Integrated Omics Analysis of Pathogenic Host Responses during Pandemic H1N1 Influenza Virus Infection: The Crucial Role of Lipid Metabolism

Protein turnover regulation is critical for influenza A virus infection

The abundance of a protein is defined by its continuous synthesis and degradation, a process known as protein turnover. Here, we systematically profiled the turnover of proteins in influenza A virus (IAV)-infected cells using a pulse-chase stable isotope labeling by amino acids in cell culture (SILAC)-based approach combined with downstream statistical modeling. We identified 1,798 virus-affected proteins with turnover changes (tVAPs) out of 7,739 detected proteins (data available at pulsechase.innatelab.org). In particular, the affected proteins were involved in RNA transcription, splicing and nuclear transport, protein translation and stability, and energy metabolism. Many tVAPs appeared to be known IAV-interacting proteins that regulate virus propagation, such as KPNA6, PPP6C, and POLR2A. Notably, our analysis identified additional IAV host and restriction factors, such as the splicing factor GPKOW, that exhibit significant turnover rate changes while their total abundance is minimally affected. Overall, we show that protein turnover is a critical factor both for virus replication and antiviral defense.

Adaptation of turnip mosaic virus to Arabidopsis thaliana involves rewiring of VPg-host proteome interactions

The outcome of a viral infection depends on a complex interplay between the host physiology and the virus, mediated through numerous protein-protein interactions. In a previous study, we used high-throughput yeast two-hybrid (HT-Y2H) to identify proteins in Arabidopsis thaliana that bind to the proteins encoded by the turnip mosaic virus (TuMV) genome. Furthermore, after experimental evolution of TuMV lineages in plants with mutations in defense-related or proviral genes, most mutations observed in the evolved viruses affected the VPg cistron. Among these mutations, D113G was a convergent mutation selected in many lineages across different plant genotypes, including cpr5-2 with constitutive expression of systemic acquired resistance. In contrast, mutation R118H specifically emerged in the jin1 mutant with affected jasmonate signaling. Using the HT-Y2H system, we analyzed the impact of these two mutations on VPg's interaction with plant proteins. Interestingly, both mutations severely compromised the interaction of VPg with the translation initiation factor eIF(iso)4E, a crucial interactor for potyvirus infection. Moreover, mutation D113G, but not R118H, adversely affected the interaction with RHD1, a zinc-finger homeodomain transcription factor involved in regulating DNA demethylation. Our results suggest that RHD1 enhances plant tolerance to TuMV infection. We also discuss our findings in a broad virus evolution context.

Ferrets as a Mammalian Model to Study Influenza Virus-Bacteria Interactions

Ferrets represent an invaluable model for the study of influenza virus pathogenicity and transmissibility. Ferrets are also employed for the study of bacterial pathogens that naturally infect humans at different anatomical sites. While viral and bacterial infection studies in isolation using animal models are important for furthering our understanding of pathogen biology and developing improved therapeutics, it is also critical to extend our knowledge to pathogen coinfections in vivo, to more closely examine interkingdom dynamics that may contribute to overall disease outcomes. We discuss how ferrets have been employed to study a diverse range of both influenza viruses and bacterial species and summarize key studies that have utilized the ferret model for primary influenza virus challenge followed by secondary bacterial infection. These copathogenesis studies have provided critical insight into the dynamic interplay between these pathogens, underscoring the utility of ferrets as a model system for investigating influenza virus-bacteria interactions.

The elucidation of plasma lipidome profiles during severe influenza in a mouse model

Although influenza virus infection has been shown to affect lipid metabolism, details remain unknown. Therefore, we elucidated the kinetic lipid profiles of mice infected with different doses of influenza virus A/Puerto Rico/8/34 (H1N1) (PR8) by measuring multiple lipid molecular species using untargeted lipidomic analysis. C57BL/6 male mice were intranasally infected with PR8 virus at 50 or 500 plaque-forming units to cause sublethal or lethal influenza, respectively. Plasma and tissue samples were collected at 1, 3, and 6 days post-infection (dpi), and comprehensive lipidomic analysis was performed using high-performance liquid chromatography-linear trap quadrupole-Orbitrap mass spectrometry, as well as gene expression analyses. The most prominent feature of the lipid profile in lethally infected mice was the elevated plasma concentrations of phosphatidylethanolamines (PEs) containing polyunsaturated fatty acid (PUFA) at 3 dpi. Furthermore, the facilitation of PUFA-containing phospholipid production in the lungs, but not in the liver, was suggested by gene expression and lipidomic analysis of tissue samples. Given the increased plasma or serum levels of PUFA-containing PEs in patients with other viral infections, especially in severe cases, the elevation of these phospholipids in circulation could be a biomarker of infection and the severity of infectious diseases.

Influenza A Virus Infection Alters Lipid Packing and Surface Electrostatic Potential of the Host Plasma Membrane

The pathogenesis of influenza A viruses (IAVs) is influenced by several factors, including IAV strain origin and reassortment, tissue tropism and host type. While such factors were mostly investigated in the context of virus entry, fusion and replication, little is known about the viral-induced changes to the host lipid membranes which might be relevant in the context of virion assembly. In this work, we applied several biophysical fluorescence microscope techniques (i.e., Förster energy resonance transfer, generalized polarization imaging and scanning fluorescence correlation spectroscopy) to quantify the effect of infection by two IAV strains of different origin on the plasma membrane (PM) of avian and human cell lines. We found that IAV infection affects the membrane charge of the inner leaflet of the PM. Moreover, we showed that IAV infection impacts lipid-lipid interactions by decreasing membrane fluidity and increasing lipid packing. Because of such alterations, diffusive dynamics of membrane-associated proteins are hindered. Taken together, our results indicate that the infection of avian and human cell lines with IAV strains of different origins had similar effects on the biophysical properties of the PM.

A binary interaction map between turnip mosaic virus and Arabidopsis thaliana proteomes

Viruses are obligate intracellular parasites that have co-evolved with their hosts to establish an intricate network of protein-protein interactions. Here, we followed a high-throughput yeast two-hybrid screening to identify 378 novel protein-protein interactions between turnip mosaic virus (TuMV) and its natural host Arabidopsis thaliana. We identified the RNA-dependent RNA polymerase NIb as the viral protein with the largest number of contacts, including key salicylic acid-dependent transcription regulators. We verified a subset of 25 interactions in planta by bimolecular fluorescence complementation assays. We then constructed and analyzed a network comprising 399 TuMV-A. thaliana interactions together with intravirus and intrahost connections. In particular, we found that the host proteins targeted by TuMV are enriched in different aspects of plant responses to infections, are more connected and have an increased capacity to spread information throughout the cell proteome, display higher expression levels, and have been subject to stronger purifying selection than expected by chance. The proviral or antiviral role of ten host proteins was validated by characterizing the infection dynamics in the corresponding mutant plants, supporting a proviral role for the transcriptional regulator TGA1. Comparison with similar studies with animal viruses, highlights shared fundamental features in their mode of action.

Cyclophosphamide Induces Lipid and Metabolite Perturbation in Amniotic Fluid during Rat Embryonic Development

Cyclophosphamide (CP) has been proven to be an embryo-fetal toxic. However, the mechanism responsible for the toxicity of the teratogenic agent has not been fully explored. This study aimed to examine the teratogenicity of CP when administered in the sensitive period of pregnant rats. The effect of CP on the lipid and metabolic profiles of amniotic fluid was evaluated using a UHPLC-Q-Exactive Orbitrap MS-based method. Metabolome analysis was performed using the MS-DIAL software with LipidBlast and NIST. Initially, we identified 636 and 154 lipid compounds in the positive and negative ion modes and 118 metabolites for differential analysis. Mainly 4 types of oxidized lipids in the amniotic fluid were found to accumulate most significantly after CP treatment, including very-long-chain unsaturated fatty acids (VLCUFAs), polyunsaturated fatty acid (PUFA)-containing triglycerides (TGs), oxidized phosphatidylcholine (PC), and sphingomyelin (SM). Tryptophan and some long-chain saturated fatty acids were lowered pronouncedly after CP treatment. These findings suggest that CP may exert teratogenic toxicity on pregnant rats through maternal and fetal oxidative stress. The UHPLC-Q-Exactive Orbitrap MS-based lipidomics approach is worthy of wider application for evaluating the potential toxicity of other agents (toxicants) during embryonic development.

