A comprehensive collection of systems biology data characterizing the host response to viral infection

A compendium of multi-omics data illuminating host responses to lethal human virus infections

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.

COVID-19: A novel holistic systems biology approach to predict its molecular mechanisms (in vitro) and repurpose drugs

PURPOSE

COVID-19 strangely kills some youth with no history of physical weakness, and in addition to the lungs, it may even directly harm other organs. Its complex mechanism has led to the loss of any significantly effective drug, and some patients with severe forms still die daily. Common methods for identifying disease mechanisms and drug design are often time-consuming or reductionist. Here, we use a novel holistic systems biology approach to predict its molecular mechanisms (in vitro), significant molecular relations with SARS, and repurpose drugs.

METHODS

We have utilized its relative phylogenic similarity to SARS. Using the available omics data for SARS and the fewer data for COVID-19 to decode the mechanisms and their significant relations, We applied the Cytoscape analyzer, MCODE, STRING, and DAVID tools to predict the topographically crucial molecules, clusters, protein interaction mappings, and functional analysis. We also applied a novel approach to identify the significant relations between the two infections using the Fischer exact test for MCODE clusters. We then constructed and analyzed a drug-gene network using PharmGKB and DrugBank (retrieved using the dgidb).

RESULTS

Some of the shared identified crucial molecules, BPs and pathways included Kaposi sarcoma-associated herpesvirus infection, Influenza A, and NOD-like receptor signaling pathways. Besides, our identified crucial molecules specific to host response against SARS-CoV-2 included FGA, BMP4, PRPF40A, and IFI16.

CONCLUSION

We also introduced seven new repurposed candidate drugs based on the drug-gene network analysis for the identified crucial molecules. Therefore, we suggest that our newly recommended repurposed drugs be further investigated in Vitro and in Vivo against COVID-19.

Integrated interactome and transcriptome analysis reveals key host factors critical for SARS-CoV-2 infection

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has seriously threatened global public health and caused huge economic losses. Omics studies of SARS-CoV-2 can help understand the interaction between the virus and host, thereby providing a new perspective in guiding the intervention and treatment of the SARS-CoV-2 infection. Since large amount of SARS-CoV-2 omics data have been accumulated in public databases, this study aimed to identify key host factors involved in SARS-CoV-2 infection through systematic integration of transcriptome and interactome data. By manually curating published studies, we obtained a comprehensive SARS-CoV-2-human protein-protein interactions (PPIs) network, comprising 3591 human proteins interacting with 31 SARS-CoV-2 viral proteins. Using the RobustRankAggregation method, we identified 123 multiple cell line common genes (CLCGs), of which 115 up-regulated CLCGs showed host enhanced innate immunity and chemotactic response signatures. Combined with network analysis, co-expression and functional enrichment analysis, we discovered four key host factors involved in SARS-CoV-2 infection: IFITM1, SERPINE1, DDX60, and TNFAIP2. Furthermore, SERPINE1 was found to facilitate SARS-CoV-2 replication, and can alleviate the endoplasmic reticulum (ER) stress induced by ORF8 protein through interaction with ORF8. Our findings highlight the importance of systematic integration analysis in understanding SARS-CoV-2-human interactions and provide valuable insights for future research on potential therapeutic targets against SARS-CoV-2 infection.

Deciphering molecular mechanisms of SARS-CoV-2 pathogenesis and drug repurposing through GRN motifs: a comprehensive systems biology study

The world has recently been plagued by a new coronavirus infection called SARS-CoV-2. This virus may lead to severe acute respiratory syndrome followed by multiple organ failure. SARS-CoV-2 has approximately 80-90% genetic similarity to SARS-CoV. Given the limited omics data available for host response to the viruses (more limited data for SARS-CoV-2), we attempted to unveil the crucial molecular mechanisms underlying the SARS-CoV-2 pathogenesis by comparing its regulatory network motifs with SARS-CoV. We also attempted to identify the non-shared crucial molecules and their functions to predict the specific mechanisms for each infection and the processes responsible for their different manifestations. Deciphering the crucial shared and non-shared mechanisms at the molecular level and signaling pathways underlying both diseases may help shed light on their pathogenesis and pave the way for other new drug repurposing against COVID-19. We constructed the GRNs for host response to SARS-CoV and SARS-CoV-2 pathogens (in vitro) and identified the significant 3-node regulatory motifs by analyzing them topologically and functionally. We attempted to identify the shared and non-shared regulatory elements and signaling pathways between their host responses. Interestingly, our findings indicated that NFKB1, JUN, STAT1, FOS, KLF4, and EGR1 were the critical shared TFs between motif-related subnetworks in both SARS and COVID-1, which are considered genes with specific functions in the immune response. Enrichment analysis revealed that the NOD-like receptor signaling, TNF signaling, and influenza A pathway were among the first significant pathways shared between SARS and COVID-19 up-regulated DEGs networks, and the term "metabolic pathways" (hsa01100) among the down-regulated DEGs networks. WEE1, PMAIP1, and TSC22D2 were identified as the top three hubs specific to SARS. However, MYPN, SPRY4, and APOL6 were the tops specific to COVID-19 in vitro. The term "Complement and coagulation cascades" pathway was identified as the first top non-shared pathway for COVID-19 and the MAPK signaling pathway for SARS. We used the identified crucial DEGs to construct a drug-gene interaction network to propose some drug candidates. Zinc chloride, Fostamatinib, Copper, Tirofiban, Tretinoin, and Levocarnitine were the six drugs with higher scores in our drug-gene network analysis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03518-x.

