scholarly journals A step-by-step sequence-based analysis of virome enrichment protocol for freshwater and sediment samples

2020 ◽  
Author(s):  
Federica Pinto ◽  
Moreno Zolfo ◽  
Francesco Beghini ◽  
Federica Armanini ◽  
Francesco Asnicar ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Microbial Community Composition ◽  
Genetic Material ◽  
Amplicon Sequencing ◽  
Sample Type ◽  
Natural Ecosystems ◽  
Sediment Samples ◽  
Water And Sediment ◽  
Water And Sediment Samples

AbstractCultivation-free metagenomic analysis afforded unprecedented details on the diversity, structure and potential functions of microbial communities in different environments. When employed to study the viral fraction of the community that is recalcitrant to cultivation, metagenomics can shed light into the diversity of viruses and their role in natural ecosystems. However, despite the increasing interest in virome metagenomics, methodological issues still hinder the proper interpretation and comparison of results across studies. Virome enrichment experimental protocols are key multi-step processes needed for separating and concentrating the viral fraction from the whole microbial community prior to sequencing. However, there is little information on their efficiency and their potential biases. To fill this gap, we used metagenomic and amplicon sequencing to examine the microbial community composition through the serial filtration and concentration steps commonly used to produce viral-enriched metagenomes. The analyses were performed on water and sediment samples from an Alpine lake. We found that, although the diversity of the retained microbial communities declined progressively during the serial filtration, the final viral fraction contained a large proportion (from 10% to 40%) of non-viral taxa, and that the efficacy of filtration showed biases based on taxonomy. Our results quantified the amount of bacterial genetic material in viromes and highlighted the influence of sample type on the enrichment efficacy. Moreover, since viral-enriched samples contained a significant portion of microbial taxa, computational sequence analysis should account for such biases in the downstream interpretation pipeline.ImportanceFiltration is a commonly used method to enrich viral particles in environmental samples. However, there is little information on its efficiency and potential biases on the final result. Using a sequence-based analysis on water and sediment samples, we found that filtration efficacy is dependent on sample type and that the final virome contained a large proportion of non-viral taxa. Our finding stressed the importance of downstream analysis to avoid biased interpretation of data.

scholarly journals Agricultural Pollution Risks Influence Microbial Ecology in Honghu Lake

2018 ◽  
Author(s):  
Maozhen Han ◽  
Melissa Dsouza ◽  
Chunyu Zhou ◽  
Hongjun Li ◽  
Junqian Zhang ◽  
...  
Keyword(s):  
Community Structure ◽  
Microbial Community ◽  
Physicochemical Properties ◽  
Microbial Communities ◽  
Microbial Ecology ◽  
Agricultural Pollution ◽  
Agricultural Activities ◽  
Sediment Samples ◽  
Water And Sediment ◽  
Honghu Lake

AbstractBackgroundAgricultural activities, such as stock-farming, planting industry, and fish aquaculture, can influence the physicochemistry and biology of freshwater lakes. However, the extent to which these agricultural activities, especially those that result in eutrophication and antibiotic pollution, effect water and sediment-associated microbial ecology, remains unclear.MethodsWe performed a geospatial analysis of water and sediment associated microbial community structure, as well as physicochemical parameters and antibiotic pollution, across 18 sites in Honghu lake, which range from impacted to less-impacted by agricultural pollution. Furthermore, the co-occurrence network of water and sediment were built and compared accorded to the agricultural activities.ResultsPhysicochemical properties including TN, TP, NO3--N, and NO2--N were correlated with microbial compositional differences in water samples. Likewise, in sediment samples, Sed-OM and Sed-TN correlated with microbial diversity. Oxytetracycline and tetracycline concentration described the majority of the variance in taxonomic and predicted functional diversity between impacted and less-impacted sites in water and sediment samples, respectively. Finally, the structure of microbial co-associations was influenced by the eutrophication and antibiotic pollution.ConclusionThese analyses of the composition and structure of water and sediment microbial communities in anthropologically-impacted lakes are imperative for effective environmental pollution monitoring. Likewise, the exploration of the associations between environmental variables (e.g. physicochemical properties, and antibiotics) and community structure is important in the assessment of lake water quality and its ability to sustain agriculture. These results show agricultural practices can negatively influence not only the physicochemical properties, but also the biodiversity of microbial communities associated with the Honghu lake ecosystem. And these results provide compelling evidence that the microbial community can be used as a sentinel of eutrophication and antibiotics pollution risk associated with agricultural activity; and that proper monitoring of this environment is vital to maintain a sustainable environment in Honghu lake.

