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*

Microbiome is associated with a wide range of diseases. The gut microbiome is also a dynamic reflection of health status, which can be modified, thus representing great potential to exploit the mechanisms that influence human physiology. Recent years have seen a dramatic rise in gut microbiome studies, which has been enabled by the rapidly evolving high-throughput sequencing methods (i.e. 16S rRNA sequencing and shotgun sequencing). As the emerging technologies for microbiome research continue to evolve (i.e. metatranscriptomics, metabolomics, culturomics, synthetic biology), microbiome research has moved beyond phylogenetic descriptions and towards mechanistic analyses. In this review, we highlight different approaches to study the microbiome, in particular, the current limitations and future promise of these techniques. This review aims to provide clinicians with a framework for studying the microbiome, as well as to accelerate the adoption of these techniques in clinical practice.
Humans ; Gastrointestinal Microbiome ; Phylogeny ; RNA, Ribosomal, 16S/genetics* ; Health Status

OBJECTIVE: Arsenic (As) and fluoride (F) are two of the most common elements contaminating groundwater resources. A growing number of studies have found that As and F can cause neurotoxicity in infants and children, leading to cognitive, learning, and memory impairments. However, early biomarkers of learning and memory impairment induced by As and/or F remain unclear. In the present study, the mechanisms by which As and/or F cause learning memory impairment are explored at the multi-omics level (microbiome and metabolome). METHODS: We stablished an SD rats model exposed to arsenic and/or fluoride from intrauterine to adult period. RESULTS: Arsenic and/fluoride exposed groups showed reduced neurobehavioral performance and lesions in the hippocampal CA1 region. 16S rRNA gene sequencing revealed that As and/or F exposure significantly altered the composition and diversity of the gut microbiome，featuring the Lachnospiraceae_NK4A136_group, Ruminococcus_1, Prevotellaceae_NK3B31_group, [Eubacterium]_xylanophilum_group. Metabolome analysis showed that As and/or F-induced learning and memory impairment may be related to tryptophan, lipoic acid, glutamate, gamma-aminobutyric acidergic (GABAergic) synapse, and arachidonic acid (AA) metabolism. The gut microbiota, metabolites, and learning memory indicators were significantly correlated. CONCLUSION Learning memory impairment triggered by As and/or F exposure may be mediated by different gut microbes and their associated metabolites.
Rats ; Animals ; Arsenic/toxicity* ; Fluorides ; RNA, Ribosomal, 16S/genetics* ; Rats, Sprague-Dawley ; Metabolome ; Microbiota

In order to determine the changes of bacterial community structure and function in the early, middle and late stage of aerobic composting of chicken manure, high-throughput sequencing and bioinformatics methods were used to determine and analyze the 16S rRNA sequence of samples at different stages of composting. Wayne analysis showed that most of the bacterial OTUs in the three composting stages were the same, and only about 10% of the operational taxonomic units (OTUs) showed stage specificity. The diversity indexes including Ace, Chao1 and Simpson showed a trend of increasing at first, followed by decreasing. However, there was no significant difference among different composting stages (P < 0.05). The dominant bacteria groups in three composting stages were analyzed at the phylum and genus levels. The dominant bacteria phyla at three composting stages were the same, but the abundances were different. LEfSe (line discriminant analysis (LDA) effect size) method was used to analyze the bacterial biological markers with statistical differences among three stages of composting. From the phylum to genus level, there were 49 markers with significant differences among different groups. The markers included 12 species, 13 genera, 12 families, 8 orders, 1 boundary, and 1 phylum. The most biomarkers were detected at early stage while the least biomarkers were detected at late stage. The microbial diversity was analyzed at the functional pathway level. The function diversity was the highest in the early stage of composting. Following the composting, the microbial function was enriched relatively while the diversity decreased. This study provides theoretical support and technical guidance for the regulation of livestock manure aerobic composting process.
Animals ; Manure/microbiology* ; Chickens/genetics* ; Composting ; RNA, Ribosomal, 16S/genetics* ; Soil ; Bacteria/genetics*