Protective Efficacy of a Mucosal Influenza Vaccine Formulation Based on the Recombinant Nucleoprotein Co-Administered with a TLR2/6 Agonist BPPcysMPEG

Current influenza vaccines target highly variable surface glycoproteins; thus, mismatches between vaccine strains and circulating strains often diminish vaccine protection. For this reason, there is still a critical need to develop effective influenza vaccines able to protect also against the drift and shift of different variants of influenza viruses. It has been demonstrated that influenza nucleoprotein (NP) is a strong candidate for a universal vaccine, which contributes to providing cross-protection in animal models. In this study, we developed an adjuvanted mucosal vaccine using the recombinant NP (rNP) and the TLR2/6 agonist S-[2,3-bispalmitoyiloxy-(2R)-propyl]-R-cysteinyl-amido-monomethoxyl-poly-ethylene-glycol (BPPcysMPEG). The vaccine efficacy was compared with that observed following parenteral vaccination of mice with the same formulation. Mice vaccinated with 2 doses of rNP alone or co-administered with BPPcysMPEG by the intranasal (i.n.) route showed enhanced antigen-specific humoral and cellular responses. Moreover, NP-specific humoral immune responses, characterized by significant NP-specific IgG and IgG subclass titers in sera and NP-specific IgA titers in mucosal territories, were remarkably increased in mice vaccinated with the adjuvanted formulation as compared with those of the non-adjuvanted vaccination group. The addition of BPPcysMPEG also improved NP-specific cellular responses in vaccinated mice, characterized by robust lymphoproliferation and mixed Th1/Th2/Th17 immune profiles. Finally, it is notable that the immune responses elicited by the novel formulation administered by the i.n. route were able to confer protection against the influenza H1N1 A/Puerto Rico/8/1934 virus.

Host genetic diversity and genetic variations of SARS-CoV-2 in COVID-19 pathogenesis and the effectiveness of vaccination

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for the outbreak of coronavirus disease 2019 (COVID-19), has shown a vast range of clinical manifestations from asymptomatic to life-threatening symptoms. To figure out the cause of this heterogeneity, studies demonstrated the trace of genetic diversities whether in the hosts or the virus itself. With this regard, this review provides a comprehensive overview of how host genetic such as those related to the entry of the virus, the immune-related genes, gender-related genes, disease-related genes, and also host epigenetic could influence the severity of COVID-19. Besides, the mutations in the genome of SARS-CoV-2 __leading to emerging of new variants__ per se affect the affinity of the virus to the host cells and enhance the immune escape capacity. The current review discusses these variants and also the latest data about vaccination effectiveness facing the most important variants.

Epigenetic perspectives of COVID-19: Virus infection to disease progression and therapeutic control

COVID-19 has caused numerous deaths as well as imposed social isolation and upheaval world-wide. Although, the genome and the composition of the virus, the entry process and replication mechanisms are well investigated from by several laboratories across the world, there are many unknown remaining questions. For example, what are the functions of membrane lipids during entry, packaging and exit of virus particles? Also, the metabolic aspects of the infected tissue cells are poorly understood. In the course of virus replication and formation of virus particles within the host cell, the enhanced metabolic activities of the host is directly proportional to viral loads. The epigenetic landscape of the host cells is also altered, particularly the expression/repression of genes associated with cellular metabolism as well as cellular processes that are antagonistic to the virus. Metabolic pathways are enzyme driven processes and the expression profile and mechanism of regulations of the respective genes encoding those enzymes during the course of pathogen invasion might be highly informative on the course of the disease. Recently, the metabolic profile of the patients' sera have been analysed from few patients. In view of this, and to gain further insights into the roles that epigenetic mechanisms might play in this scenario in regulation of metabolic pathways during the progression of COVID-19 are discussed and summarised in this contribution for ensuring best therapy.

Meta-Analysis of Two Human RNA-seq Datasets to Determine Periodontitis Diagnostic Biomarkers and Drug Target Candidates

Periodontitis is a chronic inflammatory oral disease that affects approximately 42% of adults 30 years of age or older in the United States. In response to microbial dysbiosis within the periodontal pockets surrounding teeth, the host immune system generates an inflammatory environment in which soft tissue and alveolar bone destruction occur. The objective of this study was to identify diagnostic biomarkers and the mechanistic drivers of inflammation in periodontitis to identify drugs that may be repurposed to treat chronic inflammation. A meta-analysis comprised of two independent RNA-seq datasets was performed. RNA-seq analysis, signal pathway impact analysis, protein-protein interaction analysis, and drug target analysis were performed to identify the critical pathways and key players that initiate inflammation in periodontitis as well as to predict potential drug targets. Seventy-eight differentially expressed genes, 10 significantly impacted signaling pathways, and 10 hub proteins in periodontal gingival tissue were identified. The top 10 drugs that may be repurposed for treating periodontitis were then predicted from the gene expression and pathway data. The efficacy of these drugs in treating periodontitis has yet to be investigated. However, this analysis indicates that these drugs may serve as potential therapeutics to treat inflammation in gingival tissue affected by periodontitis.

