Cigarette smoke augments CSF3 expression in neutrophils to compromise alveolar-capillary barrier function during influenza infection.

BACKGROUND: Cigarette smokers are at increased risk of acquiring influenza, developing severe disease and requiring hospitalisation/intensive care unit admission following infection. However, immune mechanisms underlying this predisposition are incompletely understood, and therapeutic strategies for influenza are limited. METHODS: We used a mouse model of concurrent cigarette smoke exposure and H1N1 influenza infection, colony-stimulating factor (CSF)3 supplementation/receptor (CSF3R) blockade and single-cell RNA sequencing (scRNAseq) to investigate this relationship. RESULTS: Cigarette smoke exposure exacerbated features of viral pneumonia such as oedema, hypoxaemia and pulmonary neutrophilia. Smoke-exposed infected mice demonstrated an increase in viral (v)RNA, but not replication-competent viral particles, relative to infection-only controls. Interstitial rather than airspace neutrophilia positively predicted morbidity in smoke-exposed infected mice. Screening of pulmonary cytokines using a novel dysregulation score identified an exacerbated expression of CSF3 and interleukin-6 in the context of smoke exposure and influenza. Recombinant (r)CSF3 supplementation during influenza aggravated morbidity, hypothermia and oedema, while anti-CSF3R treatment of smoke-exposed infected mice improved alveolar-capillary barrier function. scRNAseq delineated a shift in the distribution of Csf3 + cells towards neutrophils in the context of cigarette smoke and influenza. However, although smoke-exposed lungs were enriched for infected, highly activated neutrophils, gene signatures of these cells largely reflected an exacerbated form of typical influenza with select unique regulatory features. CONCLUSION: This work provides novel insight into the mechanisms by which cigarette smoke exacerbates influenza infection, unveiling potential therapeutic targets (e.g. excess vRNA accumulation, oedematous CSF3R signalling) for use in this context, and potential limitations for clinical rCSF3 therapy during viral infectious disease.

Identification of Drug Candidates to Suppress Cigarette Smoke-induced Inflammation via Connectivity Map Analyses.

Cigarette smoking is the main risk factor for chronic obstructive pulmonary disease, and to date, existing pharmacologic interventions have been ineffective at controlling inflammatory processes associated with the disease. To address this issue, we used the Connectivity Map (cMap) database to identify drug candidates with the potential to attenuate cigarette smoke-induced inflammation. We queried cMap using three independent in-house cohorts of healthy nonsmokers and smokers. Potential drug candidates were validated against four publicly available human datasets, as well as six independent datasets from cigarette smoke-exposed mice. Overall, these analyses yielded two potential drug candidates: kaempferol and bethanechol. Subsequently, the efficacy of each drug was validated in vivo in a model of cigarette smoke-induced inflammation. BALB/c mice were exposed to room air or cigarette smoke and treated with each of the two candidate drugs either prophylactically or therapeutically. We found that kaempferol, but not bethanechol, was able to reduce cigarette smoke-induced neutrophilia, both when administered prophylactically and when administered therapeutically. Mechanistically, kaempferol decreased expression of IL-1α and CXCL5 concentrations in the lung. Our data suggest that cMap analyses may serve as a useful tool to identify novel drug candidates against cigarette smoke-induced inflammation.

Streptococcus pneumoniae Colonization Is Required To Alter the Nasal Microbiota in Cigarette Smoke-Exposed Mice.

Smokers have nasal microbiota dysbiosis, with an increased frequency of colonizing bacterial pathogens. It is possible that cigarette smoke increases pathogen acquisition by perturbing the microbiota and decreasing colonization resistance. However, it is difficult to disentangle microbiota dysbiosis due to cigarette smoke exposure from microbiota changes caused by increased pathogen acquisition in human smokers. Using an experimental mouse model, we investigated the impact of cigarette smoke on the nasal microbiota in the absence and presence of nasal pneumococcal colonization. We observed that cigarette smoke exposure alone did not alter the nasal microbiota composition. The microbiota composition was also unchanged at 12 h following low-dose nasal pneumococcal inoculation, suggesting that the ability of the microbiota to resist initial nasal pneumococcal acquisition was not impaired in smoke-exposed mice. However, nasal microbiota dysbiosis occurred as a consequence of established high-dose nasal pneumococcal colonization at day 3 in smoke-exposed mice. Similar to clinical reports on human smokers, an enrichment of potentially pathogenic bacterial genera such as Fusobacterium, Gemella, and Neisseria was observed. Our findings suggest that cigarette smoke exposure predisposes to pneumococcal colonization independent of changes to the nasal microbiota and that microbiota dysbiosis observed in smokers may occur as a consequence of established pathogen colonization.

Cigarette Smoke Attenuates the Nasal Host Response to Streptococcus pneumoniae and Predisposes to Invasive Pneumococcal Disease in Mice.

