Reevaluating the Salty Divide: Phylogenetic Specificity of Transitions between Marine and Freshwater Systems

ABSTRACTMarine and freshwater microbial communities are phylogenetically distinct, and transitions between habitat types are thought to be infrequent. We compared the phylogenetic diversity of marine and freshwater microorganisms and identified specific lineages exhibiting notably high or low similarity between marine and freshwater ecosystems using a meta-analysis of 16S rRNA gene tag-sequencing data sets. As expected, marine and freshwater microbial communities differed in the relative abundance of major phyla and contained habitat-specific lineages. At the same time, and contrary to expectations, many shared taxa were observed in both habitats. Based on several metrics, we found thatGammaproteobacteria,Alphaproteobacteria,Bacteroidetes, andBetaproteobacteriacontained the highest number of closely related marine and freshwater sequences, suggesting comparatively recent habitat transitions in these groups. Using the abundant alphaproteobacterial group SAR11 as an example, we found evidence that new lineages, beyond the recognized LD12 clade, are detected in freshwater at low but reproducible abundances; this evidence extends beyond the 16S rRNA locus to core genes throughout the genome. Our results suggest that shared taxa are numerous, but tend to occur sporadically and at low relative abundance in one habitat type, leading to an underestimation of transition frequency between marine and freshwater habitats. Rare taxa with abundances near or below detection, including lineages that appear to have crossed the salty divide relatively recently, may possess adaptations enabling them to exploit opportunities for niche expansion when environments are disturbed or conditions change.IMPORTANCEThe distribution of microbial diversity across environments yields insight into processes that create and maintain this diversity as well as potential to infer how communities will respond to future environmental changes. We integrated data sets from dozens of freshwater lake and marine samples to compare diversity across open water habitats differing in salinity. Our novel combination of sequence-based approaches revealed lineages that likely experienced a recent transition across habitat types. These taxa are promising targets for studying physiological constraints on salinity tolerance. Our findings contribute to understanding the ecological and evolutionary controls on microbial distributions, and open up new questions regarding the plasticity and adaptability of particular lineages.

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Re-evaluating the salty divide: phylogenetic specificity of transitions between marine and freshwater systems

10.1101/347021 ◽

2018 ◽

Cited By ~ 2

Author(s):

Sara F. Paver ◽

Daniel J. Muratore ◽

Ryan J. Newton ◽

Maureen L. Coleman

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Microbial Communities ◽

Relative Abundance ◽

Environmental Changes ◽

Habitat Type ◽

Freshwater Ecosystems ◽

Rrna Gene ◽

Minimum Entropy ◽

Habitat Types

AbstractMarine and freshwater microbial communities are phylogenetically distinct and transitions between habitat types are thought to be infrequent. We compared the phylogenetic diversity of marine and freshwater microorganisms and identified specific lineages exhibiting notably high or low similarity between marine and freshwater ecosystems using a meta-analysis of 16S rRNA gene tag-sequencing datasets. As expected, marine and freshwater microbial communities differed in the relative abundance of major phyla and contained habitat-specific lineages; at the same time, however, many shared taxa were observed in both environments. Betaproteobacteria and Alphaproteobacteria sequences had the highest similarity between marine and freshwater sample pairs. Gammaproteobacteria and Alphaproteobacteria contained the highest number of Minimum Entropy Decomposition nodes shared by marine and freshwater samples. Shared nodes included lineages of the abundant alphaproteobacterial group SAR11 that have not previously been reported in 16S rRNA gene surveys of freshwater lakes. Our results suggest that shared taxa are numerous, but tend to occur sporadically and at low relative abundance in one habitat type, leading to an underestimation of transition frequency between marine and freshwater habitats. Lineages with a high degree of shared taxa or habitat-specific diversification represent targets for genome-scale investigations into microbial adaptations and evolutionary innovations. Rare taxa with abundances near or below detection, including lineages that appear to have crossed the salty divide relatively recently, may have novel adaptations enabling them to exploit opportunities for niche expansion when environments are disturbed or conditions change.ImportanceThe distribution of microbial diversity across environments yields insight into processes that create and maintain this diversity as well as potential to infer how communities will respond to future environmental changes. We integrated datasets from dozens of freshwater lake and marine samples to compare diversity across open water habitats differing in salinity. Our novel combination of sequence-based approaches revealed phyla and proteobacterial classes inferred to include more or less recent transitions across habitat types as well as specific lineages that are shared by marine and freshwater environments at the level of 16S rRNA sequence types. Our findings contribute to understanding the ecological and evolutionary controls on microbial distributions, and open up new questions regarding the plasticity and adaptability of particular lineages.