scholarly journals Effects of Actinomycete Secondary Metabolites on Sediment Microbial Communities

2016 ◽  
Vol 83 (4) ◽  
Author(s):  
Nastassia V. Patin ◽  
Michelle Schorn ◽  
Kristen Aguinaldo ◽  
Tommie Lincecum ◽  
Bradley S. Moore ◽  
...  
Keyword(s):  
Microbial Community ◽  
Secondary Metabolites ◽  
Microbial Communities ◽  
Community Composition ◽  
Marine Sediments ◽  
Microbial Community Composition ◽  
Amplicon Sequencing ◽  
Next Generation ◽  
Content Type ◽  
Predatory Bacteria

ABSTRACT Marine sediments harbor complex microbial communities that remain poorly studied relative to other biomes such as seawater. Moreover, bacteria in these communities produce antibiotics and other bioactive secondary metabolites, yet little is known about how these compounds affect microbial community structure. In this study, we used next-generation amplicon sequencing to assess native microbial community composition in shallow tropical marine sediments. The results revealed complex communities comprised of largely uncultured taxa, with considerable spatial heterogeneity and known antibiotic producers comprising only a small fraction of the total diversity. Organic extracts from cultured strains of the sediment-dwelling actinomycete genus Salinispora were then used in mesocosm studies to address how secondary metabolites shape sediment community composition. We identified predatory bacteria and other taxa that were consistently reduced in the extract-treated mesocosms, suggesting that they may be the targets of allelopathic interactions. We tested related taxa for extract sensitivity and found general agreement with the culture-independent results. Conversely, several taxa were enriched in the extract-treated mesocosms, suggesting that some bacteria benefited from the interactions. The results provide evidence that bacterial secondary metabolites can have complex and significant effects on sediment microbial communities. IMPORTANCE Ocean sediments represent one of Earth's largest and most poorly studied biomes. These habitats are characterized by complex microbial communities where competition for space and nutrients can be intense. This study addressed the hypothesis that secondary metabolites produced by the sediment-inhabiting actinomycete Salinispora arenicola affect community composition and thus mediate interactions among competing microbes. Next-generation amplicon sequencing of mesocosm experiments revealed complex communities that shifted following exposure to S. arenicola extracts. The results reveal that certain predatory bacteria were consistently less abundant following exposure to extracts, suggesting that microbial metabolites mediate competitive interactions. Other taxa increased in relative abundance, suggesting a benefit from the extracts themselves or the resulting changes in the community. This study takes a first step toward assessing the impacts of bacterial metabolites on sediment microbial communities. The results provide insight into how low-abundance organisms may help structure microbial communities in ocean sediments.

scholarly journals Bacterial Diversity of Water and Sediment Samples from Gull Point State Park (West Okoboji, Iowa) Determined Using 16S rRNA Gene Amplicon Sequencing

2021 ◽  
Vol 10 (33) ◽  
Author(s):  
John A. Kyndt
Keyword(s):  
16S Rrna ◽  
Amplicon Sequencing ◽  
Rrna Gene ◽  
Great Lakes Region ◽  
Primary State ◽  
Microbial Composition ◽  
The West ◽  
Sediment Samples ◽  
Water And Sediment ◽  
Water And Sediment Samples

Gull Point State Park is located on a peninsula on the west shore of West Okoboji Lake (Iowa, USA). It is the primary state park in the Iowa Great Lakes region. Sediment and water samples from three locations at the Gull Point pond were analyzed for their microbial composition.

