Alterations of Bacteroides sp., Neisseria sp., Actinomyces sp., and Streptococcus sp. populations in the oropharyngeal microbiome are associated with liver cirrhosis and pneumonia

Association between oral microbial dysbiosis and poor functional outcomes in stroke-associated pneumonia patients

BACKGROUND

Despite advances in our understanding of the critical role of the microbiota in stroke patients, the oral microbiome has rarely been reported to be associated with stroke-associated pneumonia (SAP). We sought to profile the oral microbial composition of SAP patients and to determine whether microbiome temporal instability and special taxa are associated with pneumonia progression and functional outcomes.

METHODS

This is a prospective, observational, single-center cohort study that examined patients with acute ischemic stroke (AIS) who were admitted within 24 h of experiencing a stroke event. The patients were divided into three groups based on the occurrence of pneumonia and the use of mechanical ventilation: nonpneumonia group, SAP group, and ventilator-associated pneumonia (VAP) group. We collected oral swabs at different time points post-admission and analyzed the microbiota using 16 S rRNA high-throughput sequencing. The microbiota was then compared among the three groups.

RESULTS

In total, 104 nonpneumonia, 50 SAP and 10 VAP patients were included in the analysis. We found that SAP and VAP patients exhibited significant dynamic differences in the diversity and composition of the oral microbiota and that the magnitude of this dysbiosis and instability increased during hospitalization. Then, by controlling the potential effect of all latent confounding variables, we assessed the changes associated with pneumonia after stroke and explored patients with a lower abundance of Streptococcus were more likely to suffer from SAP. The logistic regression analysis revealed that an increase in specific taxa in the phylum Actinobacteriota was linked to a higher risk of poor outcomes. A model for SAP patients based on oral microbiota could accurately predict 30-day clinical outcomes after stroke onset.

CONCLUSIONS

We concluded that specific oral microbiota signatures could be used to predict illness development and clinical outcomes in SAP patients. We proposed the potential of the oral microbiota as a non-invasive diagnostic biomarker in the clinical management of SAP patients.

CLINICAL TRIAL REGISTRATION

NCT04688138. Registered 29/12/2020, https://clinicaltrials.gov/ct2/show/NCT04688138 .

Oral microbiota and liver diseases

Gut microbiota plays a crucial role in our health and particularly liver diseases, including NAFLD, cirrhosis, and HCC. Oral microbiome and its role in health and disease represent an active field of research. Several lines of evidence have suggested that oral microbiota dysbiosis represents a major factor contributing to the occurrence and progression of many liver diseases. The human microbiome is valuable to the diagnosis of cancer and provides a novel strategy for targeted therapy of HCC. The most studied liver disease in relation to oral-gut-liver axis dysbiosis includes MAFLD; however, other diseases include Precancerous liver disease as viral liver diseases, liver cirrhosis, AIH and liver carcinoma (HCC). It seems that restoring populations of beneficial organisms and correcting dysbiosis appears to improve outcomes in liver disorders. We discuss the possible role of oral microbiota in these diseases.

Diurnal changes of the oral microbiome in patients with alcohol dependence

Background

Saliva secretion and oral microbiota change in rhythm with our biological clock. Dysbiosis of the oral microbiome and alcohol consumption have a two-way interactive impact, but little is known about whether the oral microbiome undergoes diurnal changes in composition and function during the daytime in patients with alcohol dependence (AD).

Methods

The impact of alcohol consumption on the diurnal salivary microbiome was examined in a case-control study of 32 AD patients and 21 healthy control (HC) subjects. We tested the changes in microbial composition and individual taxon abundance by 16S rRNA gene sequencing.

Results

The present study is the first report showing that alcohol consumption enhanced the richness of the salivary microbiome and lowered the evenness. The composition of the oral microbiota changed significantly in alcohol-dependent patients. Additionally, certain genera were enriched in the AD group, including Actinomyces, Leptotrichia, Sphaerochaeta and Cyanobacteria, all of which have pathogenic effects on the host. There is a correlation between liver enzymes and oral microbiota. KEGG function analysis also showed obvious alterations during the daytime.

