Genomic variation, origin tracing, and vaccine development of SARS-CoV-2: A systematic review

Protein structure prediction via deep learning: an in-depth review

The application of deep learning algorithms in protein structure prediction has greatly influenced drug discovery and development. Accurate protein structures are crucial for understanding biological processes and designing effective therapeutics. Traditionally, experimental methods like X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy have been the gold standard for determining protein structures. However, these approaches are often costly, inefficient, and time-consuming. At the same time, the number of known protein sequences far exceeds the number of experimentally determined structures, creating a gap that necessitates the use of computational approaches. Deep learning has emerged as a promising solution to address this challenge over the past decade. This review provides a comprehensive guide to applying deep learning methodologies and tools in protein structure prediction. We initially outline the databases related to the protein structure prediction, then delve into the recently developed large language models as well as state-of-the-art deep learning-based methods. The review concludes with a perspective on the future of predicting protein structure, highlighting potential challenges and opportunities.

Prediction of drug target interaction based on under sampling strategy and random forest algorithm

Drug target interactions (DTIs) play a crucial role in drug discovery and development. The prediction of DTIs based on computational method can effectively assist the experimental techniques for DTIs identification, which are time-consuming and expensive. However, the current computational models suffer from low accuracy and high false positive rate in the prediction of DTIs, especially for datasets with extremely unbalanced sample categories. To accurately identify the interaction between drugs and target proteins, a variety of descriptors that fully show the characteristic information of drugs and targets are extracted and applied to the integrated method random forest (RF) in this work. Here, the random projection method is adopted to reduce the feature dimension such that simplify the model calculation. In addition, to balance the number of samples in different categories, a down sampling method NearMiss (NM) which can control the number of samples is used. Based on the gold standard datasets (nuclear receptors, ion channel, GPCRs and enzymes), the proposed method achieves the auROC of 92.26%, 98.21%, 97.65%, 99.33%, respectively. The experimental results show that the proposed method yields significantly higher performance than that of state-of-the-art methods in predicting drug target interaction.

Scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtree

In metabarcoding research, such as taxon identification, phylogenetic placement plays a critical role. However, many existing phylogenetic placement methods lack comprehensive features for downstream analysis and visualization. Visualization tools often ignore placement uncertainty, making it difficult to explore and interpret placement data effectively. To overcome these limitations, we introduce a scalable approach using treeio and ggtree for parsing and visualizing phylogenetic placement data. The treeio-ggtree method supports placement filtration, uncertainty exploration, and customized visualization. It enhances scalability for large analyses by enabling users to extract subtrees from the full reference tree, focusing on specific samples within a clade. Additionally, this approach provides a clearer representation of phylogenetic placement uncertainty by visualizing associated placement information on the final placement tree.

Landscape of human organoids: Ideal model in clinics and research

In the last decade, organoid research has entered a golden era, signifying a pivotal shift in the biomedical landscape. The year 2023 marked a milestone with the publication of thousands of papers in this arena, reflecting exponential growth. However, amid this burgeoning expansion, a comprehensive and accurate overview of the field has been conspicuously absent. Our review is intended to bridge this gap, providing a panoramic view of the rapidly evolving organoid landscape. We meticulously analyze the organoid field from eight distinctive vantage points, harnessing our rich experience in academic research, industrial application, and clinical practice. We present a deep exploration of the advances in organoid technology, underpinned by our long-standing involvement in this arena. Our narrative traverses the historical genesis of organoids and their transformative impact across various biomedical sectors, including oncology, toxicology, and drug development. We delve into the synergy between organoids and avant-garde technologies such as synthetic biology and single-cell omics and discuss their pivotal role in tailoring personalized medicine, enhancing high-throughput drug screening, and constructing physiologically pertinent disease models. Our comprehensive analysis and reflective discourse provide a deep dive into the existing landscape and emerging trends in organoid technology. We spotlight technological innovations, methodological evolution, and the broadening spectrum of applications, emphasizing the revolutionary influence of organoids in personalized medicine, oncology, drug discovery, and other fields. Looking ahead, we cautiously anticipate future developments in the field of organoid research, especially its potential implications for personalized patient care, new avenues of drug discovery, and clinical research. We trust that our comprehensive review will be an asset for researchers, clinicians, and patients with keen interest in personalized medical strategies. We offer a broad view of the present and prospective capabilities of organoid technology, encompassing a wide range of current and future applications. In summary, in this review we attempt a comprehensive exploration of the organoid field. We offer reflections, summaries, and projections that might be useful for current researchers and clinicians, and we hope to contribute to shaping the evolving trajectory of this dynamic and rapidly advancing field.

The Outbreak of Acute Primary Angle-Closure Cases During the COVID-19 Omicron Variant Pandemic at a Tertiary Eye Center in Shanghai

Purpose

This study aimed to investigate the outbreak of acute primary angle-closure (APAC) during the COVID-19 Omicron variant pandemic in Shanghai.

Methods

This single-center retrospective observational study included all newly diagnosed patients with APAC in Eye, Ear, Nose, and Throat Hospital of Fudan University from December 15, 2022, to January 14, 2023 (pandemic group) during the COVID-19 pandemic of Omicron Variant, and from November 15, 2021, to February 14, 2022 (control group) when the infection rate of COVID-19 is very low in Shanghai. Demographic features, intraocular pressure, axial length, anterior chamber depth, lens thickness and pupil diameter were compared between the two groups.

Results

A total of 223 patients (261 eyes) were included in the pandemic group and 75 patients (82 eyes) in the control group. The number of APAC patients and eyes in the pandemic group is 8.92-fold and 9.55-fold of the monthly average number in the control group. The onset dates of acute angle-closure were mainly between December 17 and December 31, 2022. In the pandemic group, 72.65% of patients with APAC had a recent COVID-19 infection. Among the COVID-19-positive patients, 72% suffered APAC attacks within 24h of the occurrence of COVID-19 symptoms and 92% within 3 days. The pandemic group showed a longer time from symptoms to treatment and larger pupil diameter than the control group (7.92 ± 6.14 vs 3.63 ± 2.93 days, p = 0.006; 4.53 ± 1.17 vs 3.78 ± 1.24 mm, p = 0.003, respectively).