Simultaneous profiling and quantification of 25 eicosanoids in human serum by ultrahigh-performance liquid chromatography coupled to tandem mass spectrometry

The eicosanoid metabolic pathway is responsible for mediating the production of various inflammatory factors that are closely related to the development and resolution of inflammation. In biological matrices, the major quantifying obstacles were shown to be the oxidation and low quantities of eicosanoids and their metabolites. This study aimed to develop a reliable, sensitive ultrahigh-performance liquid chromatography coupled to a tandem mass spectrometry (UPLC–MS/MS) method to quantify eicosanoids in human serum. Solid-phase extraction (SPE) was used for sample preparation. The approach employed continuous ionization polarity switching. The target eicosanoids showed good linearity over the investigated concentration range (r2 > 0.99). The recovery rates were over 64.5%, and the matrix effects ranged from 73.0 to 128.0%. The limits of quantification were 0.048 ~ 0.44 ng/mL. For the broad concentration range, the CV % for accuracy and precision were less than ± 20%. We successfully applied this method to rapidly analyse 74 serum samples from severe influenza pneumonia, severe bacterial pneumonia and healthy individuals. Eicosanoid-related metabolite concentrations were quantified within a range similar to those of previously published articles. Compared to healthy individuals, our application found that 20-HETE, 14,15-EET and 11,12-EET were upregulated in severe influenza pneumonia patients, while LTB4 was downregulated. 8-HETE and 5-HETE were upregulated in severe bacterial pneumonia patients, while LTE4 was downregulated. This approach provides a means for monitoring the low quantities of eicosanoids in biological matrices, and our finding that different characteristic metabolite profiles may help discriminate the induction of severe pneumonia patients.

Multi-omics association analysis reveals interactions between the oropharyngeal microbiome and the metabolome in pediatric patients with influenza A virus pneumonia

Children are at high risk for influenza A virus (IAV) infections, which can develop into severe illnesses. However, little is known about interactions between the microbiome and respiratory tract metabolites and their impact on the development of IAV pneumonia in children. Using a combination of liquid chromatography tandem mass spectrometry (LC-MS/MS) and 16S rRNA gene sequencing, we analyzed the composition and metabolic profile of the oropharyngeal microbiota in 49 pediatric patients with IAV pneumonia and 42 age-matched healthy children. The results indicate that compared to healthy children, children with IAV pneumonia exhibited significant changes in the oropharyngeal macrobiotic structure (p = 0.001), and significantly lower microbial abundance and diversity (p < 0.05). These changes came with significant disturbances in the levels of oropharyngeal metabolites. Intergroup differences were observed in 204 metabolites mapped to 36 metabolic pathways. Significantly higher levels of sphingolipid (sphinganine and phytosphingosine) and propanoate (propionic acid and succinic acid) metabolism were observed in patients with IAV pneumonia than in healthy controls. Using Spearman’s rank-correlation analysis, correlations between IAV pneumonia-associated discriminatory microbial genera and metabolites were evaluated. The results indicate significant correlations and consistency in variation trends between Streptococcus and three sphingolipid metabolites (phytosphingosine, sphinganine, and sphingosine). Besides these three sphingolipid metabolites, the sphinganine-to-sphingosine ratio and the joint analysis of the three metabolites indicated remarkable diagnostic efficacy in children with IAV pneumonia. This study confirmed significant changes in the characteristics and metabolic profile of the oropharyngeal microbiome in pediatric patients with IAV pneumonia, with high synergy between the two factors. Oropharyngeal sphingolipid metabolites may serve as potential diagnostic biomarkers of IAV pneumonia in children.

The Role of Glutathione Reductase in Influenza Infection

Influenza infection induces lung epithelial cell injury via programmed cell death. Glutathione, a potent antioxidant, has been reported to be associated with influenza infection. We hypothesized that lung epithelial cell death during influenza infection is regulated by glutathione metabolism. Eight-week-old male and female BALB/c mice were infected with influenza (PR8: A/PR/8/34 [H1N1]) via intranasal instillation. Metabolomic analyses were performed on whole lung lysate after influenza infection. For in vitro analysis, Beas-2B cells were infected with influenza. RNA was extracted, and QuantiTect Primer Assay was used to assess gene expression. Glutathione concentrations were assessed by colorimetric assay. Influenza infection resulted in increased inflammation and epithelial cell injury in our murine model, leading to increased morbidity and mortality. In both our in vivo and in vitro models, influenza infection was found to induce apoptosis and necroptosis. Influenza infection led to decreased glutathione metabolism and reduced glutathione reductase activity in lung epithelial cells. Genetic inhibition of glutathione reductase suppressed apoptosis and necroptosis of lung epithelial cells. Pharmacologic inhibition of glutathione reductase reduced airway inflammation, lung injury, and cell death in our murine influenza model. Our results demonstrate that glutathione reductase activity is suppressed during influenza. Glutathione reductase inhibition prevents epithelial cell death and morbidity in our murine influenza model. Our results suggest that glutathione reductase-dependent glutathione metabolism may play an important role in the host response to viral infection by regulating lung epithelial cell death.

Association between lipid profiles and viral respiratory infections in human sputum samples

Background

Respiratory infections such as influenza account for significant global mortality each year. Generating lipid profiles is a novel and emerging research approach that may provide new insights regarding the development and progression of priority respiratory infections. We hypothesized that select clusters of lipids in human sputum would be associated with specific viral infections (Influenza (H1N1, H3N2) or Rhinovirus).

Methods

Lipid identification and semi-quantitation was determined with liquid chromatography and high-resolution mass spectrometry in induced sputum from individuals with confirmed respiratory infections (influenza (H1N1, H3N2) or rhinovirus). Clusters of lipid species and associations between lipid profiles and the type of respiratory viral agent was determined using Bayesian profile regression and multinomial logistic regression.

Results

More than 600 lipid compounds were identified across the sputum samples with the most abundant lipid classes identified as triglycerides (TG), phosphatidylethanolamines (PE), phosphatidylcholines (PC), Sphingomyelins (SM), ether-PC, and ether-PE. A total of 12 lipid species were significantly different when stratified by infection type and included acylcarnitine (AcCar) (10:1, 16:1, 18:2), diacylglycerols (DG) (16:0_18:0, 18:0_18:0), Lysophosphatidylcholine (LPC) (12:0, 20:5), PE (18:0_18:0), and TG (14:1_16:0_18:2, 15:0_17:0_19:0, 16:0_17:0_18:0, 19:0_19:0_19:0). Cluster analysis yielded three clusters of lipid profiles that were driven by just 10 lipid species (TGs and DGs). Cluster 1 had the highest levels of each lipid species and the highest prevalence of influenza A H3 infection (56%, n = 5) whereas cluster 3 had lower levels of each lipid species and the highest prevalence of rhinovirus (60%; n = 6). Using cluster 3 as the reference group, the crude odds of influenza A H3 infection compared to rhinovirus in cluster 1 was significantly (p = 0.047) higher (OR = 15.00 [95% CI: 1.03, 218.29]). After adjustment for confounders (smoking status and pulmonary comorbidities), the odds ratio (OR) became only marginally significant (p = 0.099), but the magnitude of the effect estimate was similar (OR = 16.00 [0.59, 433.03]).

