Respiratory infections are the leading causes of mortality and the current pandemic COVID‐19 is one such trauma that imposed catastrophic devastation to the health and economy of the world. Unravelling the correlations and interplay of the human microbiota in the gut–lung axis would offer incredible solutions to the underlying mystery of the disease progression. The study compared the microbiota profiles of six samples namely healthy gut, healthy lung, COVID‐19 infected gut, COVID‐19 infected lungs, Clostridium difficile infected gut and community‐acquired pneumonia infected lungs. The metagenome data sets were processed, normalized, classified and the rarefaction curves were plotted. The microbial biomarkers for COVID‐19 infections were identified as the abundance of Candida and Escherichia in lungs with Ruminococcus in the gut. Candida and Staphylococcus could play a vital role as putative prognostic biomarkers of community‐acquired pneumonia whereas abundance of Faecalibacterium and Clostridium is associated with the C. difficile infections in gut. A machine learning random forest classifier applied to the data sets efficiently classified the biomarkers. The study offers an extensive and incredible understanding of the existence of gut–lung axis during dysbiosis of two anatomically different organs.

Meta‐analysis of the microbial biomarkers in the gut–lung crosstalk in COVID‐19, community‐acquired pneumonia and Clostridium difficile infections

Lung infection is a global health problem associated with high morbidity and mortality and increasing rates of hospitalization. The correlation between pulmonary microecology and infection severity remains unclear. Therefore, the purpose of this study was to investigate the differences in lung microecology and potential biomarkers in patients with mild and severe pulmonary infection.Patients with pulmonary infection or suspected infection were divided into the mild group (140 cases) and the severe group (80 cases) according to pneomonia severity index (PSI) scores. Here, we used metagenomic next-generation sequencing (mNGS) to detect DNA mainly from bronchoalveolar lavage fluid (BALF) collected from patients to analyze changes in the lung microbiome of patients with different disease severity.We used the mNGS to analyze the pulmonary microecological composition in patients with pulmonary infection. The results of alpha diversity and beta diversity analysis showed that the microbial composition between mild and severe groups was similar on the whole. The dominant bacteria were Acinetobacter, Bacillus, Mycobacterium, Staphylococcus, and Prevotella, among others. Linear discriminant analysis effect size (LEfSe) results showed that there were significant differences in virus composition between the mild and severe patients, especially Simplexvirus and Cytomegalovirus, which were prominent in the severe group. The random forest model screened 14 kinds of pulmonary infection-related pathogens including Corynebacterium, Mycobacterium, Streptococcus, Klebsiella, and Acinetobacter. In addition, it was found that Rothia was negatively correlated with Acinetobacter, Mycobacterium, Bacillus, Enterococcus, and Klebsiella in the mild group through co-occurrence network, while no significant correlation was found in the severe group.Here, we describe the composition and diversity of the pulmonary microbiome in patients with pulmonary infection. A significant increase in viral replication was found in the severe group, as well as a significant difference in microbial interactions between patients with mild and severe lung infections, particularly the association between the common pathogenic bacteria and Rothia. This suggests that both pathogen co-viral infection and microbial interactions may influence the course of disease. Of course, more research is needed to further explore the specific mechanisms by which microbial interactions influence disease severity.

Characteristics of the pulmonary microbiota in patients with mild and severe pulmonary infection

The microbiota of the respiratory tract remains a relatively poorly studied subject. At the same time, it is involved in modulating the immune response to infectious agents in the host organism, just like the intestinal microbiota. A relationship between the composition of the respiratory microbiota and the likelihood of development and the severity of COVID-19 may be assumed. In this study, we applied the 16S rRNA metagenomic sequencing to analyze the oropharyngeal swabs from 120 COVID-19 patients collected during the first and the second waves of the COVID-19 epidemic in Russia. Differential abundance analysis with respect to comorbidities suggested association of Neisseria oralis, Neisseria mucosa, unidentified Veillonella spp., Lautropia mirabilis species with more severe lung damage, and Streptococcus salivarius, Capnocytophaga sputigena and Haemophilus parahaemolyticus with a milder course of the disease. We hypothesize that the latter bacteria (or some of them) might be beneficial for the respiratory tract and might be able to alleviate the course of the COVID-19 disease.

