scholarly journals Evaluation of Changes to the Oral Microbiome Based on 16S rRNA Sequencing among Children Treated for Cancer

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 7
Author(s):  
Patrycja Proc ◽  
Joanna Szczepańska ◽  
Beata Zarzycka ◽  
Małgorzata Szybka ◽  
Maciej Borowiec ◽  
...  

A child’s mouth is the gateway to many species of bacteria. Changes in the oral microbiome may affect the health of the entire body. The aim of the study was to evaluate the changes in the oral microbiome of childhood cancer survivors. Saliva samples before and after anti-cancer treatment were collected from 20 patients aged 6–18 years, diagnosed de novo with cancer in 2018–2019 (7 girls and 13 boys, 7.5–19 years old at the second time point). Bacterial DNA was extracted, and the microbial community profiles were assessed by 16S rRNA sequencing. The relative abundances of Cellulosilyticum and Tannerella genera were found to significantly change throughout therapy (p = 0.043 and p = 0.036, respectively). However, no differences in the alpha-diversity were observed (p = 0.817). The unsupervised classification revealed two clusters of patients: the first with significant changes in Campylobacter and Fusobacterium abundance, and the other with change in Neisseria. These two groups of patients differed in median age (10.25 vs. 16.16 years; p = 0.004) and the length of anti-cancer therapy (19 vs. 4 months; p = 0.003), but not cancer type or antibiotic treatment.

2021 ◽  
Vol 12 ◽  
Author(s):  
Emily C. Ashe ◽  
André M. Comeau ◽  
Katie Zejdlik ◽  
Seán P. O’Connell

The postmortem microbiome has recently moved to the forefront of forensic research, and many studies have focused on the idea that predictable fluctuations in decomposer communities could be used as a “microbial clock” to determine time of death. Commonly, the oral microbiome has been evaluated using 16S rRNA gene sequencing to assess the changes in community composition throughout decomposition. We sampled the hard palates of three human donors over time to identify the prominent members of the microbiome. This study combined 16S rRNA sequencing with whole metagenomic (MetaG) and metatranscriptomic (MetaT) sequencing and culturing methodologies in an attempt to broaden current knowledge about how these postmortem microbiota change and might function throughout decomposition. In all four methods, Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes were the dominant phyla, but their distributions were insufficient in separating samples based on decomposition stage or time or by donor. Better resolution was observed at the level of genus, with fresher samples from decomposition clustering away from others via principal components analysis (PCA) of the sequencing data. Key genera in driving these trends included Rothia; Lysinibacillus, Lactobacillus, Staphylococcus, and other Firmicutes; and yeasts including Candida and Yarrowia. The majority of cultures (89%) matched to sequences obtained from at least one of the sequencing methods, while 11 cultures were found in the same samples using all three methods. These included Acinetobacter gerneri, Comamonas terrigena, Morganella morganii, Proteus vulgaris, Pseudomonas koreensis, Pseudomonas moraviensis, Raoutella terrigena, Stenotrophomonas maltophilia, Bacillus cereus, Kurthia zopfii, and Lactobacillus paracasei. MetaG and MetaT data also revealed many novel insects as likely visitors to the donors in this study, opening the door to investigating them as potential vectors of microorganisms during decomposition. The presence of cultures at specific time points in decomposition, including samples for which we have MetaT data, will yield future studies tying specific taxa to metabolic pathways involved in decomposition. Overall, we have shown that our 16S rRNA sequencing results from the human hard palate are consistent with other studies and have expanded on the range of taxa shown to be associated with human decomposition, including eukaryotes, based on additional sequencing technologies.


2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Fuwei Zhao ◽  
Guimiao Jiang ◽  
Chuanliang Ji ◽  
Zhiping Zhang ◽  
Weiping Gao ◽  
...  