Conclusion

An outbreak of APAC attack was observed in our eye center during the COVID-19 Omicron variant pandemic in Shanghai. There may be a correlation between the onset of APAC and new COVID-19 Omicron variant infection, but the exact reason needs to be investigated further.

Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction

Human-virus protein-protein interactions (PPIs) play critical roles in viral infection. For example, the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds primarily to human angiotensin-converting enzyme 2 (ACE2) protein to infect human cells. Thus, identifying and blocking these PPIs contribute to controlling and preventing viruses. However, wet-lab experiment-based identification of human-virus PPIs is usually expensive, labor-intensive, and time-consuming, which presents the need for computational methods. Many machine-learning methods have been proposed recently and achieved good results in predicting human-virus PPIs. However, most methods are based on protein sequence features and apply manually extracted features, such as statistical characteristics, phylogenetic profiles, and physicochemical properties. In this work, we present an embedding-based neural framework with convolutional neural network (CNN) and bi-directional long short-term memory unit (Bi-LSTM) architecture, named Emvirus, to predict human-virus PPIs (including human-SARS-CoV-2 PPIs). In addition, we conduct cross-viral experiments to explore the generalization ability of Emvirus. Compared to other feature extraction methods, Emvirus achieves better prediction accuracy.

Revealing the roles of TLR7, a nucleic acid sensor for COVID-19 in pan-cancer

Recent studies suggested that cancer was a risk factor for coronavirus disease 2019 (COVID-19). Toll-like receptor 7 (TLR7), a severe acute respiratory syndrome 2 (SARS-CoV-2) virus's nucleic acid sensor, was discovered to be aberrantly expressed in many types of cancers. However, its expression pattern across cancers and association with COVID-19 (or its causing virus SARS-CoV-2) has not been systematically studied. In this study, we proposed a computational framework to comprehensively study the roles of TLR7 in COVID-19 and pan-cancers at genetic, gene expression, protein, epigenetic, and single-cell levels. We applied the computational framework in a few databases, including The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), Human Protein Atlas (HPA), lung gene expression data of mice infected with SARS-CoV-2, and the like. As a result, TLR7 expression was found to be higher in the lung of mice infected with SARS-CoV-2 than that in the control group. The analysis in the Opentargets database also confirmed the association between TLR7 and COVID-19. There are also a few exciting findings in cancers. First, the most common type of TLR7 was "Missense" at the genomic level. Second, TLR7 mRNA expression was significantly up-regulated in 6 cancer types and down-regulated in 6 cancer types compared to normal tissues, further validated in the HPA database at the protein level. The genes significantly co-expressed with TLR7 were mainly enriched in the toll-like receptor signaling pathway, endolysosome, and signaling pattern recognition receptor activity. In addition, the abnormal TLR7 expression was associated with mismatch repair (MMR), microsatellite instability (MSI), and tumor mutational burden (TMB) in various cancers. Mined by the ESTIMATE algorithm, the expression of TLR7 was also closely linked to various immune infiltration patterns in pan-cancer, and TLR7 was mainly enriched in macrophages, as revealed by single-cell RNA sequencing. Third, abnormal expression of TLR7 could predict the survival of Brain Lower Grade Glioma (LGG), Lung adenocarcinoma (LUAD), Skin Cutaneous Melanoma (SKCM), Stomach adenocarcinoma (STAD), and Testicular Germ Cell Tumors (TGCT) patients, respectively. Finally, TLR7 expressions were very sensitive to a few targeted drugs, such as Alectinib and Imiquimod. In conclusion, TLR7 might be essential in the pathogenesis of COVID-19 and cancers.

Recent development of surface-enhanced Raman scattering for biosensing

Surface-Enhanced Raman Scattering (SERS) technology, as a powerful tool to identify molecular species by collecting molecular spectral signals at the single-molecule level, has achieved substantial progresses in the fields of environmental science, medical diagnosis, food safety, and biological analysis. As deepening research is delved into SERS sensing, more and more high-performance or multifunctional SERS substrate materials emerge, which are expected to push Raman sensing into more application fields. Especially in the field of biological analysis, intrinsic and extrinsic SERS sensing schemes have been widely used and explored due to their fast, sensitive and reliable advantages. Herein, recent developments of SERS substrates and their applications in biomolecular detection (SARS-CoV-2 virus, tumor etc.), biological imaging and pesticide detection are summarized. The SERS concepts (including its basic theory and sensing mechanism) and the important strategies (extending from nanomaterials with tunable shapes and nanostructures to surface bio-functionalization by modifying affinity groups or specific biomolecules) for improving SERS biosensing performance are comprehensively discussed. For data analysis and identification, the applications of machine learning methods and software acquisition sources in SERS biosensing and diagnosing are discussed in detail. In conclusion, the challenges and perspectives of SERS biosensing in the future are presented.

Predicting COVID-19 Severity Integrating RNA-Seq Data Using Machine Learning Techniques

Abstract:

A fundamental challenge in the fight against COVID -19 is the development of reliable and accurate tools to predict disease progression in a patient. This information can be extremely useful in distinguishing hospitalized patients at higher risk for needing UCI from patients with low severity. How SARS-CoV-2 infection will evolve is still unclear.

Methods:

A novel pipeline was developed that can integrate RNA-Seq data from different databases to obtain a genetic biomarker COVID -19 severity index using an artificial intelligence algorithm. Our pipeline ensures robustness through multiple cross-validation processes in different steps.

