Due to the advancement of 16S rRNA sequencing technology, the lower respiratory tract microbiota, which was considered non-existent, has been revealed. The correlation between these microorganisms and diseases such as tumor has been a hot topic in recent years. As the bacteria in the surrounding can infiltrate the tumors, researchers have also begun to pay attention to the biological behavior of tumor bacteria and their interaction with tumors. In this review, we present the characteristic of the lower respiratory tract bacteria and summarize recent research findings on the relationship between these microbiota and lung cancer. On top of that, we also summarize the basic feature of bacteria in tumors and focus on the characteristic of the bacteria in lung cancer. The relationship between bacteria in lung cancer and tumor development is also been discussed. Finally, we review the potential clinical applications of bacterial communities in the lower respiratory tract and lung cancer, and summarize key points of sample collection, sequencing, and contamination control, hoping to provide new ideas for the screening and treatment of tumors.
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Humans ; Lung Neoplasms ; RNA, Ribosomal, 16S/genetics* ; Bacteria/genetics* ; Microbiota ; Respiratory System ; Lung/microbiology*

The reduction of nitrate to nitrite by the oral microbiota has been proposed to be important for oral health and results in nitric oxide formation that can improve cardiometabolic conditions. Studies of bacterial composition in subgingival plaque suggest that nitrate-reducing bacteria are associated with periodontal health, but the impact of periodontitis on nitrate-reducing capacity (NRC) and, therefore, nitric oxide availability has not been evaluated. The current study aimed to evaluate how periodontitis affects the NRC of the oral microbiota. First, 16S rRNA sequencing data from five different countries were analyzed, revealing that nitrate-reducing bacteria were significantly lower in subgingival plaque of periodontitis patients compared with healthy individuals (P < 0.05 in all five datasets with n = 20-82 samples per dataset). Secondly, subgingival plaque, saliva, and plasma samples were obtained from 42 periodontitis patients before and after periodontal treatment. The oral NRC was determined in vitro by incubating saliva with 8 mmol/L nitrate (a concentration found in saliva after nitrate-rich vegetable intake) and compared with the NRC of 15 healthy individuals. Salivary NRC was found to be diminished in periodontal patients before treatment (P < 0.05) but recovered to healthy levels 90 days post-treatment. Additionally, the subgingival levels of nitrate-reducing bacteria increased after treatment and correlated negatively with periodontitis-associated bacteria (P < 0.01). No significant effect of periodontal treatment on the baseline saliva and plasma nitrate and nitrite levels was found, indicating that differences in the NRC may only be revealed after nitrate intake. Our results suggest that an impaired NRC in periodontitis could limit dietary nitrate-derived nitric oxide levels, and the effect on systemic health should be explored in future studies.
Humans ; Nitrates ; Nitric Oxide ; Nitrites ; RNA, Ribosomal, 16S/genetics* ; Periodontitis/microbiology* ; Bacteria ; Dental Plaque/microbiology* ; Saliva/microbiology* ; Microbiota/genetics*

OBJECTIVES: This study aimed to analyze the bacteria in dental caries and establish an optimized dental-ca-ries diagnosis model based on 16S ribosomal RNA (rRNA) data of oral flora. METHODS: We searched the public databa-ses of microbiomes including NCBI, MG-RAST, EMBL-EBI, and QIITA and collected data involved in the relevant research on human oral microbiomes worldwide. The samples in the caries dataset (1 703) were compared with healthy ones (20 540) by using the microbial search engine (MSE) to obtain the microbiome novelty score (MNS) and construct a caries diagnosis model based on this index. Nonparametric multivariate ANOVA was used to analyze and compare the impact of different host factors on the oral flora MNS, and the model was optimized by controlling related factors. Finally, the effect of the model was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: 1) The oral microbiota distribution obviously differed among people with various oral-health statuses, and the species richness and species diversity index decreased. 2) ROC curve was used to evaluate the caries data set, and the area under ROC curve was AUC=0.67. 3) Among the five hosts' factors including caries status, country, age, decayed missing filled tooth (DMFT) indices, and sampling site displayed the strongest effect on MNS of samples (P=0.001). 4) The AUC of the model was 0.87, 0.74, 0.74, and 0.75 in high caries, medium caries, low caries samples in Chinese children, and mixed dental plaque samples after controlling host factors, respectively. CONCLUSIONS The model based on the analysis of 16S rRNA data of oral flora had good diagnostic efficiency.
Humans ; Child ; Bacteria/genetics* ; Dental Caries/microbiology* ; Dental Caries Susceptibility ; Microbiota/genetics* ; RNA, Ribosomal, 16S

