scholarly journals Characterization of Bacterial Community Dynamics of the Human Mouth Throughout Decomposition via Metagenomic, Metatranscriptomic, and Culturing Techniques

2021 ◽  
Vol 12 ◽  
Author(s):  
Emily C. Ashe ◽  
André M. Comeau ◽  
Katie Zejdlik ◽  
Seán P. O’Connell
Keyword(s):  
16S Rrna ◽  
Community Dynamics ◽  
Current Knowledge ◽  
16S Rrna Gene Sequencing ◽  
Oral Microbiome ◽  
16S Rrna Sequencing ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Bacterial Community Dynamics ◽  
Rrna Sequencing

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.

scholarly journals Dynamics of Soil Microbial Communities During Diazepam and Oxazepam Biodegradation in Soil Flooded by Water From a WWTP

2021 ◽  
Vol 12 ◽  
Author(s):  
Marc Crampon ◽  
Coralie Soulier ◽  
Pauline Sidoli ◽  
Jennifer Hellal ◽  
Catherine Joulian ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Soil Microbial Communities ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Treated Wastewater ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Rrna Gene Sequencing

The demand for energy and chemicals is constantly growing, leading to an increase of the amounts of contaminants discharged to the environment. Among these, pharmaceutical molecules are frequently found in treated wastewater that is discharged into superficial waters. Indeed, wastewater treatment plants (WWTPs) are designed to remove organic pollution from urban effluents but are not specific, especially toward contaminants of emerging concern (CECs), which finally reach the natural environment. In this context, it is important to study the fate of micropollutants, especially in a soil aquifer treatment (SAT) context for water from WWTPs, and for the most persistent molecules such as benzodiazepines. In the present study, soils sampled in a reed bed frequently flooded by water from a WWTP were spiked with diazepam and oxazepam in microcosms, and their concentrations were monitored for 97 days. It appeared that the two molecules were completely degraded after 15 days of incubation. Samples were collected during the experiment in order to follow the dynamics of the microbial communities, based on 16S rRNA gene sequencing for Archaea and Bacteria, and ITS2 gene for Fungi. The evolution of diversity and of specific operating taxonomic units (OTUs) highlighted an impact of the addition of benzodiazepines, a rapid resilience of the fungal community and an evolution of the bacterial community. It appeared that OTUs from the Brevibacillus genus were more abundant at the beginning of the biodegradation process, for diazepam and oxazepam conditions. Additionally, Tax4Fun tool was applied to 16S rRNA gene sequencing data to infer on the evolution of specific metabolic functions during biodegradation. It finally appeared that the microbial community in soils frequently exposed to water from WWTP, potentially containing CECs such as diazepam and oxazepam, may be adapted to the degradation of persistent contaminants.

scholarly journals Hierarchical Microbial Functions Prediction by Graph Aggregated Embedding

2021 ◽  
Vol 11 ◽  
Author(s):  
Yujie Hou ◽  
Xiong Zhang ◽  
Qinyan Zhou ◽  
Wenxing Hong ◽  
Ying Wang
Keyword(s):  
16S Rrna ◽  
Penaeus Monodon ◽  
Poor Performance ◽  
16S Rrna Gene Sequencing ◽  
Reference Database ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Significant Generalization ◽  
Rrna Gene Sequencing ◽  
Almost All

Matching 16S rRNA gene sequencing data to a metabolic reference database is a meaningful way to predict the metabolic function of bacteria and archaea, bringing greater insight to the working of the microbial community. However, some operational taxonomy units (OTUs) cannot be functionally profiled, especially for microbial communities from non-human samples cultured in defective media. Therefore, we herein report the development of Hierarchical micrObial functions Prediction by graph aggregated Embedding (HOPE), which utilizes co-occurring patterns and nucleotide sequences to predict microbial functions. HOPE integrates topological structures of microbial co-occurrence networks with k-mer compositions of OTU sequences and embeds them into a lower-dimensional continuous latent space, while maximally preserving topological relationships among OTUs. The high imbalance among KEGG Orthology (KO) functions of microbes is recognized in our framework that usually yields poor performance. A hierarchical multitask learning module is used in HOPE to alleviate the challenge brought by the long-tailed distribution among classes. To test the performance of HOPE, we compare it with HOPE-one, HOPE-seq, and GraphSAGE, respectively, in three microbial metagenomic 16s rRNA sequencing datasets, including abalone gut, human gut, and gut of Penaeus monodon. Experiments demonstrate that HOPE outperforms baselines on almost all indexes in all experiments. Furthermore, HOPE reveals significant generalization ability. HOPE's basic idea is suitable for other related scenarios, such as the prediction of gene function based on gene co-expression networks. The source code of HOPE is freely available at https://github.com/adrift00/HOPE.