Results:

CD93, RPS24, PSCA, and CD300E were identified as a COVID -19 severity gene signature. Furthermore, using the obtained gene signature, an effective multi-class classifier capable of discriminating between control, outpatient, inpatient, and ICU COVID -19 patients was optimized, achieving an accuracy of 97.5%.

Conclusion:

In summary, during this research, a new intelligent pipeline was implemented with the goal of developing a specific gene signature that can detect the severity of patients suffering COVID -19. Our approach to clinical decision support systems achieved excellent results, even when processing unseen samples. Our system can be of great clinical utility for the strategy of planning, organizing and managing human and material resources, as well as for automatically classifying the severity of patients affected by COVID -19.

Broad strategies for neutralizing SARS-CoV-2 and other human coronaviruses with monoclonal antibodies

Antibody therapeutics and vaccines for coronavirus disease 2019 (COVID-19) have been approved in many countries, with most being developed based on the original strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 has an exceptional ability to mutate under the pressure of host immunity, especially the immune-dominant spike protein of the virus, which is the target of both antibody drugs and vaccines. Given the continuous evolution of the virus and the identification of critical mutation sites, the World Health Organization (WHO) has named 5 variants of concern (VOCs): 4 are previously circulating VOCs, and 1 is currently circulating (Omicron). Due to multiple mutations in the spike protein, the recently emerged Omicron and descendent lineages have been shown to have the strongest ability to evade the neutralizing antibody (NAb) effects of current antibody drugs and vaccines. The development and characterization of broadly neutralizing antibodies (bNAbs) will provide broad strategies for the control of the sophisticated virus SARS-CoV-2. In this review, we describe how the virus evolves to escape NAbs and the potential neutralization mechanisms that associated with bNAbs. We also summarize progress in the development of bNAbs against SARS-CoV-2, human coronaviruses (CoVs) and other emerging pathogens and highlight their scientific and clinical significance.

An implementation of a hybrid method based on machine learning to identify biomarkers in the Covid-19 diagnosis using DNA sequences

Although some people do not have any chronic disease or are not in the risky age group for Covid-19, they are more vulnerable to the coronavirus. As the reason for this situation, some experts focus on the immune system of the person, while others think that the genetic history of patients may play a role. It is critical to detect corona from DNA signals as early as possible to determine the relationship between Covid-19 and genes. Thus, the effect on the severe course of the disease of variations in the genes associated with the corona disease will be revealed. In this study, a novel intelligent computer approach is proposed to identify coronavirus from nucleotide signals for the first time. The proposed method presents a multilayered feature extraction structure to extract the most effective features using an Entropy-based mapping technique, Discrete Wavelet Transform (DWT), statistical feature extractor, and Singular Value Decomposition (SVD), together. Then 94 distinctive features are selected by the ReliefF technique. Support vector machine (SVM) and k nearest neighborhood (k-NN) are chosen as classifiers. The method achieved the highest classification accuracy rate of 98.84% with an SVM classifier to detect Covid-19 from DNA signals. The proposed method is ready to be tested with a different database in the diagnosis of Covid-19 using RNA or other signals.

Ensemble learning-based feature selection for phosphorylation site detection

SARS-COV-2 is prevalent all over the world, causing more than six million deaths and seriously affecting human health. At present, there is no specific drug against SARS-COV-2. Protein phosphorylation is an important way to understand the mechanism of SARS -COV-2 infection. It is often expensive and time-consuming to identify phosphorylation sites with specific modified residues through experiments. A method that uses machine learning to make predictions about them is proposed. As all the methods of extracting protein sequence features are knowledge-driven, these features may not be effective for detecting phosphorylation sites without a complete understanding of the mechanism of protein. Moreover, redundant features also have a great impact on the fitting degree of the model. To solve these problems, we propose a feature selection method based on ensemble learning, which firstly extracts protein sequence features based on knowledge, then quantifies the importance score of each feature based on data, and finally uses the subset of important features as the final features to predict phosphorylation sites.

Nanosponges: An overlooked promising strategy to combat SARS-CoV-2

Among explored nanomaterials, nanosponge-based systems have exhibited inhibitory effects for the biological neutralization of, and antiviral delivery against, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). More studies could pave the path for clarification of their biological neutralization mechanisms as well as the assessment of their long-term biocompatibility and biosafety issues before clinical translational studies. In this review, we discuss recent advances pertaining to antiviral delivery and inhibitory effects of nanosponges against SARS-CoV-2, focusing on important challenges and opportunities. Finally, as promising approaches for recapitulating the complex structure of different organs/tissues of the body, we discuss the use of 3D in vitro models to investigate the mechanism of SARS-CoV-2 infection and to find therapeutic targets to better manage and eradicate coronavirus 2019 (COVID-19).

Identifying potential microRNA biomarkers for colon cancer and colorectal cancer through bound nuclear norm regularization

Colon cancer and colorectal cancer are two common cancer-related deaths worldwide. Identification of potential biomarkers for the two cancers can help us to evaluate their initiation, progression and therapeutic response. In this study, we propose a new microRNA-disease association identification method, BNNRMDA, to discover potential microRNA biomarkers for the two cancers. BNNRMDA better combines disease semantic similarity and Gaussian Association Profile Kernel (GAPK) similarity, microRNA function similarity and GAPK similarity, and the bound nuclear norm regularization model. Compared to other five classical microRNA-disease association identification methods (MIDPE, MIDP, RLSMDA, GRNMF, AND LPLNS), BNNRMDA obtains the highest AUC of 0.9071, demonstrating its strong microRNA-disease association identification performance. BNNRMDA is applied to discover possible microRNA biomarkers for colon cancer and colorectal cancer. The results show that all 73 known microRNAs associated with colon cancer in the HMDD database have the highest association scores with colon cancer and are ranked as top 73. Among 137 known microRNAs associated with colorectal cancer in the HMDD database, 129 microRNAs have the highest association scores with colorectal cancer and are ranked as top 129. In addition, we predict that hsa-miR-103a could be a potential biomarker of colon cancer and hsa-mir-193b and hsa-mir-7days could be potential biomarkers of colorectal cancer.

