Impact of Pneumococcal and Viral Pneumonia on the Respiratory and Intestinal Tract Microbiomes of Mice

With 2.56 million deaths worldwide annually, pneumonia is one of the leading causes of death. The most frequent causative pathogens are Streptococcus pneumoniae and influenza A virus. Lately, the interaction between the pathogens, the host, and its microbiome have gained more attention. The microbiome is known to promote the immune response toward pathogens; however, our knowledge on how infections affect the microbiome is still scarce. Here, the impact of colonization and infection with S. pneumoniae and influenza A virus on the structure and function of the respiratory and gastrointestinal microbiomes of mice was investigated. Using a meta-omics approach, we identified specific differences between the bacterial and viral infection. Pneumococcal colonization had minor effects on the taxonomic composition of the respiratory microbiome, while acute infections caused decreased microbial complexity. In contrast, richness was unaffected following H1N1 infection. Within the gastrointestinal microbiome, we found exclusive changes in structure and function, depending on the pathogen. While pneumococcal colonization had no effects on taxonomic composition of the gastrointestinal microbiome, increased abundance of Akkermansiaceae and Spirochaetaceae as well as decreased amounts of Clostridiaceae were exclusively found during invasive S. pneumoniae infection. The presence of Staphylococcaceae was specific for viral pneumonia. Investigation of the intestinal microbiomés functional composition revealed reduced expression of flagellin and rubrerythrin and increased levels of ATPase during pneumococcal infection, while increased amounts of acetyl coenzyme A (acetyl-CoA) acetyltransferase and enoyl-CoA transferase were unique after H1N1 infection. In conclusion, identification of specific taxonomic and functional profiles of the respiratory and gastrointestinal microbiome allowed the discrimination between bacterial and viral pneumonia. IMPORTANCE Pneumonia is one of the leading causes of death worldwide. Here, we compared the impact of bacterial- and viral-induced pneumonia on the respiratory and gastrointestinal microbiome. Using a meta-omics approach, we identified specific profiles that allow discrimination between bacterial and viral causative.

Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection

Influenza A Virus (IAV) infection followed by bacterial pneumonia often leads to hospitalization and death in individuals from high risk groups. Following infection, IAV triggers the process of viral RNA replication which in turn disrupts healthy gut microbial community, while the gut microbiota plays an instrumental role in protecting the host by evolving colonization resistance. Although the underlying mechanisms of IAV infection have been unraveled, the underlying complex mechanisms evolved by gut microbiota in order to induce host immune response following IAV infection remain evasive. In this work, we developed a novel Maximal-Clique based Community Detection algorithm for Weighted undirected Networks (MCCD-WN) and compared its performance with other existing algorithms using three sets of benchmark networks. Moreover, we applied our algorithm to gut microbiome data derived from fecal samples of both healthy and IAV-infected pigs over a sequence of time-points. The results we obtained from the real-life IAV dataset unveil the role of the microbial families Ruminococcaceae, Lachnospiraceae, Spirochaetaceae and Prevotellaceae in the gut microbiome of the IAV-infected cohort. Furthermore, the additional integration of metaproteomic data enabled not only the identification of microbial biomarkers, but also the elucidation of their functional roles in protecting the host following IAV infection. Our network analysis reveals a fast recovery of the infected cohort after the second IAV infection and provides insights into crucial roles of Desulfovibrionaceae and Lactobacillaceae families in combating Influenza A Virus infection. Source code of the community detection algorithm can be downloaded from https://github.com/AniBhar84/MCCD-WN.

Influenza A H1N1 Induced Disturbance of the Respiratory and Fecal Microbiome of German Landrace Pigs - a Multi-Omics Characterization

Seasonal influenza outbreaks represent a large burden for the health care system as well as the economy. While the role of the microbiome has been elucidated in the context of various diseases, the impact of respiratory viral infections on the human microbiome is largely unknown. In this study, swine was used as an animal model to characterize the temporal dynamics of the respiratory and gastrointestinal microbiome in response to an influenza A virus (IAV) infection. A multi-omics approach was applied on fecal samples to identify alterations in microbiome composition and function during IAV infection. We observed significantly altered microbial richness and diversity in the gastrointestinal microbiome after IAV infection. In particular, increased abundances of Prevotellaceae were detected, while Clostridiaceae and Lachnospiraceae decreased. Moreover, our metaproteomics data indicated that the functional composition of the microbiome was heavily affected by the influenza infection. For instance, we identified decreased amounts of flagellin, correlating with reduced abundances of Lachnospiraceae and Clostridiaceae, possibly indicating involvement of a direct immune response toward flagellated Clostridia during IAV infection. Furthermore, enzymes involved in short-chain fatty acid (SCFA) synthesis were identified in higher abundances, while metabolome analyses revealed rather stable concentrations of SCFAs. In addition, 16S rRNA gene sequencing was used to characterize effects on the composition and natural development of the upper respiratory tract microbiome. Our results showed that IAV infection resulted in significant changes in the abundance of Moraxellaceae and Pasteurellaceae in the upper respiratory tract. Surprisingly, temporal development of the respiratory microbiome structure was not affected. IMPORTANCE Here, we used swine as a biomedical model to elucidate the impact of influenza A H1N1 infection on structure and function of the respiratory and gastrointestinal tract microbiome by employing a multi-omics analytical approach. To our knowledge, this is the first study to investigate the temporal development of the porcine microbiome and to provide insights into the functional capacity of the gastrointestinal microbiome during influenza A virus infection.

A Multi-Omics Protocol for Swine Feces to Elucidate Longitudinal Dynamics in Microbiome Structure and Function

Swine are regarded as promising biomedical models, but the dynamics of their gastrointestinal microbiome have been much less investigated than that of humans or mice. The aim of this study was to establish an integrated multi-omics protocol to investigate the fecal microbiome of healthy swine. To this end, a preparation and analysis protocol including integrated sample preparation for meta-omics analyses of deep-frozen feces was developed. Subsequent data integration linked microbiome composition with function, and metabolic activity with protein inventories, i.e., 16S rRNA data and expressed proteins, and identified proteins with corresponding metabolites. 16S rRNA gene amplicon and metaproteomics analyses revealed a fecal microbiome dominated by Prevotellaceae, Lactobacillaceae, Lachnospiraceae, Ruminococcaceae and Clostridiaceae. Similar microbiome compositions in feces and colon, but not ileum samples, were observed, showing that feces can serve as minimal-invasive proxy for porcine colon microbiomes. Longitudinal dynamics in composition, e.g., temporal decreased abundance of Lactobacillaceae and Streptococcaceae during the experiment, were not reflected in microbiome function. Instead, metaproteomics and metabolomics showed a rather stable functional state, as evident from short-chain fatty acids (SCFA) profiles and associated metaproteome functions, pointing towards functional redundancy among microbiome constituents. In conclusion, our pipeline generates congruent data from different omics approaches on the taxonomy and functionality of the intestinal microbiome of swine.