Streptococcus pneumoniae is a leading cause of invasive bacterial infections, with nasal colonization an important first step in disease. While cigarette smoking is a strong risk factor for invasive pneumococcal disease, the underlying mechanisms remain unknown. This is partly due to a lack of clinically relevant animal models investigating nasal pneumococcal colonization in the context of cigarette smoke exposure. We present a model of nasal pneumococcal colonization in cigarette smoke-exposed mice and document, for the first time, that cigarette smoke predisposes to invasive pneumococcal infection and mortality in an animal model. Cigarette smoke increased the risk of bacteremia and meningitis without prior lung infection. Mechanistically, deficiency in interleukin 1α (IL-1α) or platelet-activating factor receptor (PAFR), an important host receptor thought to bind and facilitate pneumococcal invasiveness, did not rescue cigarette smoke-exposed mice from invasive pneumococcal disease. Importantly, we observed cigarette smoke to attenuate nasal inflammatory mediator expression, particularly that of neutrophil-recruiting chemokines, normally elicited by pneumococcal colonization. Smoking cessation during nasal pneumococcal colonization rescued nasal neutrophil recruitment and prevented invasive disease in mice. We propose that cigarette smoke predisposes to invasive pneumococcal disease by suppressing inflammatory processes of the upper respiratory tract. Given that smoking prevalence remains high worldwide, these findings are relevant to the continued efforts to reduce the invasive pneumococcal disease burden.

Unraveling networks of co-regulated genes on the sole basis of genome sequences.

With the growing number of available microbial genome sequences, regulatory signals can now be revealed as conserved motifs in promoters of orthologous genes (phylogenetic footprints). A next challenge is to unravel genome-scale regulatory networks. Using as sole input genome sequences, we predicted cis-regulatory elements for each gene of the yeast Saccharomyces cerevisiae by discovering over-represented motifs in the promoters of their orthologs in 19 Saccharomycetes species. We then linked all genes displaying similar motifs in their promoter regions and inferred a co-regulation network including 56,919 links between 3171 genes. Comparison with annotated regulons highlights the high predictive value of the method: a majority of the top-scoring predictions correspond to already known co-regulations. We also show that this inferred network is as accurate as a co-expression network built from hundreds of transcriptome microarray experiments. Furthermore, we experimentally validated 14 among 16 new functional links between orphan genes and known regulons. This approach can be readily applied to unravel gene regulatory networks from hundreds of microbial genomes for which no other information is available except the sequence. Long-term benefits can easily be perceived when considering the exponential increase of new genome sequences.

P2Y2 receptor regulates VCAM-1 membrane and soluble forms and eosinophil accumulation during lung inflammation.

ATP has been defined as a key mediator of asthma. In this study, we evaluated lung inflammation in mice deficient for the P2Y(2) purinergic receptor. We observed that eosinophil accumulation, a distinctive feature of lung allergic inflammation, was defective in OVA-treated P2Y(2)-deficient mice compared with OVA-treated wild type animals. Interestingly, the upregulation of VCAM-1 was lower on lung endothelial cells of OVA-treated P2Y(2)(-/-) mice compared with OVA-treated wild type animals. Adhesion assays demonstrated that the action of UTP on leukocyte adhesion through the regulation of endothelial VCAM-1 was abolished in P2Y(2)-deficient lung endothelial cells. Additionally, the level of soluble VCAM-1, reported as an inducer of eosinophil chemotaxis, was strongly reduced in the bronchoalveolar lavage fluid (BALF) of P2Y(2)-deficient mice. In contrast, we observed comparable infiltration of macrophages and neutrophils in the BALF of LPS-aerosolized P2Y(2)(+/+) and P2Y(2)(-/-) mice. This difference could be related to the much lower level of ATP in the BALF of LPS-treated mice compared with OVA-treated mice. Our data define P2Y(2) as a regulator of membrane and soluble forms of VCAM-1 and eosinophil accumulation during lung inflammation.

Harnessing the Potential of Enzymes as Inhaled Therapeutics in Respiratory Tract Diseases: A Review of the Literature.

Respiratory tract diseases (RTDs) are a global cause of mortality and affect patient well-being and quality of life. Specifically, there is a high unmet need concerning respiratory tract infections (RTIs) due to limitations of vaccines and increased antibiotic resistance. Enzyme therapeutics, and in particular plant-based enzymes, represent an underutilised resource in drug development warranting further attention. This literature review aims to summarise the current state of enzyme therapeutics in medical applications, with a focus on their potential to improve outcomes in RTDs, including RTIs. We used a narrative review approach, searching PubMed and clinicaltrials.gov with search terms including: enzyme therapeutics, enzyme therapy, inhaled therapeutics, botanical enzyme therapeutics, plant enzymes, and herbal extracts. Here, we discuss the advantages and challenges of enzyme therapeutics in the setting of RTDs and identify and describe several enzyme therapeutics currently used in the respiratory field. In addition, the review includes recent developments concerning enzyme therapies and plant enzymes in (pre-)clinical stages. The global coronavirus disease 2019 (COVID-19) pandemic has sparked development of several promising new enzyme therapeutics for use in the respiratory setting, and therefore, it is timely to provide a summary of recent developments, particularly as these therapeutics may also prove beneficial in other RTDs.

