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Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines.

ABSTRACT: Animals, plants, and algae rely on symbiotic microorganisms for their development and functioning. Genome sequencing and genomic analyses of these microorganisms provide opportunities to construct metabolic networks and to analyze the metabolism of the symbiotic communities they constitute. Genome-scale metabolic network reconstructions rest on information gained from genome annotation. As there are multiple annotation pipelines available, the question arises to what extent differences in annotation pipelines impact outcomes of these analyses. Here, we compare five commonly used pipelines (Prokka, MaGe, IMG, DFAST, RAST) from predicted annotation features (coding sequences, Enzyme Commission numbers, hypothetical proteins) to the metabolic network-based analysis of symbiotic communities (biochemical reactions, producible compounds, and selection of minimal complementary bacterial communities). While Prokka and IMG produced the most extensive networks, RAST and DFAST networks produced the fewest false positives and the most connected networks with the fewest dead-end metabolites. Our results underline differences between the outputs of the tested pipelines at all examined levels, with small differences in the draft metabolic networks resulting in the selection of different microbial consortia to expand the metabolic capabilities of the algal host. However, the consortia generated yielded similar predicted producible compounds and could therefore be considered functionally interchangeable. This contrast between selected communities and community functions depending on the annotation pipeline needs to be taken into consideration when interpreting the results of metabolic complementarity analyses. In the future, experimental validation of bioinformatic predictions will likely be crucial to both evaluate and refine the pipelines and needs to be coupled with increased efforts to expand and improve annotations in reference databases.

SUBMITTER: Karimi E

PROVIDER: S-EPMC8106915 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:The species composition of a microbial community is rarely fixed and often experiences fluctuations of varying degrees and at varying frequencies. These perturbations to a community's taxonomic profile naturally also alter the community's functional profile-the aggregate set of genes encoded by community members-ultimately altering the community's overall functional capacities. The magnitude of such functional changes and the specific shift that will occur in each function, however, are strongly dependent on how genes are distributed across community members' genomes. This gene distribution, in turn, is determined by the taxonomic composition of the community and would markedly differ, for example, between communities composed of species with similar genomic content vs. communities composed of species whose genomes encode relatively distinct gene sets. Combined, these observations suggest that community functional robustness to taxonomic perturbations could vary widely across communities with different compositions, yet, to date, a systematic study of the inherent link between community composition and robustness is lacking. RESULTS:In this study, we examined how a community's taxonomic composition influences the robustness of that community's functional profile to taxonomic perturbation (here termed taxa-function robustness) across a wide array of environments. Using a novel simulation-based computational model to quantify this taxa-function robustness in host-associated and non-host-associated communities, we find notable differences in robustness between communities inhabiting different body sites, including significantly higher robustness in gut communities compared to vaginal communities that cannot be attributed solely to differences in species richness. We additionally find between-site differences in the robustness of specific functions, some of which are potentially related to site-specific environmental conditions. These taxa-function robustness differences are most strongly associated with differences in overall functional redundancy, though other aspects of gene distribution also influence taxa-function robustness in certain body environments, and are sufficient to cluster communities by environment. Further analysis revealed a correspondence between our robustness estimates and taxonomic and functional shifts observed across human-associated communities. CONCLUSIONS:Our analysis approach revealed intriguing taxa-function robustness variation across environments and identified features of community and gene distribution that impact robustness. This approach could be further applied for estimating taxa-function robustness in novel communities and for informing the design of synthetic communities with specific robustness requirements.

Project description:BackgroundMicrobiome studies commonly use 16S rRNA gene amplicon sequencing to characterize microbial communities. Errors introduced at multiple steps in this process can affect the interpretation of the data. Here we evaluate the accuracy of operational taxonomic unit (OTU) generation, taxonomic classification, alpha- and beta-diversity measures for different settings in QIIME, MOTHUR and a pplacer-based classification pipeline, using a novel software package: DECARD.ResultsIn-silico we generated 100 synthetic bacterial communities approximating human stool microbiomes to be used as a gold-standard for evaluating the colligative performance of microbiome analysis software. Our synthetic data closely matched the composition and complexity of actual healthy human stool microbiomes. Genus-level taxonomic classification was correctly done for only 50.4-74.8% of the source organisms. Miscall rates varied from 11.9 to 23.5%. Species-level classification was less successful, (6.9-18.9% correct); miscall rates were comparable to those of genus-level targets (12.5-26.2%). The degree of miscall varied by clade of organism, pipeline and specific settings used. OTU generation accuracy varied by strategy (closed, de novo or subsampling), reference database, algorithm and software implementation. Shannon diversity estimation accuracy correlated generally with OTU-generation accuracy. Beta-diversity estimates with Double Principle Coordinate Analysis (DPCoA) were more robust against errors introduced in processing than Weighted UniFrac. The settings suggested in the tutorials were among the worst performing in all outcomes tested.ConclusionsEven when using the same classification pipeline, the specific OTU-generation strategy, reference database and downstream analysis methods selection can have a dramatic effect on the accuracy of taxonomic classification, and alpha- and beta-diversity estimation. Even minor changes in settings adversely affected the accuracy of the results, bringing them far from the best-observed result. Thus, specific details of how a pipeline is used (including OTU generation strategy, reference sets, clustering algorithm and specific software implementation) should be specified in the methods section of all microbiome studies. Researchers should evaluate their chosen pipeline and settings to confirm it can adequately answer the research question rather than assuming the tutorial or standard-operating-procedure settings will be adequate or optimal.