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

Frontiers in Microbiology ◽

10.3389/fmicb.2021.742000 ◽

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.

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

10.1101/340653 ◽

2018 ◽

Cited By ~ 1

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.

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Soil Depth Determines the Composition and Diversity of Bacterial and Archaeal Communities in a Poplar Plantation

Forests ◽

10.3390/f10070550 ◽

2019 ◽

Vol 10 (7) ◽

pp. 550 ◽

Cited By ~ 6

Author(s):

Huili Feng ◽

Jiahuan Guo ◽

Weifeng Wang ◽

Xinzhang Song ◽

Shuiqiang Yu

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Microbial Communities ◽

Relative Abundance ◽

Rrna Gene ◽

Poplar Plantation ◽

Plantation Forest ◽

Bacterial And Archaeal Communities ◽

Different Depths ◽

Archaeal Communities

Understanding the composition and diversity of soil microorganisms that typically mediate the soil biogeochemical cycle is crucial for estimating greenhouse gas flux and mitigating global changes in plantation forests. Therefore, the objectives of this study were to investigate changes in diversity and relative abundance of bacteria and archaea with soil profiles and the potential factors influencing the vertical differentiation of microbial communities in a poplar plantation. We investigated soil bacterial and archaeal community compositions and diversities by 16S rRNA gene Illumina MiSeq sequencing at different depths of a poplar plantation forest in Chenwei forest farm, Sihong County, Jiangsu, China. More than 882,422 quality-filtered 16S rRNA gene sequences were obtained from 15 samples, corresponding to 34 classified phyla and 68 known classes. Ten major bacterial phyla and two archaeal phyla were found. The diversity of bacterial and archaeal communities decreased with depth of the plantation soil. Analysis of variance (ANOVA) of relative abundance of microbial communities exhibited that Nitrospirae, Verrucomicrobia, Latescibacteria, GAL15, SBR1093, and Euryarchaeota had significant differences at different depths. The transition zone of the community composition between the surface and subsurface occurred at 10–20 cm. Overall, our findings highlighted the importance of depth with regard to the complexity and diversity of microbial community composition in plantation forest soils.

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TaxAss: Leveraging a Custom Freshwater Database Achieves Fine-Scale Taxonomic Resolution

mSphere ◽

10.1128/msphere.00327-18 ◽

2018 ◽

Vol 3 (5) ◽

Cited By ~ 25

Author(s):

