gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Zimmermann, Johannes; Kaleta, Christoph; Waschina, Silvio

doi:10.1186/s13059-021-02295-1

Cited by 180 publications

(222 citation statements)

References 118 publications

(198 reference statements)

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“…We then used ANTISMASH [ 42 ] to identify gene clusters involved in the biosynthesis of cyanopeptides and other pathways of interest. We further validated the presence of biosynthetic pathways like biotin, cobalamin, nitrogen fixation and carotenoids with gapseq [ 43 ]. As expected for distantly related bacteria, Microcystis genotypes and AB encode distinct sets of gene functions based on the presence/absence of annotated genes (Supplementary Fig.…”

Section: Resultsmentioning

confidence: 99%

“…KEGG modules differentially abundant in Microcystis or the AB genus were filtered based on a completeness greater or equal to 70%. Additionally, we used the program gapseq (v1.1) with default parameters and the MetaCyc database [ 43 , 45 ] to validate the presence the metabolic pathways involved in the biosynthesis of biotin, cobalamin, thiamine, carotenoid and nitrogen fixation.…”

Section: Methodsmentioning

confidence: 99%

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Single-colony sequencing reveals microbe-by-microbiome phylosymbiosis between the cyanobacterium Microcystis and its associated bacteria

et al. 2021

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Background Cyanobacteria from the genus Microcystis can form large mucilaginous colonies with attached heterotrophic bacteria—their microbiome. However, the nature of the relationship between Microcystis and its microbiome remains unclear. Is it a long-term, evolutionarily stable association? Which partners benefit? Here we report the genomic diversity of 109 individual Microcystis colonies—including cyanobacteria and associated bacterial genomes—isolated in situ and without culture from Lake Champlain, Canada and Pampulha Reservoir, Brazil. Results We identified 14 distinct Microcystis genotypes from Canada, of which only two have been previously reported, and four genotypes specific to Brazil. Microcystis genetic diversity was much greater between than within colonies, consistent with colony growth by clonal expansion rather than aggregation of Microcystis cells. We also identified 72 bacterial species in the microbiome. Each Microcystis genotype had a distinct microbiome composition, and more closely related genotypes had more similar microbiomes. This pattern of phylosymbiosis could be explained by co-phylogeny in only two out of the nine most prevalent associated bacterial genera, Roseomonas and Rhodobacter. These phylogenetically associated genera could enrich the metabolic repertoire of Microcystis, for example by encoding the biosynthesis of complementary carotenoid molecules. In contrast, other colony-associated bacteria showed weaker signals of co-phylogeny, but stronger evidence of horizontal gene transfer with Microcystis. These observations suggest that acquired genes are more likely to be retained in both partners (Microcystis and members of its microbiome) when they are loosely associated, whereas one gene copy is sufficient when the association is physically tight and evolutionarily long-lasting. Conclusions We have introduced a method for culture-free isolation of single colonies from nature followed by metagenomic sequencing, which could be applied to other types of microbes. Together, our results expand the known genetic diversity of both Microcystis and its microbiome in natural settings, and support their long-term, specific, and potentially beneficial associations.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Single-colony sequencing reveals microbe-by-microbiome phylosymbiosis between the cyanobacterium Microcystis and its associated bacteria

et al. 2021

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“…Moreover, the integration of multi-omics has been shown to yield superior output compared to single omic studies. For instance, the co-assembly of MG and MT sequencing reads was shown to improve the quality of assembled contigs ( Narayanasamy et al, 2016 ), which in turn improves taxonomic annotation, gene calling/annotation, binning, metabolic pathway (re) construction ( Muller et al, 2018 ; Zhou et al, 2020 ; Zimmermann et al, 2021 ), and quantification of features, e.g., taxa/genes ( Narayanasamy et al, 2016 ). Similarly, MP spectra searches are more effective when performed against gene databases derived from MG assemblies of the same sample/environment, compared to generic databases, thus improving the recruitment of measured peptides ( Tanca et al, 2016 ; Heyer et al, 2017 ; Timmins-Schiffman et al, 2017 ).…”