scholarly journals Excessive Unbalanced Meat Consumption in the First Year of Life Increases Asthma Risk in the PASTURE and LUKAS2 Birth Cohorts

2021 ◽  
Vol 12 ◽  
Author(s):  
Alexander J. Hose ◽  
Giulia Pagani ◽  
Anne M. Karvonen ◽  
Pirkka V. Kirjavainen ◽  
Caroline Roduit ◽  
...  
Keyword(s):  
16S Rrna ◽  
Meat Consumption ◽  
16S Rrna Sequencing ◽  
Atopic Asthma ◽  
First Year ◽  
Sequencing Data ◽  
Food Items ◽  
Asthma Risk ◽  
Rrna Sequencing ◽  
First Year Of Life

A higher diversity of food items introduced in the first year of life has been inversely related to subsequent development of asthma. In the current analysis, we applied latent class analysis (LCA) to systematically assess feeding patterns and to relate them to asthma risk at school age. PASTURE (N=1133) and LUKAS2 (N=228) are prospective birth cohort studies designed to evaluate protective and risk factors for atopic diseases, including dietary patterns. Feeding practices were reported by parents in monthly diaries between the 4th and 12th month of life. For 17 common food items parents indicated frequency of feeding during the last 4 weeks in 4 categories. The resulting 153 ordinal variables were entered in a LCA. The intestinal microbiome was assessed at the age of 12 months by 16S rRNA sequencing. Data on feeding practice with at least one reported time point was available in 1042 of the 1133 recruited children. Best LCA model fit was achieved by the 4-class solution. One class showed an elevated risk of asthma at age 6 as compared to the other classes (adjusted odds ratio (aOR): 8.47, 95% CI 2.52–28.56, p = 0.001) and was characterized by daily meat consumption and rare consumption of milk and yoghurt. A refined LCA restricted to meat, milk, and yoghurt confirmed the asthma risk effect of a particular class in PASTURE and independently in LUKAS2, which we thus termed unbalanced meat consumption (UMC). The effect of UMC was particularly strong for non-atopic asthma and asthma irrespectively of early bronchitis (aOR: 17.0, 95% CI 5.2–56.1, p < 0.001). UMC fostered growth of iron scavenging bacteria such as Acinetobacter (aOR: 1.28, 95% CI 1.00-1.63, p = 0.048), which was also related to asthma (aOR: 1.55, 95% CI 1.18-2.03, p = 0.001). When reconstructing bacterial metabolic pathways from 16S rRNA sequencing data, biosynthesis of siderophore group nonribosomal peptides emerged as top hit (aOR: 1.58, 95% CI 1.13-2.19, p = 0.007). By a data-driven approach we found a pattern of overly meat consumption at the expense of other protein sources to confer risk of asthma. Microbiome analysis of fecal samples pointed towards overgrowth of iron-dependent bacteria and bacterial iron metabolism as a potential explanation.

scholarly journals High-resolution microbiome analysis enabled by linking of 16S rRNA gene sequences with adjacent genomic contexts

2021 ◽  
Vol 7 (9) ◽  
Author(s):  
Žana Kapustina ◽  
Justina Medžiūnė ◽  
Gediminas Alzbutas ◽  
Irmantas Rokaitis ◽  
Karolis Matjošaitis ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
16S Rrna Sequencing ◽  
Gene Copy ◽  
Rrna Gene ◽  
Gene Sequences ◽  
Copy Numbers ◽  
Rrna Sequencing ◽  
Metagenome Sequencing ◽  
Gene Copy Numbers