Project description:BackgroundAlgae encompass a wide array of photosynthetic organisms that are ubiquitously distributed in aquatic and terrestrial habitats. Algal species often bloom in aquatic ecosystems, providing a significant autochthonous carbon input to the deeper anoxic layers in stratified water bodies. In addition, various algal species have been touted as promising candidates for anaerobic biogas production from biomass. Surprisingly, in spite of its ecological and economic relevance, the microbial community involved in algal detritus turnover under anaerobic conditions remains largely unexplored.ResultsHere, we characterized the microbial communities mediating the degradation of Chlorella vulgaris (Chlorophyta), Chara sp. strain IWP1 (Charophyceae), and kelp Ascophyllum nodosum (phylum Phaeophyceae), using sediments from an anaerobic spring (Zodlteone spring, OK; ZDT), sludge from a secondary digester in a local wastewater treatment plant (Stillwater, OK; WWT), and deeper anoxic layers from a seasonally stratified lake (Grand Lake O' the Cherokees, OK; GL) as inoculum sources. Within all enrichments, the majority of algal biomass was metabolized within 13-16 weeks, and the process was accompanied by an increase in cell numbers and a decrease in community diversity. Community surveys based on the V4 region of the 16S rRNA gene identified different lineages belonging to the phyla Bacteroidetes, Proteobacteria (alpha, delta, gamma, and epsilon classes), Spirochaetes, and Firmicutes that were selectively abundant under various substrate and inoculum conditions. Within all kelp enrichments, the microbial communities structures at the conclusion of the experiment were highly similar regardless of the enrichment source, and were dominated by the genus Clostridium, or family Veillonellaceae within the Firmicutes. In all other enrichments the final microbial community was dependent on the inoculum source, rather than the type of algae utilized as substrate. Lineages enriched included the uncultured groups VadinBC27 and WCHB1-69 within the Bacteroidetes, genus Spirochaeta and the uncultured group SHA-4 within Spirochaetes, Ruminococcaceae, Lachnospiraceae, Yongiibacter, Geosporobacter, and Acidaminobacter within the Firmicutes, and genera Kluyvera, Pantoea, Edwardsiella and Aeromonas, and Buttiauxella within the Gamma-Proteobaceteria order Enterobacteriales.ConclusionsOur results represent the first systematic survey of microbial communities mediating turnover of algal biomass under anaerobic conditions, and highlights the diversity of lineages putatively involved in the degradation process.

Project description:This study provides a deep modern insight into the phylogenetic diversity among bacterial consortia found in working and nonworking high-methane natural gas pipelines located in Poland. The working pipeline was characterized by lower biodiversity (140-154 bacterial genera from 22 to 23 classes, depending on the source of the debris) in comparison to the off-gas pipeline (169 bacterial genera from 23 classes). The sediment recovered from the working pipeline contained mostly DNA identified as belonging to the phylum Firmicutes (66.4%-45.9% operational taxonomic units [OTUs]), predominantly Bacillus (41.4%-31.1% OTUs) followed by Lysinibacillus (2.6%-1.5% OTUs) and Clostridium (2.4%-1.8% OTUs). In the nonworking pipeline, Proteobacteria (46.8% OTUs) and Cyanobacteria (27.8% OTUs) were dominant. Over 30% of the Proteobacteria sequences showed homologies to Gammaproteobacteria, with Pseudomonas (7.1%), Enhydrobacter (2.1%), Stenotrophomonas (0.5%), and Haempohilus (0.4%) among the others. Differences were noted in terms of the chemical compositions of deposits originating from the working and nonworking gas pipelines. The deposits from the nonworking gas pipeline contained iron, as well as carbon (42.58%), sulphur (15.27%), and oxygen (15.32%). This composition can be linked to both the quantity and type of the resident microorganisms. The presence of a considerable amount of silicon (17.42%), and of aluminum, potassium, calcium, and magnesium at detectable levels, may likewise affect the metabolic activity of the resident consortia in the working gas pipeline. All the analyzed sediments included both bacteria known for causing and intensifying corrosion (e.g., Pseudomonas, Desulfovibrio, Shewanella, Serratia) and bacteria that can protect the surface of pipelines against deterioration (e.g., Bacillus). Biocorrosion is not related to a single mechanism or one species of microorganism, but results from the multidirectional activity of multiple microbial communities. The analysis presented here of the state of the microbiome in a gas pipeline during the real gas transport is a particularly valuable element of this work.

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Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines.

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