Abstract Background This study aims to determine the effects of transportation on the nasal microbiota of healthy donkeys using 16S rRNA sequencing. Results Deep nasal swabs and blood were sampled from 14 donkeys before and after 21 hours’ long-distance transportation. The values of the plasma hormone (cortisol (Cor), adrenocorticotrophic hormone (ACTH)), biochemical indicators (total protein (TP), albumin (ALB), creatinine (CREA), lactic dehydrogenase (LDH), aspartate transaminase (AST), creatine kinase (CK), blood urea (UREA), plasma glucose (GLU)) and blood routine indices (white blood cell (WBC), lymphocyte (LYM), neutrophil (NEU), red blood cell (RBC), hemoglobin (HGB)) were measured. 16S rRNA sequencing was used to assess the nasal microbiota, including alpha diversity, beta diversity, and phylogenetic structures. Results showed that levels of Cor, ACTH, and heat-shock protein 90 (HSP90) were significantly increased (p < 0.05) after long-distance transportation. Several biochemical indicators (AST, CK) and blood routine indices (Neu, RBC, and HGB) increased markedly (p < 0.05), but the LYM decreased significantly (p < 0.05). Nine families and eight genera had a mean relative abundance over 1%. The predominant phyla in nasal microbiota after and before transportation were Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. Transportation stress induced significant changes in terms of nasal microbiota structure compared with those before transportation based on principal coordinate analysis (PCoA) coupled with analysis of similarities (ANOSIM) (p < 0.05). Among these changes, a notably gain in Proteobacteria and loss in Firmicutes at the phylum level was observed. Conclusions These results suggest transportation can cause stress to donkeys and change the richness and diversity of nasal microbiota. Further studies are required to understand the potential effect of these microbiota changes on the development of donkey respiratory diseases.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4005-4005
Author(s):  
Maren Schmiester ◽  
René Maier ◽  
René Riedel ◽  
Marco Frentsch ◽  
Robert R. Jenq ◽  
...  

Abstract Introduction Microbial dysbiosis is associated with increased infectious complications and poorer treatment outcomes in patients with hematologic malignancies (Galloway-Pena et al., 2016, Peled et al., 2020). While the effective reconstitution of microbial diversity by autologous fecal microbiota transfer appears within clinical reach (Malard et al, 2021), diagnosis of dysbiosis typically relies on comparatively slow and laborious molecular techniques. We validated a fast and reliable method to assess and track microbial diversity based on flow cytometric analysis (FCM) of single cell phenotypical traits in stool samples of patients undergoing therapy for aggressive lymphoma disease. Methods Stool samples of 12 patients with B-cell neoplasms and 1 patient with a T-cell neoplasm were collected at the time of diagnosis, directly before each cycle of chemoimmunotherapy administration (CHOP backbone +/- anti-CD20 therapy) and every 3 months during follow-up (2-11 samples per patient totaling n = 77 samples). Microbiome analyses were performed by sequencing the V3/V4 16S rRNA region and by FCM of light scatter properties and DNA content (DAPI staining). Phenotypic and taxonomic alpha diversity (inverse Simpson index, D2) was determined from the flow cytometric profiles and sequencing data, respectively, using the PhenoFlow R package (Props et al., 2016). Longitudinal trends in alpha diversity were evaluated with the SplinectomeR R package (Shields-Cutler et al., 2018). Results Correlation analysis and ordinary least squares regression confirmed a statistically significant association between FCM-based phenotypic and sequencing-based taxonomic alpha diversity in "real-world" patient fecal samples (Pearson's correlation r p = 0.56, r 2 = 0.32, p &lt; 0.001) (Figure 1). Despite the distinct and complex data types generated by the two methods, these results demonstrate that phenotypic diversity measurements obtained from FCM can serve as proxies for taxonomic diversity measurements obtained from 16S rRNA sequencing. Next, we analyzed the dynamics of microbial alpha diversity in our cohort over the course of therapy and follow-up using a spline-based approach optimized for longitudinal data. We demonstrated a statistically significant non-linear trend of alpha diversity with a decrease during the period of chemotherapy administration followed by reconstitution during the immunotherapy and follow-up period (Figure 2) using both FCM and 16S rRNA sequencing (p = 0.001). Notably, the samples collected during the treatment phase were obtained 2-3 weeks after the last administration of chemoimmunotherapy, depicting a progressive and sustained dysbiosis in patients during the months of chemotherapy. Conclusions We demonstrated a clear correlation between microbial alpha diversity as determined by 16S rRNA sequencing and by our flow cytometric-based approach. Using FCM analysis, we found a marked decline in diversity during chemotherapy administration. These results are in line with the dysbiosis pattern observed by 16S rRNA sequencing and with previous studies (Rashidi et al., 2019). Our findings thus demonstrate that FCM could provide a fast, first-line point-of-care monitoring of microbial diversity dynamics. Figure 1 Figure 1. Disclosures Jenq: Merck: Consultancy; Karius: Consultancy; Microbiome DX: Consultancy; Seres: Membership on an entity's Board of Directors or advisory committees, Patents & Royalties; Kaleido: Membership on an entity's Board of Directors or advisory committees; MaaT Pharma: Membership on an entity's Board of Directors or advisory committees; Prolacta: Membership on an entity's Board of Directors or advisory committees; LISCure: Membership on an entity's Board of Directors or advisory committees. Bullinger: Daiichi Sankyo: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Sanofi: Honoraria; Amgen: Honoraria; Gilead: Consultancy; Jazz Pharmaceuticals: Consultancy, Honoraria, Research Funding; Hexal: Consultancy; Novartis: Consultancy, Honoraria; Bayer: Research Funding; Pfizer: Consultancy, Honoraria; Seattle Genetics: Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Menarini: Consultancy; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Astellas: Honoraria. Na: Octapharma: Honoraria, Research Funding; Shire/Takeda: Honoraria, Research Funding; Bristol Myers Squibb: Research Funding.