scholarly journals Assessing Microbial Communities Related to Mercury Transformations in Contaminated Streambank Soils

2021 ◽  
Vol 232 (1) ◽  
Author(s):  
Yazeed Abdelmageed ◽  
Carrie Miller ◽  
Carrie Sanders ◽  
Timothy Egbo ◽  
Alexander Johs ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Contaminated Soils ◽  
Microbial Community Composition ◽  
Inorganic Mercury ◽  
Amplicon Sequencing ◽  
Soil Samples ◽  
Rrna Gene ◽  
Enrichment Cultures ◽  
Relative Abundances

AbstractIn nature, the bioaccumulative potent neurotoxin methylmercury (MeHg) is produced from inorganic mercury (Hg) predominantly by anaerobic microorganisms. Hg-contaminated soils are a potential source of MeHg due to microbial activity. We examine streambank soils collected from the contaminated East Fork Poplar Creek (EFPC) in Tennessee, USA, where seasonal variations in MeHg levels have been observed throughout the year, suggesting active microbial Hg methylation. In this study, we characterized the microbial community in contaminated bank soil samples collected from two locations over a period of one year and compared the results to soil samples from an uncontaminated reference site with similar geochemistry (n = 12). Microbial community composition and diversity were assessed by 16S rRNA gene amplicon sequencing. Furthermore, to isolate potential methylators from soils, enrichment cultures were prepared using selective media. A set of three clade-specific primers targeting the gene hgcA were used to detect Hg methylators among the δ-Proteobacteria in EFPC bank soils across all seasons. Two families among the δ-Proteobacteria that have been previously associated with Hg methylation, Geobacteraceae and Syntrophobacteraceae, were found to be predominant with relative abundances of 0.13% and 4.0%, respectively. However, in soil enrichment cultures, Firmicutes were predominant among families associated with Hg methylation. Specifically, Clostridiaceae and Peptococcaceae and their genera Clostridium and Desulfosporosinus were among the ten most abundant genera with relative abundances of 2.6% and 1.7%, respectively. These results offer insights into the role of microbial communities on Hg transformation processes in contaminated bank soils in EFPC. Identifying the biogeochemical drivers of MeHg production is critical for future remediation efforts.

scholarly journals Wetland plant evolutionary history influences soil and endophyte microbial community composition

2020 ◽  
Author(s):  
Marisa B. Szubryt ◽  
Kelly Skinner ◽  
Edward J. O’Loughlin ◽  
Jason Koval ◽  
Stephanie M. Greenwald ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Composition ◽  
Plant Species ◽  
Evolutionary History ◽  
Microbial Community Composition ◽  
Methane Flux ◽  
Sample Type ◽  
Wetland Plant ◽  
Wetland Ecosystems

AbstractMethane is a microbially derived greenhouse gas whose emissions are highly variable throughout wetland ecosystems. Differences in plant community composition account for some of this variability, suggesting an influence of plant species on microbial community structure and function in these ecosystems. Given that closely related plant species have similar morphological and biochemical features, we hypothesize that plant evolutionary history is related to differences in microbial community composition. To examine species-specific patterns in microbiomes, we selected five monoculture-forming wetland plant species based on the evolutionary distances among them. We detected significant differences in microbial communities between sample types (unvegetated soil, bulk soil, rhizosphere soil, internal root tissues, and internal leaf tissues) associated with these plant species based on 16S relative abundances. We additionally found that differences in plant evolutionary history were correlated with variation in microbial communities across plant species within each sample type. Using qPCR, we observed substantial differences in overall methanogen and methanotroph population sizes between plant species and sample types. Methanogens tended to be most abundant in rhizosphere soils while methanotrophs were the most abundant in roots. Given that microbes influence methane flux and that plants affect methanogen and methanotroph populations, plant species contribute to variable degrees of methane emissions. Incorporating the influence of plant evolutionary history into future modeling efforts may improve predictions of wetland methane emission since microbial community differences correlate with differences in plant evolutionary history.

scholarly journals Machine Learning Predicts the Presence of 2,4,6-Trinitrotoluene in Sediments of a Baltic Sea Munitions Dumpsite Using Microbial Community Compositions

