RCT were included and showed no statistical difference in seroconversion rates between the two vaccines (RR = 0.93[0.76,1.13]). There was no statistical difference in seroconversion rates between the sexes, for men (RR = 0.93[0.69,1.25]) and women (RR = 0.91[0.62,1.33]). Among the adverse effects there was no statistically significant difference in fever (RR = 1.06[0.44,2.57]), while for injection site pain there was a statistically significant difference (RR = 1.14[1.18,1.84]). There was no significant difference in seroconversion rates in patients with KTR who received the two additional vaccines. Patients injected with the viral vector vaccine were less painful than those injected with the mRNA vaccine.

The ORF7a protein of SARS-CoV-2 initiates autophagy and limits autophagosome-lysosome fusion via degradation of SNAP29 to promote virus replication.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is closely related to various cellular aspects associated with autophagy. However, how SARS-CoV-2 mediates the subversion of the macroautophagy/autophagy pathway remains largely unclear. In this study, we demonstrate that overexpression of the SARS-CoV-2 ORF7a protein activates LC3-II and leads to the accumulation of autophagosomes in multiple cell lines, while knockdown of the viral ORF7a gene via shRNAs targeting ORF7a sgRNA during SARS-CoV-2 infection decreased autophagy levels. Mechanistically, the ORF7a protein initiates autophagy via the AKT-MTOR-ULK1-mediated pathway, but ORF7a limits the progression of autophagic flux by activating CASP3 (caspase 3) to cleave the SNAP29 protein at aspartic acid residue 30 (D30), ultimately impairing complete autophagy. Importantly, SARS-CoV-2 infection-induced accumulated autophagosomes promote progeny virus production, whereby ORF7a downregulates SNAP29, ultimately resulting in failure of autophagosome fusion with lysosomes to promote viral replication. Taken together, our study reveals a mechanism by which SARS-CoV-2 utilizes the autophagic machinery to facilitate its own propagation via ORF7a.

TREM-2 is a sensor and activator of T cell response in SARS-CoV-2 infection.

Limited understanding of T cell responses against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has impeded vaccine development and drug discovery for coronavirus disease 2019 (COVID-19). We found that triggering receptor expressed on myeloid cells 2 (TREM-2) was induced in T cells in the blood and lungs of patients with COVID-19. After binding to SARS-CoV-2 membrane (M) protein through its immunoglobulin domain, TREM-2 then activated the CD3ζ/ZAP70 complex, leading to STAT1 phosphorylation and T-bet transcription. In vitro stimulation with M protein-reconstituted pseudovirus or recombinant M protein, and TREM-2 promoted the T helper cell 1 (TH1) cytokines interferon-γ and tumor necrosis factor. In vivo infection of CD4­TREM-2 conditional knockout mice with murine coronavirus mouse hepatitis virus A-59 showed that intrinsic TREM-2 in T cells enhanced TH1 response and viral clearance, thus aggravating lung destruction. These findings demonstrate a previously unidentified role for TREM-2 in SARS-CoV-2 infection, and suggest potential strategies for drug discovery and clinical management of COVID-19.

SARS-CoV-2 ORF10 suppresses the antiviral innate immune response by degrading MAVS through mitophagy.

The global coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused severe morbidity and mortality in humans. It is urgent to understand the function of viral genes. However, the function of open reading frame 10 (ORF10), which is uniquely expressed by SARS-CoV-2, remains unclear. In this study, we showed that overexpression of ORF10 markedly suppressed the expression of type I interferon (IFN-I) genes and IFN-stimulated genes. Then, mitochondrial antiviral signaling protein (MAVS) was identified as the target via which ORF10 suppresses the IFN-I signaling pathway, and MAVS was found to be degraded through the ORF10-induced autophagy pathway. Furthermore, overexpression of ORF10 promoted the accumulation of LC3 in mitochondria and induced mitophagy. Mechanistically, ORF10 was translocated to mitochondria by interacting with the mitophagy receptor Nip3-like protein X (NIX) and induced mitophagy through its interaction with both NIX and LC3B. Moreover, knockdown of NIX expression blocked mitophagy activation, MAVS degradation, and IFN-I signaling pathway inhibition by ORF10. Consistent with our observations, in the context of SARS-CoV-2 infection, ORF10 inhibited MAVS expression and facilitated viral replication. In brief, our results reveal a novel mechanism by which SARS-CoV-2 inhibits the innate immune response; that is, ORF10 induces mitophagy-mediated MAVS degradation by binding to NIX.

