Multiomics: unraveling the panoramic landscapes of SARS-CoV-2 infection

Integrated multi-omics analysis reveals liver metabolic reprogramming by fish iridovirus and antiviral function of alpha-linolenic acid

Iridovirus poses a substantial threat to global aquaculture due to its high mortality rate; however, the molecular mechanisms underpinning its pathogenesis are not well elucidated. Here, a multi-omics approach was applied to groupers infected with Singapore grouper iridovirus (SGIV), focusing on the roles of key metabolites. Results showed that SGIV induced obvious histopathological damage and changes in metabolic enzymes within the liver. Furthermore, SGIV significantly reduced the contents of lipid droplets, triglycerides, cholesterol, and lipoproteins. Metabolomic analysis indicated that the altered metabolites were enriched in 19 pathways, with a notable down-regulation of lipid metabolites such as glycerophosphates and alpha-linolenic acid (ALA), consistent with disturbed lipid homeostasis in the liver. Integration of transcriptomic and metabolomic data revealed that the top enriched pathways were related to cell growth and death and nucleotide, carbohydrate, amino acid, and lipid metabolism, supporting the conclusion that SGIV infection induced liver metabolic reprogramming. Further integrative transcriptomic and proteomic analysis indicated that SGIV infection activated crucial molecular events in a phagosome-immune depression-metabolism dysregulation-necrosis signaling cascade. Of note, integrative multi-omics analysis demonstrated the consumption of ALA and linoleic acid (LA) metabolites, and the accumulation of L-glutamic acid (GA), accompanied by alterations in immune, inflammation, and cell death-related genes. Further experimental data showed that ALA, but not GA, suppressed SGIV replication by activating antioxidant and anti-inflammatory responses in the host. Collectively, these findings provide a comprehensive resource for understanding host response dynamics during fish iridovirus infection and highlight the antiviral potential of ALA in the prevention and treatment of iridoviral diseases.

Infection with SARS-CoV-2 can cause pancreatic impairment

Evidence suggests associations between COVID-19 patients or vaccines and glycometabolic dysfunction and an even higher risk of the occurrence of diabetes. Herein, we retrospectively analyzed pancreatic lesions in autopsy tissues from 67 SARS-CoV-2 infected non-human primates (NHPs) models and 121 vaccinated and infected NHPs from 2020 to 2023 and COVID-19 patients. Multi-label immunofluorescence revealed direct infection of both exocrine and endocrine pancreatic cells by the virus in NHPs and humans. Minor and limited phenotypic and histopathological changes were observed in adult models. Systemic proteomics and metabolomics results indicated metabolic disorders, mainly enriched in insulin resistance pathways, in infected adult NHPs, along with elevated fasting C-peptide and C-peptide/glucose ratio levels. Furthermore, in elder COVID-19 NHPs, SARS-CoV-2 infection causes loss of beta (β) cells and lower expressed-insulin in situ characterized by islet amyloidosis and necrosis, activation of α-SMA and aggravated fibrosis consisting of lower collagen in serum, an increase of pancreatic inflammation and stress markers, ICAM-1 and G3BP1, along with more severe glycometabolic dysfunction. In contrast, vaccination maintained glucose homeostasis by activating insulin receptor α and insulin receptor β. Overall, the cumulative risk of diabetes post-COVID-19 is closely tied to age, suggesting more attention should be paid to blood sugar management in elderly COVID-19 patients.

SiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data

Cellular populations simultaneously encode multiple biological attributes, including spatial configuration, temporal trajectories, and cell-cell interactions. Some of these signals may be overshadowed by others and harder to recover, despite the great progress made to computationally reconstruct biological processes from single-cell data. To address this, we present SiFT, a kernel-based projection method for filtering biological signals in single-cell data, thus uncovering underlying biological processes. SiFT applies to a wide range of tasks, from the removal of unwanted variation in the data to revealing hidden biological structures. We demonstrate how SiFT enhances the liver circadian signal by filtering spatial zonation, recovers regenerative cell subpopulations in spatially-resolved liver data, and exposes COVID-19 disease-related cells, pathways, and dynamics by filtering healthy reference signals. SiFT performs the correction at the gene expression level, can scale to large datasets, and compares favorably to state-of-the-art methods.

