Characterization of Lung and Oral Microbiomes in Lung Cancer Patients Using Culturomics and 16S rRNA Gene Sequencing

The Metabolic Potential of the Human Lung Microbiome

The human lung microbiome remains largely underexplored, despite its potential implications in the pharmacokinetics of inhaled drugs and its involvement in lung diseases. Interactions within these bacterial communities and with the host are complex processes which often involve microbial small molecules. In this study, we employed a computational approach to describe the metabolic potential of the human lung microbiome. By utilizing antiSMASH and BiG-SCAPE software, we identified 1831 biosynthetic gene clusters for the production of specialized metabolites in a carefully compiled genome database of lung-associated bacteria and fungi. It was shown that RiPPs represent the largest class of natural products within the bacteriome, while NRPs constitute the largest class of natural products in the lung mycobiome. All predicted BGCs were further categorized into 767 gene cluster families, and a subsequent network analysis highlighted that these families are widely distributed and contain many uncharacterized members. Moreover, in-depth annotation allowed the assignment of certain gene clusters to putative lung-specific functions within the microbiome, such as osmoadaptation or surfactant synthesis. This study establishes the lung microbiome as a prolific source for secondary metabolites and lays the groundwork for detailed investigation of this unique environment.

Analyzing the characteristics of respiratory microbiota after the placement of an airway stent for malignant central airway obstruction

Malignant central airway stenosis is treated with airway stent placement, but post-placement microbial characteristics remain unclear. We studied microbial features in 60 patients post-stent placement, focusing on changes during granulation tissue proliferation. Samples were collected before stent (N = 29), after stent on day 3 (N = 20), and after granulation tissue formation (AS-GTF, N = 43). Metagenomic sequencing showed significant respiratory tract microbiota changes with granulation tissue. The microbiota composition, dominated by Actinobacteria, Firmicutes, and Proteobacteria, was similar among the groups. At the species level, the AS-GTF group exhibited significant differences, with Peptostreptococcus stomatis and Achromobacter xylosoxidans enriched. Analysis based on tracheoesophageal fistula presence identified Tannerella forsythia and Stenotrophomonas maltophilia as the main differential species, enriched in the fistula subgroup. Viral and fungal detection showed Human gammaherpesvirus 4 and Candida albicans as the main species, respectively. These findings highlight microbiota changes after stent placement, potentially associated with granulation tissue proliferation, informing stent placement therapy and anti-infective treatment optimization.

IMPORTANCE

Malignant central airway stenosis is a life-threatening condition that can be effectively treated with airway stent placement. However, despite its clinical importance, the microbial characteristics of the respiratory tract following stent insertion remain poorly understood. This study addresses this gap by investigating the microbial features in patients with malignant central airway stenosis after stent placement, with a specific focus on microbial changes during granulation tissue proliferation. The findings reveal significant alterations in the diversity and structure of the respiratory tract microbiota following the placement of malignant central airway stents. Notably, certain bacterial species, including Peptostreptococcus stomatis and Achromobacter xylosoxidans, exhibit distinct patterns in the after-stent granulation tissue formation group. Additionally, the presence of tracheoesophageal fistula further influences the microbial composition. These insights provide valuable references for optimizing stent placement therapy and enhancing clinical anti-infective strategies.

Organoids as an in vitro model to study human tumors and bacteria

Organoids faithfully replicate the morphological structure, physiological functions, stable phenotype of the source tissue. Recent research indicates that bacteria can significantly influence the initiation, advancement, and treatment of tumors. This article provides a comprehensive review of the applications of organoid technology in tumor research, the relationship between bacteria and the genesis and development of tumors, and the exploration of the impact of bacteria on tumors and their applications in research.

Microbes for lung cancer detection: feasibility and limitations

As the second most common cancer in the world, the development of lung cancer is closely related to factors such as heredity, environmental exposure, and lung microenvironment, etc. Early screening and diagnosis of lung cancer can be helpful for the treatment of patients. Currently, CT screening and histopathologic biopsy are widely used in the clinical detection of lung cancer, but they have many disadvantages such as false positives and invasive operations. Microbes are another genome of the human body, which has recently been shown to be closely related to chronic inflammatory, metabolic processes in the host. At the same time, they are important players in cancer development, progression, treatment, and prognosis. The use of microbes for cancer therapy has been extensively studied, however, the diagnostic role of microbes is still unclear. This review aims to summarize recent research on using microbes for lung cancer detection and present the current shortcomings of microbes in collection and detection. Finally, it also looks ahead to the clinical benefits that may accrue to patients in the future about screening and early detection.