Conclusions

In this study, human sputum lipid profiles were shown to be associated with distinct types of viral infection. Better understanding the relationship between respiratory infections of global importance and lipids contributes to advancing knowledge of pathogenesis of infections including identifying populations with increased susceptibility and developing effective therapeutics and biomarkers of health status.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02091-w.

Spatial Metabolomics Reveals Localized Impact of Influenza Virus Infection on the Lung Tissue Metabolome

The influenza virus (IAV) is a major cause of respiratory disease, with significant infection increases in pandemic years. Vaccines are a mainstay of IAV prevention but are complicated by IAV’s vast strain diversity and manufacturing and vaccine uptake limitations. While antivirals may be used for treatment of IAV, they are most effective in early stages of the infection, and several virus strains have become drug resistant. Therefore, there is a need for advances in IAV treatment, especially host-directed therapeutics. Given the spatial dynamics of IAV infection and the relationship between viral spatial distribution and disease severity, a spatial approach is necessary to expand our understanding of IAV pathogenesis. We used spatial metabolomics to address this issue. Spatial metabolomics combines liquid chromatography-tandem mass spectrometry of metabolites extracted from systematic organ sections, 3D models, and computational techniques to develop spatial models of metabolite location and their role in organ function and disease pathogenesis. In this project, we analyzed serum and systematically sectioned lung tissue samples from uninfected or infected mice. Spatial mapping of sites of metabolic perturbations revealed significantly lower metabolic perturbation in the trachea compared to other lung tissue sites. Using random forest machine learning, we identified metabolites that responded differently in each lung position based on infection, including specific amino acids, lipids and lipid-like molecules, and nucleosides. These results support the implementation of spatial metabolomics to understand metabolic changes upon respiratory virus infection.

IMPORTANCE The influenza virus is a major health concern. Over 1 billion people become infected annually despite the wide distribution of vaccines, and antiviral agents are insufficient to address current clinical needs. In this study, we used spatial metabolomics to understand changes in the lung and serum metabolome of mice infected with influenza A virus compared to uninfected controls. We determined metabolites altered by infection in specific lung tissue sites and distinguished metabolites perturbed by infection between lung tissue and serum samples. Our findings highlight the utility of a spatial approach to understanding the intersection between the lung metabolome, viral infection, and disease severity. Ultimately, this approach will expand our understanding of respiratory disease pathogenesis.

Altered Lipid Profile in COVID-19 Patients and Metabolic Reprogramming

Background

Coronavirus disease 2019 (COVID-19) is a global pandemic. Previous studies have reported dyslipidemia in patients with COVID-19. Herein, we conducted a retrospective study and a bioinformatics analysis to evaluate the essential data of the lipid profile as well as the possible mechanism in patients with COVID-19.

Methods

First of all, the retrospective study included three cohorts: patients with COVID-19, a healthy population, and patients with chronic obstructive pulmonary disease (COPD). For each subject, serum lipid profiles in the biochemical data were compared, including triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Furthermore, bioinformatics analyses were performed for exploring the biological or immunological mechanisms.

Results

In line with the biochemical data of the three cohorts, the statistical result displayed that patients with COVID-19 were more likely to have lower levels of TC and HDL-C as compared with healthy individuals. The differential proteins associated with COVID-19 are involved in the lipid pathway and can target and regulate cytokines and immune cells. Additionally, a heatmap revealed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections were possibly involved in lipid metabolic reprogramming. The viral proteins, such as spike (S) and non-structural protein 2 (Nsp2) of SARS-CoV-2, may be involved in metabolic reprogramming.

Conclusion

The metabolic reprogramming after SARS-CoV-2 infections is probably associated with the immune and clinical phenotype of patients. Hence, metabolic reprogramming may be targeted for developing antivirals against COVID-19.

Greasing the inflammatory pathogenesis of viral pneumonias in diabetes

Type 2 diabetes (T2D) and obesity are independent risk factors for increased morbidity and mortality associated with influenza and SARS-CoV-2 infection. Skewed cellular metabolism shapes immune cell inflammatory responsiveness and function in obesity, T2D, and infection. However, altered immune cell responsiveness and levels of systemic proinflammatory mediators, partly independent of peripheral immune cell contribution, are linked with SARS-CoV-2-associated disease severity. Despite such knowledge, the role of tissue parenchymal cell-driven inflammatory responses, and specifically those dominantly modified in obesity (e.g., adipocytes), in influenza and SARS-CoV-2 infection pathogenesis remain poorly defined. Whether obesity-dependent skewing of adipocyte cellular metabolism uncovers inflammatory clades and promotes the existence of a 'pathogenic-inflammatory' adipocyte phenotype that amplifies SARS-CoV-2 infection diseases severity in individuals with obesity and individuals with obesity and T2D has not been examined. Here, using the knowledge gained from studies of immune cell responses in obesity, T2D, and infection, we highlight the key knowledge gaps underlying adipocyte cellular functions that may sculpt and grease pathogenic processes associated with influenza and SARS-CoV-2 disease severity in diabetes.

Metabolic Modifications by Common Respiratory Viruses and Their Potential as New Antiviral Targets

Respiratory viruses are known to be the most frequent causative mediators of lung infections in humans, bearing significant impact on the host cell signaling machinery due to their host-dependency for efficient replication. Certain cellular functions are actively induced by respiratory viruses for their own benefit. This includes metabolic pathways such as glycolysis, fatty acid synthesis (FAS) and the tricarboxylic acid (TCA) cycle, among others, which are modified during viral infections. Here, we summarize the current knowledge of metabolic pathway modifications mediated by the acute respiratory viruses respiratory syncytial virus (RSV), rhinovirus (RV), influenza virus (IV), parainfluenza virus (PIV), coronavirus (CoV) and adenovirus (AdV), and highlight potential targets and compounds for therapeutic approaches.

Pulmonary infection induces persistent, pathogen-specific lipidomic changes influencing trained immunity

Resolution of infection results in development of trained innate immunity which is typically beneficial for defense against unrelated secondary infection. Epigenetic changes including modification of histones via binding of various polar metabolites underlie the establishment of trained innate immunity. Therefore, host metabolism and this response are intimately linked. However, little is known regarding the influence of lipids on the development and function of trained immunity. Utilizing two models of pulmonary bacterial infection combined with multi-omic approaches, we identified persistent, pathogen-specific changes to the lung lipidome that correlated with differences in the trained immune response against a third unrelated pathogen. Further, we establish the specific cellular populations in the lung that contribute to this altered lipidome. Together these results expand our understanding of the pulmonary trained innate immune response and the contributions of host lipids in informing that response.

•

Pathogens exert differential effects on pulmonary efferocytosis post-infection

•

Differences in efferocytosis are mediated by macrophage subsets

•

Unique immune lipid mediator milieus are linked to these macrophage subsets

•

Changes in the lipid landscape impact trained immunity to an unrelated infection

ImmuCellDB: An Indicative Database of Immune Cell Composition From Different Tissues and Disease Conditions in Mouse and Human

Immune cell composition is highly divergent across different tissues and diseases. A comprehensive resource of tissue immune cells across different conditions in mouse and human will thus provide great understanding of the immune microenvironment of many diseases. Recently, computational methods for estimating immune cell abundance from tissue transcriptome data have been developed and are now widely used. Using these computational tools, large-scale estimation of immune cell composition across tissues and conditions should be possible using gene expression data collected from public databases. In total, 266 tissue types and 706 disease types in humans, as well as 143 tissue types and 61 disease types, and 206 genotypes in mouse had been included in a database we have named ImmuCellDB (http://wap-lab.org:3200/ImmuCellDB/). In ImmuCellDB, users can search and browse immune cell proportions based on tissues, disease or genotype in mouse or humans. Additionally, the variation and correlation of immune cell abundance and gene expression level between different conditions can be compared and viewed in this database. We believe that ImmuCellDB provides not only an indicative view of tissue-dependent or disease-dependent immune cell profiles, but also represents an easy way to pre-determine immune cell abundance and gene expression profiles for specific situations.