CoVProt: Toward a Mass Spectrometry Data Portal for COVID-19 Proteomics Research and Development

The coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc globally. Beyond the pandemic, the long-term effects of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus in multiple organ systems are yet to be deciphered. This calls for continued systems science research. Moreover, the host response to SARS-CoV-2 varies person-to-person and gives rise to different degrees of morbidity and mortality. Mass spectrometry (MS) has been a proven asset in studies of the SARS-CoV-2 from an omics systems science lens. To strengthen the proteomics research dedicated to COVID-19, we introduce here a web-based portal, CoVProt. The portal is work in progress and aims for a comprehensive curation of MS-based proteomics data of COVID-19 clinical samples for deep proteomic investigations, data visualization, and easy data accessibility for life sciences innovations and planetary health research community. Currently, CoVProt contains information on 2725 different proteins and 37,125 different peptides from six data sets covering a total of 202 clinical samples. Moreover, all pertinent data sets extracted from the literature have been reanalyzed using a common analysis pipeline developed by combining multiple tools. Going forward, we anticipate that the CoVProt portal will also provide access to the clinical parameters of the patients. The CoVProt (v1.0) portal addresses an existing significant gap to study COVID-19 host proteomics, which, to the best of our knowledge, is the first effort in this direction. We believe that CoVProt is poised to make contributions as a community resource for proteomic applications and aims to broadly support clinical studies to facilitate the discovery of COVID-19 biomarkers and therapeutics with translational potential.

S100A8/A9: An emerging player in sepsis and sepsis-induced organ injury

Sepsis, the foremost contributor to mortality in intensive care unit patients, arises from an uncontrolled systemic response to invading infections, resulting in extensive harm across multiple organs and systems. Recently, S100A8/A9 has emerged as a promising biomarker for sepsis and sepsis-induced organ injury, and targeting S100A8/A9 appeared to ameliorate inflammation-induced tissue damage and improve adverse outcomes. S100A8/A9, a calcium-binding heterodimer mainly found in neutrophils and monocytes, serves as a causative molecule with pro-inflammatory and immunosuppressive properties, which are vital in the pathogenesis of sepsis. Therefore, improving our comprehension of how S100A8/A9 acts as a pathological player in the development of sepsis is imperative for advancing research on sepsis. Our review is the first-to the best of our knowledge-to discuss the biology of S100A8/A9 and its release mechanisms, summarize recent advances concerning the vital roles of S100A8/A9 in sepsis and the consequential organ damage, and underscore its potential as a promising diagnostic biomarker and therapeutic target for sepsis.

A robust platform for integrative spatial multi-omics analysis to map immune responses to SARS-CoV-2 infection in lung tissues

The SARS-CoV-2 (COVID-19) virus has caused a devastating global pandemic of respiratory illness. To understand viral pathogenesis, methods are available for studying dissociated cells in blood, nasal samples, bronchoalveolar lavage fluid and similar, but a robust platform for deep tissue characterization of molecular and cellular responses to virus infection in the lungs is still lacking. We developed an innovative spatial multi-omics platform to investigate COVID-19-infected lung tissues. Five tissue-profiling technologies were combined by a novel computational mapping methodology to comprehensively characterize and compare the transcriptome and targeted proteome of virus infected and uninfected tissues. By integrating spatial transcriptomics data (Visium, GeoMx and RNAScope) and proteomics data (CODEX and PhenoImager HT) at different cellular resolutions across lung tissues, we found strong evidence for macrophage infiltration and defined the broader microenvironment surrounding these cells. By comparing infected and uninfected samples, we found an increase in cytokine signalling and interferon responses at different sites in the lung and showed spatial heterogeneity in the expression level of these pathways. These data demonstrate that integrative spatial multi-omics platforms can be broadly applied to gain a deeper understanding of viral effects on cellular environments at the site of infection and to increase our understanding of the impact of SARS-CoV-2 on the lungs.