Conclusion

Alcohol drinking influences diurnal changes in the oral microbiota, leading to flora disturbance and related functional impairment. In particular, the diurnal changes of the oral microbiota may open avenues for potential interventions that can relieve the detrimental consequences of AD.

Shen-Ling-Bai-Zhu-San Enhances the Antipneumonia Effect of Cefixime in Children by Ameliorating Gut Microflora, Inflammation, and Immune Response

Objective

Shen-Ling-Bai-Zhu-San (SLBZS) is used for treating gastrointestinal disorders. However, the role of SLBZS in treating pneumonia in children is still unclear.

Methods

In this study, children (≥2 and <9 years) with pneumonia were treated with 0.1 g cefixime (cefixime group) or 0.1 g cefixime + 9 g SLBZS (SLBZS + cefixime). The drugs were administered twice daily for 10 days. The therapeutic effects of the two groups were compared. The white blood cell (WBC), neutrophil, and lymphocyte counts; neutrophil-lymphocyte ratio (NLR); serum inflammatory factor levels; and gut microflora were assessed.

Results

The clinical efficacy of SLBZS + cefixime treatment of pneumonia in children was higher than that of cefixime alone (93.3% vs. 86.7%). Both cefixime and SLBZS + cefixime treatments decreased the area of pulmonary inflammatory lesions, reduced white blood cell and neutrophil counts, neutrophil-lymphocyte ratio, inflammation, and increased lymphocyte count in children with pneumonia compared with those before treatment. Moreover, SLBZS enhanced the anti-inflammation and immunity-enhancing effects of cefixime in children with pneumonia. SLBZS + cefixime treatment decreased Enterobacter, Enterococcus, Bacteroides, and Fusobacterium counts and increased Bifidobacterium and Lactobacillus counts. Compared with the cefixime treatment group, the count of the six bacterial strains in the SLBZS + cefixime treatment group was closer to the normal level.

Conclusion

SLBZS enhanced the antipneumonia effect of cefixime in children with pneumonia by ameliorating gut microflora, inflammation, and immune response.

Human disease prediction from microbiome data by multiple feature fusion and deep learning

Human disease prediction from microbiome data has broad implications in metagenomics. It is rare for the existing methods to consider abundance profiles from both known and unknown microbial organisms, or capture the taxonomic relationships among microbial taxa, leading to significant information loss. On the other hand, deep learning has shown unprecedented advantages in classification tasks for its feature-learning ability. However, it encounters the opposite situation in metagenome-based disease prediction since high-dimensional low-sample-size metagenomic datasets can lead to severe overfitting; and black-box model fails in providing biological explanations. To circumvent the related problems, we developed MetaDR, a comprehensive machine learning-based framework that integrates various information and deep learning to predict human diseases. Experimental results indicate that MetaDR achieves competitive prediction performance with a reduction in running time, and effectively discovers the informative features with biological insights.

Vital Members in the More Dysbiotic Oropharyngeal Microbiotas in H7N9-Infected Patients

The dysbiosis of oropharyngeal (OP) microbiota is associated with multiple diseases, including H7N9 infection. Different OP microbial colonization states may reflect different severities or stages of disease and affect the effectiveness of the treatments. Current study aims to determine the vital bacteria that could possibly drive the OP microbiota in the H7N9 patients to more severe microbial dysbiosis state. The OP microbiotas of 42 H7N9 patients and 30 healthy subjects were analyzed by a series of bioinformatics and statistical analyses. Two clusters of OP microbiotas in H7N9 patients, i.e., Cluster_1_Diseased and Cluster_2_Diseased, were determined at two microbial colonization states by Partition Around Medoids (PAM) clustering analysis, each characterized by distinct operational taxonomic units (OTUs) and functional metabolites. Cluster_1_Diseased was determined at more severe dysbiosis status compared with Cluster_2_Diseased, while OTU143_Capnocytophaga and OTU269_Treponema acted as gatekeepers for both of the two clustered microbiotas. Nine OTUs assigned to seven taxa, i.e., Alloprevotella, Atopobium, Megasphaera, Oribacterium, Prevotella, Stomatobaculum, and Veillonella, were associated with both H7N9 patients with and without secondary bacterial lung infection in Cluster_1. In addition, two groups of healthy cohorts may have potential different susceptibilities to H7N9 infection. These findings suggest that two OP microbial colonization states of H7N9 patients were at different dysbiosis states, which may help determine the health status of H7N9 patients, as well as the susceptibility of healthy subjects to H7N9 infection.