Significance of Immune Status of SARS-CoV-2 Infected Patients in Determining the Efficacy of Therapeutic Interventions

Coronavirus disease 2019 (COVID-19) is now being investigated for its distinctive patterns in the course of disease development which can be indicated with miscellaneous immune responses in infected individuals. Besides this series of investigations on the pathophysiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), significant fundamental immunological and physiological processes are indispensable to address clinical markers of COVID-19 disease and essential to identify or design effective therapeutics. Recent developments in the literature suggest that deficiency of type I interferon (IFN) in serum samples can be used to represent a severe progression of COVID-19 disease and can be used as the basis to develop combined immunotherapeutic strategies. Precise control over inflammatory response is a significant aspect of targeting viral infections. This account presents a brief review of the pathophysiological characteristics of the SARS-CoV-2 virus and the understanding of the immune status of infected patients. We further discuss the immune system’s interaction with the SARS-CoV-2 virus and their subsequent involvement of dysfunctional immune responses during the progression of the disease. Finally, we highlight some of the implications of the different approaches applicable in developing promising therapeutic interventions that redirect immunoregulation and viral infection.

Therapeutic targets and interventional strategies in COVID-19: mechanisms and clinical studies

Owing to the limitations of the present efforts on drug discovery against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the lack of the understanding of the biological regulation mechanisms underlying COVID-19, alternative or novel therapeutic targets for COVID-19 treatment are still urgently required. SARS-CoV-2 infection and immunity dysfunction are the two main courses driving the pathogenesis of COVID-19. Both the virus and host factors are potential targets for antiviral therapy. Hence, in this study, the current therapeutic strategies of COVID-19 have been classified into "target virus" and "target host" categories. Repurposing drugs, emerging approaches, and promising potential targets are the implementations of the above two strategies. First, a comprehensive review of the highly acclaimed old drugs was performed according to evidence-based medicine to provide recommendations for clinicians. Additionally, their unavailability in the fight against COVID-19 was analyzed. Next, a profound analysis of the emerging approaches was conducted, particularly all licensed vaccines and monoclonal antibodies (mAbs) enrolled in clinical trials against primary SARS-CoV-2 and mutant strains. Furthermore, the pros and cons of the present licensed vaccines were compared from different perspectives. Finally, the most promising potential targets were reviewed, and the update of the progress of treatments has been summarized based on these reviews.

Preprocessing of Public RNA-Sequencing Datasets to Facilitate Downstream Analyses of Human Diseases

Publicly available RNA-sequencing (RNA-seq) data are a rich resource for elucidating the mechanisms of human disease; however, preprocessing these data requires considerable bioinformatic expertise and computational infrastructure. Analyzing multiple datasets with a consistent computational workflow increases the accuracy of downstream meta-analyses. This collection of datasets represents the human intracellular transcriptional response to disorders and diseases such as acute lymphoblastic leukemia (ALL), B-cell lymphomas, chronic obstructive pulmonary disease (COPD), colorectal cancer, lupus erythematosus; as well as infection with pathogens including Borrelia burgdorferi, hantavirus, influenza A virus, Middle East respiratory syndrome coronavirus (MERS-CoV), Streptococcus pneumoniae, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We calculated the statistically significant differentially expressed genes and Gene Ontology terms for all datasets. In addition, a subset of the datasets also includes results from splice variant analyses, intracellular signaling pathway enrichments as well as read mapping and quantification. All analyses were performed using well-established algorithms and are provided to facilitate future data mining activities, wet lab studies, and to accelerate collaboration and discovery.

Differential role of sphingomyelin in influenza virus, rhinovirus and SARS-CoV-2 infection of Calu-3 cells

Host cell lipids play a pivotal role in the pathogenesis of respiratory virus infection. However, a direct comparison of the lipidomic profile of influenza virus and rhinovirus infections is lacking. In this study, we first compared the lipid profile of influenza virus and rhinovirus infection in a bronchial epithelial cell line. Most lipid features were downregulated for both influenza virus and rhinovirus, especially for the sphingomyelin features. Pathway analysis showed that sphingolipid metabolism was the most perturbed pathway. Functional study showed that bacterial sphingomyelinase suppressed influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication, but promoted rhinovirus replication. These findings suggest that sphingomyelin pathway can be a potential target for antiviral therapy, but should be carefully evaluated as it has opposite effects on different respiratory viruses. Furthermore, the differential effect of sphingomyelinase on rhinovirus and influenza virus may explain the interference between rhinovirus and influenza virus infection.