The value of prospective metabolomic susceptibility endotypes: broad applicability for infectious diseases

BACKGROUND

As new infectious diseases (ID) emerge and others continue to mutate, there remains an imminent threat, especially for vulnerable individuals. Yet no generalizable framework exists to identify the at-risk group prior to infection. Metabolomics has the advantage of capturing the existing physiologic state, unobserved via current clinical measures. Furthermore, metabolomics profiling during acute disease can be influenced by confounding factors such as indications, medical treatments, and lifestyles.

METHODS

We employed metabolomic profiling to cluster infection-free individuals and assessed their relationship with COVID severity and influenza incidence/recurrence.

FINDINGS

We identified a metabolomic susceptibility endotype that was strongly associated with both severe COVID (ORICUadmission = 6.7, p-value = 1.2 × 10-08, ORmortality = 4.7, p-value = 1.6 × 10-04) and influenza (ORincidence = 2.9; p-values = 2.2 × 10-4, βrecurrence = 1.03; p-value = 5.1 × 10-3). We observed similar severity associations when recapitulating this susceptibility endotype using metabolomics from individuals during and after acute COVID infection. We demonstrate the value of using metabolomic endotyping to identify a metabolically susceptible group for two-and potentially more-IDs that are driven by increases in specific amino acids, including microbial-related metabolites such as tryptophan, bile acids, histidine, polyamine, phenylalanine, and tyrosine metabolism, as well as carbohydrates involved in glycolysis.

INTERPRETATIONS

These metabolites may be identified prior to infection to enable protective measures for these individuals.

FUNDING

The Longitudinal EMR and Omics COVID-19 Cohort (LEOCC) and metabolomic profiling were supported by the National Heart, Lung, and Blood Institute and the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health.

Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.

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.

Host Response of Syrian Hamster to SARS-CoV-2 Infection including Differences with Humans and between Sexes

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has highlighted the importance of having proper tools and models to study the pathophysiology of emerging infectious diseases to test therapeutic protocols, assess changes in viral phenotypes, and evaluate the effects of viral evolution. This study provided a comprehensive characterization of the Syrian hamster (Mesocricetus auratus) as an animal model for SARS-CoV-2 infection using different approaches (description of clinical signs, viral load, receptor profiling, and host immune response) and targeting four different organs (lungs, intestine, brain, and PBMCs). Our data showed that both male and female hamsters were susceptible to the infection and developed a disease similar to the one observed in patients with COVID-19 that included moderate to severe pulmonary lesions, inflammation, and recruitment of the immune system in the lungs and at the systemic level. However, all animals recovered within 14 days without developing the severe pathology seen in humans, and none of them died. We found faint evidence for intestinal and neurological tropism associated with the absence of lesions and a minimal host response in intestines and brains, which highlighted another crucial difference with the multiorgan impairment of severe COVID-19. When comparing male and female hamsters, we observed that males sustained higher viral RNA shedding and replication in the lungs, suffered from more severe symptoms and histopathological lesions, and triggered higher pulmonary inflammation. Overall, these data confirmed the Syrian hamster as a suitable model for mild to moderate COVID-19 and reflected sex-related differences in the response against the virus observed in humans.

Protective effect of ursodeoxycholic acid on COVID-19 in patients with chronic liver disease

Objective

Ursodeoxycholic acid (UDCA) may reduce susceptibility to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection by downregulating angiotensin-converting enzyme 2 (ACE2), based on recent experimental investigation. This study aimed to determine the potential protective effect of UDCA against SARS-CoV-2 infection in patients with chronic liver disease.