2021 ◽  
Vol 12 ◽  
Author(s):  
René Janßen ◽  
Aaron J. Beck ◽  
Johannes Werner ◽  
Olaf Dellwig ◽  
Johannes Alneberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Microbial Community ◽  
Baltic Sea ◽  
Microbial Communities ◽  
Environmental Variables ◽  
Microbial Community Composition ◽  
Amplicon Sequencing ◽  
R Package ◽  
Environmental Drivers ◽  
Rrna Gene

Bacteria are ubiquitous and live in complex microbial communities. Due to differences in physiological properties and niche preferences among community members, microbial communities respond in specific ways to environmental drivers, potentially resulting in distinct microbial fingerprints for a given environmental state. As proof of the principle, our goal was to assess the opportunities and limitations of machine learning to detect microbial fingerprints indicating the presence of the munition compound 2,4,6-trinitrotoluene (TNT) in southwestern Baltic Sea sediments. Over 40 environmental variables including grain size distribution, elemental composition, and concentration of munition compounds (mostly at pmol⋅g–1 levels) from 150 sediments collected at the near-to-shore munition dumpsite Kolberger Heide by the German city of Kiel were combined with 16S rRNA gene amplicon sequencing libraries. Prediction was achieved using Random Forests (RFs); the robustness of predictions was validated using Artificial Neural Networks (ANN). To facilitate machine learning with microbiome data we developed the R package phyloseq2ML. Using the most classification-relevant 25 bacterial genera exclusively, potentially representing a TNT-indicative fingerprint, TNT was predicted correctly with up to 81.5% balanced accuracy. False positive classifications indicated that this approach also has the potential to identify samples where the original TNT contamination was no longer detectable. The fact that TNT presence was not among the main drivers of the microbial community composition demonstrates the sensitivity of the approach. Moreover, environmental variables resulted in poorer prediction rates than using microbial fingerprints. Our results suggest that microbial communities can predict even minor influencing factors in complex environments, demonstrating the potential of this approach for the discovery of contamination events over an integrated period of time. Proven for a distinct environment future studies should assess the ability of this approach for environmental monitoring in general.

scholarly journals Assessing species biomass contributions in microbial communities via metaproteomics

2017 ◽  
Author(s):  
Manuel Kleiner ◽  
Erin Thorson ◽  
Christine E. Sharp ◽  
Xiaoli Dong ◽  
Dan Liu ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Composition ◽  
Microbial Mats ◽  
Soda Lakes ◽  
Microbial Community Composition ◽  
Amplicon Sequencing ◽  
Rrna Gene ◽  
Cell Numbers

AbstractAssessment of microbial community composition is the cornerstone of microbial ecology. Microbial community composition can be analyzed by quantifying cell numbers or by quantifying biomass for individual populations. However, as cell volumes can differ by orders of magnitude, these two approaches yield vastly different results. Methods for quantifying cell numbers are already available (e.g. fluorescence in situ hybridization, 16S rRNA gene amplicon sequencing), yet methods for assessing community composition in terms of biomass are lacking.We developed metaproteomics based methods for assessing microbial community composition using protein abundance as a measure for biomass contributions of individual populations. We optimized the accuracy and sensitivity of the method using artificially assembled microbial communities and found that it is less prone to some of the biases found in sequencing-based methods. We applied the method using communities from two different environments, microbial mats from two alkaline soda lakes and saliva from multiple individuals.

scholarly journals CaptureSeq: Hybridization-Based Enrichment of cpn60 Gene Fragments Reveals the Community Structures of Synthetic and Natural Microbial Ecosystems

2021 ◽  
Vol 9 (4) ◽  
pp. 816
Author(s):  
Matthew G. Links ◽  
Tim J. Dumonceaux ◽  
E. Luke McCarthy ◽  
Sean M. Hemmingsen ◽  
Edward Topp ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Dna Sequences ◽  
Pcr Amplification ◽  
Shotgun Sequencing ◽  
Natural Ecosystems ◽  
Gene Markers ◽  
Microbial Profile ◽  
Microbial Ecosystems ◽  
Pcr Targeting