This study aimed to investigate the recovery effect of Zuogui Jiangtang Qinggan Prescription on intestinal flora homeostasis control and intestinal mucosal barrier in type 2 diabetes mellitus(T2DM) with nonalcoholic fatty liver disease(NAFLD) induced by a high-fat diet. NAFLD was established in MKR transgenic mice(T2DM mice) by a high-fat diet(HFD), and subsequently treated for 8 weeks with Zuogui Jiangtang Qinggan Prescription(7.5, 15 g·kg~(-1)) and metformin(0.067 g·kg~(-1)). Triglyceride and liver function were assessed using serum. The hematoxylin-eosin(HE) staining and Masson staining were used to stain the liver tissue, while HE staining and AB-PAS staining were used to stain the intestine tissue. 16S rRNA sequencing was utilized to track the changes in the intestinal flora of the mice in each group. Polymerase chain reaction(PCR) and immunofluorescence were used to determine the protein and mRNA expression levels of ZO-1, Occludin, and Claudin-1. The results demonstrated that Zuogui Jiangtang Qinggan Prescription increased the body mass of T2DM mice with NAFLD and decreased the hepatic index. It down-regulated the serum biomarkers of liver function and dyslipidemia such as alanine aminotransferase(ALT), aspartate transaminase(AST), and triglycerides(TG), increased insulin sensitivity, and improved glucose tolerance. According to the results of 16S rRNA sequencing, the Zuogui Jiangtang Qinggan Prescription altered the composition and abundance of the intestinal flora, increasing the relative abundances of Muribaculaceae, Lactobacillaceae, Lactobacillus, Akkermansia, and Bacteroidota and decreasing the relative abundances of Lachnospiraceae, Firmicutes, Deslfobacteria, Proteobacteria, and Desulfovibrionaceae. According to the pathological examination of the intestinal mucosa, Zuogui Jiangtang Qinggan Prescritpion increased the expression levels of the tight junction proteins ZO-1, Occludin, and Claudin-1, promoted intestinal mucosa repair, protected intestinal villi, and increased the height of intestinal mucosa villi and the number of goblet cells. By enhancing intestinal mucosal barrier repair and controlling intestinal microbiota homeostasis, Zuogui Jiangtang Qinggan Prescription reduces intestinal mucosal damage induced by T2DM and NAFLD.
Mice ; Animals ; Non-alcoholic Fatty Liver Disease/metabolism* ; Gastrointestinal Microbiome ; RNA, Ribosomal, 16S ; Diabetes Mellitus, Type 2/metabolism* ; Occludin/pharmacology* ; Claudin-1/metabolism* ; Intestinal Mucosa ; Liver ; Triglycerides/metabolism* ; Diet, High-Fat ; Homeostasis ; Mice, Inbred C57BL

This study aimed to investigate the biological effects and underlying mechanisms of the total ginsenosides from Panax ginseng stems and leaves on lipopolysaccharide(LPS)-induced acute lung injury(ALI) in mice. Sixty male C57BL/6J mice were randomly divided into a control group, a model group, the total ginsenosides from P. ginseng stems and leaves normal administration group(61.65 mg·kg~(-1)), and low-, medium-, and high-dose total ginsenosides from P. ginseng stems and leaves groups(15.412 5, 30.825, and 61.65 mg·kg~(-1)). Mice were administered for seven continuous days before modeling. Twenty-four hours after modeling, mice were sacrificed to obtain lung tissues and calculate lung wet/dry ratio. The number of inflammatory cells in bronchoalveolar lavage fluid(BALF) was detected. The levels of interleukin-1β(IL-1β), interleukin-6(IL-6), and tumor necrosis factor-α(TNF-α) in BALF were detected. The mRNA expression levels of IL-1β, IL-6, and TNF-α, and the levels of myeloperoxidase(MPO), glutathione peroxidase(GSH-Px), superoxide dismutase(SOD), and malondialdehyde(MDA) in lung tissues were determined. Hematoxylin-eosin(HE) staining was used to observe the pathological changes in lung tissues. The gut microbiota was detected by 16S rRNA sequencing, and gas chromatography-mass spectrometry(GC-MS) was applied to detect the content of short-chain fatty acids(SCFAs) in se-rum. The results showed that the total ginsenosides from P. ginseng stems and leaves could reduce lung index, lung wet/dry ratio, and lung damage in LPS-induced ALI mice, decrease the number of inflammatory cells and levels of inflammatory factors in BALF, inhibit the mRNA expression levels of inflammatory factors and levels of MPO and MDA in lung tissues, and potentiate the activity of GSH-Px and SOD in lung tissues. Furthermore, they could also reverse the gut microbiota disorder, restore the diversity of gut microbiota, increase the relative abundance of Lachnospiraceae and Muribaculaceae, decrease the relative abundance of Prevotellaceae, and enhance the content of SCFAs(acetic acid, propionic acid, and butyric acid) in serum. This study suggested that the total ginsenosides from P. ginseng stems and leaves could improve lung edema, inflammatory response, and oxidative stress in ALI mice by regulating gut microbiota and SCFAs metabolism.
Mice ; Male ; Animals ; Ginsenosides/pharmacology* ; Tumor Necrosis Factor-alpha/metabolism* ; Interleukin-6 ; Panax/genetics* ; Lipopolysaccharides/adverse effects* ; Gastrointestinal Microbiome ; RNA, Ribosomal, 16S ; Mice, Inbred C57BL ; Acute Lung Injury/genetics* ; Lung/metabolism* ; Superoxide Dismutase/metabolism* ; Plant Leaves/metabolism* ; RNA, Messenger