Bacterial factors required for Streptococcus pneumoniae coinfection with influenza A virus

BACKGROUND

Streptococcus pneumoniae is a common cause of post-influenza secondary bacterial infection, which results in excessive morbidity and mortality. Although 13-valent pneumococcal conjugate vaccine (PCV13) vaccination programs have decreased the incidence of pneumococcal pneumonia, PCV13 failed to prevent serotype 3 pneumococcal disease as effectively as other vaccine serotypes. We aimed to investigate the mechanisms underlying the co-pathogenesis of influenza virus and serotype 3 pneumococci.

METHODS

We carried out a genome-wide screening of a serotype 3 S. pneumoniae transposon insertion mutant library in a mouse model of coinfection with influenza A virus (IAV) to identify the bacterial factors required for this synergism.

RESULTS

Direct, high-throughput sequencing of transposon insertion sites identified 24 genes required for both coinfection and bacterial infection alone. Targeted deletion of the putative aminotransferase (PA) gene decreased bacterial growth, which was restored by supplementation with methionine. The bacterial burden in a coinfection with the PA gene deletion mutant and IAV in the lung was lower than that in a coinfection with wild-type pneumococcus and IAV, but was significantly higher than that in an infection with the PA gene deletion mutant alone. These data suggest that IAV infection alters host metabolism to benefit pneumococcal fitness and confer higher susceptibility to pneumococcal infection. We further demonstrated that bacterial growth was increased by supplementation with methionine or IAV-infected mouse lung homogenates.

CONCLUSIONS

The data indicates that modulation of host metabolism during IAV infection may serve as a potential therapeutic intervention against secondary bacterial infections caused by serotype 3 pneumococci during IAV outbreaks in the future.

Low serum level of apolipoprotein A1 may predict the severity of COVID-19: A retrospective study

BACKGROUND

Dyslipidemia has been observed in patients with coronavirus disease 2019 (COVID-19). This study aimed to investigate blood lipid profiles in patients with COVID-19 and to explore their predictive values for COVID-19 severity.

METHODS

A total of 142 consecutive patients with COVID-19 were included in this single-center retrospective study. Blood lipid profile characteristics were investigated in patients with COVID-19 in comparison with 77 age- and gender-matched healthy subjects, their predictive values for COVID-19 severity were analyzed by using multivariable logistic regression analysis, and their prediction efficiencies were evaluated by using receiver operator characteristic (ROC) curves.

RESULTS

There were 125 and 17 cases in the non-severe and severe groups, respectively. Total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein A1 (ApoA1) gradually decreased across the groups in the following order: healthy controls, non-severe group, and severe group. ApoA1 was identified as an independent risk factor for COVID-19 severity (adjusted odds ratio [OR]: 0.865, 95% confidence interval [CI]: 0.800-0.935, p < 0.001), along with interleukin-6 (IL-6) (adjusted OR: 1.097, 95% CI: 1.034-1.165, p = 0.002). ApoA1 exhibited the highest area under the ROC curve (AUC) among all single markers (AUC: 0.896, 95% CI: 0.834-0.941); moreover, the risk model established using ApoA1 and IL-6 enhanced prediction efficiency (AUC: 0.977, 95% CI: 0.932-0.995).

CONCLUSION

Blood lipid profiles in patients with COVID-19 are quite abnormal compared with those in healthy subjects, especially in severe cases. Serum ApoA1 may represent a good indicator for predicting the severity of COVID-19.

Aberrant NAD+ metabolism underlies Zika virus-induced microcephaly

Zika virus (ZIKV) infection during pregnancy can cause microcephaly in newborns, yet the underlying mechanisms remain largely unexplored. Here, we reveal extensive and large-scale metabolic reprogramming events in ZIKV-infected mouse brains by performing a multi-omics study comprising transcriptomics, proteomics, phosphoproteomics and metabolomics approaches. Our proteomics and metabolomics analyses uncover dramatic alteration of nicotinamide adenine dinucleotide (NAD⁺)-related metabolic pathways, including oxidative phosphorylation, TCA cycle and tryptophan metabolism. Phosphoproteomics analysis indicates that MAPK and cyclic GMP-protein kinase G signaling may be associated with ZIKV-induced microcephaly. Notably, we demonstrate the utility of our rich multi-omics datasets with follow-up in vivo experiments, which confirm that boosting NAD⁺ by NAD⁺ or nicotinamide riboside supplementation alleviates cell death and increases cortex thickness in ZIKV-infected mouse brains. Nicotinamide riboside supplementation increases the brain and body weight as well as improves the survival in ZIKV-infected mice. Our study provides a comprehensive resource of biological data to support future investigations of ZIKV-induced microcephaly and demonstrates that metabolic alterations can be potentially exploited for developing therapeutic strategies.

Diagnostic Potential of the Plasma Lipidome in Infectious Disease: Application to Acute SARS-CoV-2 Infection

Improved methods are required for investigating the systemic metabolic effects of SARS-CoV-2 infection and patient stratification for precision treatment. We aimed to develop an effective method using lipid profiles for discriminating between SARS-CoV-2 infection, healthy controls, and non-SARS-CoV-2 respiratory infections. Targeted liquid chromatography-mass spectrometry lipid profiling was performed on discovery (20 SARS-CoV-2-positive; 37 healthy controls; 22 COVID-19 symptoms but SARS-CoV-2negative) and validation (312 SARS-CoV-2-positive; 100 healthy controls) cohorts. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and Kruskal-Wallis tests were applied to establish discriminant lipids, significance, and effect size, followed by logistic regression to evaluate classification performance. OPLS-DA reported separation of SARS-CoV-2 infection from healthy controls in the discovery cohort, with an area under the curve (AUC) of 1.000. A refined panel of discriminant features consisted of six lipids from different subclasses (PE, PC, LPC, HCER, CER, and DCER). Logistic regression in the discovery cohort returned a training ROC AUC of 1.000 (sensitivity = 1.000, specificity = 1.000) and a test ROC AUC of 1.000. The validation cohort produced a training ROC AUC of 0.977 (sensitivity = 0.855, specificity = 0.948) and a test ROC AUC of 0.978 (sensitivity = 0.948, specificity = 0.922). The lipid panel was also able to differentiate SARS-CoV-2-positive individuals from SARS-CoV-2-negative individuals with COVID-19-like symptoms (specificity = 0.818). Lipid profiling and multivariate modelling revealed a signature offering mechanistic insights into SARS-CoV-2, with strong predictive power, and the potential to facilitate effective diagnosis and clinical management.

Fueling influenza and the immune response: Implications for metabolic reprogramming during influenza infection and immunometabolism

Recent studies support the notion that glycolysis and oxidative phosphorylation are rheostats in immune cells whose bioenergetics have functional outputs in terms of their biology. Specific intrinsic and extrinsic molecular factors function as molecular potentiometers to adjust and control glycolytic to respiratory power output. In many cases, these potentiometers are used by influenza viruses and immune cells to support pathogenesis and the host immune response, respectively. Influenza virus infects the respiratory tract, providing a specific environmental niche, while immune cells encounter variable nutrient concentrations as they migrate in response to infection. Immune cell subsets have distinct metabolic programs that adjust to meet energetic and biosynthetic requirements to support effector functions, differentiation, and longevity in their ever-changing microenvironments. This review details how influenza coopts the host cell for metabolic reprogramming and describes the overlap of these regulatory controls in immune cells whose function and fate are dictated by metabolism. These details are contextualized with emerging evidence of the consequences of influenza-induced changes in metabolic homeostasis on disease progression.

Metabolite profiles associated with disease progression in influenza infection

BACKGROUND

We performed metabolomic profiling to identify metabolites that correlate with disease progression and death.

