scholarly journals Temperate Coastal Microbial Communities Rapidly Respond to Low Concentrations of Partially Weathered Diesel

2021 ◽  
Author(s):  
Camilla M. Ryther ◽  
Alice C. Ortmann ◽  
Gary Wohlgeschaffen ◽  
Brian J. Robinson
Keyword(s):  
Microbial Communities ◽  
Small Concentration ◽  
Energy Conditions ◽  
Volatile Components ◽  
Prokaryotic Community ◽  
Sequencing Data ◽  
Run Off ◽  
Low Concentrations ◽  
Road Surfaces ◽  
Benzene Toluene

AbstractDiesel is frequently encountered in coastal ecosystems due to land run-off from road surfaces. The current study investigates how partially weathered diesel at environmentally relevant concentrations, as may be seen during a run-off event, affect coastal microbial communities. A mesocosm experiment using seawater from the Bedford Basin, Nova Scotia, was followed for 72 h after the addition of partially weathered diesel. Sequencing data suggests partially weathered diesel acts quickly to alter the prokaryotic community, as both opportunistic (Vibrio and Lentibacter) and oil-degrading (Colwellia, Sulfitobacter, and Pseudoalteromonas) bacteria proliferated after 24 h in comparison to the control. In addition, total prokaryotes seemed to recover in abundance after 24 h, where eukaryotes only ceased to decrease slightly at 72 h, likely because of an inability to adapt to the oil-laden conditions, unlike the prokaryotes. Considering there were no highly volatile components (benzene, toluene, ethylbenzene, and xylene) present in the diesel when the communities were exposed, the results indicate that even a relatively small concentration of diesel run-off can cause a drastic change to the microbial community under low energy conditions. Higher energy conditions due to wave action may mitigate the response of the microbial communities by dilution and additional weathering of the diesel.

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 Bayesian Classification of Microbial Communities Based on 16S rRNA Metagenomic Data

2018 ◽  
Author(s):  
Arghavan Bahadorinejad ◽  
Ivan Ivanov ◽  
Johanna W Lampe ◽  
Meredith AJ Hullar ◽  
Robert S Chapkin ◽  
...  
Keyword(s):  
16S Rrna ◽  
Sample Size ◽  
Microbial Communities ◽  
State Of The Art ◽  
Metagenomic Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Sample Data

AbstractWe propose a Bayesian method for the classification of 16S rRNA metagenomic profiles of bacterial abundance, by introducing a Poisson-Dirichlet-Multinomial hierarchical model for the sequencing data, constructing a prior distribution from sample data, calculating the posterior distribution in closed form; and deriving an Optimal Bayesian Classifier (OBC). The proposed algorithm is compared to state-of-the-art classification methods for 16S rRNA metagenomic data, including Random Forests and the phylogeny-based Metaphyl algorithm, for varying sample size, classification difficulty, and dimensionality (number of OTUs), using both synthetic and real metagenomic data sets. The results demonstrate that the proposed OBC method, with either noninformative or constructed priors, is competitive or superior to the other methods. In particular, in the case where the ratio of sample size to dimensionality is small, it was observed that the proposed method can vastly outperform the others.Author summaryRecent studies have highlighted the interplay between host genetics, gut microbes, and colorectal tumor initiation/progression. The characterization of microbial communities using metagenomic profiling has therefore received renewed interest. In this paper, we propose a method for classification, i.e., prediction of different outcomes, based on 16S rRNA metagenomic data. The proposed method employs a Bayesian approach, which is suitable for data sets with small ration of number of available instances to the dimensionality. Results using both synthetic and real metagenomic data show that the proposed method can outperform other state-of-the-art metagenomic classification algorithms.

scholarly journals CAMISIM: Simulating metagenomes and microbial communities

2018 ◽  
Author(s):  
Adrian Fritz ◽  
Peter Hofmann ◽  
Stephan Majda ◽  
Eik Dahms ◽  
Johannes Dröge ◽  
...  
Keyword(s):  
Microbial Communities ◽  
De Novo ◽  
Real Data ◽  
Small Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Taxonomic Profiling ◽  
Benchmark Data ◽  
Sequencing Technologies ◽  
Wide Range