Microbial communities of the upper respiratory tract in mild and severe COVID-19 patients: a possible link with the disease course

The only animals other than bats reported to have been infected with SARS-CoV-2-related coronaviruses (SARS2r-CoVs) prior to the COVID-19 pandemic are pangolins. In early 2020 multiple papers reported the identification of two clades of SARS2r-CoVs, GD and GX, infecting pangolins. However the RNA-Seq datasets supporting pangolin genome assembly were widely contaminated, contained synthetic vectors or were heavily enriched or filtered with little but coronavirus sequences left in the datasets. Here we investigate two pangolin fecal samples sequenced by Li et al. (2021) provided in support of GD PCoV infection of pangolins in Guangdong and find the read distribution consistent with PCR amplicon contamination and SARS-CoV-2 contamination, and further identify the presence of synthetic plasmid sequences. We also build upon our previous work to further analyze the dataset GX/P3B by Lam et al. (2020), which is the only non enriched/heavily filtered pangolin tissue dataset sequenced by Lam et al. (2020). We identify synthetic vectors and confirm human genomic origin samples in the dataset. Finally, we find human mitochondrial sequences in all pangolin organ datasets and mouse and tiger mitochondrial sequences in selected pangolin organ datasets sequenced by Liu et al. (2019). We infer that human and mouse genomic origin sequences were probably sourced from contamination prior to sequencing, while tiger origin sequence contamination may have occurred due to index hopping during sequencing. These observations are problematic for attributing pangolins as SARS2r-CoV hosts in the datasets examined. The forensic methods developed and used here can be applied to examine any third party SRA data sets.

Further analysis of metagenomic datasets containing GD and GX pangolin CoVs indicates widespread contamination, undermining pangolin host attribution (preprint)

Background: This study aimed to identify pathogens and factors that predict the outcome of severe COVID-19 by utilizing metagenomic next-generation sequencing (mNGS) technology. 
Methods: We retrospectively analyzed data from 56 severe COVID-19 patients admitted to our hospital between December 2022 and March 2023. We analyzed the pathogen types and strains detected through mNGS and conventional microbiological testing and collected general patient information. 
Results: In this study, 42 pathogens were detected using mNGS and conventional microbiological testing. mNGS had a significantly higher detection rate of 90.48% compared to 71.43% for conventional testing (P=0.026). A total of 196 strains were detected using both methods, with a significantly higher detection rate of 70.92% for mNGS compared to 49.49% for conventional testing (P=0.000). The 56 patients were divided into a survival group (33 cases) and a death group (23 cases) based on clinical outcomes. The survival group had significantly lower age, number of pathogens detected by mNGS, number of pathogens detected by conventional testing, APACHE-II score, SOFA score, high-sensitivity troponin, creatine kinase-MB subtype, and lactate dehydrogenase compared to the death group (P<0.05). Multivariate logistic regression analysis showed that these factors were risk factors for mortality in severe COVID-19 patients (P<0.05). In contrast, ROC curve analysis revealed that these factors had diagnostic values for mortality, with AUC values ranging from 0.657 to 0.963. The combined diagnosis of these indicators had an AUC of 0.924. 
Conclusion: The use of mNGS technology can significantly enhance the detection of pathogens in severe cases of COVID-19 and also has a solid ability to predict clinical outcomes.

pdf/exploring-the-pathogen-diagnosis-and-prognostic-factors-of-27ga33jrze.pdf

Exploring the Pathogen Diagnosis and Prognostic Factors of Severe COVID-19 Using Metagenomic Next-Generation Sequencing: A Retrospective Study

Lung microbiota plays an important role in many diseases including lower respiratory tract infections (LRTI) and pneumonia. This study aimed to explore the effects of community-acquired pneumonia (CAP) on microbial diversity and identify potential biomarkers of respiratory tract in CAP LRTI patients. In the current study, a comprehensive bioinformatics analysis was performed based on metagenomic next generation sequencing technology, followed by alpha and beta diversity, LEfSe, and co-occurrence network analysis, and random forest model construction. Our results showed that CAP dramatically influenced taxon abundance, and the significant differences in microbiota including Proteobacteria, Bacteroidetes, Euryarchaeota, Firmicutes and Spirochaetes were observed at the phylum level. Co-occurrence network selected out novel modules involved in microbial proliferation-associated pathways. A random forest model screened Klebsiella pneumoniae and Bacillus cereus as potential diagnostic biomarkers with high AUC values. The microbial composition was different between CAP LRTI patients and non-CAP LRTI patients. Klebsiella pneumoniae and Bacillus cereus were strongly associated with increased severity of LRTI with a pneumonia history. Our findings provided an insight for a better understanding of community and structure of lung microbiota for future diagnosis and treatment in LRTI patients with a history of pneumonia. Moreover, these microbes were considered as potential biomarkers for predicting the risks for the treatment strategies of LRTI.

Characteristics of the lung microbiota in lower respiratory tract infections with and without history of pneumonia.

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Characteristics of the lung microbiota in lower respiratory tract infections with and without history of pneumonia.