Objective: To detect and analyze the characteristics of oral microbiota in species composition, function and metabolism among caries, periodontitis and oral healthy individuals, hunting for the microbiome-derived biomarkers with specificity and sensitivity to estimate the occurrence of these two diseases. Methods: Saliva samples were collected from 10 patients with high caries risk [decayed-missing-filled teeth (DMFT)≥6, HC group] in Department of Endodontics, 10 patients with periodontitis of grade Ⅱ A-Ⅲ C (PG group) in Department of Periodontology and 10 oral healthy individuals (HH group) from School of Stomatology, The Fourth Military Medical University during from March 2022 to June 2022. A baseline examination was conducted on all participants, including their oral conditions of caries and periodontal health. Metagenomic sequencing (Illumina PE150 platform) and liquid chromatography-mass spectrometry were used to detect microorganisms and their metabolites in the samples respectively. The sequencing data were analyzed to obtain the information of microbial taxonomic composition, functional genes and metabolites in each group of samples. The basic oral conditions and saliva samples of subjects in each group were evaluated and collected by the same professional endodontist. Results: There were no significant difference in baseline characteristics such as age and sex among the subjects in each group (P>0.05). DMFT in HC group (9.0±1.7) was significantly higher than that in HH group (0) and PG group (0) (F=243.00, P<0.001). Sequencing data analysis showed that the taxonomic compositions of salivary microbiota in each group were mainly Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria and Fusobacteria at the phylum level, and Streptococcus, Neisseria, Rothia, Prevotella at the genus level. Differential analysis showed that, compared with the HH group, HC group and PG group had significant differences in taxonomic composition (P<0.05), and the most significant among them was Prevotella. At the species level, Prevotella pallens was the most significant change in HC group, and Porphyromonas gingivalis in PG group. Metabolite analysis showed that there were significant differences in metabolites between HC group and PG group. The results showed that, compared with the HH group, the most significant metabolite change was 3-hydroxy-1, 5-diphenylpentan-1-one in HC group (P=0.001) and N1 acetylspermine in PG group (P=0.002) respectively. Compared with the PG group, the metabolite of HC group with the most significant difference is D-glucosamine 6-phosphate (P=0.006). The metabolism gene function analysis showed that, the enrichment of carbohydrate metabolism related genes was highest in HC group, followed with HH group, and it was lowest in PG group. In addition, compared with the HH group, the abundance of functional genes related to glucose metabolism, such as ABC transporter and phosphotransferase system, were significantly decreased in PG group (P<0.05), but significantly increased in HC group (P<0.05). Conclusions: There is a significant correlation between the alternation of carbohydrate metabolism of salivary microbiota with the occurrence of caries and periodontitis. In the future, Prevotella pallens and 3-hydroxy-1, 5-diphenylpentan-1-one may be the potential biomarkers of caries; while Porphyromonas gingivalis and N1 acetylspermine work in the predictions of periodontitis.
Humans ; Saliva/microbiology* ; Dental Caries Susceptibility ; Periodontitis/microbiology* ; Microbiota/genetics* ; Porphyromonas gingivalis/genetics* ; RNA, Ribosomal, 16S/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