METHODS

We performed a study of adults hospitalized with Influenza A(H1N1)pdm09. Cases (n = 32) were defined by a composite outcome of death or transfer to the intensive care unit during the 60-day follow-up period. Controls (n = 64) were survivors who did not require transfer to the ICU. Four hundred and eight metabolites from eight families were measured on plasma sample at enrollment using a mass spectrometry based Biocrates platform. Conditional logistic regression was used to summarize the association of the individual metabolites and families with the composite outcome and its major two components.

RESULTS

The ten metabolites with the strongest association with disease progression belonged to five different metabolite families with sphingolipids being the most common. The acylcarnitines, glycerides, sphingolipids and biogenic metabolite families had the largest odds ratios based on the composite endpoint. The tryptophan odds ratio for the composite is largely associated with death (OR 17.33: 95% CI, 1.60-187.76).

CONCLUSIONS

Individuals that develop disease progression when infected with Influenza H1N1 have a metabolite signature that differs from survivors. Low levels of tryptophan had a strong association with death.

REGISTRY

ClinicalTrials.gov Identifier: NCT01056185.

Host-derived lipids orchestrate pulmonary γδ T cell response to provide early protection against influenza virus infection

Innate immunity is important for host defense by eliciting rapid anti-viral responses and bridging adaptive immunity. Here, we show that endogenous lipids released from virus-infected host cells activate lung γδ T cells to produce interleukin 17 A (IL-17A) for early protection against H1N1 influenza infection. During infection, the lung γδ T cell pool is constantly supplemented by thymic output, with recent emigrants infiltrating into the lung parenchyma and airway to acquire tissue-resident feature. Single-cell studies identify IL-17A-producing γδ T (Tγδ17) cells with a phenotype of TCRγδhiCD3hiAQP3hiCXCR6hi in both infected mice and patients with pneumonia. Mechanistically, host cell-released lipids during viral infection are presented by lung infiltrating CD1d+ B-1a cells to activate IL-17A production in γδ T cells via γδTCR-mediated IRF4-dependent transcription. Reduced IL-17A production in γδ T cells is detected in mice either lacking B-1a cells or with ablated CD1d in B cells. Our findings identify a local host-immune crosstalk and define important cellular and molecular mediators for early innate defense against lung viral infection.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

Ceramide synthase 2 deletion decreases the infectivity of HIV-1

The lipid composition of HIV-1 virions is enriched in sphingomyelin (SM), but the roles that SM or other sphingolipids (SLs) might play in the HIV-1 replication pathway have not been elucidated. In human cells, SL levels are regulated by ceramide synthase (CerS) enzymes that produce ceramides, which can be converted to SMs, hexosylceramides, and other SLs. In many cell types, CerS2, which catalyzes the synthesis of very long chain ceramides, is the major CerS. We have examined how CerS2 deficiency affects the assembly and infectivity of HIV-1. As expected, we observed that very long chain ceramide, hexosylceramide, and SM were reduced in CerS2 knockout cells. CerS2 deficiency did not affect HIV-1 assembly or the incorporation of the HIV-1 envelope (Env) protein into virus particles, but it reduced the infectivites of viruses produced in the CerS2-deficient cells. The reduced viral infection levels were dependent on HIV-1 Env, since HIV-1 particles that were pseudotyped with the vesicular stomatitis virus glycoprotein did not exhibit reductions in infectivity. Moreover, cell-cell fusion assays demonstrated that the functional defect of HIV-1 Env in CerS2-deficient cells was independent of other viral proteins. Overall, our results indicate that the altered lipid composition of CerS2-deficient cells specifically inhibit the HIV-1 Env receptor binding and/or fusion processes.

Progress and challenge for computational quantification of tissue immune cells

Tissue immune cells have long been recognized as important regulators for the maintenance of balance in the body system. Quantification of the abundance of different immune cells will provide enhanced understanding of the correlation between immune cells and normal or abnormal situations. Currently, computational methods to predict tissue immune cell compositions from bulk transcriptomes have been largely developed. Therefore, summarizing the advantages and disadvantages is appropriate. In addition, an examination of the challenges and possible solutions for these computational models will assist the development of this field. The common hypothesis of these models is that the expression of signature genes for immune cell types might represent the proportion of immune cells that contribute to the tissue transcriptome. In general, we grouped all reported tools into three groups, including reference-free, reference-based scoring and reference-based deconvolution methods. In this review, a summary of all the currently reported computational immune cell quantification tools and their applications, limitations, and perspectives are presented. Furthermore, some critical problems are found that have limited the performance and application of these models, including inadequate immune cell type, the collinearity problem, the impact of the tissue environment on the immune cell expression level, and the deficiency of standard datasets for model validation. To address these issues, tissue specific training datasets that include all known immune cells, a hierarchical computational framework, and benchmark datasets including both tissue expression profiles and the abundances of all the immune cells are proposed to further promote the development of this field.

Host factors involved in influenza virus infection

Influenza virus causes an acute febrile respiratory disease in humans that is commonly known as 'flu'. Influenza virus has been around for centuries and is one of the most successful, and consequently most studied human viruses. This has generated tremendous amount of data and information, thus it is pertinent to summarise these for, particularly interdisciplinary readers. Viruses are acellular organisms and exist at the interface of living and non-living. Due to this unique characteristic, viruses require another organism, i.e. host to survive. Viruses multiply inside the host cell and are obligate intracellular pathogens, because their relationship with the host is almost always harmful to host. In mammalian cells, the life cycle of a virus, including influenza is divided into five main steps: attachment, entry, synthesis, assembly and release. To complete these steps, some viruses, e.g. influenza utilise all three parts - plasma membrane, cytoplasm and nucleus, of the cell; whereas others, e.g. SARS-CoV-2 utilise only plasma membrane and cytoplasm. Hence, viruses interact with numerous host factors to complete their life cycle, and these interactions are either exploitative or antagonistic in nature. The host factors involved in the life cycle of a virus could be divided in two broad categories - proviral and antiviral. This perspective has endeavoured to assimilate the information about the host factors which promote and suppress influenza virus infection. Furthermore, an insight into host factors that play a dual role during infection or contribute to influenza virus-host adaptation and disease severity has also been provided.

Baicalin Inhibits Influenza A Virus Infection via Promotion of M1 Macrophage Polarization

Background and Aims

The natural compound baicalin (BA) possesses potent antiviral properties against the influenza virus. However, the underlying molecular mechanisms of this antiviral activity and whether macrophages are involved remain unclear. In this study, we, therefore, investigated the effect of BA on macrophages.

Methods

We studied macrophage recruitment, functional phenotypes (M1/M2), and the cellular metabolism via flow cytometry, qRT-PCR, immunofluorescence, a cell culture transwell system, and GC-MS–based metabolomics both in vivo in H1N1 A virus-infected mice and in vitro.

Results

BA treatment drastically reduced macrophage recruitment (CD11b⁺, F4/80⁺) by approximately 90% while maintaining the proportion of M1-polarized macrophages in the bronchoalveolar lavage fluid of infected mice. This BA-stimulated macrophage M1 phenotype shift was further verified in vitro in ANA-1 and primary peritoneal macrophages by measuring macrophage M1 polarization signals (CD86, iNOS, TNF-α, iNOS/Arg-1 ratio, and IL-1β cleavage). Meanwhile, we observed an activation of the IFN pathway (upregulation of IFN-β and IRF-3), an inhibition of influenza virus replication (as measured by the M gene), and distinct cellular metabolic responses in BA-treated cells.

Conclusion

BA triggered macrophage M1 polarization, IFN activation, and other cellular reactions, which are beneficial for inhibition of H1N1 A virus infection.