Methods

Patients with chronic liver disease receiving UDCA (taking UDCA ≥1 month) at Beijing Ditan Hospital between January 2022 and December 2022 were consecutively enrolled. These patients were matched in a 1:1 ratio to those with liver disease not receiving UDCA during the same period by using a propensity score matching analysis with nearest neighbor matching algorithm. We conducted a phone survey of coronavirus disease 2019 (COVID-19) infection during the early phase of the pandemic liberation (from 15 December 2022 to 15 January 2023). The risk of COVID-19 was compared in two matched cohorts of 225 UDCA users and 225 non-UDCA users based on patient self-report.

Results

In the adjusted analysis, the control group was superior to the UDCA group in COVID-19 vaccination rates and liver function indicators, including γ-glutamyl transpeptidase and alkaline phosphatase (p < 0.05). UDCA was associated with a lower incidence of SARS-CoV-2 infection (UDCA 85.3% vs. control 94.2%, p = 0.002), more mild cases (80.0% vs. 72.0%, p = 0.047), and shorter median time from infection to recovery (5 vs. 7 days, p < 0.001). Logistic regression analysis showed that UDCA was a significant protective factor against COVID-19 infection (OR: 0.32, 95%CI: 0.16-0.64, p = 0.001). Furthermore, diabetes mellitus (OR: 2.48, 95%CI: 1.11-5.54, p = 0.027) and moderate/severe infection (OR: 8.94, 95%CI: 1.07-74.61, p = 0.043) were more likely to prolong the time from infection to recovery.

Conclusion

UDCA therapy may be beneficial in reducing COVID-19 infection risk, alleviating symptoms, and shortening the recovery time in patients with chronic liver disease. However, it should be emphasized that the conclusions were based on patient self-report rather than classical COVID-19 detection by experimental investigations. Further large clinical and experimental studies are needed to validate these findings.

A prophylactic effect of aluminium-based adjuvants against respiratory viruses via priming local innate immunity

Infection caused by respiratory viruses can lead to a severe respiratory disease and even death. Vaccination is the most effective way to prevent the disease, but it cannot be quickly applied when facing an emerging infectious disease. Here, we demonstrated that immunization with an aluminium-zinc hybrid particulate adjuvant (FH-001) alone, bearing great resemblance in morphology with commonly used aluminium-based adjuvants in vaccines, could quickly induce mice to generate a broadly protective immune response to resist the lethal challenge of influenza B viruses. Furthermore, a multi-omics-based analysis revealed that the alveolar macrophage and type I interferon pathway, rather than adaptive immunity and type II interferon pathway, were essential for the observed prophylactic effect of FH-001. More importantly, a similar protective effect was observed against influenza A virus strain A/Shanghai/02/2013(H7N9), A/California/04/2009(H1N1) and respiratory syncytial virus. Therefore, we introduced here a new and promising strategy that can be quickly applied during the outbreak of emerging respiratory viruses.

Advances in rapid detection of SARS-CoV-2 by mass spectrometry

COVID-19 has already been lasting for more than two years and it has been severely affecting the whole world. Still, detection of SARS-CoV-2 remains the frontline approach to combat the pandemic, and the reverse transcription polymerase chain reaction (RT-PCR)-based method is the well recognized detection method for the enormous analytical demands. However, the RT-PCR method typically takes a relatively long time, and can produce false positive and false negative results. Mass spectrometry (MS) is a very commonly used technique with extraordinary sensitivity, specificity and speed, and can produce qualitative and quantitative information of various analytes, which cannot be achieved by RT-PCR. Since the pandemic outbreak, various mass spectrometric approaches have been developed for rapid detection of SARS-CoV-2, including the LC-MS/MS approaches that could allow analysis of several hundred clinical samples per day with one MS system, MALDI-MS approaches that could directly analyze clinical samples for the detection, and efforts for the on-site detection with portable devices. In this review, these mass spectrometric approaches were summarized, and their pros and cons as well as further development were also discussed.