Robin R. Rohwer ◽

Joshua J. Hamilton ◽

Ryan J. Newton ◽

Katherine D. McMahon

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Microbial Communities ◽

Microbial Community Composition ◽

Taxonomic Resolution ◽

Rrna Gene ◽

Data Sets ◽

Data Set ◽

Comprehensive Database ◽

Fine Resolution

ABSTRACT Taxonomy assignment of freshwater microbial communities is limited by the minimally curated phylogenies used for large taxonomy databases. Here we introduce TaxAss, a taxonomy assignment workflow that classifies 16S rRNA gene amplicon data using two taxonomy reference databases: a large comprehensive database and a small ecosystem-specific database rigorously curated by scientists within a field. We applied TaxAss to five different freshwater data sets using the comprehensive SILVA database and the freshwater-specific FreshTrain database. TaxAss increased the percentage of the data set classified compared to using only SILVA, especially at fine-resolution family to species taxon levels, while across the freshwater test data sets classifications increased by as much as 11 to 40% of total reads. A similar increase in classifications was not observed in a control mouse gut data set, which was not expected to contain freshwater bacteria. TaxAss also maintained taxonomic richness compared to using only the FreshTrain across all taxon levels from phylum to species. Without TaxAss, most organisms not represented in the FreshTrain were unclassified, but at fine taxon levels, incorrect classifications became significant. We validated TaxAss using simulated amplicon data derived from full-length clone libraries and found that 96 to 99% of test sequences were correctly classified at fine resolution. TaxAss splits a data set’s sequences into two groups based on their percent identity to reference sequences in the ecosystem-specific database. Sequences with high similarity to sequences in the ecosystem-specific database are classified using that database, and the others are classified using the comprehensive database. TaxAss is free and open source and is available at https://www.github.com/McMahonLab/TaxAss. IMPORTANCE Microbial communities drive ecosystem processes, but microbial community composition analyses using 16S rRNA gene amplicon data sets are limited by the lack of fine-resolution taxonomy classifications. Coarse taxonomic groupings at the phylum, class, and order levels lump ecologically distinct organisms together. To avoid this, many researchers define operational taxonomic units (OTUs) based on clustered sequences, sequence variants, or unique sequences. These fine-resolution groupings are more ecologically relevant, but OTU definitions are data set dependent and cannot be compared between data sets. Microbial ecologists studying freshwater have curated a small, ecosystem-specific taxonomy database to provide consistent and up-to-date terminology. We created TaxAss, a workflow that leverages this database to assign taxonomy. We found that TaxAss improves fine-resolution taxonomic classifications (family, genus, and species). Fine taxonomic groupings are more ecologically relevant, so they provide an alternative to OTU-based analyses that is consistent and comparable between data sets.

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Updating urinary microbiome analyses to enhance biologic interpretation

10.1101/2021.09.30.21264391 ◽

2021 ◽

Author(s):

Nazema Y Siddiqui ◽

Li Ma ◽

Linda Brubaker ◽

Jialiang Mao ◽

Carter Hoffman ◽

...

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Microbial Communities ◽

Bacterial Genome ◽

Rrna Gene ◽

Sequence Variant ◽

Sequencing Data ◽

Variable Regions ◽

Urinary Microbiome ◽

The Impact

Objective: An approach for assessing the urinary microbiome is 16S rRNA gene sequencing, where a segment of the bacterial genome is amplified and sequenced. Methods used to analyze these data are rapidly evolving, although the research implications are not known. This re-analysis of an existing dataset aimed to determine the impact of updated bioinformatic and statistical techniques. Methods: A prior Pelvic Floor Disorders Network (PFDN) study compared the urinary microbiome in 123 women with mixed urinary incontinence (MUI) and 84 controls. We used the PFDN unprocessed sequencing data of V1-V3 and V4-V6 16S variable regions, processed operational taxonomic unit (OTU) tables, and de-identified clinical data. We processed sequencing data with an updated bioinformatic pipeline, which used DADA2 to generate amplicon sequence variant (ASV) tables. Taxa from ASV tables were compared to OTU tables generated from the original processing; taxa from different variable regions (e.g., V1-V3 versus V4-V6) after updated processing were also compared. After updated processing, data were analyzed with multiple filtering thresholds. Several techniques were tested to cluster samples into microbial communities. Multivariable regression was used to test for associations between microbial communities and MUI, while controlling for potentially confounding variables. Results: Of taxa identified through updated bioinformatic processing, only 40% were identified originally, though taxa identified through both methods represented >99% of sequencing data in terms of relative abundance. When different 16S rRNA gene regions were sequenced from the same samples, there were differences noted in recovered taxa. When the original clustering methods were applied to reprocessed sequencing data, we confirmed differences in microbial communities associated with MUI. However, when samples were clustered with a different methodology, microbial communities were no longer associated with MUI. Conclusions: Updated bioinformatic processing techniques recover many different taxa compared to prior techniques, though most of these differences exist in low abundance taxa that occupy a small proportion of the overall microbiome. Detection of high abundance taxa are not significantly impacted by bioinformatic strategy. However, there are different biases for less abundant taxa; these differences as well as downstream clustering methodology and filtering thresholds may affect interpretation of overall results.