Coronavirus Disease 19 and Future Ecological Crises: Hopes from Epigenomics and Unraveling Genome Regulation in Humans and Infectious Agents

With coronavirus disease 19 (COVID-19), we have witnessed a shift from public health to planetary health and a growing recognition of the importance of systems science in developing effective solutions against pandemics in the 21st century. COVID-19 and the history of frequent infectious outbreaks in the last two decades suggest that COVID-19 is likely a dry run for future ecological crises. Now is the right time to plan ahead and deploy the armamentarium of systems science scholarship for planetary health. The science of epigenomics, which investigates both genetic and nongenetic traits regarding heritable phenotypic alterations, and new approaches to understanding genome regulation in humans and pathogens offer veritable prospects to boost the global scientific capacities to innovate therapeutics and diagnostics against novel and existing infectious agents. Several reversible epigenetic alterations, such as chromatin remodeling and histone methylation, control and influence gene expression. COVID-19 lethality is linked, in part, to the cytokine storm, age, and status of the immune system in a given person. Additionally, due to reduced human mobility and daily activities, effects of the pandemic on the environment have been both positive and negative. For example, reduction in environmental pollution and lesser extraction from nature have potential positive corollaries on water and air quality. Negative effects include pollution as plastics and other materials were disposed in unconventional places and spaces in the course of the pandemic. I discuss the opportunities and challenges associated with the science of epigenomics, specifically with an eye to inform and prevent future ecological crises and pandemics that are looming on the horizon in the 21st century. In particular, this article underscores that epigenetics of both viruses and the host may influence virus infectivity and severity of attendant disease.

Epigenetic mechanisms influencing COVID-19

The COVID-19 pandemic is one of the most significant public health threats in recent history and has impacted the lives of almost everyone worldwide. Epigenetic mechanisms contribute to many aspects of the SARS-CoV-2 replication cycle, including expression levels of viral receptor ACE2, expression of cytokine genes as part of the host immune response, and the implication of various histone modifications in several aspects of COVID-19. SARS-CoV-2 proteins physically associate with many different host proteins over the course of infection, and notably there are several interactions between viral proteins and epigenetic enzymes such as HDACs and bromodomain-containing proteins as shown by correlation-based studies. The many contributions of epigenetic mechanisms to the viral life cycle and the host immune response to infection have resulted in epigenetic factors being identified as emerging biomarkers for COVID-19, and project epigenetic modifiers as promising therapeutic targets to combat COVID-19. This review article highlights the major epigenetic pathways at play during COVID-19 disease and discusses ongoing clinical trials that will hopefully contribute to slowing the spread of SARS-CoV-2.

The epigenetic implication in coronavirus infection and therapy

Epigenetics is a relatively new field of science that studies the genetic and non-genetic aspects related to heritable phenotypic changes, frequently caused by environmental and metabolic factors. In the host, the epigenetic machinery can regulate gene expression through a series of reversible epigenetic modifications, such as histone methylation and acetylation, DNA/RNA methylation, chromatin remodeling, and non-coding RNAs. The coronavirus disease 19 (COVID-19) is a highly transmittable and pathogenic viral infection. The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which emerged in Wuhan, China, and spread worldwide, causes it. COVID-19 severity and consequences largely depend on patient age and health status. In this review, we will summarize and comparatively analyze how viruses regulate the host epigenome. Mainly, we will be focusing on highly pathogenic respiratory RNA virus infections such as coronaviruses. In this context, epigenetic alterations might play an essential role in the onset of coronavirus disease complications. Although many therapeutic approaches are under study, more research is urgently needed to identify effective vaccine or safer chemotherapeutic drugs, including epigenetic drugs, to cope with this viral outbreak and to develop pre- and post-exposure prophylaxis against COVID-19.

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Establishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of MERS, SARS1 and SARS2 infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family HCTs encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository.

Comparative Transcriptomic Analysis of Rhinovirus and Influenza Virus Infection

Rhinovirus (RV) and influenza virus are the most frequently detected respiratory viruses among adult patients with community acquired pneumonia. Previous clinical studies have identified major differences in the clinical presentations and inflammatory or immune response during these infections. A systematic transcriptomic analysis directly comparing influenza and RV is lacking. Here, we sought to compare the transcriptomic response to these viral infections. Human airway epithelial Calu-3 cells were infected with contemporary clinical isolates of RV, influenza A virus (IAV), or influenza B virus (IBV). Host gene expression was determined using RNA-seq. Differentially expressed genes (DEGs) with respect to mock-infected cells were identified using the overlapping gene-set of four different statistical models. Transcriptomic analysis showed that RV-infected cells have a more blunted host response with fewer DEGs than IAV or IBV-infected cells. IFNL1 and CXCL10 were among the most upregulated DEGs during RV, IAV, and IBV infection. Other DEGs that were highly expressed for all 3 viruses were mainly genes related to type I or type III interferons (RSAD2, IDO1) and chemokines (CXCL11). Notably, ICAM5, a known receptor for enterovirus D68, was highly expressed during RV infection only. Gene Set Enrichment Analysis (GSEA) confirmed that pathways associated with interferon response, innate immunity, or regulation of inflammatory response, were most perturbed for all three viruses. Network analysis showed that steroid-related pathways were enriched. Taken together, our data using contemporary virus strains suggests that genes related to interferon and chemokine predominated the host response associated with RV, IAV, and IBV infection. Several highly expressed genes, especially ICAM5 which is preferentially-induced during RV infection, deserve further investigation.

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Establishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of CoV infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family target genes encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository.