Polyurethane (PUR) plastics is widely used because of its unique physical and chemical properties. However, unreasonable disposal of the vast amount of used PUR plastics has caused serious environmental pollution. The efficient degradation and utilization of used PUR plastics by means of microorganisms has become one of the current research hotspots, and efficient PUR degrading microbes are the key to the biological treatment of PUR plastics. In this study, an Impranil DLN-degrading bacteria G-11 was isolated from used PUR plastic samples collected from landfill, and its PUR-degrading characteristics were studied. Strain G-11 was identified as Amycolatopsis sp. through 16S rRNA gene sequence alignment. PUR degradation experiment showed that the weight loss rate of the commercial PUR plastics upon treatment of strain G-11 was 4.67%. Scanning electron microscope (SEM) showed that the surface structure of G-11-treated PUR plastics was destroyed with an eroded morphology. Contact angle and thermogravimetry analysis (TGA) showed that the hydrophilicity of PUR plastics increased along with decreased thermal stability upon treatment by strain G-11, which were consistent with the weight loss and morphological observation. These results indicated that strain G-11 isolated from landfill has potential application in biodegradation of waste PUR plastics.
Plastics/metabolism* ; Polyurethanes/chemistry* ; RNA, Ribosomal, 16S ; Bacteria/genetics* ; Biodegradation, Environmental

OBJECTIVES: To investigate the distribution characteristics and correlation of intestinal and pharyngeal microbiota in early neonates. METHODS: Full-term healthy neonates who were born in Shanghai Pudong New Area Maternal and Child Health Hospital from September 2021 to January 2022 and were given mixed feeding were enrolled. The 16S rRNA sequencing technique was used to analyze the stool and pharyngeal swab samples collected on the day of birth and days 5-7 after birth, and the composition and function of intestinal and pharyngeal microbiota were analyzed and compared. RESULTS: The diversity analysis showed that the diversity of pharyngeal microbiota was higher than that of intestinal microbiota in early neonates, but the difference was not statistically significant (P>0.05). On the day of birth, the relative abundance of Proteobacteria in the intestine was significantly higher than that in the pharynx (P<0.05). On days 5-7 after birth, the relative abundance of Actinobacteria and Proteobacteria in the intestine was significantly higher than that in the pharynx (P<0.05), and the relative abundance of Firmicutes in the intestine was significantly lower than that in the pharynx (P<0.05). At the genus level, there was no significant difference in the composition of dominant bacteria between the intestine and the pharynx on the day of birth (P>0.05), while on days 5-7 after birth, there were significant differences in the symbiotic bacteria of Streptococcus, Staphylococcus, Rothia, Bifidobacterium, and Escherichia-Shigella between the intestine and the pharynx (P<0.05). The analysis based on the database of Clusters of Orthologous Groups of proteins showed that pharyngeal microbiota was more concentrated on chromatin structure and dynamics and cytoskeleton, while intestinal microbiota was more abundant in RNA processing and modification, energy production and conversion, amino acid transport and metabolism, carbohydrate transport and metabolism, coenzyme transport and metabolism, and others (P<0.05). The Kyoto Encyclopedia of Genes and Genomes analysis showed that compared with pharyngeal microbiota, intestinal microbiota was more predictive of cell motility, cellular processes and signal transduction, endocrine system, excretory system, immune system, metabolic diseases, nervous system, and transcription parameters (P<0.05). CONCLUSIONS The composition and diversity of intestinal and pharyngeal microbiota of neonates are not significantly different at birth. The microbiota of these two ecological niches begin to differentiate and gradually exhibit distinct functions over time.
Humans ; Infant, Newborn ; Bacteria ; China ; High-Throughput Nucleotide Sequencing ; Intestines ; Microbiota ; Pharynx/microbiology* ; RNA, Ribosomal, 16S/genetics*