Proteomic insights into SARS-CoV-2 infection mechanisms, diagnosis, therapies and prognostic monitoring methods

At the end of 2019, the COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection, seriously damaged world public health security. Several protein markers associated with virus infection have been extensively explored to combat the ever-increasing challenge posed by SARS-CoV-2. The proteomics of COVID-19 deepened our understanding of viral particles and their mechanisms of host invasion, providing us with information on protein changes in host tissues, cells and body fluids following infection in COVID-19 patients. In this review, we summarize the proteomic studies of SARS-CoV-2 infection and review the current understanding of COVID-19 in terms of the quantitative and qualitative proteomics of viral particles and host entry factors from the perspective of protein pathological changes in the organism following host infection.

Circulating pyruvate is a potent prognostic marker for critical COVID-19 outcomes

Background

Coronavirus-19 (COVID-19) disease is driven by an unchecked immune response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus which alters host mitochondrial-associated mechanisms. Compromised mitochondrial health results in abnormal reprogramming of glucose metabolism, which can disrupt extracellular signalling. We hypothesized that examining mitochondrial energy-related signalling metabolites implicated in host immune response to SARS-CoV-2 infection would provide potential biomarkers for predicting the risk of severe COVID-19 illness.

Methods

We used a semi-targeted serum metabolomics approach in 273 patients with different severity grades of COVID-19 recruited at the acute phase of the infection to determine the relative abundance of tricarboxylic acid (Krebs) cycle-related metabolites with known extracellular signaling properties (pyruvate, lactate, succinate and α-ketoglutarate). Abundance levels of energy-related metabolites were evaluated in a validation cohort (n=398) using quantitative fluorimetric assays.

Results

Increased levels of four energy-related metabolites (pyruvate, lactate, a-ketoglutarate and succinate) were found in critically ill COVID-19 patients using semi-targeted and targeted approaches (p&lt;0.05). The combined strategy proposed herein enabled us to establish that circulating pyruvate levels (p&lt;0.001) together with body mass index (p=0.025), C-reactive protein (p=0.039), D-Dimer (p&lt;0.001) and creatinine (p=0.043) levels, are independent predictors of critical COVID-19. Furthermore, classification and regression tree (CART) analysis provided a cut-off value of pyruvate in serum (24.54 µM; p&lt;0.001) as an early criterion to accurately classify patients with critical outcomes.

Conclusion

Our findings support the link between COVID-19 pathogenesis and immunometabolic dysregulation, and show that fluorometric quantification of circulating pyruvate is a cost-effective clinical decision support tool to improve patient stratification and prognosis prediction.

Single-cell analysis of the adaptive immune response to SARS-CoV-2 infection and vaccination

Amid the ongoing Coronavirus Disease 2019 (COVID-19) pandemic, vaccination and early therapeutic interventions are the most effective means to combat and control the severity of the disease. Host immune responses to SARS-CoV-2 and its variants, particularly adaptive immune responses, should be fully understood to develop improved strategies to implement these measures. Single-cell multi-omic technologies, including flow cytometry, single-cell transcriptomics, and single-cell T-cell receptor (TCR) and B-cell receptor (BCR) profiling, offer a better solution to examine the protective or pathological immune responses and molecular mechanisms associated with SARS-CoV-2 infection, thus providing crucial support for the development of vaccines and therapeutics for COVID-19. Recent reviews have revealed the overall immune landscape of natural SARS-CoV-2 infection, and this review will focus on adaptive immune responses (including T cells and B cells) to SARS-CoV-2 revealed by single-cell multi-omics technologies. In addition, we explore how the single-cell analyses disclose the critical components of immune protection and pathogenesis during SARS-CoV-2 infection through the comparison between the adaptive immune responses induced by natural infection and by vaccination.