Shotgun metagenome data sets of microbial communities are highly diverse, not only due to the natural variation of the underlying biological systems, but also due to differences in laboratory protocols, replicate numbers, and sequencing technologies. Accordingly, to effectively assess the performance of metagenomic analysis software, a wide range of benchmark data sets are required. Here, we describe the CAMISIM microbial community and metagenome simulator. The software can model different microbial abundance profiles, multi-sample time series and differential abundance studies, includes real and simulated strain-level diversity, and generates second and third generation sequencing data from taxonomic profiles or de novo. Gold standards are created for sequence assembly, genome binning, taxonomic binning, and taxonomic profiling. CAMSIM generated the benchmark data sets of the first CAMI challenge. For two simulated multi-sample data sets of the human and mouse gut microbiomes we observed high functional congruence to the real data. As further applications, we investigated the effect of varying evolutionary genome divergence, sequencing depth, and read error profiles on two popular metagenome assemblers, MEGAHIT and metaSPAdes, on several thousand small data sets generated with CAMISIM. CAMISIM can simulate a wide variety of microbial communities and metagenome data sets together with truth standards for method evaluation. All data sets and the software are freely available at: https://github.com/CAMI-challenge/CAMISIM

scholarly journals Resilience of Microbial Communities after Hydrogen Peroxide Treatment of a Eutrophic Lake to Suppress Harmful Cyanobacterial Blooms

2021 ◽  
Vol 9 (7) ◽  
pp. 1495
Author(s):  
Tim Piel ◽  
Giovanni Sandrini ◽  
Gerard Muyzer ◽  
Corina P. D. Brussaard ◽  
Pieter C. Slot ◽  
...  
Keyword(s):  
Hydrogen Peroxide ◽  
Microbial Community ◽  
Microbial Communities ◽  
Microbial Community Composition ◽  
Glycoside Hydrolases ◽  
Microbial Community Analysis ◽  
Cyanobacterial Blooms ◽  
Temporary Increase ◽  
Low Concentrations ◽  
Harmful Cyanobacterial Blooms

Applying low concentrations of hydrogen peroxide (H2O2) to lakes is an emerging method to mitigate harmful cyanobacterial blooms. While cyanobacteria are very sensitive to H2O2, little is known about the impacts of these H2O2 treatments on other members of the microbial community. In this study, we investigated changes in microbial community composition during two lake treatments with low H2O2 concentrations (target: 2.5 mg L−1) and in two series of controlled lake incubations. The results show that the H2O2 treatments effectively suppressed the dominant cyanobacteria Aphanizomenon klebahnii, Dolichospermum sp. and, to a lesser extent, Planktothrix agardhii. Microbial community analysis revealed that several Proteobacteria (e.g., Alteromonadales, Pseudomonadales, Rhodobacterales) profited from the treatments, whereas some bacterial taxa declined (e.g., Verrucomicrobia). In particular, the taxa known to be resistant to oxidative stress (e.g., Rheinheimera) strongly increased in relative abundance during the first 24 h after H2O2 addition, but subsequently declined again. Alpha and beta diversity showed a temporary decline but recovered within a few days, demonstrating resilience of the microbial community. The predicted functionality of the microbial community revealed a temporary increase of anti-ROS defenses and glycoside hydrolases but otherwise remained stable throughout the treatments. We conclude that the use of low concentrations of H2O2 to suppress cyanobacterial blooms provides a short-term pulse disturbance but is not detrimental to lake microbial communities and their ecosystem functioning.

scholarly journals Human gut microbial communities dictate efficacy of anti-PD-1 therapy in a humanized microbiome mouse model of glioma

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Kory J Dees ◽  
Hyunmin Koo ◽  
J Fraser Humphreys ◽  
Joseph A Hakim ◽  
David K Crossman ◽  
...  
Keyword(s):  
Mouse Model ◽  
Microbial Communities ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Microbial Composition ◽  
Healthy Human ◽  
Fecal Transplantation ◽  
Host Interaction ◽  
Shotgun Metagenomic Sequencing ◽  
Mouse Lines

Abstract Background Although immunotherapy works well in glioblastoma (GBM) preclinical mouse models, the therapy has not demonstrated efficacy in humans. To address this anomaly, we developed a novel humanized microbiome (HuM) model to study the response to immunotherapy in a preclinical mouse model of GBM. Methods We used 5 healthy human donors for fecal transplantation of gnotobiotic mice. After the transplanted microbiomes stabilized, the mice were bred to generate 5 independent humanized mouse lines (HuM1-HuM5). Results Analysis of shotgun metagenomic sequencing data from fecal samples revealed a unique microbiome with significant differences in diversity and microbial composition among HuM1-HuM5 lines. All HuM mouse lines were susceptible to GBM transplantation, and exhibited similar median survival ranging from 19 to 26 days. Interestingly, we found that HuM lines responded differently to the immune checkpoint inhibitor anti-PD-1. Specifically, we demonstrate that HuM1, HuM4, and HuM5 mice are nonresponders to anti-PD-1, while HuM2 and HuM3 mice are responsive to anti-PD-1 and displayed significantly increased survival compared to isotype controls. Bray-Curtis cluster analysis of the 5 HuM gut microbial communities revealed that responders HuM2 and HuM3 were closely related, and detailed taxonomic comparison analysis revealed that Bacteroides cellulosilyticus was commonly found in HuM2 and HuM3 with high abundances. Conclusions The results of our study establish the utility of humanized microbiome mice as avatars to delineate features of the host interaction with gut microbial communities needed for effective immunotherapy against GBM.