OBJECTIVE: To investigate whether astragalus polysaccharides (APS) combined with berberine (BBR) can reduce high-fat diet (HFD)-induced obesity in mice. METHODS: Except for normal mice, 32 HFD-induced obese mice were randomized into HFD, APS (1,000 mg/kg APS), BBR (200 mg/kg BBR), and APS plus BBR (1,000 mg/kg APS plus 200 mg/kg BBR) groups, respectively. After 6-week treatment (once daily by gavage), the obesity phenotype and pharmacodynamic effects were evaluated by histopathological examination of epididymal fat, liver, and colon using hematoxylin-eosin staining and serum biochemical analyses by an automated chemistry analyzer. The feces were collected at the 12 th week, and taxonomic and functional profiles of gut microbiota were analyzed by 16S ribosomal ribonucleic acid (16S rRNA) sequencing. RESULTS: Compared with HFD group, the average body weight of APS plus BBR group was decreased (P<0.01), accompanied with the reduced fat accumulation, enhanced colonic integrity, insulin sensitivity and glucose homeostasis (P<0.05 or P<0.01). Importantly, APS combined with BBR treatment was more effective than APS or BBR alone in improving HFD-induced insulin resistance (P<0.05 or P<0.01). 16S rRNA sequence-based analysis of fecal samples demonstrated that APS combined with BBR treatment exhibited a better impact on HFD-induced gut microbiota dysbiosis, exclusively via the enriched abundances of Bacteroides, which corresponded to the large increase of predicted bacterial genes involved in carbohydrate metabolism. CONCLUSION APS combined with BBR may synergistically reduce obesity and modulate the gut microbiota in HFD-fed mice.
Mice ; Animals ; Diet, High-Fat ; Berberine/therapeutic use* ; Mice, Obese ; RNA, Ribosomal, 16S/genetics* ; Gastrointestinal Microbiome ; Obesity/drug therapy* ; Insulin Resistance ; Mice, Inbred C57BL

Objective: To analyze the short-term effect of individual atmospheric PM_2.5 exposure on the diversity, enterotype, and community structure of gut microbiome in healthy elderly people in Jinan, Shandong province. Methods: The present panel study recruited 76 healthy elderly people aged 60-69 years old in Dianliu Street, Lixia District, Jinan, Shandong Province, and followed them up five times from September 2018 to January 2019. The relevant information was collected by questionnaire, physical examination, precise monitoring of individual PM_2.5 exposure, fecal sample collection and gut microbiome 16S rDNA sequencing. The Dirichlet multinomial mixtures (DMM) model was used to analyze the enterotype. Linear mixed effect model and generalized linear mixed effect model were used to analyze the effect of PM_2.5 exposure on gut microbiome α diversity indices (Shannon, Simpson, Chao1, and ACE indices), enterotype and abundance of core species. Results: Each of the 76 subjects participated in at least two follow-up visits, resulting in a total of 352 person-visits. The age of 76 subjects was (65.0±2.8) years old with BMI (25.0±2.4) kg/m². There were 38 males accounting for 50% of the subjects. People with an educational level of primary school or below accounted for 10.5% of the 76 subjects, and those with secondary school and junior college or above accounting for 71.1% and 18.4%. The individual PM_2.5 exposure concentration of 76 subjects during the study period was (58.7±53.7) μg/m³. DMM model showed that the subjects could be divided into four enterotypes, which were mainly driven by Bacteroides, Faecalibacterium, Lachnospiraceae, Prevotellaceae, and Ruminococcaceae. Linear mixed effects model showed that different lag periods of PM_2.5 exposure were significantly associated with a lower gut α diversity index (FDR<0.05 after correction). Further analysis showed that PM_2.5 exposure was significantly associated with changes in the abundances of Firmicutes (Megamonas, Blautia, Streptococcus, etc.) and Bacteroidetes (Alistipes) (FDR<0.05 after correction). Conclusion: Short-term PM_2.5 exposure is significantly associated with a decrease in gut microbiome diversity and changes in the abundance of several species of Firmicutes and Bacteroidetes in the elderly. It is necessary to further explore the underlying mechanisms between PM_2.5 exposure and the gut microbiome, so as to provide a scientific basis for promoting the intestinal health of the elderly.
Aged ; Humans ; Male ; Middle Aged ; Feces/microbiology* ; Gastrointestinal Microbiome ; Particulate Matter ; RNA, Ribosomal, 16S/genetics* ; Female