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways.

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources.

Disruption of Physiological Rhythms Persist Following Cessation of Cigarette Smoke Exposure in Mice.

BACKGROUND: Physiological rhythms in mammals are essential for maintaining health, whereas disruptions may cause or exacerbate disease pathogenesis. As such, our objective was to characterize how cigarette smoke exposure affects physiological rhythms of otherwise healthy mice using telemetry and cosinor analysis. METHODS: Female BALB/c mice were implanted with telemetry devices to measure body temperature, heart rate, systolic blood pressure (SBP), and activity. Following baseline measurements, mice were exposed to cigarette smoke for approximately 50 min twice daily during weekdays over 24 weeks. Physiological parameters were recorded after 1, 4, 8, and 24 weeks of exposure or after 4 weeks cessation following 4 weeks of cigarette smoke exposure. RESULTS: Acute cigarette smoke exposure resulted in anapyrexia, and bradycardia, with divergent effects on SBP. Long term, cigarette smoke exposure disrupted physiological rhythms after just 1 week, which persisted across 24 weeks of exposure (as shown by mixed effects on mesor, amplitude, acrophase, and goodness-of-fit using cosinor analysis). Four weeks of cessation was insufficient to allow full recovery of rhythms. CONCLUSION: Our characterization of the pathophysiology of cigarette smoke exposure on physiological rhythms of mice suggests that rhythm disruption may precede and contribute to disease pathogenesis. These findings provide a clear rationale and guide for the future use of chronotherapeutics.

Total particulate matter concentration skews cigarette smoke's gene expression profile.

Exposure of small animals to cigarette smoke is widely used as a model to study the pathogenesis of chronic obstructive pulmonary disease. However, protocols and exposure systems utilised vary substantially and it is unclear how these different systems compare. We analysed the gene expression profile of six publically available murine datasets from different cigarette smoke-exposure systems and related the gene signatures to three clinical cohorts. 234 genes significantly regulated by cigarette smoke in at least one model were used to construct a 55-gene network containing 17 clusters. Increasing numbers of differentially regulated clusters were associated with higher total particulate matter concentrations in the different datasets. Low total particulate matter-induced genes mainly related to xenobiotic/detoxification responses, while higher total particulate matter activated immune/inflammatory processes in addition to xenobiotic/detoxification responses. To translate these observations to the clinic, we analysed the regulation of the revealed network in three human cohorts. Similar to mice, we observed marked differences in the number of regulated clusters between the cohorts. These differences were not determined by pack-year. Although none of the experimental models exhibited a complete alignment with any of the human cohorts, some exposure systems showed higher resemblance. Thus, depending on the cohort, clinically observed changes in gene expression may be mirrored more closely by specific cigarette smoke exposure systems. This study emphasises the need for careful validation of animal models.

Protective role of P2Y2 receptor against lung infection induced by pneumonia virus of mice.

ATP released in the early inflammatory processes acts as a danger signal by binding to purinergic receptors expressed on immune cells. A major contribution of the P2Y(2) receptor of ATP/UTP to dendritic cell function and Th2 lymphocyte recruitment during asthmatic airway inflammation was previously reported. We investigated here the involvement of P2Y(2) receptor in lung inflammation initiated by pneumonia virus of mice infection. We demonstrated that P2Y(2) (-/-) mice display a severe increase in morbidity and mortality rate in response to the virus. Lower survival of P2Y(2) (-/-) mice was not significantly correlated with excessive inflammation despite the higher level of neutrophil recruiters in their bronchoalveolar fluids. Interestingly, we observed reduced ATP level and lower numbers of dendritic cells, CD4(+) T cells and CD8(+) T cells in P2Y(2) (-/-) compared to P2Y(2) (+/+) infected lungs. Lower level of IL-12 and higher level of IL-6 in bronchoalveolar fluid support an inhibition of Th1 response in P2Y(2) (-/-) infected mice. Quantification of DC recruiter expression revealed comparable IP-10 and MIP-3α levels but a reduced BRAK level in P2Y(2) (-/-) compared to P2Y(2) (+/+) bronchoalveolar fluids. The increased morbidity and mortality of P2Y(2) (-/-) mice could be the consequence of a lower viral clearance leading to a more persistent viral load correlated with the observed higher viral titer. The decreased viral clearance could result from the defective Th1 response to PVM with a lack of DC and T cell infiltration. In conclusion, P2Y(2) receptor, previously described as a target in cystic fibrosis therapy and as a mediator of Th2 response in asthma, may also regulate Th1 response protecting mice against lung viral infection.

Network Analysis Tools: from biological networks to clusters and pathways.

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.