Human microbiome is a diagnostic biomarker in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the third leading cause of cancer mortality worldwide. Increasing evidence indicates a close relationship between HCC and the human microbiota. Herein, we reviewed the important potential of the human microbiota as a diagnostic biomarker of HCC.

DATA SOURCES

Several innovative studies have investigated the characteristics of the gut and oral microbiomes in patients with HCC and proposed that the human microbiome has the potential to be a diagnostic biomarker of HCC. Literature from February 1999 to February 2019 was searched in the PubMed database using the keywords "microbiota" or "microbiome" or "microbe" and "liver cancer" or "hepatocellular carcinoma", and the results of clinical and experimental studies were analyzed.

RESULTS

Specific changes occur in the human microbiome of patients with HCC. Moreover, the gut microbiome and oral microbiome can be used as non-invasive diagnostic biomarkers for HCC. Furthermore, they also have certain diagnostic potential for precancerous diseases of HCC. The diagnostic potential of the blood microbiota and ascites microbiota in HCC will be gradually discovered in the future.

CONCLUSIONS

The human microbiome is valuable to the diagnosis of HCC and provides a novel strategy for targeted therapy of HCC. The human microbiome may be widely used in the diagnosis, treatment and prognosis for multiple system diseases or cancers in the future.

Oropharyngeal Microbiome in Obstructive Sleep Apnea: Decreased Diversity and Abundance

STUDY OBJECTIVES

To explore and analyze diversity and abundance of oropharyngeal microbiota in patients with obstructive sleep apnea (OSA).

METHODS

This was a cross-sectional study. Middle-aged men, suspected to have OSA, referred to full-night polysomnography, and willing to provide oropharyngeal swab samples, were consecutively enrolled. OSA severity was assessed by apnea-hypopnea index (AHI) as non-OSA (AHI < 5 events/h) and OSA (AHI ≥ 15 events/h). Bacterial DNA of oropharyngeal samples was extracted and quality test performed. Oropharyngeal microbiota was analyzed using 16S ribosomal DNA (rDNA) sequencing, and bioinformatic analysis carried out after sequencing.

RESULTS

Samples from 51 men (25 in the non-OSA group and 26 in the OSA group) were sent for examination. Of these, 40 samples were found to have sufficient concentration of DNA and were analyzed for bioinformatics. In alpha diversity analysis, the OSA group exhibited significantly lower sobs (198.33 ± 21.71 versus 216.57 ± 26.21, P = .022), chao (221.30 ± 26.62 versus 243.86 ± 26.20, P = .014), ace (222.17 ± 27.15 versus 242.42 ± 25.81, P = .028) and shannon index (3.14 ± 0.23 versus 3.31 ± 0.26, P = .035), suggesting a reduction in microbial species diversity. We further divided participants into non-OSA, moderate OSA, and severe OSA groups and observed a significant decrease in the bacterial biodiversity of OSA groups compared with the non-OSA group, with the most significant decrease occurring in the moderate OSA group. Principal coordinate analysis showed two extremely different oropharyngeal microbial communities in non-OSA and OSA groups. More interestingly, proportion of Neisseria was slightly higher in the severe OSA group (20.64%), followed by the moderate OSA and non-OSA groups (12.57% and 9.69%, respectively). Glaciecola was not detected in the OSA groups compared to the non-OSA group (0 versus 0.772 ± 0.4754, P < .001).