A Bibliometric Analysis of Corona Pandemic in Social Sciences: A Review of Influential Aspects and Conceptual Structure

Corona pandemic has affected the whole world, and it is a highly researched area in biological sciences. As the current pandemic has affected countries socially and economically, the purpose of this bibliometric analysis is to provide a holistic review of the corona pandemic in the field of social sciences. This study aims to highlight significant, influential aspects, research streams, and themes. We have reviewed 395 journal articles related to coronavirus in the field of social sciences from 2003 to 2020. We have deployed 'biblioshiny' a web-interface of the 'bibliometrix 3.0' package of R-studio to conduct bibliometric analysis and visualization. In the field of social sciences, we have reported influential aspects of coronavirus literature. We have found that the 'Morbidity and Mortality Weekly Report' is the top journal. The core article of coronavirus literature is 'Guidelines for preventing health-care-associated pneumonia'. The most commonly used word, in titles, abstracts, author's keywords, and keywords plus, is 'SARS'. Top affiliation is 'The University of Hong Kong'. Hong Kong is a leading country based on citations, and the USA is on top based on total publications. We have used a conceptual framework to identify potential research streams and themes in coronavirus literature. Four research streams are found by deploying a co-occurrence network. These research streams are 'Social and economic effects of epidemic disease', 'Infectious disease calamities and control', 'Outbreak of COVID 19,' and 'Infectious diseases and the role of international organizations'. Finally, a thematic map is used to provide a holistic understanding by dividing significant themes into basic or transversal, emerging or declining, motor, highly developed, but isolated themes. These themes and subthemes have proposed future directions and critical areas of research.

Thoughts on the alveolar phase of COVID-19

COVID-19 can be divided into three clinical stages, and one can speculate that these stages correlate with where the infection resides. For the asymptomatic phase, the infection mostly resides in the nose, where it elicits a minimal innate immune response. For the mildly symptomatic phase, the infection is mostly in the pseudostratified epithelium of the larger airways and is accompanied by a more vigorous innate immune response. In the conducting airways, the epithelium can recover from the infection, because the keratin 5 basal cells are spared and they are the progenitor cells for the bronchial epithelium. There may be more severe disease in the bronchioles, where the club cells are likely infected. The devastating third phase is in the gas exchange units of the lung, where ACE2-expressing alveolar type II cells and perhaps type I cells are infected. The loss of type II cells results in respiratory insufficiency due to the loss of pulmonary surfactant, alveolar flooding, and possible loss of normal repair, since type II cells are the progenitors of type I cells. The loss of type I and type II cells will also block normal active resorption of alveolar fluid. Subsequent endothelial damage leads to transudation of plasma proteins, formation of hyaline membranes, and an inflammatory exudate, characteristic of ARDS. Repair might be normal, but if the type II cells are severely damaged alternative pathways for epithelial repair may be activated, which would result in some residual lung disease.

Digital microbiology

BACKGROUND

Digitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects.

OBJECTIVE

This review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process.

SOURCES

We used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology.

CONTENT

We describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory.

IMPLICATIONS

We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.

Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures

By 27 February 2020, the outbreak of coronavirus disease 2019 (COVID-19) caused 82 623 confirmed cases and 2858 deaths globally, more than severe acute respiratory syndrome (SARS) (8273 cases, 775 deaths) and Middle East respiratory syndrome (MERS) (1139 cases, 431 deaths) caused in 2003 and 2013, respectively. COVID-19 has spread to 46 countries internationally. Total fatality rate of COVID-19 is estimated at 3.46% by far based on published data from the Chinese Center for Disease Control and Prevention (China CDC). Average incubation period of COVID-19 is around 6.4 days, ranges from 0 to 24 days. The basic reproductive number (R₀ ) of COVID-19 ranges from 2 to 3.5 at the early phase regardless of different prediction models, which is higher than SARS and MERS. A study from China CDC showed majority of patients (80.9%) were considered asymptomatic or mild pneumonia but released large amounts of viruses at the early phase of infection, which posed enormous challenges for containing the spread of COVID-19. Nosocomial transmission was another severe problem. A total of 3019 health workers were infected by 12 February 2020, which accounted for 3.83% of total number of infections, and extremely burdened the health system, especially in Wuhan. Limited epidemiological and clinical data suggest that the disease spectrum of COVID-19 may differ from SARS or MERS. We summarize latest literatures on genetic, epidemiological, and clinical features of COVID-19 in comparison to SARS and MERS and emphasize special measures on diagnosis and potential interventions. This review will improve our understanding of the unique features of COVID-19 and enhance our control measures in the future.

Myeloid TBK1 Signaling Contributes to the Immune Response to Influenza

Macrophages provide key elements of the host response to influenza A virus (IAV) infection, including expression of type I IFN and inflammatory cytokines and chemokines. TBK1 (TNF receptor-associated factor family member-associated NF-κB activator-binding kinase 1) contributes to IFN expression and antiviral responses in some cell types, but its role in the innate response to IAV in vivo is unknown. We hypothesized that macrophage TBK1 contributes to both IFN and non-IFN components of host defense and IAV pathology. We generated myeloid-conditional TBK1 knockout mice and assessed the in vitro and in vivo consequences of IAV infection. Myeloid-specific loss of TBK1 in vivo resulted in less severe host response to IAV, as assessed by decreased mortality, weight loss, and hypoxia and less inflammatory changes in BAL fluid relative to wild-type mice despite no differences in viral load. Mice lacking myeloid TBK1 showed less recruitment of CD64⁺SiglecF^-Ly6C^hi inflammatory macrophages, less expression of inflammatory cytokines in the BAL fluid, and less expression of both IFN regulatory factor and NF-κB target genes in the lung. Analysis of sorted alveolar macrophages, inflammatory macrophages, and lung interstitial macrophages revealed that each subpopulation requires TBK1 for distinct components of the response to IAV infection. Our findings define roles for myeloid TBK1 in IAV-induced lung inflammation apart from IFN type I expression and point to myeloid TBK1 as a central and cell type-specific regulator of virus-induced lung damage.