Author(s):  
Tong Tong Wu ◽  
Jin Xiao ◽  
Michael B. Sohn ◽  
Kevin A. Fiscella ◽  
Christie Gilbert ◽  
...  

Untreated tooth decays affect nearly one third of the world and is the most prevalent disease burden among children. The disease progression of tooth decay is multifactorial and involves a prolonged decrease in pH, resulting in the demineralization of tooth surfaces. Bacterial species that are capable of fermenting carbohydrates contribute to the demineralization process by the production of organic acids. The combined use of machine learning and 16s rRNA sequencing offers the potential to predict tooth decay by identifying the bacterial community that is present in an individual’s oral cavity. A few recent studies have demonstrated machine learning predictive modeling using 16s rRNA sequencing of oral samples, but they lack consideration of the multifactorial nature of tooth decay, as well as the role of fungal species within their models. Here, the oral microbiome of mother–child dyads (both healthy and caries-active) was used in combination with demographic–environmental factors and relevant fungal information to create a multifactorial machine learning model based on the LASSO-penalized logistic regression. For the children, not only were several bacterial species found to be caries-associated (Prevotella histicola, Streptococcus mutans, and Rothia muciloginosa) but also Candida detection and lower toothbrushing frequency were also caries-associated. Mothers enrolled in this study had a higher detection of S. mutans and Candida and a higher plaque index. This proof-of-concept study demonstrates the significant impact machine learning could have in prevention and diagnostic advancements for tooth decay, as well as the importance of considering fungal and demographic–environmental factors.


2012 ◽  
Vol 2 (2) ◽  
pp. 111
Author(s):  
Sung-Hee Oh ◽  
Min-Chul Cho ◽  
Jae-Wook Kim ◽  
Dongheui An ◽  
Mun-Hui Jeong ◽  
...  

Author(s):  
Isabel Abellan-Schneyder ◽  
Andrea Janina Bayer ◽  
Sandra Reitmeier ◽  
Klaus Neuhaus

Author(s):  
Andrea Janina Bayer ◽  
Sandra Reitmeier ◽  
Klaus Neuhaus ◽  
Isabel Abellan-Schneyder

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Haleh Forouhandeh ◽  
Sepideh Zununi Vahed ◽  
Hossein Ahangari ◽  
Vahideh Tarhriz ◽  
Mohammad Saeid Hejazi

Abstract Lighvan cheese (Lighvan panir) is among the most famous traditional cheese in Iran for its desired aroma and flavor. Undoubtedly, the lactic acid bacteria especially the genus Lactobacillus are the critical factors in developing the aroma, flavor, and texture in Lighvan cheese. In this study, the Lactobacillus population of the main Lighvan cheese was investigated. The Lactobacillus of the main Lighvan cheese was isolated using specific culture methods according to previously published Guidelines. Then, the phylogenetic features were investigated and the phenotypic characteristics were examined using specific culture methods. Twenty-eight Gram-positive bacterial species were identified belonged to the genus Lactobacillus. According to the same sequences as each other, three groups (A, B, and C) of isolates were categorized with a high degree of similarity to L. fermentum (100%) and L. casei group (L. casei, L. paracasei, and L. rhamnosus) (99.0 to 100%). Random amplified polymorphic DNA (RAPD) fingerprint analysis manifested the presence of three clusters that were dominant in traditional Lighvan cheese. Cluster І was divided into 4 sub-clusters. By the result of carbohydrate fermentation pattern and 16S rRNA sequencing, isolates were identified as L. rhamnosus. The isolates in clusters II and III represented L. paracasei and L. fermentum, respectively as they were identified by 16S rRNA sequencing and fermented carbohydrate patterns. Our result indicated that the specific aroma and flavor of traditional Lighvan cheese can be related to its Lactobacillus population including L. fermentum, L. casei, L. paracasei, and L. rhamnosus. Graphical abstract