CONCLUSIONS

Middle-aged men with OSA showed less oropharyngeal species diversity and altered abundance, on which further confirmation is warranted.

Microbiome diurnal rhythmicity and its impact on host physiology and disease risk

Host-microbiome interactions constitute key determinants of host physiology, while their dysregulation is implicated in a wide range of human diseases. The microbiome undergoes diurnal variation in composition and function, and this in turn drives oscillations in host gene expression and functions. In this review, we discuss the newest developments in understanding circadian host-microbiome interplays, and how they may be relevant in health and disease contexts. We summarize the molecular mechanisms by which the microbiome influences host function in a diurnal manner, and inversely describe how the host orchestrates circadian rhythmicity of the microbiome. Furthermore, we highlight the future perspectives and challenges in studying this new and exciting facet of host-microbiome interactions. Finally, we illustrate how the elucidation of the microbiome chronobiology may pave the way for novel therapeutic approaches.

Comparison of Oropharyngeal Microbiota in Healthy Piglets and Piglets With Respiratory Disease

Porcine respiratory disease (PRD) is responsible for severe economic losses in the swine industry worldwide. Our objective was to characterize the oropharyngeal microbiota of suckling piglets and compare the microbiota of healthy piglets and piglets with PRD. Oropharyngeal swabs were collected from healthy (Healthy_A, n = 6; Healthy_B, n = 4) and diseased (PRD_A, n = 18; PRD_B, n = 5) piglets at 2-3 weeks of age from two swine farms in Guangdong province, China. Total DNA was extracted from each sample and the V3-V4 hypervariable region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq platform. No statistically significant differences were observed in bacterial diversity and richness between the healthy and PRD groups in the two farms except for Shannon index in farm A. Principal coordinates analysis (PCoA) showed structural segregation between diseased and healthy groups and between groups of different farms. Among all samples, the phyla Firmicutes, Proteobacteria, and Bacteroidetes were predominant. At the genus level, Streptococcus, Lactobacillus, and Actinobacillus were the core genera in the oropharynx of healthy piglets from the two farms. Significant differences in bacterial taxa were found when the microbiota was compared regarding the health status. In farm A, the percentages of Moraxella and Veillonella were higher in the PRD group, while only Porphyromonas was significantly increased in the PRD group in farm B (p < 0.05). Compared to PRD groups, statistically significant predominance of Lactobacillus was observed in the healthy groups from both farms (p < 0.05). Our findings revealed that Moraxella, Veillonella, and Porphyromonas may play a potential role in PRD and Lactobacillus may have a protective role against respiratory diseases.

Case report of Actinomyces turicensis meningitis as a complication of purulent mastoiditis

BACKGROUND

Central nervous system (CNS) infections caused by Actinomyces spp. including brain abscess, actinomycoma, subdural empyema and epidural abscess are well described, however reports of Actinomyces-associated meningitis are scarcely reported.

CASE REPORT

We present the case of a 43-year-old Hungarian male patient with poor socioeconomic status who developed acute bacterial meningitis caused by Actinomyces turicensis originating from the left side mastoiditis. The bacterial cultures of both cerebrospinal fluid (CSF) and purulent discharge collected during the mastoid surgery showed slow growing Gram-positive rods that were identified by automated systems (API, VITEK) as A. turicensis The bacterial identification was confirmed by 16S rRNA PCR and subsequent nucleic acid sequencing. No bacterial growth was detected in blood culture bottles after 5 days of incubation. Hence, multiple antibacterial treatments and surgical intervention the patient passed away.

CONCLUSIONS

Anaerobes are rarely involved in CNS infections therefore anaerobic culture of CSF samples is routinely not performed. However, anaerobic bacteria should be considered as potential pathogens when certain risk factors are present, such as paranasal sinusitis, mastoiditis in patients with poor socioeconomic condition. To the best of our knowledge, our case report is the first description of A. turicensis meningitis that has been diagnosed as consequence of purulent mastoiditis.