Palmitic Acid Promotes Virus Replication in Fish Cell by Modulating Autophagy Flux and TBK1-IRF3/7 Pathway

Palmitic acid is the most common saturated fatty acid in animals, plants, and microorganisms. Studies highlighted that palmitic acid plays a significant role in diverse cellular processes and viral infections. Accumulation of palmitic acid was observed in fish cells (grouper spleen, GS) infected with Singapore grouper iridovirus (SGIV). The fluctuated content levels after viral infection suggested that palmitic acid was functional in virus-cell interactions. In order to investigate the roles of palmitic acid in SGIV infection, the effects of palmitic acid on SGIV induced cytopathic effect, expression levels of viral genes, viral proteins, as well as virus production were evaluated. The infection and replication of SGIV were increased after exogenous addition of palmitic acid but suppressed after knockdown of fatty acid synthase (FASN), of which the primary function was to catalyze palmitate synthesis. Besides, the promotion of virus replication was associated with the down-regulating of interferon-related molecules, and the reduction of IFN1 and ISRE promotor activities by palmitic acid. We also discovered that palmitic acid restricted TBK1, but not MDA5-induced interferon immune responses. On the other hand, palmitic acid decreased autophagy flux in GS cells via suppressing autophagic degradation, and subsequently enhanced viral replication. Together, our findings indicate that palmitic acid is not only a negative regulator of TBK1-IRF3/7 pathway, but also a suppressor of autophagic flux. Finally, palmitic acid promotes the replication of SGIV in fish cells.

Transcriptomic and Metabolic Responses to a Live-Attenuated Francisella tularensis Vaccine

The immune response to live-attenuated Francisella tularensis vaccine and its host evasion mechanisms are incompletely understood. Using RNA-Seq and LC–MS on samples collected pre-vaccination and at days 1, 2, 7, and 14 post-vaccination, we identified differentially expressed genes in PBMCs, metabolites in serum, enriched pathways, and metabolites that correlated with T cell and B cell responses, or gene expression modules. While an early activation of interferon α/β signaling was observed, several innate immune signaling pathways including TLR, TNF, NF-κB, and NOD-like receptor signaling and key inflammatory cytokines such as Il-1α, Il-1β, and TNF typically activated following infection were suppressed. The NF-κB pathway was the most impacted and the likely route of attack. Plasma cells, immunoglobulin, and B cell signatures were evident by day 7. MHC I antigen presentation was more actively up-regulated first followed by MHC II which coincided with the emergence of humoral immune signatures. Metabolomics analysis showed that glycolysis and TCA cycle-related metabolites were perturbed including a decline in pyruvate. Correlation networks that provide hypotheses on the interplay between changes in innate immune, T cell, and B cell gene expression signatures and metabolites are provided. Results demonstrate the utility of transcriptomics and metabolomics for better understanding molecular mechanisms of vaccine response and potential host–pathogen interactions.

mRNA display with library of even-distribution reveals cellular interactors of influenza virus NS1

A comprehensive examination of protein-protein interactions (PPIs) is fundamental for the understanding of cellular machineries. However, limitations in current methodologies often prevent the detection of PPIs with low abundance proteins. To overcome this challenge, we develop a mRNA display with library of even-distribution (md-LED) method that facilitates the detection of low abundance binders with high specificity and sensitivity. As a proof-of-principle, we apply md-LED to IAV NS1 protein. Complementary to AP-MS, md-LED enables us to validate previously described PPIs as well as to identify novel NS1 interactors. We show that interacting with FASN allows NS1 to directly regulate the synthesis of cellular fatty acids. We also use md-LED to identify a mutant of NS1, D92Y, results in a loss of interaction with CPSF1. The use of high-throughput sequencing as the readout for md-LED enables sensitive quantification of interactions, ultimately enabling massively parallel experimentation for the investigation of PPIs.

Influenza infection rewires energy metabolism and induces browning features in adipose cells and tissues

Like all obligate intracellular pathogens, influenza A virus (IAV) reprograms host cell's glucose and lipid metabolism to promote its own replication. However, the impact of influenza infection on white adipose tissue (WAT), a key tissue in the control of systemic energy homeostasis, has not been yet characterized. Here, we show that influenza infection induces alterations in whole-body glucose metabolism that persist long after the virus has been cleared. We report depot-specific changes in the WAT of IAV-infected mice, notably characterized by the appearance of thermogenic brown-like adipocytes within the subcutaneous fat depot. Importantly, viral RNA- and viral antigen-harboring cells are detected in the WAT of infected mice. Using in vitro approaches, we find that IAV infection enhances the expression of brown-adipogenesis-related genes in preadipocytes. Overall, our findings shed light on the role that the white adipose tissue, which lies at the crossroads of nutrition, metabolism and immunity, may play in influenza infection.

Acute experimental infection of bats and ferrets with Hendra virus: Insights into the early host response of the reservoir host and susceptible model species

Bats are the natural reservoir host for a number of zoonotic viruses, including Hendra virus (HeV) which causes severe clinical disease in humans and other susceptible hosts. Our understanding of the ability of bats to avoid clinical disease following infection with viruses such as HeV has come predominantly from in vitro studies focusing on innate immunity. Information on the early host response to infection in vivo is lacking and there is no comparative data on responses in bats compared with animals that succumb to disease. In this study, we examined the sites of HeV replication and the immune response of infected Australian black flying foxes and ferrets at 12, 36 and 60 hours post exposure (hpe). Viral antigen was detected at 60 hpe in bats and was confined to the lungs whereas in ferrets there was evidence of widespread viral RNA and antigen by 60 hpe. The mRNA expression of IFNs revealed antagonism of type I and III IFNs and a significant increase in the chemokine, CXCL10, in bat lung and spleen following infection. In ferrets, there was an increase in the transcription of IFN in the spleen following infection. Liquid chromatography tandem mass spectrometry (LC-MS/MS) on lung tissue from bats and ferrets was performed at 0 and 60 hpe to obtain a global overview of viral and host protein expression. Gene Ontology (GO) enrichment analysis of immune pathways revealed that six pathways, including a number involved in cell mediated immunity were more likely to be upregulated in bat lung compared to ferrets. GO analysis also revealed enrichment of the type I IFN signaling pathway in bats and ferrets. This study contributes important comparative data on differences in the dissemination of HeV and the first to provide comparative data on the activation of immune pathways in bats and ferrets in vivo following infection.

Bats are natural reservoirs for a number of viruses, including HeV that cause severe disease in humans and other susceptible hosts. We examined acute HeV infection in pteropid bats, compared to ferrets, a species that develops fulminating disease following exposure to HeV, similar to humans. Analysis of HeV replication and transcription of innate immune genes was performed at 12, 36 and 60 hpe and global proteomics was performed on tissues at 60 hpe to obtain insight into the mechanisms responsible for innocuous (bats) compared to fatal (ferrets) HeV infection. We confirmed that both animal species had become infected on the basis of detection of viral RNA in bat lung (60 hpe) and ferret lung, lymph node, spleen, heart and intestine (36 and/or 60 hpe). Analysis of the transcription of IFNs and CXCL10, combined with global proteomics analysis revealed differences in the activation of the immune response between bats and ferrets, consistent with the difference in the control of viral replication and the development of pathology associated with disease between the two species. This study represents the first in vivo comparison between bats and a susceptible host and contributes important information on the kinetics and control of HeV in these two model species.

Metabolic host response and therapeutic approaches to influenza infection

Based on available metabolomic studies, influenza infection affects a variety of cellular metabolic pathways to ensure an optimal environment for its replication and production of viral particles. Following infection, glucose uptake and aerobic glycolysis increase in infected cells continually, which results in higher glucose consumption. The pentose phosphate shunt, as another glucose-consuming pathway, is enhanced by influenza infection to help produce more nucleotides, especially ATP. Regarding lipid species, following infection, levels of triglycerides, phospholipids, and several lipid derivatives undergo perturbations, some of which are associated with inflammatory responses. Also, mitochondrial fatty acid β-oxidation decreases significantly simultaneously with an increase in biosynthesis of fatty acids and membrane lipids. Moreover, essential amino acids are demonstrated to decline in infected tissues due to the production of large amounts of viral and cellular proteins. Immune responses against influenza infection, on the other hand, could significantly affect metabolic pathways. Mainly, interferon (IFN) production following viral infection affects cell function via alteration in amino acid synthesis, membrane composition, and lipid metabolism. Understanding metabolic alterations required for influenza virus replication has revealed novel therapeutic methods based on targeted inhibition of these cellular metabolic pathways.

