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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.
An Innovative Protocol for Metaproteomic Analyses of Microbial Pathogens in Cystic Fibrosis Sputum
(2021)
Hallmarks of cystic fibrosis (CF) are increased viscosity of mucus and impaired mucociliary clearance within the airways due to mutations of the cystic fibrosis conductance regulator gene. This facilitates the colonization of the lung by microbial pathogens and the concomitant establishment of chronic infections leading to tissue damage, reduced lung function, and decreased life expectancy. Although the interplay between key CF pathogens plays a major role during disease progression, the pathophysiology of the microbial community in CF lungs remains poorly understood. Particular challenges in the analysis of the microbial population present in CF sputum is (I) the inhomogeneous, viscous, and slimy consistence of CF sputum, and (II) the high number of human proteins masking comparably low abundant microbial proteins. To address these challenges, we used 21 CF sputum samples to develop a reliable, reproducible and widely applicable protocol for sputum processing, microbial enrichment, cell disruption, protein extraction and subsequent metaproteomic analyses. As a proof of concept, we selected three sputum samples for detailed metaproteome analyses and complemented and validated metaproteome data by 16S sequencing, metabolomic as well as microscopic analyses. Applying our protocol, the number of bacterial proteins/protein groups increased from 199-425 to 392-868 in enriched samples compared to nonenriched controls. These early microbial metaproteome data suggest that the arginine deiminase pathway and multiple proteases and peptidases identified from various bacterial genera could so far be underappreciated in their contribution to the CF pathophysiology. By providing a standardized and effective protocol for sputum processing and microbial enrichment, our study represents an important basis for future studies investigating the physiology of microbial pathogens in CF in vivo – an important prerequisite for the development of novel antimicrobial therapies to combat chronic recurrent airway infection in CF.
Lichens represent self-supporting symbioses, which occur in a wide range of terrestrial habitats and which contribute significantly to mineral cycling and energy flow at a global scale. Lichens usually grow much slower than higher plants. Nevertheless, lichens can contribute substantially to biomass production. This review focuses on the lichen symbiosis in general and especially on the model species Lobaria pulmonaria L. Hoffm., which is a large foliose lichen that occurs worldwide on tree trunks in undisturbed forests with long ecological continuity. In comparison to many other lichens, L. pulmonaria is less tolerant to desiccation and highly sensitive to air pollution. The name-giving mycobiont (belonging to the Ascomycota), provides a protective layer covering a layer of the green-algal photobiont (Dictyochloropsis reticulata) and interspersed cyanobacterial cell clusters (Nostoc spec.). Recently performed metaproteome analyses confirm the partition of functions in lichen partnerships. The ample functional diversity of the mycobiont contrasts the predominant function of the photobiont in production (and secretion) of energy-rich carbohydrates, and the cyanobiont’s contribution by nitrogen fixation. In addition, high throughput and state-of-the-art metagenomics and community fingerprinting, metatranscriptomics, and MS-based metaproteomics identify the bacterial community present on L. pulmonaria as a surprisingly abundant and structurally integrated element of the lichen symbiosis. Comparative metaproteome analyses of lichens from different sampling sites suggest the presence of a relatively stable core microbiome and a sampling site-specific portion of the microbiome. Moreover, these studies indicate how the microbiota may contribute to the symbiotic system, to improve its health, growth and fitness.