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PSXIV-23 Characterization of microbial communities associated with the rumen lining, digesta and rumen fluid from beef cattle consuming a high energy diet using 16S rRNA gene amplicon sequencing

Journal of Animal Science ◽

10.1093/jas/skz258.878 ◽

2019 ◽

Vol 97 (Supplement_3) ◽

pp. 446-446

Author(s):

Arquimides Reyes ◽

Margaret Weinroth ◽

Cory Wolfe ◽

Robert Delmore ◽

Terry Engle ◽

...

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Beef Cattle ◽

Microbial Communities ◽

Relative Abundance ◽

Rumen Fluid ◽

Amplicon Sequencing ◽

High Energy ◽

Feedlot Cattle ◽

Rrna Gene

Abstract The true etiology of liver abscesses is not well known. Therefore, the objective of this study was to characterize the microbial communities in the rumen lining, digesta, and rumen fluid from beef cattle consuming a high energy diet, using 16S rRNA gene amplicon sequencing. Twelve crossbred feedlot steers (450 ±10 kg; ~ 3.0 years of age) fitted with ruminal fistulas, consuming a high energy finishing diet (1.43 NEg, Mcal/kg DM) for 21 d were utilized in this experiment. Microbial DNA from three regions within the rumen [rumen lining (ventral/lateral), digesta (geometric center of the rumen), and rumen fluid] was extracted and the V4 region of the 16S rRNA gene was amplified and sequenced. Across all sample regions, bacterial sequences were classified into 34 phyla, 76 classes, 143 orders, and 254 families. Bacteroidetes and Firmicutes were the predominant phyla present across all samples. The relative abundance of Bacteroidetes detected in rumen fluid was lesser (P < 0.05) when compared to bacteria sampled from the rumen lining and digesta. In contrast, the relative abundance of Firmicutes were greater (P < 0.05) in rumen fluid and the rumen lining when compared to digesta samples. There are very few publications describing the complex community of the rumen microbiome. To our knowledge this is the first publication categorizing microbial populations in three distinct locations within the rumen using next generation sequencing in feedlot cattle.

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Rapid PCR identification of Prevotella copri in an Australian cohort of pregnant women

Journal of Developmental Origins of Health and Disease ◽

10.1017/s2040174419000849 ◽

2019 ◽

Vol 11 (3) ◽

pp. 228-234 ◽

Cited By ~ 1

Author(s):

Lawrence Gray ◽

Kyoko Hasebe ◽

Martin O’Hely ◽

Anne-Louise Ponsonby ◽

Peter Vuillermin ◽

...

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Real Time ◽

Relative Abundance ◽

Real Time Pcr ◽

Cost Effective ◽

Birth Cohort Study ◽

Rrna Gene ◽

Sequencing Data ◽

Cost Effective Technique

AbstractGut bacteria from the genus Prevotella are found in high abundance in faeces of non-industrialised communities but low abundance in industrialised, Westernised communities. Prevotella copri is one of the principal Prevotella species within the human gut. As it has been associated with developmental health and disease states, we sought to (i) develop a real-time polymerase chain reaction (PCR) to rapidly determine P. copri abundance and (ii) investigate its abundance in a large group of Australian pregnant mothers.The Barwon Infant Study is a pre-birth cohort study (n = 1074). Faecal samples were collected from mothers at 36 weeks gestation. Primers with a probe specific to the V3 region of P. copri 16S rRNA gene were designed and optimised for real-time PCR. Universal 16S rRNA gene primers amplified pan-bacterial DNA in parallel. Relative abundance of P. copri was calculated using a 2-ΔCt method.Relative abundance of P. copri by PCR was observed in 165/605 (27.3%) women. The distribution was distinctly bimodal, defining women with substantial (n = 115/165, 69.7%) versus very low P. copri expression (n = 50/165, 30.3%). In addition, abundance of P. copri by PCR correlated with 16S rRNA gene MiSeq sequencing data (r2 = 0.67, P < 0.0001, n = 61).We have developed a rapid and cost-effective technique for identifying the relative abundance of P. copri using real-time PCR. The expression of P. copri was evident in only a quarter of the mothers, and either at substantial or very low levels. PCR detection of P. copri may facilitate assessment of this species in large, longitudinal studies across multiple populations and in various clinical settings.