IMMU-09. HUMAN MICROBIOTA INFLUENCE THE EFFICACY OF IMMUNOTHERAPY IN A MOUSE MODEL OF GLIOBLASTOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi93-vi94
Author(s):  
Kory Dees ◽  
Hyunmin Koo ◽  
James Humphreys ◽  
Joseph Hakim ◽  
David Crossman ◽  
...  
Keyword(s):  
Mouse Model ◽  
Microbial Communities ◽  
Mouse Models ◽  
Gut Microbiome ◽  
Positive Response ◽  
Checkpoint Inhibitors ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Microbial Composition ◽  
Healthy Human

Abstract Although immunotherapy works well in glioblastoma (GBM) pre-clinical mouse models, the therapy has unfortunately not demonstrated efficacy in humans. In melanoma and other cancers, the composition of the gut microbiome has been shown to determine responsiveness or resistance to immune checkpoint inhibitors (anti-PD-1). Most pre-clinical cancer studies have been done in mouse models using mouse gut microbiomes, but there are significant differences between mouse and human microbial gut compositions. To address this inconsistency, we developed a novel humanized microbiome (HuM) model to study the response to immunotherapy in a pre-clinical mouse model of GBM. We used five healthy human donors for fecal transplantation of gnotobiotic mice. After the transplanted microbiomes stabilized, the mice were bred to generate five independent humanized mouse lines (HuM1-HuM5). Analysis of shotgun metagenomic sequencing data from fecal samples revealed a unique microbiome with significant differences in diversity and microbial composition among HuM1-HuM5 lines. Interestingly, we found that the HuM lines responded differently to anti-PD-1. Specifically, we demonstrate that HuM2 and HuM3 mice are responsive to anti-PD-1 and displayed significantly increased survival compared to isotype controls, while HuM1, HuM4, and HuM5 mice are resistant to anti-PD-1. These mice are genetically identical, and only differ in the composition of the gut microbiome. In a correlative experiment, we found that disrupting the responder HuM2 microbiome with antibiotics abrogated the positive response to anti-PD-1, indicating that HuM2 microbiota must be present in the mice to elicit the positive response to anti-PD-1 in the GBM model. The question remains of whether the “responsive” microbial communities in HuM2 and HuM3 can be therapeutically exploited and applicable in other tumor models, or if the “resistant” microbial communities in HuM1, HuM4, and HuM5 can be depleted and/or replaced. Future studies will assess responder microbial transplants as a method of enhancing immunotherapy.

Pathogen identification in prostate cancer biopsies using transcriptome sequencing.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e16545-e16545
Author(s):  
Carisa M Shah ◽  
Rohan Bareja ◽  
Olivier Elemento
Keyword(s):  
Prostate Cancer ◽  
Bone Metastases ◽  
Microbial Communities ◽  
Rna Sequencing ◽  
Metastatic Prostate Cancer ◽  
Microbial Pathogens ◽  
Sequencing Data ◽  
Streptomyces Species ◽  
Microbial Species ◽  
Tumor Biopsies

e16545 Background: Tumor biopsies may frequently be associated with microbial species due to proximity with microbial communities or due to contamination during tissue processing. We examined sequencing data from tumor biopsies to explore the tumor-associated microbiome in prostate cancer. Methods: Patients were enrolled in a prospective Precision Medicine study to evaluate genomic alterations based on freshly obtained tissue biopsies. Total RNA was prepared for RNA sequencing using the standard Illumina mRNA sample preparation protocol. Paired-end RNA-sequencing at read lengths of 50 or 51 bp was performed with the HiSeq 2000 (Illumina). A total of ~268 million paired-end reads were generated, corresponding to 27 billion bases. Kraken and FusionCatcher were used to identify the metagenome that maps to microbial species. R was used to analyze the microbial pathogens and to create a heatmap of microbial abundance. We focused our analysis on prostate cancer. Results: From 1/30/2013 to 12/16/15, 83 patients with urothelial cancers were identified, of which 32 patients with prostate cancer (n = 23 from primary tumor, n = 9 metastatic prostate cancer) were sequenced. Of the 9 metastatic samples, 5 were metastatic prostate cancer to bone, 4 were other metastases. A giant cell tumor of bone was used as a control. The metagenome was identified and mapped to the Kraken pathogen database. 43 unique pathogens characterized the microbiome associated with prostate cancer, including Streptomyces species, Acinetobacter baumannii, Enterobacteriaceae, and Corynebacterium species. The most prevalent species in prostate cancer bone metastases were also the most prevalent in primary prostate tumors. Prostate cancer bone metastases clustered separately from the primary prostate cancer samples, the other metastases, and primary bone cancer. Conclusions: We have identified a putative microbiome signature associated with prostate cancer. Prostate cancer bone metastases appear to cluster separately from prostate cancer, suggesting a non-random association with microbial communities. Ongoing work is looking to (1) validate microbial association using orthogonal analyses and (2) determine the exact origin of the signature.