Objective: To explore the changes in bacterial community structure, antibiotic resistance genome, and pathogen virulence genome in river water before and after the river flowing through Haikou City and their transmission and dispersal patterns and to reveal anthropogenic disturbance's effects on microorganisms and resistance genes in the aquatic environment. Methods: The Nandu River was divided into three study areas: the front, middle and rear sections from the upstream before it flowed through Haikou City to the estuary. Three sampling sites were selected in each area, and six copies of the sample were collected in parallel at each site and mixed for 3 L per sample. Microbial community structure, antibiotic resistance, virulence factors, and mobile genetic elements were analyzed through bioinformatic data obtained by metagenomic sequencing and full-length sequencing of 16S rRNA genes. Variations in the distribution of bacterial communities between samples and correlation of transmission patterns were analyzed by principal co-ordinates analysis, procrustes analysis, and Mantel test. Results: As the river flowed through Haikou City, microbes' alpha diversity gradually decreased. Among them, Proteobacteria dominates in the bacterial community in the front, middle, and rear sections, and the relative abundance of Proteobacteria in the middle and rear sections was higher than that in the front segment. The diversity and abundance of antibiotic resistance genes, virulence factors, and mobile genetic elements were all at low levels in the front section and all increased significantly after flow through Haikou City. At the same time, horizontal transmission mediated by mobile genetic elements played a more significant role in the spread of antibiotic-resistance genes and virulence factors. Conclusions: Urbanization significantly impacts river bacteria and the resistance genes, virulence factors, and mobile genetic elements they carry. The Nandu River in Haikou flows through the city, receiving antibiotic-resistant and pathogen-associated bacteria excreted by the population. In contrast, antibiotic-resistant genes and virulence factors are enriched in bacteria, which indicates a threat to environmental health and public health. Comparison of river microbiomes and antibiotic resistance genomes before and after flow through cities is a valuable early warning indicator for monitoring the spread of antibiotic resistance.
Humans ; Rivers ; Virulence Factors/genetics* ; RNA, Ribosomal, 16S/genetics* ; Microbiota/genetics* ; Anti-Bacterial Agents ; Drug Resistance, Microbial/genetics*

OBJECTIVE: To describe the submucosal microbial profiles of peri-implantitis and healthy implants, and to explore bacteria that might be correlated with clinical parameters. METHODS: In the present cross-sectional study, 49 patients were recruited. Each patient contributed with one implant, submucosal biofilms were collected from 20 healthy implants and 29 implants with peri-implantitis. DNA was extracted and bacterial 16S ribosomal RNA (16S rRNA) genes were amplified. Submucosal biofilms were analyzed using 16S rRNA sequencing at Illumina MiSeq platform. Differences between the groups were determined by analyzing α diversity, microbial component and microbial structure. The potential correlation between the bacteria with pocket probing depth (PPD) of peri-implant calculated by Spearman correlation analysis. RESULTS: The α diversity of submucosal microbial of health group was significantly lower than that in peri-implantitis group (Chao1 index: 236.85±66.13 vs. 150.54±57.43, P < 0.001; Shannon index: 3.42±0.48 vs. 3.02±0.65, P=0.032). Principal coordinated analysis showed that the submucosal microbial structure had significant difference between healthy and peri-implantitis groups [R²=0.243, P=0.001, analysis of similarities (ANOSIM)]. Compared with healthy implants, relative abundance of periodontal pathogens were higher in peri-implantitis, including members of the red complex (Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola) and some members of orange complex (Precotella intermedia, Eubacterium nodatum, Parvimonas micra), as well as some new periodontal pathogens, such as Fillifactor alocis, Fretibacterium fastidiosum, Desulfobulbus sp._HMT_041, and Porphyromonas endodontalis. Spearman correlation analysis revealed that the relative abundance of Treponema denticola (r=0.686, P < 0.001), Tannerella forsythia (r=0.675, P < 0.001), Fretibacterium sp. (r=0.671, P < 0.001), Desulfobulbus sp._HMT_041 (r=0.664, P < 0.001), Filifactor alocis (r=0.642, P < 0.001), Fretibacterium fastidiosum (r=0.604, P < 0.001), Porphyromonas gingivalis (r=0.597, P < 0.001), Porphyromonas endodontalis (r=0.573, P < 0.001) were positive correlated with PPD. While the relative abundance of Rothia aeria (r=-0.615, P < 0.001) showed negatively correlation with PPD. CONCLUSION Marked differences were observed in the microbial profiles of healthy implants and peri-implantitis. The members of red and orange complex as well as some new periodontal pathogens seem to play an important role in peri-implant disease. Compared with healthy implants, the submucosal microbial of peri-implantitis were characterized by high species richness and diversity.
Humans ; Peri-Implantitis/microbiology* ; Cross-Sectional Studies ; RNA, Ribosomal, 16S/genetics* ; Bacterial Load ; Porphyromonas gingivalis ; Dental Implants