The Influence of SARS-CoV-2 Infection on Lipid Metabolism—The Potential Use of Lipid-Lowering Agents in COVID-19 Management

Several studies have indicated lipid metabolism alterations during COVID-19 infection, specifically a decrease in high-density lipoprotein (HDL) and low-density lipoprotein (LDL) concentrations and an increase in triglyceride (TG) levels during the infection. However, a decline in triglycerides can also be observed in critical cases. A direct correlation can be observed between a decrease in serum cholesterol, HDL-C, LDL-C and TGs, and the severity of the disease; these laboratory findings can serve as potential markers for patient outcomes. The transmission of coronavirus increases proportionally with rising levels of cholesterol in the cell membrane. This is due to the fact that cholesterol increases the number of viral entry spots and the concentration of angiotensin-converting enzyme 2 (ACE2) receptor, crucial for viral penetration. Studies have found that lower HDL-C levels correspond with a higher susceptibility to SARS-CoV-2 infection and infections in general, while higher HDL-C levels were related to a lower risk of developing them. However, extremely high HDL-C levels in serum increase the risk of infectious diseases and is associated with a higher risk of cardiovascular events. Low HDL-C levels are already accepted as a marker for risk stratification in critical illnesses, and higher HDL-C levels prior to the infection is associated with a lower risk of death in older patients. The correlation between LDL-C levels and disease severity is still unclear. However, TG levels were significantly higher in non-surviving severe patients compared to those that survived; therefore, elevated TG-C levels in COVID-19 patients may be considered an indicator of uncontrolled inflammation and an increased risk of death.

Prioritizing potential circRNA biomarkers for bladder cancer and bladder urothelial cancer based on an ensemble model

Bladder cancer is the most common cancer of the urinary system. Bladder urothelial cancer accounts for 90% of bladder cancer. These two cancers have high morbidity and mortality rates worldwide. The identification of biomarkers for bladder cancer and bladder urothelial cancer helps in their diagnosis and treatment. circRNAs are considered oncogenes or tumor suppressors in cancers, and they play important roles in the occurrence and development of cancers. In this manuscript, we developed an Ensemble model, CDA-EnRWLRLS, to predict circRNA-Disease Associations (CDA) combining Random Walk with restart and Laplacian Regularized Least Squares, and further screen potential biomarkers for bladder cancer and bladder urothelial cancer. First, we compute disease similarity by combining the semantic similarity and association profile similarity of diseases and circRNA similarity by combining the functional similarity and association profile similarity of circRNAs. Second, we score each circRNA-disease pair by random walk with restart and Laplacian regularized least squares, respectively. Third, circRNA-disease association scores from these models are integrated to obtain the final CDAs by the soft voting approach. Finally, we use CDA-EnRWLRLS to screen potential circRNA biomarkers for bladder cancer and bladder urothelial cancer. CDA-EnRWLRLS is compared to three classical CDA prediction methods (CD-LNLP, DWNN-RLS, and KATZHCDA) and two individual models (CDA-RWR and CDA-LRLS), and obtains better AUC of 0.8654. We predict that circHIPK3 has the highest association with bladder cancer and may be its potential biomarker. In addition, circSMARCA5 has the highest association with bladder urothelial cancer and may be its possible biomarker.

Finding Lung-Cancer-Related lncRNAs Based on Laplacian Regularized Least Squares With Unbalanced Bi-Random Walk

Lung cancer is one of the leading causes of cancer-related deaths. Thus, it is important to find its biomarkers. Furthermore, there is an increasing number of studies reporting that long noncoding RNAs (lncRNAs) demonstrate dense linkages with multiple human complex diseases. Inferring new lncRNA-disease associations help to identify potential biomarkers for lung cancer and further understand its pathogenesis, design new drugs, and formulate individualized therapeutic options for lung cancer patients. This study developed a computational method (LDA-RLSURW) by integrating Laplacian regularized least squares and unbalanced bi-random walk to discover possible lncRNA biomarkers for lung cancer. First, the lncRNA and disease similarities were computed. Second, unbalanced bi-random walk was, respectively, applied to the lncRNA and disease networks to score associations between diseases and lncRNAs. Third, Laplacian regularized least squares were further used to compute the association probability between each lncRNA-disease pair based on the computed random walk scores. LDA-RLSURW was compared using 10 classical LDA prediction methods, and the best AUC value of 0.9027 on the lncRNADisease database was obtained. We found the top 30 lncRNAs associated with lung cancers and inferred that lncRNAs TUG1, PTENP1, and UCA1 may be biomarkers of lung neoplasms, non-small–cell lung cancer, and LUAD, respectively.

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4⁺ T cells, CD8⁺ T cells, monocytes, dendritic cells, B cells, and natural killer cells. Various immune cells are involved and make up the complex and unique immune system response to COVID-19, providing characteristics that set it apart from other respiratory infectious diseases. In the present study, we identified cell markers for differentiating COVID-19 from common inflammatory responses, non-COVID-19 severe respiratory diseases, and healthy populations based on single-cell profiling of the gene expression of six immune cell types by using Boruta and mRMR feature selection methods. Some features such as IFI44L in B cells, S100A8 in monocytes, and NCR2 in natural killer cells are involved in the innate immune response of COVID-19. Other features such as ZFP36L2 in CD4⁺ T cells can regulate the inflammatory process of COVID-19. Subsequently, the IFS method was used to determine the best feature subsets and classifiers in the six immune cell types for two classification algorithms. Furthermore, we established the quantitative rules used to distinguish the disease status. The results of this study can provide theoretical support for a more in-depth investigation of COVID-19 pathogenesis and intervention strategies.

Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19

Amid the COVID‐19 crisis, we put sizeable efforts to collect a high number of experimentally validated drug–virus association entries from literature by text mining and built a human drug–virus association database. To the best of our knowledge, it is the largest publicly available drug–virus database so far. Next, we develop a novel weight regularization matrix factorization approach, termed WRMF, for in silico drug repurposing by integrating three networks: the known drug–virus association network, the drug–drug chemical structure similarity network, and the virus–virus genomic sequencing similarity network. Specifically, WRMF adds a weight to each training sample for reducing the influence of negative samples (i.e. the drug–virus association is unassociated). A comparison on the curated drug–virus database shows that WRMF performs better than a few state‐of‐the‐art methods. In addition, we selected the other two different public datasets (i.e. Cdataset and HMDD V2.0) to assess WRMF's performance. The case study also demonstrated the accuracy and reliability of WRMF to infer potential drugs for the novel virus. In summary, we offer a useful tool including a novel drug–virus association database and a powerful method WRMF to repurpose potential drugs for new viruses.

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.

Recognition of Immune Cell Markers of COVID-19 Severity with Machine Learning Methods

COVID-19 is hypothesized to be linked to the host's excessive inflammatory immunological response to SARS-CoV-2 infection, which is regarded to be a major factor in disease severity and mortality. Numerous immune cells play a key role in immune response regulation, and gene expression analysis in these cells could be a useful method for studying disease states, assessing immunological responses, and detecting biomarkers. Here, we developed a machine learning procedure to find biomarkers that discriminate disease severity in individual immune cells (B cell, CD4+ cell, CD8+ cell, monocyte, and NK cell) using single-cell gene expression profiles of COVID-19. The gene features of each profile were first filtered and ranked using the Boruta feature selection method and mRMR, and the resulting ranked feature lists were then fed into the incremental feature selection method to determine the optimal number of features with decision tree and random forest algorithms. Meanwhile, we extracted the classification rules in each cell type from the optimal decision tree classifiers. The best gene sets discovered in this study were analyzed by GO and KEGG pathway enrichment, and some important biomarkers like TLR2, ITK, CX3CR1, IL1B, and PRDM1 were validated by recent literature. The findings reveal that the optimal gene sets for each cell type can accurately classify COVID-19 disease severity and provide insight into the molecular mechanisms involved in disease progression.

A Retrospective and Multicenter Study on COVID-19 in Inner Mongolia: Evaluating the Influence of Sampling Locations on Nucleic Acid Test and the Dynamics of Clinical and Prognostic Indexes

COVID-19 is spreading widely, and the pandemic is seriously threatening public health throughout the world. A comprehensive study on the optimal sampling types and timing for an efficient SARS-CoV-2 test has not been reported. We collected clinical information and the values of 55 biochemical indices for 237 COVID-19 patients, with 37 matched non-COVID-19 pneumonia patients and 131 healthy people in Inner Mongolia as control. In addition, the results of dynamic detection of SARS-CoV-2 using oropharynx swab, pharynx swab, and feces were collected from 197 COVID-19 patients. SARS-CoV-2 RNA positive in feces specimen was present in approximately one-third of COVID-19 patients. The positive detection rate of SARS-CoV-2 RNA in feces was significantly higher than both in the oropharynx and nasopharynx swab (P < 0.05) in the late period of the disease, which is not the case in the early period of the disease. There were statistically significant differences in the levels of blood LDH, CRP, platelet count, neutrophilic granulocyte count, white blood cell number, and lymphocyte count between COVID-19 and non-COVID-19 pneumonia patients. Finally, we developed and compared five machine-learning models to predict the prognosis of COVID-19 patients based on biochemical indices at disease onset and demographic characteristics. The best model achieved an area under the curve of 0.853 in the 10-fold cross-validation.

Genetic differentiation and diversity of SARS-CoV-2 omicron variant in Its early outbreak

The recently emerged Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has quickly spread around the world. Although many consensus mutations of the Omicron variant have been recognized, little is known about its genetic variation during its transmission in the population. Here, we comprehensively analyzed the genetic differentiation and diversity of the Omicron variant during its early outbreak. We found that Omicron achieved more structural variations, especially deletions, on the SARS-CoV-2 genome than the other four variants of concern (Alpha, Beta, Gamma, and Delta) in the same timescale. In addition, the Omicron variant acquired, except for 50 consensus mutations, seven great new non-synonymous nucleotide substitutions during its spread. Three of them are on the S protein, including S_A701V, S_L1081V, and S_R346K, which belong to the receptor-binding domain (RBD). The Omicron BA.1 branch could be divided into five divergent groups spreading across different countries and regions based on these seven novel mutations. Furthermore, we found that the Omicron variant possesses more mutations related to a faster transmission rate than the other SARS-CoV-2 variants by assessing the relationship between the genetic diversity and transmission rate. The findings indicated that more attention should be paid to the significant genetic differentiation and diversity of the Omicron variant for better disease prevention and control.

Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization With Kernel Diffusion

