Impact of influenza virus infection on lung microbiome in adults with severe pneumonia

Deciphering respiratory viral infections by harnessing organ-on-chip technology to explore the gut-lung axis

The lung microbiome has recently gained attention for potentially affecting respiratory viral infections, including influenza A virus, respiratory syncytial virus (RSV) and SARS-CoV-2. We will discuss the complexities of the lung microenvironment in the context of viral infections and the use of organ-on-chip (OoC) models in replicating the respiratory tract milieu to aid in understanding the role of temporary microbial colonization. Leveraging the innovative capabilities of OoC, particularly through integrating gut and lung models, opens new avenues to understand the mechanisms linking inter-organ crosstalk and respiratory infections. We will discuss technical aspects of OoC lung models, ranging from the selection of cell substrates for extracellular matrix mimicry, mechanical strain, breathing mechanisms and air-liquid interface to the integration of immune cells and use of microscopy tools for algorithm-based image analysis and systems biology to study viral infection in vitro. OoC offers exciting new options to study viral infections across host species and to investigate human cellular physiology at a personalized level. This review bridges the gap between complex biological phenomena and the technical prowess of OoC models, providing a comprehensive roadmap for researchers in the field.

Characterization of serum metabolome and respiratory microbiota in children with influenza A virus infection

The risk of children being infected with Influenza A virus (IAV) is high, and if not treated promptly, it can lead to serious illness. Compared with control group, IAV infection decreased the contents of platelet, white blood cell, lymphocyte, eosinophil, basophil, CD3+ T cells, CD4+ T cells, CD8+ T cells, and B cells, while increasing the number of red blood cell. Additionally, IAV infection increased serum concentrations of total protein, albumin and lipase, while decreasing the contents of calcium, triglyceride, total bilirubin, direct bilirubin, indirect bilirubin and gamma-glutamyltransferase. However, the interactions between the respiratory microbiome and metabolites and their impact on IAV in children remains unclear. Ultra performance liquid chromatography quadrupole time of flight mass spectrometry (UPLC-QTOF/MS) and 16S rRNA gene sequencing were employed to analysis the respiratory microbiome and serum metabolic characteristics of 85 patients with IAV infection and age-matched 55 controls with respiratory disease who tested negative for 13 types of respiratory pathogens. The serum metabolic profile of IAV patients was significantly changed, and the purine metabolism was destroyed. Purine metabolism was also enriched in H3N2 patients compared to H1N1, with increased xanthine, deoxyguanosine, and inosine. The respiratory microbiome structure in children with IAV, including H1N1 and H3N2, was significantly different from that of the control, with significantly increased Chao index. The Mantel test revealed the correlation and consistency in the trends of Haemophilus, Ureaplasma and Inosine. This study revealed the characteristics of the respiratory microbiome and serum metabolites in pediatric patients with IAV, providing a new direction for exploring the pathogenesis of IAV in children.

Metagenomic next generation sequencing of bronchoalveolar lavage fluids for the identification of pathogens in patients with pulmonary infection: A retrospective study

Due to the limitations of traditional laboratory methods (TMs), identification of causative pathogens of numerous pulmonary infections (PIs) remains difficult. This study evaluated the value of metagenomic next generation sequencing (mNGS) in the identification of various respiratory pathogens. A total of 207 patients with TMs and mNGS data were collected for this retrospective study. TMs included sputum culture, blood, and bronchoalveolar lavage fluid (BALF) analysis, or polymerase chain reaction analysis of throat swabs. Otherwise, BALF was collected and analyzed using mNGS. For bacterial pathogens, sensitivities of mNGS as compared to TMs were 76.74 % and 58.14 % (P=0.012). For fungal pathogens, the detection rate of mNGS sensitivity was higher as compared to that of TMs (93.68 % vs 22.11 %; P<0.001). The positive predictive value and negative predictive value were also greater for mNGS. Use of mNGS for BALF analysis offers good specificity and thus facilitates to the clinical diagnosis of PIs.

Highly diverse sputum microbiota correlates with the disease severity in patients with community-acquired pneumonia: a longitudinal cohort study

BACKGROUND

Community-acquired pneumonia (CAP) is a common and serious condition that can be caused by a variety of pathogens. However, much remains unknown about how these pathogens interact with the lower respiratory commensals, and whether any correlation exists between the dysbiosis of the lower respiratory microbiota and disease severity and prognosis.

METHODS

We conducted a retrospective cohort study to investigate the composition and dynamics of sputum microbiota in patients diagnosed with CAP. In total, 917 sputum specimens were collected consecutively from 350 CAP inpatients enrolled in six hospitals following admission. The V3-V4 region of the 16 S rRNA gene was then sequenced.