Unified feature association networks through integration of transcriptomic and proteomic data

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.

Analysis of clinical characteristics of macrophage capping protein (CAPG) gene expressed in glioma based on TCGA data and clinical experiments

Macrophage capping protein (CAPG) genes were investigated based on The Cancer Genome Atlas (TCGA) database and clinical experiments. Glioblastoma (GBM) genes expression profiling chip of 529 disease samples and 10 normal samples selected from TCGA database were used for analysis, 25 brain glioma tissue samples and 15 normal brain tissues were collected in the Department of Neurosurgery of the First Affiliated Hospital of Xinjiang Medical University in China from 2016 to 2017 to analyze CAPG genes. TCGA results showed that the expression level of CAPG genes in GBM was higher than that in normal tissues, and the expression level of men, aged over 46 years and high grade gliomas in pathological stages was higher than that of women, aged ≤46 and low grade gliomas in pathological stages, and the survival time of high expression was shorter than that of low expression. The expression level of CAPG in glioma tissues was higher than that in normal tissues, and the expression level of CAPG in males was higher than that in females, as males had lymphatic transfer and low differentiation compared with females, but the expression level was not related to age. Survival analysis showed that higher expression level indicated shorter survival time, they were positively correlated. The expression of CAPG in glioma is high, and it is highly expressed with the severity of the disease, and it is also obviously related to the prognosis. Therefore, CAPG could be used as a biomarker for pathological grade and prognosis in glioma. However, the related studies are not consistent on the expression of different sex and ages, so further study is needed.

MERS-CoV and H5N1 influenza virus antagonize antigen presentation by altering the epigenetic landscape

Convergent evolution dictates that diverse groups of viruses will target both similar and distinct host pathways to manipulate the immune response and improve infection. In this study, we sought to leverage this uneven viral antagonism to identify critical host factors that govern disease outcome. Utilizing a systems-based approach, we examined differential regulation of IFN-γ-dependent genes following infection with robust respiratory viruses including influenza viruses [A/influenza/Vietnam/1203/2004 (H5N1-VN1203) and A/influenza/California/04/2009 (H1N1-CA04)] and coronaviruses [severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV)]. Categorizing by function, we observed down-regulation of gene expression associated with antigen presentation following both H5N1-VN1203 and MERS-CoV infection. Further examination revealed global down-regulation of antigen-presentation gene expression, which was confirmed by proteomics for both H5N1-VN1203 and MERS-CoV infection. Importantly, epigenetic analysis suggested that DNA methylation, rather than histone modification, plays a crucial role in MERS-CoV-mediated antagonism of antigen-presentation gene expression; in contrast, H5N1-VN1203 likely utilizes a combination of epigenetic mechanisms to target antigen presentation. Together, the results indicate a common mechanism utilized by H5N1-VN1203 and MERS-CoV to modulate antigen presentation and the host adaptive immune response.

Childhood tolerance of severe influenza: a mortality analysis in mice

During the 1918 influenza pandemic, children experienced substantially lower mortality than adults, a striking but unexplained finding. Whether this was due to enhanced resistance (reduced virus load) or better tolerance (reduced impact of infection) has not been defined. We found that prepubertal mice infected with H1N1 influenza virus also showed greater survival than infected pubertal mice, despite similar virus loads. Transcriptome profiling of infected lungs identified estrogen as a regulator of susceptibility in both sexes and also linked better survival to late expression of IL-1β. Blocking puberty with gonadectomy or a gonadotropin-releasing hormone antagonist improved survival. Estrogen or testosterone (which can be converted to estrogen) restored susceptibility of gonadectomized pubertal mice to influenza mortality, but dihydrotestosterone (which cannot be converted to estrogen) did not. Estrogen receptor blockade with fulvestrant in both male and female pubertal mice resulted in improved survival, even when given 3 days after infection. Moreover, late, but not early, IL-1β neutralization after infection was also protective. These findings indicate that pubertal increases in estrogen in both sexes are associated with increased mortality during influenza. This helps explain the reduced mortality of children seen with influenza in 1918 and might also be relevant to childhood tolerance to many other infectious diseases.

Influenza-Omics and the Host Response: Recent Advances and Future Prospects

Influenza A viruses (IAV) continually evolve and have the capacity to cause global pandemics. Because IAV represents an ongoing threat, identifying novel therapies and host innate immune factors that contribute to IAV pathogenesis is of considerable interest. This review summarizes the relevant literature as it relates to global host responses to influenza infection at both the proteome and transcriptome level. The various-omics infection systems that include but are not limited to ferrets, mice, pigs, and even the controlled infection of humans are reviewed. Discussion focuses on recent advances, remaining challenges, and knowledge gaps as it relates to influenza-omics infection outcomes.