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PIME: a package for discovery of novel differences among microbial communities

10.1101/632182 ◽

2019 ◽

Author(s):

Luiz Fernando W. Roesch ◽

Priscila Thiago Dobbler ◽

Victor Satler Pylro ◽

Bryan Kolaczkowski ◽

Jennifer C. Drew ◽

...

Keyword(s):

16S Rrna ◽

Microbial Communities ◽

Relative Abundance ◽

Error Detection ◽

Rrna Gene ◽

Treatment Prevalence ◽

Massive Sequencing ◽

Microbial Associations ◽

Reliability And Robustness ◽

Low Prevalence

AbstractMassive sequencing of genetic markers, such as the 16S rRNA gene for prokaryotes, allows the comparative analysis of diversity and abundance of whole microbial communities. However, the data used for profiling microbial communities is usually low in signal and high in noise preventing the identification of real differences among treatments. PIME (Prevalence Interval for Microbiome Evaluation) fills this gap by removing those taxa that may be high in relative abundance in just a few samples but have a low prevalence overall. The reliability and robustness of PIME were compare against the existing methods and verified by a number of approaches using 16S rRNA independent datasets. To remove the noise, PIME filters microbial taxa not shared in a per treatment prevalence interval starting at 5% with increments of 5% at each filtering step. For each prevalence interval, hundreds of decision trees are calculated to predict the likelihood of detecting differences in treatments. The best prevalence-filtered dataset is user-selected by choosing the prevalence interval that keeps the majority of the 16S rRNA reads in the dataset and shows the lowest error rate. To obtain the likelihood of introducing bias while building prevalence-filtered datasets, an error detection step based in random permutations is also included. A reanalysis of previews published datasets with PIME uncovered previously missed microbial associations improving the ability to detect important organisms, which may be masked when only relative abundance is considered.

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Keystone Genes Confer Superior Performance in Hyperthermophilic Composting

10.21203/rs.3.rs-151019/v1 ◽

2021 ◽

Author(s):

Peng Cui ◽

Hanpeng Liao ◽

Chaofan Ai ◽

Zhongbing Xu ◽

Zhi Chen ◽

...

Keyword(s):

Microbial Communities ◽

Relative Abundance ◽

Anthropogenic Activities ◽

N Cycling ◽

Superior Performance ◽

Rrna Gene ◽

Sequencing Data ◽

Solid Waste Disposal ◽

Organic Solid ◽

Improved Performance

Abstract Background: Large amounts of organic solid wastes originating from anthropogenic activities have imposed enormous pressure on the environment and human health. Our previous studies showed that compared with conventional thermophilic composting (cTC), hyperthermophilic composting (hTC) exhibits superior performance in organic solid waste disposal by providing advantages such as improved composting temperature, nitrogen conservation (NC), nitrous oxide (N2O) mitigation and germination index (GI). However, it remains unclear how hTC communities drive improved performance. Here, we used GeoChip 5.0M coupled with high-throughput 16S rRNA gene sequencing data to investigate the variations in carbon (C)-degrading and nitrogen (N)-cycling genes and microbial communities and their linkages with selected performance indices (composting temperature, NC, N2O emission rate and GI) in hTC and cTC in factory-scale experiments, aiming to identify the keystone biotic drivers for the improved performance. Results: We showed that hTC significantly altered functional composition structures compared with those in cTC, which was driven by taxonomic shift in microbial communities. Specifically, hTC significantly increased the relative abundance of C-degrading genes and decreased the relative abundance of N-cycling genes during composting. These significantly shifted genes were the keystone genes dominating the improved performance of hTC, as indicated by a random forest model. Furthermore, network and partial least squares path modeling analysis suggested that the keystone genes continued to dominantly drive the improved performance after multiple biotic (community composition and other genes) drivers were simultaneously considering in hTC. Conclusions: Together, our study provides evidence that keystone genes potentially play a pivotal role in improving composting temperature, N2O mitigation, NC and GI in hTC and emphasizes the importance of understanding the variation in functions for targeted manipulation of composting practices.

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