Salivary metabolomic biomarkers for non-invasive lung cancer detection

Identifying novel biomarkers for early detection of lung cancer is crucial. Non-invasively available saliva is an ideal biofluid for biomarker exploration; however, the rationale underlying biomarker detection from organs distal to the oral cavity in saliva requires clarification. Therefore, we analyzed metabolomic profiles of cancer tissues compared with those of adjacent non-cancerous tissues, as well as plasma and saliva samples collected from patients with lung cancer (n = 109 pairs). Additionally, we analyzed plasma and saliva samples collected from control participants (n = 83 and 71, respectively). Capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry were performed to comprehensively quantify hydrophilic metabolites. Paired tissues were compared, revealing 53 significantly different metabolites. Plasma and saliva showed 44 and 40 significantly different metabolites, respectively, between patients and controls. Of these, 12 metabolites exhibited significant differences in all three comparisons and primarily belonged to the polyamine and amino acid pathways; N1-acetylspermidine exhibited the highest discrimination ability. A combination of 12 salivary metabolites was evaluated using a machine learning method to differentiate patients with lung cancer from controls. Salivary data were randomly split into training and validation datasets. Areas under the receiver operating characteristic curve were 0.744 for cross-validation using training data and 0.792 for validation data. This model exhibited a higher discrimination ability for N1-acetylspermidine than that for other metabolites. The probability of lung cancer calculated using this model was independent of most patient characteristics. These results suggest that consistently different salivary biomarkers in both plasma and lung tissues might facilitate non-invasive lung cancer screening.

Saliva‑microbiome‑derived signatures: expected to become a potential biomarker for pulmonary nodules (MCEPN-1)

BACKGROUND

Oral microbiota imbalance is associated with the progression of various lung diseases, including lung cancer. Pulmonary nodules (PNs) are often considered a critical stage for the early detection of lung cancer; however, the relationship between oral microbiota and PNs remains unknown.

METHODS

We conducted a 'Microbiome with pulmonary nodule series study 1' (MCEPN-1) where we compared PN patients and healthy controls (HCs), aiming to identify differences in oral microbiota characteristics and discover potential microbiota biomarkers for non-invasive, radiation-free PNs diagnosis and warning in the future. We performed 16 S rRNA amplicon sequencing on saliva samples from 173 PN patients and 40 HCs to compare the characteristics and functional changes in oral microbiota between the two groups. The random forest algorithm was used to identify PN salivary microbial markers. Biological functions and potential mechanisms of differential genes in saliva samples were preliminarily explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Groups (COG) analyses.

RESULTS

The diversity of salivary microorganisms was higher in the PN group than in the HC group. Significant differences were noted in community composition and abundance of oral microorganisms between the two groups. Neisseria, Prevotella, Haemophilus and Actinomyces, Porphyromonas, Fusobacterium, 7M7x, Granulicatella and Selenomonas were the main differential genera between the PN and HC groups. Fusobacterium, Porphyromonas, Parvimonas, Peptostreptococcus and Haemophilus constituted the optimal marker sets (area under curve, AUC = 0.80), which can distinguish between patients with PNs and HCs. Further, the salivary microbiota composition was significantly correlated with age, sex, and smoking history (P < 0.001), but not with personal history of cancer (P > 0.05). Bioinformatics analysis of differential genes showed that patients with PN showed significant enrichment in protein/molecular functions related to immune deficiency and energy metabolisms, such as the cytoskeleton protein RodZ, nicotinamide adenine dinucleotide phosphate dehydrogenase (NADPH) dehydrogenase, major facilitator superfamily transporters and AraC family transcription regulators.