Identification of Novel Therapeutic Candidates Against SARS-CoV-2 Infections: An Application of RNA Sequencing Toward mRNA Based Nanotherapeutics

Due to fast transmission and various circulating SARS-CoV-2 variants, a significant increase of coronavirus 2019 infection cases with acute respiratory symptoms has prompted worries about the efficiency of current vaccines. The possible evasion from vaccine immunity urged scientists to identify novel therapeutic targets for developing improved vaccines to manage worldwide COVID-19 infections. Our study sequenced pooled peripheral blood mononuclear cells transcriptomes of SARS-CoV-2 patients with moderate and critical clinical outcomes to identify novel potential host receptors and biomarkers that can assist in developing new translational nanomedicines and vaccine therapies. The dysregulated signatures were associated with humoral immune responses in moderate and critical patients, including B-cell activation, cell cycle perturbations, plasmablast antibody processing, adaptive immune responses, cytokinesis, and interleukin signaling pathway. The comparative and longitudinal analysis of moderate and critically infected groups elucidated diversity in regulatory pathways and biological processes. Several immunoglobin genes (IGLV9-49, IGHV7-4, IGHV3-64, IGHV1-24, IGKV1D-12, and IGKV2-29), ribosomal proteins (RPL29, RPL4P2, RPL5, and RPL14), inflammatory response related cytokines including Tumor Necrosis Factor (TNF, TNFRSF17, and TNFRSF13B), C-C motif chemokine ligands (CCL3, CCL25, CCL4L2, CCL22, and CCL4), C-X-C motif chemokine ligands (CXCL2, CXCL10, and CXCL11) and genes related to cell cycle process and DNA proliferation (MYBL2, CDC20, KIFC1, and UHCL1) were significantly upregulated among SARS-CoV-2 infected patients. 60S Ribosomal protein L29 (RPL29) was a highly expressed gene among all COVID-19 infected groups. Our study suggested that identifying differentially expressed genes (DEGs) based on disease severity and onset can be a powerful approach for identifying potential therapeutic targets to develop effective drug delivery systems against SARS-CoV-2 infections. As a result, potential therapeutic targets, such as the RPL29 protein, can be tested in vivo and in vitro to develop future mRNA-based translational nanomedicines and therapies to combat SARS-CoV-2 infections.

REGLIV: Molecular regulation data of diverse living systems facilitating current multiomics research

Multiomics is a powerful technique in molecular biology that facilitates the identification of new associations among different molecules (genes, proteins & metabolites). It has attracted tremendous research interest from the scientists worldwide and has led to an explosive number of published studies. Most of these studies are based on the regulation data provided in available databases. Therefore, it is essential to have molecular regulation data that are strictly validated in the living systems of various cell lines and in vivo models. However, no database has been developed yet to provide comprehensive molecular regulation information validated by living systems. Herein, a new database, Molecular Regulation Data of Living System Facilitating Multiomics Study (REGLIV) is introduced to describe various types of molecular regulation tested by the living systems. (1) A total of 2996 regulations describe the changes in 1109 metabolites triggered by alterations in 284 genes or proteins, and (2) 1179 regulations describe the variations in 926 proteins induced by 125 endogenous metabolites. Overall, REGLIV is unique in (a) providing the molecular regulation of a clearly defined regulatory direction other than simple correlation, (b) focusing on molecular regulations that are validated in a living system not simply in an in vitro test, and (c) describing the disease/tissue/species specific property underlying each regulation. Therefore, REGLIV has important implications for the future practice of not only multiomics, but also other fields relevant to molecular regulation. REGLIV is freely accessible at: https://idrblab.org/regliv/.

Investigating expressed RNA variants that are related to disease severity in SARS-CoV-2-infected patients with mild-to-severe disease

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to be a significant public health challenge globally. SARS-CoV-2 is a novel virus, and the understanding of what constitutes expressed RNAseq variants in healthy, convalescent, severe, moderate, and those admitted to the intensive care unit (ICU) is yet to be presented. We characterize the different expressed RNAseq variants in healthy, severe, moderate, ICU, and convalescent individuals.