Searching for a Lifeline: Transcriptome Profiling Studies of Influenza Susceptibility and Resistance

Excess or dysregulated host inflammatory responses cause much of the morbidity and mortality caused by severe influenza. Given the limitations of vaccines and antiviral drugs, novel therapeutics to modulate host responses and improve outcomes in severe influenza are needed. One strategy is to learn from the direct comparison of high-survivor versus high-mortality animal models. This review surveys the results of lung transcriptome profiling studies in murine models that directly compare susceptible versus resistant hosts challenged with identical influenza infections. The potential contributions and limitations of these studies are discussed. To amplify their power, the studies are subjected to a meta-analysis, which helps identify frequently dysregulated pathways and potentially novel areas for investigation. Using connectivity map-based tools (LINCS), transcriptome signatures linked to susceptibility can identify candidate drugs that merit testing for in vivo efficacy.

Epigenetic Landscape during Coronavirus Infection

Coronaviruses (CoV) comprise a large group of emerging human and animal pathogens, including the highly pathogenic severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) strains. The molecular mechanisms regulating emerging coronavirus pathogenesis are complex and include virus–host interactions associated with entry, replication, egress and innate immune control. Epigenetics research investigates the genetic and non-genetic factors that regulate phenotypic variation, usually caused by external and environmental factors that alter host expression patterns and performance without any change in the underlying genotype. Epigenetic modifications, such as histone modifications, DNA methylation, chromatin remodeling, and non-coding RNAs, function as important regulators that remodel host chromatin, altering host expression patterns and networks in a highly flexible manner. For most of the past two and a half decades, research has focused on the molecular mechanisms by which RNA viruses antagonize the signaling and sensing components that regulate induction of the host innate immune and antiviral defense programs upon infection. More recently, a growing body of evidence supports the hypothesis that viruses, even lytic RNA viruses that replicate in the cytoplasm, have developed intricate, highly evolved, and well-coordinated processes that are designed to regulate the host epigenome, and control host innate immune antiviral defense processes, thereby promoting robust virus replication and pathogenesis. In this article, we discuss the strategies that are used to evaluate the mechanisms by which viruses regulate the host epigenome, especially focusing on highly pathogenic respiratory RNA virus infections as a model. By combining measures of epigenome reorganization with RNA and proteomic datasets, we articulate a spatial-temporal data integration approach to identify regulatory genomic clusters and regions that play a crucial role in the host’s innate immune response, thereby defining a new viral antagonism mechanism following emerging coronavirus infection.

Annotating Protein Functional Residues by Coupling High-Throughput Fitness Profile and Homologous-Structure Analysis

Identification and annotation of functional residues are fundamental questions in protein sequence analysis. Sequence and structure conservation provides valuable information to tackle these questions. It is, however, limited by the incomplete sampling of sequence space in natural evolution. Moreover, proteins often have multiple functions, with overlapping sequences that present challenges to accurate annotation of the exact functions of individual residues by conservation-based methods. Using the influenza A virus PB1 protein as an example, we developed a method to systematically identify and annotate functional residues. We used saturation mutagenesis and high-throughput sequencing to measure the replication capacity of single nucleotide mutations across the entire PB1 protein. After predicting protein stability upon mutations, we identified functional PB1 residues that are essential for viral replication. To further annotate the functional residues important to the canonical or noncanonical functions of viral RNA-dependent RNA polymerase (vRdRp), we performed a homologous-structure analysis with 16 different vRdRp structures. We achieved high sensitivity in annotating the known canonical polymerase functional residues. Moreover, we identified a cluster of noncanonical functional residues located in the loop region of the PB1 β-ribbon. We further demonstrated that these residues were important for PB1 protein nuclear import through the interaction with Ran-binding protein 5. In summary, we developed a systematic and sensitive method to identify and annotate functional residues that are not restrained by sequence conservation. Importantly, this method is generally applicable to other proteins about which homologous-structure information is available.

IMPORTANCE

To fully comprehend the diverse functions of a protein, it is essential to understand the functionality of individual residues. Current methods are highly dependent on evolutionary sequence conservation, which is usually limited by sampling size. Sequence conservation-based methods are further confounded by structural constraints and multifunctionality of proteins. Here we present a method that can systematically identify and annotate functional residues of a given protein. We used a high-throughput functional profiling platform to identify essential residues. Coupling it with homologous-structure comparison, we were able to annotate multiple functions of proteins. We demonstrated the method with the PB1 protein of influenza A virus and identified novel functional residues in addition to its canonical function as an RNA-dependent RNA polymerase. Not limited to virology, this method is generally applicable to other proteins that can be functionally selected and about which homologous-structure information is available.

Influenza Research Database: An integrated bioinformatics resource for influenza virus research

The Influenza Research Database (IRD) is a U.S. National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Bioinformatics Resource Center dedicated to providing bioinformatics support for influenza virus research. IRD facilitates the research and development of vaccines, diagnostics and therapeutics against influenza virus by providing a comprehensive collection of influenza-related data integrated from various sources, a growing suite of analysis and visualization tools for data mining and hypothesis generation, personal workbench spaces for data storage and sharing, and active user community support. Here, we describe the recent improvements in IRD including the use of cloud and high performance computing resources, analysis and visualization of user-provided sequence data with associated metadata, predictions of novel variant proteins, annotations of phenotype-associated sequence markers and their predicted phenotypic effects, hemagglutinin (HA) clade classifications, an automated tool for HA subtype numbering conversion, linkouts to disease event data and the addition of host factor and antiviral drug components. All data and tools are freely available without restriction from the IRD website at https://www.fludb.org.

Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens

Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection.

An important challenge in infectious disease research is to understand how the human immune system responds to different types of pathogenic infections. An important component of mounting proper response is the transcriptional regulatory network that specifies the context-specific gene expression program in the host cell. However, our understanding of this regulatory network and how it drives context-specific transcriptional programs is incomplete. To address this gap, we performed a network-based analysis of host response to influenza viruses that integrated high-throughput mRNA- and protein measurements and protein-protein interaction networks to identify virus and pathogenicity-specific modules and their upstream physical regulatory programs. We inferred regulatory networks for human cell line and mouse host systems, which recapitulated several known regulators and pathways of the immune response and viral life cycle. We used the networks to study time point and strain-specific subnetworks and to prioritize important regulators of host response. We predicted several novel regulators, both at the mRNA and protein levels, and experimentally verified their role in the virus life cycle based on their ability to significantly impact viral replication.

Systems immunology: Beyond antibody titers

Despite the evident success of currently available vaccines to prevent infectious diseases, we still lack a full understanding of the mechanisms by which vaccines induce protective immune responses. Systems immunology applies multifaceted analytical tools to better understand the immune responses to vaccines by deep characterization of the cellular components, regulatory pathways, antibody responses and immune gene profiles with the ultimate goal of identifying the complex cellular, genetic and regulatory factors and mechanisms that contribute to effective and protective immune responses.

Human H7N9 virus induces a more pronounced pro-inflammatory cytokine but an attenuated interferon response in human bronchial epithelial cells when compared with an epidemiologically-linked chicken H7N9 virus

Background

Avian influenza virus H7N9 has jumped species barrier, causing sporadic human infections since 2013. We have previously isolated an H7N9 virus from a patient, and an H7N9 virus from a chicken in a live poultry market where the patient visited during the incubation period. These two viruses were genetically highly similar. This study sought to use a human bronchial epithelial cell line model to infer the virulence of these H7N9 viruses in humans.

Methods

Human bronchial epithelial cell line Calu-3 was infected with two H7N9 viruses (human H7N9-HU and chicken H7N9-CK), a human H5N1 virus and a human 2009 pandemic H1N1 virus. The infected cell lysate was collected at different time points post-infection for the determination of the levels of pro-inflammatory cytokines (tumor necrosis factor α [TNF-α] and interleukin 6 [IL-6]), anti-inflammatory cytokines (interleukin 10 [IL-10] and transforming growth factor beta [TGF-β]), chemokines (interleukin 8 [IL-8] and monocyte chemoattractant protein 1 [MCP-1]), and interferons (interferon β [IFN-β] and interferon lambda 1 [IFNL1]). The viral load in the cell lysate was also measured.

Results

Comparison of the human and chicken H7N9 viruses showed that H7N9-HU induced significantly higher levels of TNF-α at 12 h post-infection, and significantly higher levels of IL-8 from 12 to 48 h post-infection than those of H7N9-CK. However, the level of IFNL1 was lower for H7N9-HU than that of H7N9-CK at 48 h post-infection (P < 0.001). H7N9-HU had significantly higher viral loads than H7N9-CK at 3 and 6 h post-infection. H5N1 induced significantly higher levels of TNF-α, IL-6, IL-8, IL-10 and MCP-1 than those of H7N9 viruses at 48 h post-infection. Conversely, H1N1 induced lower levels of TNF-α, IL-10, MCP-1, IFNL1 and IFN-β when compared with H7N9 viruses at the same time point.

Conclusions

H7N9-HU induced higher levels of pro-inflammatory IL-6 and IL-8 and exhibited a more rapid viral replication than H7N9-CK. However, the level of antiviral IFNL1 was lower for H7N9-HU than H7N9-CK. Our results suggest that the gained properties in modulating human innate immunity by H7N9-HU transformed it to be a more virulent virus in humans than H7N9-CK.

Multi-task consensus clustering of genome-wide transcriptomes from related biological conditions

MOTIVATION

Identifying the shared and pathogen-specific components of host transcriptional regulatory programs is important for understanding the principles of regulation of immune response. Recent efforts in systems biology studies of infectious diseases have resulted in a large collection of datasets measuring host transcriptional response to various pathogens. Computational methods to identify and compare gene expression modules across different infections offer a powerful way to identify strain-specific and shared components of the regulatory program. An important challenge is to identify statistically robust gene expression modules as well as to reliably detect genes that change their module memberships between infections.

RESULTS

We present MULCCH (MULti-task spectral Consensus Clustering for Hierarchically related tasks), a consensus extension of a multi-task clustering algorithm to infer high-confidence strain-specific host response modules under infections from multiple virus strains. On simulated data, MULCCH more accurately identifies genes exhibiting pathogen-specific patterns compared to non-consensus and nonmulti-task clustering approaches. Application of MULCCH to mammalian transcriptional response to a panel of influenza viruses showed that our method identifies clusters with greater coherence compared to non-consensus methods. Further, MULCCH derived clusters are enriched for several immune system-related processes and regulators. In summary, MULCCH provides a reliable module-based approach to identify molecular pathways and gene sets characterizing commonality and specificity of host response to viruses of different pathogenicities.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://bitbucket.org/roygroup/mulcch

CONTACT

sroy@biostat.wisc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.