2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Leena Malayil ◽  
Suhana Chattopadhyay ◽  
Emmanuel F. Mongodin ◽  
Amy R. Sapkota

AbstractNontraditional irrigation water sources (e.g., recycled water, brackish water) may harbor human pathogens, including Vibrio spp., that could be present in a viable-but-nonculturable (VBNC) state, stymieing current culture-based detection methods. To overcome this challenge, we coupled 5-bromo-2′-deoxyuridine (BrdU) labeling, enrichment techniques, and 16S rRNA sequencing to identify metabolically-active Vibrio spp. in nontraditional irrigation water (recycled water, pond water, non-tidal freshwater, and tidal brackish water). Our coupled BrdU-labeling and sequencing approach revealed the presence of metabolically-active Vibrio spp. at all sampling sites. Whereas, the culture-based method only detected vibrios at three of the four sites. We observed the presence of V. cholerae, V. vulnificus, and V. parahaemolyticus using both methods, while V. aesturianus and V. shilonii were detected only through our labeling/sequencing approach. Multiple other pathogens of concern to human health were also identified through our labeling/sequencing approach including P. shigelloides, B. cereus and E. cloacae. Most importantly, 16S rRNA sequencing of BrdU-labeled samples resulted in Vibrio spp. detection even when our culture-based methods resulted in negative detection. This suggests that our novel approach can effectively detect metabolically-active Vibrio spp. that may have been present in a VBNC state, refining our understanding of the prevalence of vibrios in nontraditional irrigation waters.


Metabolites ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 487
Author(s):  
Tao Zhang ◽  
Hao Ding ◽  
Lan Chen ◽  
Yueyue Lin ◽  
Yongshuang Gong ◽  
...  

Elucidation of the mechanism of lipogenesis and fat deposition is essential for controlling excessive fat deposition in chicken. Studies have shown that gut microbiota plays an important role in regulating host lipogenesis and lipid metabolism. However, the function of gut microbiota in the lipogenesis of chicken and their relevant mechanisms are poorly understood. In the present study, the gut microbiota of chicken was depleted by oral antibiotics. Changes in cecal microbiota and metabolomics were detected by 16S rRNA sequencing and ultra-high performance liquid chromatography coupled with MS/MS (UHPLC–MS/MS) analysis. The correlation between antibiotic-induced dysbiosis of gut microbiota and metabolites and lipogenesis were analysed. We found that oral antibiotics significantly promoted the lipogenesis of chicken. 16S rRNA sequencing indicated that oral antibiotics significantly reduced the diversity and richness and caused dysbiosis of gut microbiota. Specifically, the abundance of Proteobacteria was increased considerably while the abundances of Bacteroidetes and Firmicutes were significantly decreased. At the genus level, the abundances of genera Escherichia-Shigella and Klebsiella were significantly increased while the abundances of 12 genera were significantly decreased, including Bacteroides. UHPLC-MS/MS analysis showed that antibiotic-induced dysbiosis of gut microbiota significantly altered cecal metabolomics and caused declines in abundance of 799 metabolites and increases in abundance of 945 metabolites. Microbiota-metabolite network revealed significant correlations between 4 differential phyla and 244 differential metabolites as well as 15 differential genera and 304 differential metabolites. Three metabolites of l-glutamic acid, pantothenate acid and N-acetyl-l-aspartic acid were identified as potential metabolites that link gut microbiota and lipogenesis in chicken. In conclusion, our results showed that antibiotic-induced dysbiosis of gut microbiota promotes lipogenesis of chicken by altering relevant metabolomics. The efforts in this study laid a basis for further study of the mechanisms that gut microbiota regulates lipogenesis and fat deposition of chicken.


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