Sequence-based characterization of bacterial communities has long been a hostage of limitations of both 16S rRNA gene and whole metagenome sequencing. Neither approach is universally applicable, and the main efforts to resolve constraints have been devoted to improvement of computational prediction tools. Here, we present semi-targeted 16S rRNA sequencing (st16S-seq), a method designed for sequencing V1–V2 regions of the 16S rRNA gene along with the genomic locus upstream of the gene. By in silico analysis of 13 570 bacterial genome assemblies, we show that genome-linked 16S rRNA sequencing is superior to individual hypervariable regions or full-length gene sequences in terms of classification accuracy and identification of gene copy numbers. Using mock communities and soil samples we experimentally validate st16S-seq and benchmark it against the established microbial classification techniques. We show that st16S-seq delivers accurate estimation of 16S rRNA gene copy numbers, enables taxonomic resolution at the species level and closely approximates community structures obtainable by whole metagenome sequencing.

scholarly journals The archives are half-empty: an assessment of the availability of microbial community sequencing data

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Stephanie D. Jurburg ◽  
Maximilian Konzack ◽  
Nico Eisenhauer ◽  
Anna Heintz-Buschart
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
16S Rrna Gene Sequencing ◽  
Data Reuse ◽  
Data Availability ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Data Archiving ◽  
Sequencing Studies ◽  
Rrna Gene Sequencing

AbstractAs DNA sequencing has become more popular, the public genetic repositories where sequences are archived have experienced explosive growth. These repositories now hold invaluable collections of sequences, e.g., for microbial ecology, but whether these data are reusable has not been evaluated. We assessed the availability and state of 16S rRNA gene amplicon sequences archived in public genetic repositories (SRA, EBI, and DDJ). We screened 26,927 publications in 17 microbiology journals, identifying 2015 16S rRNA gene sequencing studies. Of these, 7.2% had not made their data public at the time of analysis. Among a subset of 635 studies sequencing the same gene region, 40.3% contained data which was not available or not reusable, and an additional 25.5% contained faults in data formatting or data labeling, creating obstacles for data reuse. Our study reveals gaps in data availability, identifies major contributors to data loss, and offers suggestions for improving data archiving practices.

Accurate Five-category Classification for Colorectal Cancer Using Gut Microbiome 16S rRNA Sequencing

2021 ◽  
Author(s):  
Liying Zhang ◽  
Jiaqi Zhu ◽  
Qiutao Ding ◽  
Yanqi Huang ◽  
Hongbo Zhang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
16S Rrna ◽  
Gut Microbiome ◽  
Large Scale ◽  
Operational Taxonomic Unit ◽  
16S Rrna Sequencing ◽  
Screening Tests ◽  
Advanced Adenoma ◽  
Sequencing Data ◽  
Rrna Sequencing

Abstract The association between the gut microbiome and the five stages of colorectal cancer (CRC) (healthy, polyposis, nonadvanced adenoma, advanced adenoma, and cancer) remains unclear. We performed 16S rRNA sequencing of the V3-V4 amplicon from 999 samples from subjects at various stages of CRC development and constructed an accurate predictive random forest model for CRC development. In the testing set, our five-category CRC prediction classifier had accuracies of 0.84 and 0.74 using the relative operational taxonomic unit (OTU) abundances and relative genus abundances, respectively. Specifically, the OTU-based classifier had a sensitivity of 0.97 and specificity of 0.97 for CRC samples, and the genus-based classifier had a sensitivity of 0.97 and specificity of 0.95 for CRC samples. Meanwhile, the gut microbiota was found to differ at all stages of CRC development. The differential abundances of closely related bacteria were used to accurately classify the five stages of CRC development. Additionally, both unannotated and annotated OTUs played important roles in classifier modelling. Our work not only provides valuable 16S rRNA sequencing data from patients and healthy individuals on a large scale but also identifies reproducible gut microbiome biomarkers for CRC staging and highlights their potential applications as noninvasive microbiome biomarkers for diagnosis and as predictive CRC screening tests.