Nanomaterial Effects on Viral Infection

The potential for environmental and occupational exposures of populations to nanomaterials (NMs) has fostered concerns of associated adverse health effects, with a particular emphasis on pulmonary injury and disease. Many studies have revealed that several types of NMs can evoke a variety of biological responses, such as pulmonary inflammation and oxidative stress, which contribute to allergy, fibrosis, and granuloma formation. Less attention has been paid to health effects that may result from exposure to NMs and additional stressors such as pathogens, with a particular focus on susceptibility to viral infection. This chapter will summarize the current body of literature related to NMs and viral exposures with a primary focus on immune modulation. A summary of the studies performed and major findings to date will be discussed, highlighting proposed molecular mechanisms behind NM-driven host susceptibility, challenges, limitations, and future research needs. Specific mechanisms discussed include direct interaction between NMs and biological molecules, activation of pattern recognition receptors (PRRs) and related signaling pathways, production of oxidative stress and mitochondrial dysfunction, inflammasome activation, and modulation of lipid signaling networks.

Metabolomic Analysis of Influenza A Virus A/WSN/1933 (H1N1) Infected A549 Cells during First Cycle of Viral Replication

Influenza A virus (IAV) has developed strategies to utilize host metabolites which, after identification and isolation, can be used to discover the value of immunometabolism. During this study, to mimic the metabolic processes of influenza virus infection in human cells, we infect A549 cells with H1N1 (WSN) influenza virus and explore the metabolites with altered levels during the first cycle of influenza virus infection using ultra-high-pressure liquid chromatography-quadrupole time-of-flight mass spectrometer (UHPLC-Q-TOF MS) technology. We annotate the metabolites using MetaboAnalyst and the Kyoto Encyclopedia of Genes and Genomes pathway analyses, which reveal that IAV regulates the abundance of the metabolic products of host cells during early infection to provide the energy and metabolites required to efficiently complete its own life cycle. These metabolites are correlated with the tricarboxylic acid (TCA) cycle and mainly are involved in purine, lipid, and glutathione metabolisms. Concurrently, the metabolites interact with signal receptors in A549 cells to participate in cellular energy metabolism signaling pathways. Metabonomic analyses have revealed that, in the first cycle, the virus not only hijacks cell metabolism for its own replication, but also affects innate immunity, indicating a need for further study of the complex relationship between IAV and host cells.

The Role of EGFR in Influenza Pathogenicity: Multiple Network-Based Approaches to Identify a Key Regulator of Non-lethal Infections

Despite high sequence similarity between pandemic and seasonal influenza viruses, there is extreme variation in host pathogenicity from one viral strain to the next. Identifying the underlying mechanisms of variability in pathogenicity is a critical task for understanding influenza virus infection and effective management of highly pathogenic influenza virus disease. We applied a network-based modeling approach to identify critical functions related to influenza virus pathogenicity using large transcriptomic and proteomic datasets from mice infected with six influenza virus strains or mutants. Our analysis revealed two pathogenicity-related gene expression clusters; these results were corroborated by matching proteomics data. We also identified parallel downstream processes that were altered during influenza pathogenesis. We found that network bottlenecks (nodes that bridge different network regions) were highly enriched in pathogenicity-related genes, while network hubs (highly connected network nodes) were significantly depleted in these genes. We confirmed that this trend persisted in a distinct virus: Severe Acute Respiratory Syndrome Coronavirus (SARS). The role of epidermal growth factor receptor (EGFR) in influenza pathogenesis, one of the bottleneck regulators with corroborating signals across transcript and protein expression data, was tested and validated in additional mouse infection experiments. We demonstrate that EGFR is important during influenza infection, but the role it plays changes for lethal versus non-lethal infections. Our results show that by using association networks, bottleneck genes that lack hub characteristics can be used to predict a gene's involvement in influenza virus pathogenicity. We also demonstrate the utility of employing multiple network approaches for analyzing host response data from viral infections.

Metabolite, Protein, and Lipid Extraction (MPLEx): A Method that Simultaneously Inactivates Middle East Respiratory Syndrome Coronavirus and Allows Analysis of Multiple Host Cell Components Following Infection

Mass spectrometry (MS)-based, integrated proteomics, metabolomics, and lipidomics (collectively, multi-omics) studies provide a very detailed snapshot of virus-induced changes to the host following infection and can lead to the identification of novel prophylactic and therapeutic targets for preventing or lessening disease severity. Multi-omics studies with Middle East respiratory syndrome coronavirus (MERS-CoV) are challenging as the requirements of biosafety level 3 containment limit the numbers of samples that can be safely managed. To address these issues, the multi-omics sample preparation technique MPLEx (metabolite, protein, and lipid extraction) was developed to partition a single sample into three distinct parts (metabolites, proteins, and lipids) for multi-omics analysis, while simultaneously inactivating MERS-CoV by solubilizing and disrupting the viral envelope and denaturing viral proteins. Here we describe the MPLEx protocol, highlight the step of inactivation, and describe the details of downstream processing, instrumental analysis of the three separate analytes, and their subsequent informatics pipelines.

Linking Cell Dynamics With Gene Coexpression Networks to Characterize Key Events in Chronic Virus Infections

The host immune response against infection requires the coordinated action of many diverse cell subsets that dynamically adapt to a pathogen threat. Due to the complexity of such a response, most immunological studies have focused on a few genes, proteins, or cell types. With the development of “omic”-technologies and computational analysis methods, attempts to analyze and understand complex system dynamics are now feasible. However, the decomposition of transcriptomic data sets generated from complete organs remains a major challenge. Here, we combined Weighted Gene Coexpression Network Analysis (WGCNA) and Digital Cell Quantifier (DCQ) to analyze time-resolved mouse splenic transcriptomes in acute and chronic Lymphocytic Choriomeningitis Virus (LCMV) infections. This enabled us to generate hypotheses about complex immune functioning after a virus-induced perturbation. This strategy was validated by successfully predicting several known immune phenomena, such as effector cytotoxic T lymphocyte (CTL) expansion and exhaustion. Furthermore, we predicted and subsequently verified experimentally macrophage-CD8 T cell cooperativity and the participation of virus-specific CD8⁺ T cells with an early effector transcriptome profile in the host adaptation to chronic infection. Thus, the linking of gene expression changes with immune cell kinetics provides novel insights into the complex immune processes within infected tissues.

Inflammatory responses relate to distinct bronchoalveolar lavage lipidome in community-acquired pneumonia patients: a pilot study

Background

Community-acquired pneumonia (CAP) is a leading cause of morbidity and mortality worldwide. Antibiotics are losing their effectiveness due to the emerging infectious diseases, the scarcity of novel antibiotics, and the contributions of antibiotic misuse and overuse to resistance. Characterization of the lipidomic response to pneumonia and exploring the “lipidomic phenotype” can provide new insight into the underlying mechanisms of pathogenesis and potential avenues for diagnostic and therapeutic treatments.

Methods

Lipid profiles of bronchoalveolar lavage fluid (BALF) samples were generated through untargeted lipidomic profiling analysis using high-performance liquid chromatography with mass spectrometry (HPLC-MS). Principal component analysis (PCA) was applied to identify possible sources of variations among samples. Partitioning clustering analysis (k-means) was employed to evaluate the existence of distinct lipidomic clusters.