Materials and methods

The bulk RNA sequencing data with identifier PRJNA639275 were downloaded from Sequence Reads Archive (SRA). The individuals were divided into: (1) healthy, n = 34, moderate, n = 8, convalescent, n = 2, severe, n = 16, and ICU, n = 8. Fastqc version 0.11.9 and Cutadapt version 3.7 were used to assess the read quality and perform adapter trimming, respectively. STAR was used to align reads to the reference genome, and GATK best practice was followed to call variants using the rnavar pipeline, part of the nf-core pipelines.

Results

Our analysis demonstrated that different sets of unique RNAseq variants characterize convalescent, moderate, severe, and those admitted to the ICU. The data show that the individuals who recover from SARS-CoV-2 infection have the same set of expressed variants as the healthy controls. We showed that the healthy and SARS-CoV-2-infected individuals display different sets of expressed variants characteristic of the patient phenotype.

Conclusion

The individuals with severe, moderate, those admitted to the ICU, and convalescent display a unique set of variants. The findings in this study will inform the test kit development and SARS-CoV-2 patients classification to enhance the management and control of SARS-CoV-2 infection in our population.

The Central Role of Extracellular Vesicles in the Mechanisms of Thrombosis in COVID-19 Patients With Cancer and Therapeutic Strategies

Cancer patients have increased SARS-CoV-2 susceptibility and are prone to developing severe COVID-19 infections. The incidence of venous thrombosis is approximately 20% in COVID-19 patients with cancer. It has been suggested that thrombus formation has been suggested to correlate with severe clinical manifestations, mortality, and sequelae. In this review, we primarily elaborate on the pathophysiological mechanisms of thrombosis in COVID-19 patients with cancer, emphasize the role of microparticles (MPs) and phosphatidylserine (PS) in coagulation, and propose an antithrombotic strategy. The coagulation mechanisms of COVID-19 and cancer synergistically amplify the coagulation cascade, and collectively promotes pulmonary microvascular occlusion. During systemic coagulation, the virus activates immune cells to release abundant proinflammatory cytokines, referred to as cytokine storm, resulting in the apoptosis of tumor and blood cells and subsequent MPs release. Additionally, we highlight that tumor cells contribute to MPs and coagulation by apoptosis owing to insufficient blood supply. A positive feedback loop of cytokines storm and MPs storm promotes microvascular coagulation storm, leading to microthrombi formation and inadequate blood perfusion. Microthrombi-damaged endothelial cells (ECs), tumor, and blood cells further aggravate the apoptosis of the cells and facilitate MPs storm. PS, especially on MPs, plays a pivotal role in the blood coagulation process, contributing to clot initiation, amplification, and propagation. Since coagulation is a common pathway of COVID-19 and cancer, and associated with mortality, patients would benefit from antithrombotic therapy. The above results lead us to assert that early stage antithrombotic therapy is optimal. This strategy is likely to maintain blood flow patency contributing to viral clearance, attenuating the formation of cytokines and MPs storm, maintaining oxygen saturation, and avoiding the progress of the disease.

Towards a Systems Immunology Approach to Understanding Correlates of Protective Immunity against HCV

Over the past decade, tremendous progress has been made in systems biology-based approaches to studying immunity to viral infections and responses to vaccines. These approaches that integrate multiple facets of the immune response, including transcriptomics, serology and immune functions, are now being applied to understand correlates of protective immunity against hepatitis C virus (HCV) infection and to inform vaccine development. This review focuses on recent progress in understanding immunity to HCV using systems biology, specifically transcriptomic and epigenetic studies. It also examines proposed strategies moving forward towards an integrated systems immunology approach for predicting and evaluating the efficacy of the next generation of HCV vaccines.