RESULTS

The sputum microbiota in 71% of the samples were predominately composed of respiratory commensals. Conversely, 15% of the samples demonstrated dominance by five opportunistic pathogens. Additionally, 5% of the samples exhibited sterility, resembling the composition of negative controls. Compared to non-severe CAP patients, severe cases exhibited a more disrupted sputum microbiota, characterized by the highly dominant presence of potential pathogens, greater deviation from a healthy state, more significant alterations during hospitalization, and sparser bacterial interactions. The sputum microbiota on admission demonstrated a moderate prediction of disease severity (AUC = 0.74). Furthermore, different pathogenic infections were associated with specific microbiota alterations. Acinetobacter and Pseudomonas were more abundant in influenza A infections, with Acinetobacter was also enriched in Klebsiella pneumoniae infections.

CONCLUSION

Collectively, our study demonstrated that pneumonia may not consistently correlate with severe dysbiosis of the respiratory microbiota. Instead, the degree of microbiota dysbiosis was correlated with disease severity in CAP patients.

Comparing sputum microbiota characteristics between severe and critically ill influenza patients

Background

Currently, limited attention has been directed toward utilizing clinical cohorts as a starting point to elucidate alterations in the lower respiratory tract (LRT) microbiota following influenza A virus (IAV) infection.

Objectives

Our objective was to undertake a comparative analysis of the diversity and composition of sputum microbiota in individuals afflicted by severe and critically ill influenza patients.

Methods

Sputum specimens were procured from patients diagnosed with IAV infection for the purpose of profiling the microbiota using 16S-rDNA sequencing. To ascertain taxonomic differences between the severe and critically ill influenza cohorts, we leveraged Linear Discriminant Analysis Effect Size (LEfSe). Additionally, Spearman correlation analysis was employed to illuminate associations between sputum microbiota and influenza Ct values alongside laboratory indicators.

Results

Our study encompassed a total cohort of 64 patients, comprising 48 within the severe group and 16 within the critically ill group. Intriguingly, Bacteroidetes exhibited significant depletion in the critically ill cohort (p=0.031). The sputum microbiomes of the severe influenza group were hallmarked by an overrepresentation of Neisseria, Porphyromonas, Actinobacillus, Alloprevotella, TM7x, and Clostridia_UCG-014, yielding ROC-plot AUC values of 0.71, 0.68, 0.60, 0.70, 0.70, and 0.68, respectively. Notably, Alloprevotella exhibited an inverse correlation with influenza Ct values. Moreover, C-reactive protein (CRP) manifested a positive correlation with Haemophilus and Porphyromonas.

Conclusion

The outcomes of this investigation lay the groundwork for future studies delving into the connection between the LRT microbiome and respiratory disorders. Further exploration is warranted to elucidate the intricate mechanisms underlying the interaction between IAV and Alloprevotella, particularly in disease progression.

Association between gut microbiota and influenza: a bidirectional two-sample mendelian randomization study

BACKGROUND

Previous observational studies have indicated a correlation between the gut microbiota and influenza; however, the exact nature of the bidirectional causal connection remains uncertain.

METHOD

A two-way, two-sample Mendelian randomization (MR) study was conducted to evaluate the possible causal connection between the gut microbiota and the two outcomes of influenza (pneumonia without influenza and influenza pneumonia). The statistical analysis of gut microbiota is derived from the information of the most extensive meta-analysis (GWAS) conducted by the MiBioGen Alliance, encompassing a sample size of 18,340.The summary statistical data for influenza (not pneumonia, n = 291,090) and influenza pneumonia (n = 342,499) are from GWAS data published by FinnGen consortium R8.Estimate and summarize Single-nucleotide polymorphisms (SNPs) using Inverse variance weighted (IVW), MR Egger, and Weighted median (WM) in bidirectional MR analysis. To assess the heterogeneity, horizontal pleiotropy, and stability of SNPs, we employed Cochran's Q test, MR Egger intercept test, and sensitivity analysis.

RESULT

The IVW analysis indicated that there was a significant association between influenza infection and five bacterial taxa. Additionally, the abundance changes of seven gut microbiota were found to be causally related to influenza infection. In addition, seven bacterial taxa showed a significant association with the occurrence of influenza pneumonia. The findings from the WM analysis largely support the outcomes of IVW, however, the results of MR egger analysis do not align with IVW. Furthermore, there is no proof to substantiate the cause-and-effect relationship between influenza pneumonia and the composition of gut microbiota.

CONCLUSION

This analysis demonstrates a possible bidirectional causal connection between the prevalence of particular gut microbiota and the occurrence of influenza infection. The presence of certain gut microbiota may potentially contribute to the development of pneumonia caused by influenza. Additional investigation into the interaction between particular bacterial communities and influenza can enhance efforts in preventing, monitoring, and treating influenza.