CONCLUSIONS

Our study provides the first evidence that the salivary microbiota can serve as potential biomarkers for identifying PN. We observed a significant association between changes in the oral microbiota and PNs, indicating the potential of salivary microbiota as a new non-invasive biomarker for PNs.

TRIAL REGISTRATION

Clinical trial registration number: ChiCTR2200062140; Date of registration: 07/25/2022.

Microbiome and lung cancer: carcinogenic mechanisms, early cancer diagnosis, and promising microbial therapies

Microbiomes in the lung, gut, and oral cavity are correlated with lung cancer initiation and progression. While correlations have been preliminarily established in earlier studies, delving into microbe-mediated carcinogenic mechanisms will extend our understanding from correlation to causation. Building upon the causative relationships between microbiome and lung cancer, a novel concept of microbial biomarkers has emerged, mainly encompassing cancer-specific bacteria and circulating microbiome DNA. They might function as noninvasive liquid biopsy techniques for lung cancer early detection. Furthermore, potential microbial therapies have displayed initial efficacy in lung cancer treatment, providing multiple avenues for therapeutic intervention. Herein, we will discuss the molecular mechanisms and signaling pathways through which microbes influence lung cancer initiation and development. Additionally, we will summarize recent findings on microbial biomarkers as a member of tumor liquid biopsy techniques and provide an overview of the latest advances in various microbe-assisted/mediated therapeutic approaches for lung cancer.

Symbiotic microbial communities in various locations of the lung cancer respiratory tract along with potential host immunological processes affected

Lung cancer has the highest mortality rate among all cancers worldwide. The 5-year overall survival rate for non-small cell lung cancer (NSCLC) is estimated at around 26%, whereas for small cell lung cancer (SCLC), the survival rate is only approximately 7%. This disease places a significant financial and psychological burden on individuals worldwide. The symbiotic microbiota in the human body has been significantly associated with the occurrence, progression, and prognosis of various diseases, such as asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis. Studies have demonstrated that respiratory symbiotic microorganisms and their metabolites play a crucial role in modulating immune function and contributing to the pathophysiology of lung cancer through their interactions with the host. In this review, we provide a comprehensive overview of the microbial characteristics associated with lung cancer, with a focus on the respiratory tract microbiota from different locations, including saliva, sputum, bronchoalveolar lavage fluid (BALF), bronchial brush samples, and tissue. We describe the respiratory tract microbiota's biodiversity characteristics by anatomical region, elucidating distinct pathological features, staging, metastasis, host chromosomal mutations, immune therapies, and the differentiated symbiotic microbiota under the influence of environmental factors. Our exploration investigates the intrinsic mechanisms linking the microbiota and its host. Furthermore, we have also provided a comprehensive review of the immune mechanisms by which microbiota are implicated in the development of lung cancer. Dysbiosis of the respiratory microbiota can promote or inhibit tumor progression through various mechanisms, including DNA damage and genomic instability, activation and regulation of the innate and adaptive immune systems, and stimulation of epithelial cells leading to the upregulation of carcinogenesis-related pathways.

Airway microbiota correlated with pulmonary exacerbation in primary ciliary dyskinesia patients

IMPORTANCE

PCD is a rare disease characterized by productive cough, rhinitis, and recurrent infections of the upper and lower airways. Because the diagnosis of PCD is often delayed, patients receive more antibiotics, experience a heavier financial burden, and have a worse prognosis; thus, it is very important to identify the pathogeny and use the correct antibiotic. In this large single-center study of PCD microbiota, we identified an outline of the bacterial microbes from the respiratory tract; furthermore, we found that the microbiota diversity in pediatric sputum was richer than that in pediatric BALF through sequencing, indicating a heterogeneous community structure. The microbiota diversity and richness were lower during pulmonary exacerbation than during pulmonary stabilization. A significantly higher abundance of Pseudomonas had a moderate distinguishing effect for lung exacerbation, which attracted more attention for the study of Pseudomonas therapy in pediatric patients with PCD.

Oral microbiome alterations in epilepsy and after seizure control

Background

The existing diagnostic methods of epilepsy such as history collection and electroencephalogram have great limitations in practice, so more reliable and less difficult diagnostic methods are needed.