Coronavirus disease 2019 (COVID-19) is rapidly spreading. Researchers around the world are dedicated to finding the treatment clues for COVID-19. Drug repositioning, as a rapid and cost-effective way for finding therapeutic options from available FDA-approved drugs, has been applied to drug discovery for COVID-19. In this study, we develop a novel drug repositioning method (VDA-KLMF) to prioritize possible anti-SARS-CoV-2 drugs integrating virus sequences, drug chemical structures, known Virus-Drug Associations, and Logistic Matrix Factorization with Kernel diffusion. First, Gaussian kernels of viruses and drugs are built based on known VDAs and nearest neighbors. Second, sequence similarity kernel of viruses and chemical structure similarity kernel of drugs are constructed based on biological features and an identity matrix. Third, Gaussian kernel and similarity kernel are diffused. Forth, a logistic matrix factorization model with kernel diffusion is proposed to identify potential anti-SARS-CoV-2 drugs. Finally, molecular dockings between the inferred antiviral drugs and the junction of SARS-CoV-2 spike protein-ACE2 interface are implemented to investigate the binding abilities between them. VDA-KLMF is compared with two state-of-the-art VDA prediction models (VDA-KATZ and VDA-RWR) and three classical association prediction methods (NGRHMDA, LRLSHMDA, and NRLMF) based on 5-fold cross validations on viruses, drugs, and VDAs on three datasets. It obtains the best recalls, AUCs, and AUPRs, significantly outperforming other five methods under the three different cross validations. We observe that four chemical agents coming together on any two datasets, that is, remdesivir, ribavirin, nitazoxanide, and emetine, may be the clues of treatment for COVID-19. The docking results suggest that the key residues K353 and G496 may affect the binding energies and dynamics between the inferred anti-SARS-CoV-2 chemical agents and the junction of the spike protein-ACE2 interface. Integrating various biological data, Gaussian kernel, similarity kernel, and logistic matrix factorization with kernel diffusion, this work demonstrates that a few chemical agents may assist in drug discovery for COVID-19.

Multivalent Vaccine Strategies in Battling the Emergence of COVID-19 Variants

The emergence of new SARS-CoV-2 variants has led to increased transmission and more severe cases of COVID-19, with some having the ability to escape the existing vaccines. This review discusses the importance of developing new vaccine strategies to keep pace with these variants to more effectively manage the pandemic. Many of the new vaccine approaches include multivalent display of the most highly mutated regions in the SARS-CoV-2 spike protein such that they resemble a virus particle and can stimulate an effective neutralization response. It is hoped that such approaches help to manage the existing pandemic and provide a robust infrastructure toward fast tracking responses across the world in case of future pandemics.

Molecular Evolution of Severe Acute Respiratory Syndrome Coronavirus 2: Hazardous and More Hazardous Strains Behind the Coronavirus Disease 2019 Pandemic and Their Targeting by Drugs and Vaccines

Within almost the last 2 years, the world has been shaken by the coronavirus disease 2019 (COVID-19) pandemic, which has affected the lives of all people. With nearly 4.92 million deaths by October 19, 2021, and serious health damages in millions of people, COVID-19 has been the most serious global challenge after the Second World War. Besides lost lives and long-term health problems, devastating impact on economics, education, and culture will probably leave a lasting impression on the future. Therefore, the actual extent of losses will become obvious only after years. Moreover, despite the availability of different vaccines and vaccination programs, it is still impossible to forecast what the next steps of the virus are or how near we are to the end of the pandemic. In this article, the route of molecular evolution of the coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is thoroughly compiled, highlighting the changes that the virus has undergone during the last 2 years and discussing the approaches that the medical community has undertaken in the fight against virus-induced damages.

Fundamentals of genomic epidemiology, lessons learned from the coronavirus disease 2019 (COVID-19) pandemic, and new directions

The coronavirus disease 2019 (COVID-19) pandemic was one of the significant causes of death worldwide in 2020. The disease is caused by severe acute coronavirus syndrome (SARS) coronavirus 2 (SARS-CoV-2), an RNA virus of the subfamily Orthocoronavirinae related to 2 other clinically relevant coronaviruses, SARS-CoV and MERS-CoV. Like other coronaviruses and several other viruses, SARS-CoV-2 originated in bats. However, unlike other coronaviruses, SARS-CoV-2 resulted in a devastating pandemic. The SARS-CoV-2 pandemic rages on due to viral evolution that leads to more transmissible and immune evasive variants. Technology such as genomic sequencing has driven the shift from syndromic to molecular epidemiology and promises better understanding of variants. The COVID-19 pandemic has exposed critical impediments that must be addressed to develop the science of pandemics. Much of the progress is being applied in the developed world. However, barriers to the use of molecular epidemiology in low- and middle-income countries (LMICs) remain, including lack of logistics for equipment and reagents and lack of training in analysis. We review the molecular epidemiology literature to understand its origins from the SARS epidemic (2002-2003) through influenza events and the current COVID-19 pandemic. We advocate for improved genomic surveillance of SARS-CoV and understanding the pathogen diversity in potential zoonotic hosts. This work will require training in phylogenetic and high-performance computing to improve analyses of the origin and spread of pathogens. The overarching goals are to understand and abate zoonosis risk through interdisciplinary collaboration and lowering logistical barriers.

Lipidomics and metabolomics signatures of SARS-CoV-2 mediators/receptors in peripheral leukocytes, jejunum and colon

Cell surface receptor-mediated viral entry plays a critical role in this infection. Well-established SARS-CoV-2 receptors such as ACE2 and TMPRSS2 are highly expressed in the gastrointestinal tract. In fact, there are evidences that SARS-CoV-2 infects epithelial cells from the digestive system. However, emerging research has identified novel mediators such as DPP9, TYK2, and CCR2, all playing a critical role in inflammation. We evaluated the expression of SARS-CoV-2 receptors in peripheral leukocytes (n = 469), jejunum (n = 30), and colon (n = 37) of three independent cohorts by real-time PCR, RNA-sequencing, and microarray transcriptomics. We also performed HPCL-MS/MS lipidomics and metabolomics analyses to identify signatures linked to SARS-CoV-2 receptors. We found markedly higher peripheral leukocytes ACE2 expression levels in women compared to men, whereas the intestinal expression of TMPRSS2 was positively associated with BMI. Consistent lipidomics signatures associated with the expression of these mediators were found in both tissues and peripheral leukocytes involving n-3 long-chain PUFAs and arachidonic acid-derived eicosanoids, which play a key role in the regulation of inflammation and may interfere with viral entry and replication. Medium- and long-chain hydroxy acids, which have shown to interfere in viral replication, were also liked to SARS-CoV2 receptors. Gonadal steroids were also associated with the expression of some of these receptors, even after controlling for sex. The expression of SARS-CoV2 receptors was associated with several metabolic and nutritional traits in different cell types. This information may be useful in the design of potential therapies targeted at coronavirus entry.