scholarly journals Deciphering the low abundance microbiota of presumed aseptic hip and knee implants

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257471
Author(s):  
Charles Carr ◽  
Hannah Wilcox ◽  
Jeremy P. Burton ◽  
Sharanya Menon ◽  
Kait F. Al ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Computational Tools ◽  
Cutibacterium Acnes ◽  
Joint Implants ◽  
Rrna Gene Sequencing

16S rRNA gene sequencing of DNA extracted from clinically uninfected hip and knee implant samples has revealed polymicrobial populations. However, previous studies assessed 16S rRNA gene sequencing as a technique for the diagnosis of periprosthetic joint infections, leaving the microbiota of presumed aseptic hip and knee implants largely unstudied. These communities of microorganisms might play important roles in aspects of host health, such as aseptic loosening. Therefore, this study sought to characterize the bacterial composition of presumed aseptic joint implant microbiota using next generation 16S rRNA gene sequencing, and it evaluated this method for future investigations. 248 samples were collected from implants of 41 patients undergoing total hip or knee arthroplasty revision for presumed aseptic failure. DNA was extracted using two methodologies—one optimized for high throughput and the other for human samples—and amplicons of the V4 region of the 16S rRNA gene were sequenced. Sequencing data were analyzed and compared with ancillary specific PCR and microbiological culture. Computational tools (SourceTracker and decontam) were used to detect and compensate for environmental and processing contaminants. Microbial diversity of patient samples was higher than that of open-air controls and differentially abundant taxa were detected between these conditions, possibly reflecting a true microbiota that is present in clinically uninfected joint implants. However, positive control-associated artifacts and DNA extraction methodology significantly affected sequencing results. As well, sequencing failed to identify Cutibacterium acnes in most culture- and PCR-positive samples. These challenges limited characterization of bacteria in presumed aseptic implants, but genera were identified for further investigation. In all, we provide further support for the hypothesis that there is likely a microbiota present in clinically uninfected joint implants, and we show that methods other than 16S rRNA gene sequencing may be ideal for its characterization. This work has illuminated the importance of further study of microbiota of clinically uninfected joint implants with novel molecular and computational tools to further eliminate contaminants and artifacts that arise in low bacterial abundance samples.

scholarly journals Identification of Mycobacterium neoaurumIsolated from a Neutropenic Patient with Catheter-Related Bacteremia by 16S rRNA Sequencing

2000 ◽  
Vol 38 (9) ◽  
pp. 3515-3517 ◽  
Author(s):  
Patrick C. Y. Woo ◽  
Hoi-Wah Tsoi ◽  
Kit-Wah Leung ◽  
Peggy N. L. Lum ◽  
Andy S. P. Leung ◽  
...  
Keyword(s):  
16S Rrna ◽  
Lymphoblastic Leukemia ◽  
Similarity Index ◽  
16S Rrna Sequencing ◽  
Identification System ◽  
Rrna Gene ◽  
Microbial Identification ◽  
First Case ◽  
Rrna Sequencing ◽  
Mycobacterial Strain

A rapidly growing pigmented mycobacterial strain with an ambiguous biochemical profile was isolated from the blood culture taken through the Hickman catheter of a 9-year-old girl with acute lymphoblastic leukemia. Whole-cell fatty acid analysis showed that the best match profile was that of Mycobacterium aurum, but the similarity index was only 0.217, meaning that there were no good matches between the isolate and the organisms in the database of the Microbial Identification System. The 16S rRNA gene of the mycobacterial strain was amplified, agarose gel purified, and sequenced. There were 44 base differences between the gene sequence of the isolate and that ofM. aurum but only one base difference between the sequence of the isolate and that of Mycobacterium neoaurum, showing that the isolate was indeed a strain of M. neoaurum by using this “gold standard.” This represents the first case ofM. neoaurum infection documented by 16S rRNA sequencing.

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