Methods

By characterizing oral microbiota in patients diagnosed with epilepsy (EPs) and patients whose seizures were under control (EPRs), we sought to discover biomarkers for different disease states. 16S rRNA gene sequencing was performed on 480 tongue swabs [157 EPs, 22 EPRs, and 301 healthy controls (HCs)].

Results

Compared with normal individuals, patients with epilepsy exhibit increased alpha diversity in their oral microbiota, and the oral microbial communities of the two groups demonstrate significant beta diversity differences. EPs exhibit a significant increase in the abundance of 26 genera, including Streptococcus, Granulicatella, and Kluyvera, while the abundance of 14 genera, including Peptostreptococcus, Neisseria, and Schaalia, is significantly reduced. The area under the receiver operating characteristic curve (AUC) of oral microbial markers in the training cohort and validation cohort was 98.85% and 97.23%, respectively. Importantly, the AUC of the biomarker set achieved 92.44% of additional independent validation sets. In addition, EPRs also have their own unique oral community.

Conclusion

This study describes the characterization of the oral microbiome in EP and EPR and demonstrates the potential of the specific microbiome as a non-invasive diagnostic tool for epilepsy.

[Analysis of Salivary Microbiota Characteristics in Patients With Pulmonary Nodules: A Prospective Nonrandomized Concurrent Controlled Trial]

Objective

To uncover and identify the differences in salivary microbiota profiles and their potential roles between patients with pulmonary nodules (PN) and healthy controls, and to propose new candidate biomarkers for the early warning of PN.

Methods

16S rRNA amplicon sequencing was performed with the saliva samples of 173 PN patients, or the PN group, and 40 health controls, or the HC group, to compare the characteristics, including diversity, community composition, differential species, and functional changes of salivary microbiota in the two groups. Random forest algorithm was used to identify salivary microbial markers of PN and their predictive value for PN was assessed by area under the curve (AUC). Finally, the biological functions and potential mechanisms of differentially-expressed genes in saliva samples were preliminarily investigated on the basis of predictive functional profiling of Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2).

Results

The α diversity and β diversity of salivary microbiota in the PN group were higher than those in the HC group (P<0.05). Furthermore, there were significant differences in the community composition and the abundance of oral microorganisms between the PN and the HC groups (P<0.05). Random forest algorithm was applied to identify differential microbial species. Porphyromonas, Haemophilus, and Fusobacterium constituted the optimal marker sets (AUC=0.79, 95% confidence interval: 0.71-0.86), which can be used to effectively identify patients with PN. Bioinformatics analysis of the differentially-expressed genes revealed that patients with PN showed significant enrichment in protein/molecular functions involved in immune deficiency and redox homeostasis.

Conclusion

Changes in salivary microbiota are closely associated with PN and may induce the development of PN or malignant transformation of PN, which indicates the potential of salivary microbiota to be used as a new non-invasive humoral marker for the early diagnosis of PN.

The application of multi-omics in the respiratory microbiome: Progresses, challenges and promises

The study of the respiratory microbiome has entered a multi-omic era. Through integrating different omic data types such as metagenome, metatranscriptome, metaproteome, metabolome, culturome and radiome surveyed from respiratory specimens, holistic insights can be gained on the lung microbiome and its interaction with host immunity and inflammation in respiratory diseases. The power of multi-omics have moved the field forward from associative assessment of microbiome alterations to causative understanding of the lung microbiome in the pathogenesis of chronic, acute and other types of respiratory diseases. However, the application of multi-omics in respiratory microbiome remains with unique challenges from sample processing, data integration, and downstream validation. In this review, we first introduce the respiratory sample types and omic data types applicable to studying the respiratory microbiome. We next describe approaches for multi-omic integration, focusing on dimensionality reduction, multi-omic association and prediction. We then summarize progresses in the application of multi-omics to studying the microbiome in respiratory diseases. We finally discuss current challenges and share our thoughts on future promises in the field.