Unique subgingival microbiota associated with periodontitis in cirrhosis patients

Liver cirrhosis is a severe disease with major impact on the overall health of the patient including poor oral health. Lately, there has been increasing focus on oral diseases as cirrhosis-related complications due to the potential impact on systemic health and ultimately mortality. Periodontitis is one of the most common oral diseases in cirrhosis patients. However, no studies have investigated the composition of the subgingival microbiome in patients suffering from periodontitis and liver cirrhosis. We analysed the subgingival microbiome in 21 patients with periodontitis and cirrhosis using long-reads Illumina sequencing. The subgingival microbiota was dominated by bacteria belonging to the Firmicutes phylum and to a lesser extend the Actinobacteria and Bacteroidetes phyla. Bacteria usually considered periodontal pathogens, like Porhyromonas ginigivalis, Tannerella forsythia, Treponema denticola, generally showed low abundancy. Comparing the microbiota in our patients with that of periodontitis patients and healthy controls of three other studies revealed that the periodontitis-associated subgingival microbiota in cirrhosis patients is composed of a unique microbiota of bacteria not normally associated with periodontitis. We hypothesise that periodontitis in cirrhosis patients is a consequence of dysbiosis due to a compromised immune system that renders commensal bacteria pathogenic.

Respiratory microbiota and lower respiratory tract disease

INTRODUCTION

The respiratory airways harbor a complex succession of ecological niches with distinct but related bacterial communities. Particular challenges of respiratory microbiome research have led to limited scientific output compared to other human microbiomes. Areas covered: In this review, we summarize the current state of knowledge of the bacterial respiratory microbiome, with a particular focus on associations between the respiratory microbiome and lower respiratory tract conditions. Expert commentary: There is growing evidence that the respiratory microbiome is associated with lower respiratory infectious diseases and related conditions. Most respiratory microbiome reports are metataxonomic cross-sectional or case-control studies with relatively small sample sizes. Large, prospective projects with metatranscriptomics or metabolomics approach are needed to unravel the effect of the respiratory microbiome on health-related conditions. Moreover, standardization in sampling, library preparation, sequencing techniques and data analysis should be encouraged.

Metagenomics Biomarkers Selected for Prediction of Three Different Diseases in Chinese Population

The dysbiosis of human microbiome has been proven to be associated with the development of many human diseases. Metagenome sequencing emerges as a powerful tool to investigate the effects of microbiome on diseases. Identification of human gut microbiome markers associated with abnormal phenotypes may facilitate feature selection for multiclass classification. Compared with binary classifiers, multiclass classification models deploy more complex discriminative patterns. Here, we developed a pipeline to address the challenging characterization of multilabel samples. In this study, a total of 300 biomarkers were selected from the microbiome of 806 Chinese individuals (383 controls, 170 with type 2 diabetes, 130 with rheumatoid arthritis, and 123 with liver cirrhosis), and then logistic regression prediction algorithm was applied to those markers as the model intrinsic features. The estimated model produced an F₁ score of 0.9142, which was better than other popular classification methods, and an average receiver operating characteristic (ROC) of 0.9475 showed a significant correlation between these selected biomarkers from microbiome and corresponding phenotypes. The results from this study indicate that machine learning is a vital tool in data mining from microbiome in order to identify disease-related biomarkers, which may contribute to the application of microbiome-based precision medicine in the future.