Energy, environmental, economic and social equity (4E) pressures of COVID-19 vaccination mismanagement: A global perspective

Vaccination now offers a way to resolve the COVID-19 pandemic. However, it is critical to recognise the full energy, environmental, economic and social equity (4E) impacts of the vaccination life cycle. The full 4E impacts include the design and trials, order management, material preparation, manufacturing, cold chain logistics, low-temperature storage, crowd management and end-of-life waste management. A life cycle perspective is necessary for sustainable vaccination management because a prolonged immunisation campaign for COVID-19 is likely. The impacts are geographically dispersed across sectors and regions, creating real and virtual 4E footprints that occur at different timescales. Decision-makers in industry and governments have to act, unify, resolve, and work together to implement more sustainable COVID-19 vaccination management globally and locally to minimise the 4E footprints. Potential practices include using renewable energy in production, storage, transportation and waste treatment, using better product design for packaging, using the Internet of Things (IoT) and big data analytics for better logistics, using real-time database management for better tracking of deliveries and public vaccination programmes, and using coordination platforms for more equitable vaccine access. These practices raise global challenges but suggest solutions with a 4E perspective, which could mitigate the impacts of global vaccination campaigns and prepare sustainably for future pandemics and global warming.

Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis

Over 50% of diffuse large B-cell lymphoma (DLBCL) patients are diagnosed at an advanced stage. Although there are a few therapeutic strategies for DLBCL, most of them are more effective in limited-stage cancer patients. The prognosis of patients with advanced-stage DLBCL is usually poor with frequent recurrence and metastasis. In this study, we aimed to identify gene expression and network differences between limited- and advanced-stage DLBCL patients, with the goal of identifying potential agents that could be used to relieve the severity of DLBCL. Specifically, RNA sequencing data of DLBCL patients at different clinical stages were collected from the cancer genome atlas (TCGA). Differentially expressed genes were identified using DESeq2, and then, weighted gene correlation network analysis (WGCNA) and differential module analysis were performed to find variations between different stages. In addition, important genes were extracted by key driver analysis, and potential agents for DLBCL were identified according to gene-expression perturbations and the Crowd Extracted Expression of Differential Signatures (CREEDS) drug signature database. As a result, 20 up-regulated and 73 down-regulated genes were identified and 79 gene co-expression modules were found using WGCNA, among which, the thistle1 module was highly related to the clinical stage of DLBCL. KEGG pathway and GO enrichment analyses of genes in the thistle1 module indicated that DLBCL progression was mainly related to the NOD-like receptor signaling pathway, neutrophil activation, secretory granule membrane, and carboxylic acid binding. A total of 47 key drivers were identified through key driver analysis with 11 up-regulated key driver genes and 36 down-regulated key diver genes in advanced-stage DLBCL patients. Five genes (MMP1, RAB6C, ACCSL, RGS21 and MOCOS) appeared as hub genes, being closely related to the occurrence and development of DLBCL. Finally, both differentially expressed genes and key driver genes were subjected to CREEDS analysis, and 10 potential agents were predicted to have the potential for application in advanced-stage DLBCL patients. In conclusion, we propose a novel pipeline to utilize perturbed gene-expression signatures during DLBCL progression for identifying agents, and we successfully utilized this approach to generate a list of promising compounds.

Artificial Intelligence Systems for Diagnosis and Clinical Classification of COVID-19

Objectives: COVID-19 is highly infectious and has been widely spread worldwide, with more than 159 million confirmed cases and more than 3 million deaths as of May 11, 2021. It has become a serious public health event threatening people's lives and safety. Due to the rapid transmission and long incubation period, shortage of medical resources would easily occur in the short term of discovering disease cases. Therefore, we aimed to construct an artificial intelligent framework to rapidly distinguish patients with COVID-19 from common pneumonia and non-pneumonia populations based on computed tomography (CT) images. Furthermore, we explored artificial intelligence (AI) algorithms to integrate CT features and laboratory findings on admission to predict the clinical classification of COVID-19. This will ease the burden of doctors in this emergency period and aid them to perform timely and appropriate treatment on patients. Methods: We collected all CT images and clinical data of novel coronavirus pneumonia cases in Inner Mongolia, including domestic cases and those imported from abroad; then, three models based on transfer learning to distinguish COVID-19 from other pneumonia and non-pneumonia population were developed. In addition, CT features and laboratory findings on admission were combined to predict clinical types of COVID-19 using AI algorithms. Lastly, Spearman's correlation test was applied to study correlations of CT characteristics and laboratory findings. Results: Among three models to distinguish COVID-19 based on CT, vgg19 showed excellent diagnostic performance, with area under the curve (AUC) of the receiver operating characteristic (ROC) curve at 95%. Together with laboratory findings, we were able to predict clinical types of COVID-19 with AUC of the ROC curve at 90%. Furthermore, biochemical markers, such as C-reactive protein (CRP), LYM, and lactic dehydrogenase (LDH) were identified and correlated with CT features. Conclusion: We developed an AI model to identify patients who were positive for COVID-19 according to the results of the first CT examination after admission and predict the progression combined with laboratory findings. In addition, we obtained important clinical characteristics that correlated with the CT image features. Together, our AI system could rapidly diagnose COVID-19 and predict clinical types to assist clinicians perform appropriate clinical management.