Comparative analysis of microbiome between accurately identified 16S rDNA and quantified bacteria in simulated samples

2014 ◽  
Vol 63 (3) ◽  
pp. 433-440 ◽  
Author(s):  
Haiyin Wang ◽  
Pengcheng Du ◽  
Juan Li ◽  
Yuanyuan Zhang ◽  
Wen Zhang ◽  
...  
Keyword(s):  
Microbial Communities ◽  
16S Rdna ◽  
Gold Standard ◽  
Clinical Utility ◽  
Bacterial Species ◽  
Relative Proportion ◽  
Rrna Gene ◽  
Low Concentrations ◽  
Rdna Sequencing ◽  
V3 Region

Although 16S rRNA gene (rDNA) sequencing is the gold standard for categorizing bacteria or characterizing microbial communities its clinical utility is limited by bias in metagenomic studies, in either the experiments or the data analyses. To evaluate the efficiency of current metagenomic methods, we sequenced seven simulated samples of ten bacterial species mixed at different concentrations. The V3 region of 16S rDNA was targeted and used to determine the distribution of bacterial species. The number of target sequences in individual simulated samples was in the range 1–1000 to provide a better reflection of natural microbial communities. However, for a given bacterial species present in the same proportion but at different concentrations, the observed percentage of 16S rDNAs was similar, except at very low concentrations that cannot be detected by real-time PCR. These results confirmed that the comparative microbiome in a sample characterized by 16S rDNA sequencing is sufficient to detect not only potential infectious pathogens, but also the relative proportion of 16S rDNA in the sample.

scholarly journals Phylogenetic Molecular Ecological Network of Soil Microbial Communities in Response to Elevated CO2

mBio ◽  
2011 ◽  
Vol 2 (4) ◽  
Author(s):  
Jizhong Zhou ◽  
Ye Deng ◽  
Feng Luo ◽  
Zhili He ◽  
Yunfeng Yang
Keyword(s):  
Microbial Communities ◽  
Microbial Ecology ◽  
High Throughput ◽  
Network Topology ◽  
Ecosystem Functioning ◽  
Soil Microbial Communities ◽  
Microbial Populations ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Soil Microbial

ABSTRACT Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO2, is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO2 enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO2 levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management. IMPORTANCE The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO2 level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research.

Microbial population dynamics in laboratory-scale activated sludge reactors

2002 ◽  
Vol 46 (1-2) ◽  
pp. 19-27 ◽  
Author(s):  
K. Kaewpipat ◽  
C.P.L. Grady
Keyword(s):  
Activated Sludge ◽  
Microbial Communities ◽  
System Performance ◽  
Denaturing Gradient Gel Electrophoresis ◽  
Treatment Plant ◽  
Laboratory Scale ◽  
Rrna Gene ◽  
Significant Finding ◽  
Prokaryotic Community ◽  
Activated Sludge Systems

As a first step in understanding nonlinear dynamics in activated sludge systems, two laboratory-scale sequencing batch reactors were operated under identical conditions and changes in their microbial communities were followed through microscopic examination, macroscopic observation, and denaturing gradient gel electrophoresis (DGGE) of PCR-amplified 16S rRNA gene segments from the prokaryotic community. Two experiments were performed. The first used activated sludge from a local wastewater treatment plant to start the replicate reactors. The second used the biomass from the first experiment as a source by intermixing the two and equally redistributing the biomass into the two replicate reactors. For both experiments, the two reactors behaved fairly similarly and had similar microbial communities for a period of 60 days following start-up. Beyond that, the microbial communities in the two reactors in the first experiment diverged in composition, while those in the second experiment remained fairly similar. This suggests that the degree of change occurring in replicate reactors depends upon the severity of perturbation to which they are exposed. The DGGE data showed that the bacterial communities in both experiments were highly dynamic, even though the system performance of the replicate reactors were very similar, suggesting that dynamics within the prokaryotic community is not necessarily reflected in system performance. Moreover, a significant finding from this study is that replicate activated sludge systems are not identical, although they can be very similar if started appropriately.

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