Background. The molecular profiling of complex microbial communities has become the basis for examining the relationship between the microbiome composition, structure and metabolic functions of those communities. Microbial community structure can be partially assessed with “universal” PCR targeting taxonomic or functional gene markers. Increasingly, shotgun metagenomic DNA sequencing is providing more quantitative insight into microbiomes. However, both amplicon-based and shotgun sequencing approaches have shortcomings that limit the ability to study microbiome dynamics. Methods. We present a novel, amplicon-free, hybridization-based method (CaptureSeq) for profiling complex microbial communities using probes based on the chaperonin-60 gene. Molecular profiles of a commercially available synthetic microbial community standard were compared using CaptureSeq, whole metagenome sequencing, and 16S universal target amplification. Profiles were also generated for natural ecosystems including antibiotic-amended soils, manure storage tanks, and an agricultural reservoir. Results. The CaptureSeq method generated a microbial profile that encompassed all of the bacteria and eukaryotes in the panel with greater reproducibility and more accurate representation of high G/C content microorganisms compared to 16S amplification. In the natural ecosystems, CaptureSeq provided a much greater depth of coverage and sensitivity of detection compared to shotgun sequencing without prior selection. The resulting community profiles provided quantitatively reliable information about all three domains of life (Bacteria, Archaea, and Eukarya) in the different ecosystems. The applications of CaptureSeq will facilitate accurate studies of host-microbiome interactions for environmental, crop, animal and human health. Conclusions: cpn60-based hybridization enriched for taxonomically informative DNA sequences from complex mixtures. In synthetic and natural microbial ecosystems, CaptureSeq provided sequences from prokaryotes and eukaryotes simultaneously, with quantitatively reliable read abundances. CaptureSeq provides an alternative to PCR amplification of taxonomic markers with deep community coverage while minimizing amplification biases.

scholarly journals Reduced metagenome sequencing for strain-resolution taxonomic profiles

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Lars Snipen ◽  
Inga-Leena Angell ◽  
Torbjørn Rognes ◽  
Knut Rudi
Keyword(s):  
Microbial Community ◽  
Microbial Community Composition ◽  
Amplicon Sequencing ◽  
Good Alternative ◽  
Shotgun Sequencing ◽  
Least Square ◽  
Full Potential ◽  
Shotgun Metagenomics ◽  
Metagenome Sequencing ◽  
Reference Genomes

Abstract Background Studies of shifts in microbial community composition has many applications. For studies at species or subspecies levels, the 16S amplicon sequencing lacks resolution and is often replaced by full shotgun sequencing. Due to higher costs, this restricts the number of samples sequenced. As an alternative to a full shotgun sequencing we have investigated the use of Reduced Metagenome Sequencing (RMS) to estimate the composition of a microbial community. This involves the use of double-digested restriction-associated DNA sequencing, which means only a smaller fraction of the genomes are sequenced. The read sets obtained by this approach have properties different from both amplicon and shotgun data, and analysis pipelines for both can either not be used at all or not explore the full potential of RMS data. Results We suggest a procedure for analyzing such data, based on fragment clustering and the use of a constrained ordinary least square de-convolution for estimating the relative abundance of all community members. Mock community datasets show the potential to clearly separate strains even when the 16S is 100% identical, and genome-wide differences is < 0.02, indicating RMS has a very high resolution. From a simulation study, we compare RMS to shotgun sequencing and show that we get improved abundance estimates when the community has many very closely related genomes. From a real dataset of infant guts, we show that RMS is capable of detecting a strain diversity gradient for Escherichia coli across time. Conclusion We find that RMS is a good alternative to either metabarcoding or shotgun sequencing when it comes to resolving microbial communities at the strain level. Like shotgun metagenomics, it requires a good database of reference genomes and is well suited for studies of the human gut or other communities where many reference genomes exist. A data analysis pipeline is offered, as an R package at https://github.com/larssnip/microRMS.

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