Addressing the 'hypoxia paradox' in severe COVID-19: literature review and report of four cases treated with erythropoietin analogues

BACKGROUND

Since fall 2019, SARS-CoV-2 spread world-wide, causing a major pandemic with estimated ~ 220 million subjects affected as of September 2021. Severe COVID-19 is associated with multiple organ failure, particularly of lung and kidney, but also grave neuropsychiatric manifestations. Overall mortality reaches > 2%. Vaccine development has thrived in thus far unreached dimensions and will be one prerequisite to terminate the pandemic. Despite intensive research, however, few treatment options for modifying COVID-19 course/outcome have emerged since the pandemic outbreak. Additionally, the substantial threat of serious downstream sequelae, called 'long COVID' and 'neuroCOVID', becomes increasingly evident. Among candidates that were suggested but did not yet receive appropriate funding for clinical trials is recombinant human erythropoietin. Based on accumulating experimental and clinical evidence, erythropoietin is expected to (1) improve respiration/organ function, (2) counteract overshooting inflammation, (3) act sustainably neuroprotective/neuroregenerative. Recent counterintuitive findings of decreased serum erythropoietin levels in severe COVID-19 not only support a relative deficiency of erythropoietin in this condition, which can be therapeutically addressed, but also made us coin the term 'hypoxia paradox'. As we review here, this paradox is likely due to uncoupling of physiological hypoxia signaling circuits, mediated by detrimental gene products of SARS-CoV-2 or unfavorable host responses, including microRNAs or dysfunctional mitochondria. Substitution of erythropoietin might overcome this 'hypoxia paradox' caused by deranged signaling and improve survival/functional status of COVID-19 patients and their long-term outcome. As supporting hints, embedded in this review, we present 4 male patients with severe COVID-19 and unfavorable prognosis, including predicted high lethality, who all profoundly improved upon treatment which included erythropoietin analogues.

SHORT CONCLUSION

Substitution of EPO may-among other beneficial EPO effects in severe COVID-19-circumvent downstream consequences of the 'hypoxia paradox'. A double-blind, placebo-controlled, randomized clinical trial for proof-of-concept is warranted.

A Computational Framework to Identify Transcriptional and Network Differences between Hepatocellular Carcinoma and Normal Liver Tissue and Their Applications in Repositioning Drugs

Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies worldwide. Although there have been extensive studies on the molecular mechanisms of its carcinogenesis, FDA-approved drugs for HCC are rare. Side effects, development time, and cost of these drugs are the major bottlenecks, which can be partially overcome by drug repositioning. In this study, we developed a computational framework to study the mechanisms of HCC carcinogenesis, in which drug perturbation-induced gene expression signatures were utilized for repositioning of potential drugs. Specifically, we first performed differential expression analysis and coexpression network module analysis on the HCC dataset from The Cancer Genome Atlas database. Differential gene expression analysis identified 1,337 differentially expressed genes between HCC and adjacent normal tissues, which were significantly enriched in functions related to various pathways, including α-adrenergic receptor activity pathway and epinephrine binding pathway. Weighted gene correlation network analysis (WGCNA) suggested that the number of coexpression modules was higher in HCC tissues than in normal tissues. Finally, by correlating differentially expressed genes with drug perturbation-related signatures, we prioritized a few potential drugs, including nutlin and eribulin, for the treatment of hepatocellular carcinoma. The drugs have been reported by a few experimental studies to be effective in killing cancer cells.

Case Report: A Re-Positive Case of SARS-CoV-2 Associated With Glaucoma

The current pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has already become a global threat to the human population. Infection with SARS-CoV-2 leads to a wide spectrum of clinical manifestations. Ocular abnormalities have been reported in association with COVID-19, but the nature of the impairments was not specified. Here, we report a case of a female patient diagnosed with glaucoma on re-hospitalization for ocular complications two months after being discharged from the hospital upon recovery from COVID-19. Meanwhile, the patient was found re-positive for SARS-CoV-2 in the upper respiratory tract. The infection was also diagnosed in the aqueous humor through immunostaining with antibodies against the N protein and S protein of SARS-CoV-2. Considering the eye is an immune-privileged site, we speculate that SARS-CoV-2 survived in the eye and resulted in the patient testing re-positive for SARS-CoV-2.

Role of Pegylated Interferon in Patients with Chronic Liver Diseases in the Context of SARS-CoV-2 Infection

The coronavirus 2019 (COVID-19) pandemic has resulted in 168 million cases and about 3.5 million deaths (as of May 26, 2021) during the last 18 months. These 18 months of the COVID-19 pandemic have been characterized by phases or waves of new cases, the emergence of new variants of the deadly virus, and several new complications. After providing emergency approval to several drugs and adherence to several public health measures with frequent full and partial lockdowns, the incidence of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could not be contained till now on a global basis. Although prophylactic vaccines have inspired optimism, the scarcity of vaccines and several vaccine-related regulations indicate that the vaccine's benefit would not be reaching the people of developing countries anytime soon. In the course of our clinical practice, we used pegylated interferon (Peg-IFN) in 35 patients with chronic liver diseases (CLD), and we found that only two of them were infected with SARS-CoV-2 that was mild in nature. These two patients with CLD have a mild course of disease cured without any specific therapy. Patients with CLD are usually immune-compromised. However, three CLD patients remained free of SARS-CoV-2 although they had COVID-19 patients among their family members. Next, we accomplished two studies for assessing the immune-modulatory capacities of Peg-IFN, 1 and 12 injections following administration of Peg-IFN. The data revealed that peripheral blood mononuclear cells (PBMCs) of Peg-IFN-administered CLD patients produced significantly higher levels of some cytokines of innate immunity in comparison with the cytokines produced by PBMC of CLD patients before Peg-IFN intake. The pattern of cytokine responses and absence of infection of SARS-CoV-2 in 33 of 35 CLD patients represent some preliminary observations indicating a possible role of Peg-IFN in patients with CLD. The study may be extended to other chronic infections and cancers in which patients receive Peg-IFN. The role of Peg-IFN for pre- or postexposure prophylaxis in the acquisition of SARS-CoV-2 infection and influencing the natural course of COVID-19 remains to be clarified.