Exploring the impact of fungal spores from agricultural environments on the mice lung microbiome and metabolic profile

Exposure to particulate matter (PM) from agricultural environments has been extensively reported to cause respiratory health concerns in both animals and agricultural workers. Furthermore, PM from agricultural environments, containing fungal spores, has emerged as a significant threat to public health and the environment. Despite its potential toxicity, the impact of fungal spores present in PM from agricultural environments on the lung microbiome and metabolic profile is not well understood. To address this gap in knowledge, we developed a mice model of immunodeficiency using cyclophosphamide and subsequently exposed the mice to fungal spores via the trachea. By utilizing metabolomics techniques and 16 S rRNA sequencing, we conducted a comprehensive investigation into the alterations in the lung microbiome and metabolic profile of mice exposed to fungal spores. Our study uncovered significant modifications in both the lung microbiome and metabolic profile post-exposure to fungal spores. Additionally, fungal spore exposure elicited noticeable changes in α and β diversity, with these microorganisms being closely associated with inflammatory factors. Employing non-targeted metabolomics analysis via GC-TOF-MS, a total of 215 metabolites were identified, among which 42 exhibited significant differences. These metabolites are linked to various metabolic pathways, with amino sugar and nucleotide sugar metabolism, as well as galactose metabolism, standing out as the most notable pathways. Cysteine and methionine metabolism, along with glycine, serine and threonine metabolism, emerged as particularly crucial pathways. Moreover, these metabolites demonstrated a strong correlation with inflammatory factors and exhibited significant associations with microbial production. Overall, our findings suggest that disruptions to the microbiome and metabolome may hold substantial relevance in the mechanism underlying fungal spore-induced lung damage in mice.

Characterization of Lung Microbiomes in Pneumonic Hu Sheep Using Culture Technique and 16S rRNA Gene Sequencing

Hu sheep, a locally bred species in China known for its high productivity, is currently suffering from pneumonia. Here, we combine high-throughput 16SrRNA gene sequencing and bacterial culturing to examine the bacterial community in pneumonic Hu Sheep lungs (p < 0.05). The results showed that the abundance and diversity of lung bacteria in healthy sheep were significantly higher than those in pneumonia sheep (p = 0.139), while there was no significant difference between moderate and severe pneumonia. Furthermore, the composition of the lung microbiota community underwent significant alterations between different levels of pneumonia severity. The application of LEfSe analysis revealed a notable enrichment of Mannheimiae within the lungs of sheep afflicted with moderate pneumonia (p < 0.01), surpassing the levels observed in their healthy counterparts. Additionally, Fusobacterium emerged as the prevailing bacterial group within the lungs of sheep suffering from severe pneumonia. Integrating the results of bacterial isolation and identification, we conclusively determined that Mannheimia haemolytica was the primary pathogenic bacterium within the lungs of sheep afflicted with moderate pneumonia. Furthermore, the exacerbation of pneumonia may be attributed to the synergistic interplay between Fusobacterium spp. and other bacterial species. Our results provide new insights for guiding preventive and therapeutic measures for pneumonia of different severities in sheep.

Potential role of intratumor bacteria outside the gastrointestinal tract: More than passengers

INTRODUCTION

Tumor-associated bacteria and gut microbiota have gained significant attention in recent years due to their potential role in cancer development and therapeutic response. This review aims to discuss the contributions of intratumor bacteria outside the gastrointestinal tract, in addition to exploring the mechanisms, functions, and implications of these bacteria in cancer therapy.

METHODS

We reviewed current literature on intratumor bacteria and their impact on tumorigenesis, progression, metastasis, drug resistance, and anti-tumor immune modulation. Additionally, we examined techniques used to detect intratumor bacteria, precautions necessary when handling low microbial biomass tumor samples, and the recent progress in bacterial manipulation for tumor treatment.

RESULTS

Research indicates that each type of cancer uniquely interacts with its microbiome, and bacteria can be detected even in non-gastrointestinal tumors with low bacterial abundance. Intracellular bacteria have the potential to regulate tumor cells' biological behavior and contribute to critical aspects of tumor development. Furthermore, bacterial-based anti-tumor therapies have shown promising results in cancer treatment.

CONCLUSIONS

Understanding the complex interactions between intratumor bacteria and tumor cells could lead to the development of more precise cancer treatment strategies. Further research into non-gastrointestinal tumor-associated bacteria is needed to identify new therapeutic approaches and expand our knowledge of the microbiota's role in cancer biology.