Results

PCA showed that BALF lipidomes differed significantly between CAP (n = 52) and controls (n = 68, including 35 healthy volunteers and 33 patients with non-infectious lung diseases); while no clear separation was found between severe CAP and non-severe CAP cases. Lactosylceramides were the most prominently elevated lipid constituent in CAP. Clustering analysis revealed three separate lipid profiles; subjects in each cluster exhibited significant differences in disease severity, incidence of hypoxemia, percentages of phagocytes in BALF, and serum concentrations of albumin and total cholesterol (all p < 0.05). In addition, SM (d34:1) was negatively related to macrophage (adjusted r = − 0.462, p < 0.0001) and PE (18:1p/20:4) was positively correlated with polymorphonuclear neutrophil (PMN) percentages of BALF (adjusted r = 0.541, p < 0.0001). The 30-day mortality did not differ amongst three clusters (p < 0.05).

Conclusions

Our data suggest that specific lower airway lipid composition is related to different intensities of host inflammatory responses, and may contribute to functionally relevant shifts in disease pathogenesis in CAP individuals. These findings argue for the need to tailor therapy based on specific lipid profiles and related inflammatory status.

Trial registration

ClinicalTrials.gov (NCT03093220). Registered on 28 March 2017 (retrospectively registered).

Electronic supplementary material

The online version of this article (10.1186/s12931-019-1028-8) contains supplementary material, which is available to authorized users.

Advances in Analyzing Virus-Induced Alterations of Host Cell Splicing

Alteration of host cell splicing is a common feature of many viral infections which is underappreciated because of the complexity and technical difficulty of studying alternative splicing (AS) regulation. Recent advances in RNA sequencing technologies revealed that up to several hundreds of host genes can show altered mRNA splicing upon viral infection. The observed changes in AS events can be either a direct consequence of viral manipulation of the host splicing machinery or result indirectly from the virus-induced innate immune response or cellular damage. Analysis at a higher resolution with single-cell RNAseq, and at a higher scale with the integration of multiple omics data sets in a systems biology perspective, will be needed to further comprehend this complex facet of virus-host interactions.

Systems vaccinology and big data in the vaccine development chain

Systems vaccinology has proven a fascinating development in the last decade. Where traditionally vaccine development has been dominated by trial and error, systems vaccinology is a tool that provides novel and comprehensive understanding if properly used. Data sets retrieved from systems‐based studies endorse rational design and effective development of safe and efficacious vaccines. In this review we first describe different omics‐techniques that form the pillars of systems vaccinology. In the second part, the application of systems vaccinology in the different stages of vaccine development is described. Overall, this review shows that systems vaccinology has become an important tool anywhere in the vaccine development chain.

Importance of 1918 virus reconstruction to current assessments of pandemic risk

Reconstruction of the 1918 influenza virus has facilitated considerable advancements in our understanding of this extraordinary pandemic virus. However, the benefits of virus reconstruction are not limited to this one strain. Here, we provide an overview of laboratory studies which have evaluated the reconstructed 1918 virus, and highlight key discoveries about determinants of virulence and transmissibility associated with this virus in mammals. We further discuss recent and current pandemic threats from avian and swine reservoirs, and provide specific examples of how reconstruction of the 1918 pandemic virus has improved our ability to contextualize research employing novel and emerging strains. As influenza viruses continue to evolve and pose a threat to human health, studying past pandemic viruses is key to future preparedness efforts.

An Integrated, High-Throughput Strategy for Multiomic Systems Level Analysis

Proteomics, metabolomics, and transcriptomics generate comprehensive data sets, and current biocomputational capabilities allow their efficient integration for systems biology analysis. Published multiomics studies cover methodological advances as well as applications to biological questions. However, few studies have focused on the development of a high-throughput, unified sample preparation approach to complement high-throughput omic analytics. This report details the automation, benchmarking, and application of a strategy for transcriptomic, proteomic, and metabolomic analyses from a common sample. The approach, sample preparation for multi-omics technologies (SPOT), provides equivalent performance to typical individual omic preparation methods but greatly enhances throughput and minimizes the resources required for multiomic experiments. SPOT was applied to a multiomics time course experiment for zinc-treated HL-60 cells. The data reveal Zn effects on NRF2 antioxidant and NFkappaB signaling. High-throughput approaches such as these are critical for the acquisition of temporally resolved, multicondition, large multiomic data sets such as those necessary to assess complex clinical and biological concerns. Ultimately, this type of approach will provide an expanded understanding of challenging scientific questions across many fields.

Moving Forward: Recent Developments for the Ferret Biomedical Research Model

Since the initial report in 1911, the domestic ferret has become an invaluable biomedical research model. While widely recognized for its utility in influenza virus research, ferrets are used for a variety of infectious and noninfectious disease models due to the anatomical, metabolic, and physiological features they share with humans and their susceptibility to many human pathogens. However, there are limitations to the model that must be overcome for maximal utility for the scientific community. Here, we describe important recent advances that will accelerate biomedical research with this animal model.

Integrated Omics: Tools, Advances, and Future Approaches

With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

Fatty Acid Metabolism is Associated With Disease Severity After H7N9 Infection

BACKGROUND

Human infections with the H7N9 virus could lead to lung damage and even multiple organ failure, which is closely associated with a high mortality rate. However, the metabolic basis of such systemic alterations remains unknown.

METHODS

This study included hospitalized patients (n = 4) with laboratory-confirmed H7N9 infection, healthy controls (n = 9), and two disease control groups comprising patients with pneumonia (n = 9) and patients with pneumonia who received steroid treatment (n = 10). One H7N9-infected patient underwent lung biopsy for histopathological analysis and expression analysis of genes associated with lung homeostasis. H7N9-induced systemic alterations were investigated using metabolomic analysis of sera collected from the four patients by using ultra-performance liquid chromatography-mass spectrometry. Chest digital radiography and laboratory tests were also conducted.

FINDINGS

Two of the four patients did not survive the clinical treatments with antiviral medication, steroids, and oxygen therapy. Biopsy revealed disrupted expression of genes associated with lung epithelial integrity. Histopathological analysis demonstrated severe lung inflammation after H7N9 infection. Metabolomic analysis indicated that fatty acid metabolism may be inhibited during H7N9 infection. Serum levels of palmitic acid, erucic acid, and phytal may negatively correlate with the extent of lung inflammation after H7N9 infection. The changes in fatty acid levels may not be due to steroid treatment or pneumonia.

INTERPRETATION

Altered structural and secretory properties of the lung epithelium may be associated with the severity of H7N9-infection-induced lung disease. Moreover, fatty acid metabolism level may predict a fatal outcome after H7N9 virus infection.

seq-ImmuCC: Cell-Centric View of Tissue Transcriptome Measuring Cellular Compositions of Immune Microenvironment From Mouse RNA-Seq Data

The RNA sequencing approach has been broadly used to provide gene-, pathway-, and network-centric analyses for various cell and tissue samples. However, thus far, rich cellular information carried in tissue samples has not been thoroughly characterized from RNA-Seq data. Therefore, it would expand our horizons to better understand the biological processes of the body by incorporating a cell-centric view of tissue transcriptome. Here, a computational model named seq-ImmuCC was developed to infer the relative proportions of 10 major immune cells in mouse tissues from RNA-Seq data. The performance of seq-ImmuCC was evaluated among multiple computational algorithms, transcriptional platforms, and simulated and experimental datasets. The test results showed its stable performance and superb consistency with experimental observations under different conditions. With seq-ImmuCC, we generated the comprehensive landscape of immune cell compositions in 27 normal mouse tissues and extracted the distinct signatures of immune cell proportion among various tissue types. Furthermore, we quantitatively characterized and compared 18 different types of mouse tumor tissues of distinct cell origins with their immune cell compositions, which provided a comprehensive and informative measurement for the immune microenvironment inside tumor tissues. The online server of seq-ImmuCC are freely available at http://wap-lab.org:3200/immune/.