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review.

Since the first case of coronavirus disease 2019 (COVID-19) was discovered in December 2019, COVID-19 swiftly spread over the world. By the end of March 2021, more than 136 million patients have been infected. Since the second and third waves of the COVID-19 outbreak are in full swing, investigating effective and timely solutions for patients' check-ups and treatment is important. Although the SARS-CoV-2 virus-specific reverse transcription polymerase chain reaction test is recommended for the diagnosis of COVID-19, the test results are prone to be false negative in the early course of COVID-19 infection. To enhance the screening efficiency and accessibility, chest images captured via X-ray or computed tomography (CT) provide valuable information when evaluating patients with suspected COVID-19 infection. With advanced artificial intelligence (AI) techniques, AI-driven models training with lung scans emerge as quick diagnostic and screening tools for detecting COVID-19 infection in patients. In this article, we provide a comprehensive review of state-of-the-art AI-empowered methods for computational examination of COVID-19 patients with lung scans. In this regard, we searched for papers and preprints on bioRxiv, medRxiv, and arXiv published for the period from January 1, 2020, to March 31, 2021, using the keywords of COVID, lung scans, and AI. After the quality screening, 96 studies are included in this review. The reviewed studies were grouped into three categories based on their target application scenarios: automatic detection of coronavirus disease, infection segmentation, and severity assessment and prognosis prediction. The latest AI solutions to process and analyze chest images for COVID-19 treatment and their advantages and limitations are presented. In addition to reviewing the rapidly developing techniques, we also summarize publicly accessible lung scan image sets. The article ends with discussions of the challenges in current research and potential directions in designing effective computational solutions to fight against the COVID-19 pandemic in the future.

Mathematical model of the feedback between global supply chain disruption and COVID-19 dynamics.

The pandemic of COVID-19 has become one of the greatest threats to human health, causing severe disruptions in the global supply chain, and compromising health care delivery worldwide. Although government authorities sought to contain the spread of SARS-CoV-2, by restricting travel and in-person activities, failure to deploy time-sensitive strategies in ramping-up of critical resource production exacerbated the outbreak. Here, we developed a mathematical model to analyze the effects of the interaction between supply chain disruption and infectious disease dynamics using coupled production and disease networks built on global data. Analysis of the supply chain model suggests that time-sensitive containment strategies could be created to balance objectives in pandemic control and economic losses, leading to a spatiotemporal separation of infection peaks that alleviates the societal impact of the disease. A lean resource allocation strategy can reduce the impact of supply chain shortages from 11.91 to 1.11% in North America. Our model highlights the importance of cross-sectoral coordination and region-wise collaboration to optimally contain a pandemic and provides a framework that could advance the containment and model-based decision making for future pandemics.

Intelligent Manufacturing Systems in COVID-19 Pandemic and Beyond: Framework and Impact Assessment

Pandemics like COVID-19 have created a spreading and ever-higher healthy threat to the humans in the manufacturing system which incurs severe disruptions and complex issues to industrial networks. The intelligent manufacturing (IM) systems are promising to create a safe working environment by using the automated manufacturing assets which are monitored by the networked sensors and controlled by the intelligent decision-making algorithms. The relief of the production disruption by IM technologies facilitates the reconnection of the good and service flows in the network, which mitigates the severity of industrial chain disruption. In this study, we create a novel intelligent manufacturing framework for the production recovery under the pandemic and build an assessment model to evaluate the impacts of the IM technologies on industrial networks. Considering the constraints of the IM resources, we formulate an optimization model to schedule the allocation of IM resources according to the mutual market demands and the severity of the pandemic.