OBJECTIVE: To investigate the gut microbiota in newly diagnosed IgA nephropathy patients with chronic kidney disease (CKD) stages 1-2 and the association between the gut microbiota and the clinical risk factors of IgA nephropathy. METHODS: Fresh fecal samples were collected from nineteen newly diagnosed IgA nephropathy patients with CKD stages 1-2 and fifteen age- and sex-matched healthy controls. Fecal bacterial DNA was extracted and microbiota composition were characterized using 16S ribosomal RNA (16S rRNA) high-throughput sequencing for the V3-V4 region. The Illumina Miseq platform was used to analyze the results of 16S rRNA high-throughput sequencing of fecal flora. At the same time, the clinical risk factors of IgA nephropathy patients were collected to investigate the association between the gut microbiota and the clinical risk factors. RESULTS: (1) At the phylum level, the abundance of Bacteroidetes was significantly reduced (P=0.046), and the abundance of Actinobacteria was significantly increased (P=0.001). At the genus level, the abundance of Escherichia-Shigella, Bifidobacte-rium, Dorea and others were significantly increased (P < 0.05). The abundance of Lachnospira, Coprococcus_2 and Sutterella was significantly reduced (P < 0.05). (2) There was no significant difference in the abundance of gut microbiota between the newly diagnosed IgA nephropathy patients and the healthy control group (P>0.05), but there were differences in the structure of the gut microbiota between the two groups. The results of LEfSe analysis showed that there were 16 differential bacteria in the newly diagnosed IgA nephropathy patients and healthy controls. Among them, the abundance of the newly diagnosed IgA nephropathy patients was increased in Enterobacteriales, Actinobacteria, Escherichia-Shigella, etc. The healthy control group was increased in Bacteroidetes and Lachnospira. (3) The result of redundancy analysis (RDA) showed that Bifidobacterium was positively correlated with serum IgA levels, 24-hour urinary protein levels and the presence of hypertension. Lachnoclostridium was positively correlated with the presence of hypertension. Escherichia-Shigella was positively correlated with urine red blood cells account. Bifidobacterium was positively correlated with the proliferation of capillaries. Faecalibacterium was positively correlated with cell/fibrocytic crescents. Ruminococcus_2 was positively correlated with mesangial cell proliferation, glomerular segmental sclerosis and renal tubular atrophy/interstitial fibrosis. CONCLUSION The gut microbiota in the newly diagnosed IgA nephropathy patients with CKD stages 1-2 is different from that of the healthy controls. Most importantly, some gut bacteria are related to the clinical risk factors of IgA nephropathy. Further research is needed to understand the potential role of these bacteria in IgA nephropathy.
Humans ; Gastrointestinal Microbiome ; RNA, Ribosomal, 16S/genetics* ; Glomerulonephritis, IGA ; Bacteria/genetics* ; Risk Factors ; Renal Insufficiency, Chronic