Disordered oropharyngeal microbial communities in H7N9 patients with or without secondary bacterial lung infection

Secondary bacterial lung infection (SBLI) is a serious complication in patients with H7N9 virus infection, and increases disease severity. The oropharyngeal (OP) microbiome helps prevent colonisation of respiratory pathogens. We aimed to investigate the OP microbiome of H7N9 patients with/without secondary bacterial pneumonia using 16S rRNA gene sequencing. OP swab samples were collected from 51 H7N9 patients (21 with SBLI and 30 without) and 30 matched healthy controls (HCs) and used for comparative composition, diversity and richness analyses of microbial communities. Principal coordinates analysis successfully distinguished between the OP microbiomes of H7N9 patients and healthy subjects, and the OP microbiome diversity of patients with SBLI was significantly increased. There was significant dysbiosis of the OP microbiome in H7N9 patients, with an abundance of Leptotrichia, Oribacterium, Streptococcus, Atopobium, Eubacterium, Solobacterium and Rothia species in patients with SBLI, and Filifactor, Megasphaera and Leptotrichia species in patients without SBLI, when compared with HCs. Importantly, Haemophilus and Bacteroides species were enriched in HCs. These findings revealed dysbiosis of the OP microbiota in H7N9 patients, and identified OP microbial risk indicators of SBLI, suggesting that the OP microbiome could provide novel and non-invasive diagnostic biomarkers for early microbiota-targeted prophylactic therapies for SBLI prevention.

Microbial investigations in throat swab and tracheal aspirate specimens are beneficial to predict the corresponding endotracheal tube biofilm flora among intubated neonates with ventilator-associated pneumonia

Ventilator-associated pneumonia (VAP) is a common nosocomial infection in neonatal intensive care units with high morbidity and mortality. Bacterial biofilm in the endotracheal tube (ET) provides a notable and persistent source of pathogens that may cause VAP, and thus is important for VAP detection. However, during intubation microbial investigations in ET, samples are unavailable due to the infeasibility of collecting ET samples during intubation of neonates. It is therefore of great importance to find alternative sources of samples that can help identify the ET biofilm flora. In the present study, the microbial signatures of throat swabs and tracheal aspirates were compared with ET biofilm samples from VAP neonates using 16S ribosomal RNA gene polymerase chain reaction, denaturing gradient gel electrophoresis (DGGE), cloning and sequencing. Sequences were assigned to phylogenetic species using BLAST. Microbial diversity and richness among the three types of specimens were compared based on their DGGE fingerprints, and taxonomic characteristics based on the BLAST results. The microbial richness and diversity of ET biofilms were similar to tracheal aspirate yet significantly different from throat swab samples (P<0.05). Compared with ET biofilms, the overall constituent ratio of microflora was significantly different in throat swab and tracheal aspirate samples (P<0.05). However tracheal aspirate samples were useful for predicting Staphylococcus sp. in ET biofilms with a sensitivity of 85.7% and a specificity of 83.3%. The sensitivity for the combination of tracheal aspirate and throat swab samples to detect Staphylococcus sp. in ET biofilms was 100%. The detection of Pseudomonas sp. in throat swabs assisted its identification in ET biofilms (sensitivity 33.3% and specificity 100%). The results of the present study suggest that microbial investigations in throat swab and tracheal aspirate samples are beneficial for identifying the ET biofilm flora. There may therefore be clinical applications of using substituent samples to identify pathogens in ET biofilms for VAP surveillance among intubated neonates.

Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification

Background. Text data of 16S rRNA are informative for classifications of microbiota-associated diseases. However, the raw text data need to be systematically processed so that features for classification can be defined/extracted; moreover, the high-dimension feature spaces generated by the text data also pose an additional difficulty. Results. Here we present a Phylogenetic Tree-Based Motif Finding algorithm (PMF) to analyze 16S rRNA text data. By integrating phylogenetic rules and other statistical indexes for classification, we can effectively reduce the dimension of the large feature spaces generated by the text datasets. Using the retrieved motifs in combination with common classification methods, we can discriminate different samples of both pneumonia and dental caries better than other existing methods. Conclusions. We extend the phylogenetic approaches to perform supervised learning on microbiota text data to discriminate the pathological states for pneumonia and dental caries. The results have shown that PMF may enhance the efficiency and reliability in analyzing high-dimension text data.