Section: Multi-omic Considerations and Experimental Design For Longitudinal Studiesmentioning

confidence: 99%

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

et al. 2021

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In recent years, multi-omic studies have enabled resolving community structure and interrogating community function of microbial communities. Simultaneous generation of metagenomic, metatranscriptomic, metaproteomic, and (meta) metabolomic data is more feasible than ever before, thus enabling in-depth assessment of community structure, function, and phenotype, thus resulting in a multitude of multi-omic microbiome datasets and the development of innovative methods to integrate and interrogate those multi-omic datasets. Specifically, the application of reference-independent approaches provides opportunities in identifying novel organisms and functions. At present, most of these large-scale multi-omic datasets stem from spatial sampling (e.g., water/soil microbiomes at several depths, microbiomes in/on different parts of the human anatomy) or case-control studies (e.g., cohorts of human microbiomes). We believe that longitudinal multi-omic microbiome datasets are the logical next step in microbiome studies due to their characteristic advantages in providing a better understanding of community dynamics, including: observation of trends, inference of causality, and ultimately, prediction of community behavior. Furthermore, the acquisition of complementary host-derived omics, environmental measurements, and suitable metadata will further enhance the aforementioned advantages of longitudinal data, which will serve as the basis to resolve drivers of community structure and function to understand the biotic and abiotic factors governing communities and specific populations. Carefully setup future experiments hold great potential to further unveil ecological mechanisms to evolution, microbe-microbe interactions, or microbe-host interactions. In this article, we discuss the challenges, emerging strategies, and best-practices applicable to longitudinal microbiome studies ranging from sampling, biomolecular extraction, systematic multi-omic measurements, reference-independent data integration, modeling, and validation.

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“…Another tool, Functional Ontology Assignments for Metagenomes (FOAM), although including biogeochemical cycling genes, does not permit visualization to facilitate interpreting functional profiles, and it annotates all protein sequences with a universal threshold value, which may lead to prediction biases ( Prestat et al, 2014 ). Some tools can be used in the analysis of genome, metagenome or metatranscriptome, e.g., METABOLIC ( Zhou et al, 2020 ), iPATH ( Darzi et al, 2018 ), gapseq ( Zimmermann et al, 2021 ), MEGAN ( Huson et al, 2007 ), and SAMSA2 ( Westreich et al, 2018 ). The METABOLIC ( Zhou et al, 2020 ) toolkit can assess microbial ecology and biogeochemistry based on evaluating the completeness of pathways in genomes or/and metagenome-assembled genomes, but is not directly based on calculating the relative abundance of pathways.…”

Section: Introductionmentioning

confidence: 99%

“…The METABOLIC ( Zhou et al, 2020 ) toolkit can assess microbial ecology and biogeochemistry based on evaluating the completeness of pathways in genomes or/and metagenome-assembled genomes, but is not directly based on calculating the relative abundance of pathways. iPath ( Darzi et al, 2018 ) and gapseq ( Zimmermann et al, 2021 ) are applications for the visualization and analysis of metabolic pathways in a cellular genome or a set of gene sequences, but not metagenomes. These two applications do not specialize in the biogeochemical cycle and cannot calculate the relative abundance of pathways.…”

Section: Introductionmentioning

confidence: 99%

DiTing: A Pipeline to Infer and Compare Biogeochemical Pathways From Metagenomic and Metatranscriptomic Data

et al. 2021

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Metagenomics and metatranscriptomics are powerful methods to uncover key micro-organisms and processes driving biogeochemical cycling in natural ecosystems. Databases dedicated to depicting biogeochemical pathways (for example, metabolism of dimethylsulfoniopropionate (DMSP), which is an abundant organosulfur compound) from metagenomic/metatranscriptomic data are rarely seen. Additionally, a recognized normalization model to estimate the relative abundance and environmental importance of pathways from metagenomic and metatranscriptomic data has not been organized to date. These limitations impact the ability to accurately relate key microbial-driven biogeochemical processes to differences in environmental conditions. Thus, an easy-to-use, specialized tool that infers and visually compares the potential for biogeochemical processes, including DMSP cycling, is urgently required. To solve these issues, we developed DiTing, a tool wrapper to infer and compare biogeochemical pathways among a set of given metagenomic or metatranscriptomic reads in one step, based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) and a manually created DMSP cycling gene database. Accurate and specific formulae for over 100 pathways were developed to calculate their relative abundance. Output reports detail the relative abundance of biogeochemical pathways in both text and graphical format. DiTing was applied to simulated metagenomic data and resulted in consistent genetic features of simulated benchmark genomic data. Subsequently, when applied to natural metagenomic and metatranscriptomic data from hydrothermal vents and the Tara Ocean project, the functional profiles predicted by DiTing were correlated with environmental condition changes. DiTing can now be confidently applied to wider metagenomic and metatranscriptomic datasets, and it is available at https://github.com/xuechunxu/DiTing.

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gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Cited by 180 publications

References 118 publications

Single-colony sequencing reveals microbe-by-microbiome phylosymbiosis between the cyanobacterium Microcystis and its associated bacteria

Single-colony sequencing reveals microbe-by-microbiome phylosymbiosis between the cyanobacterium Microcystis and its associated bacteria

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

DiTing: A Pipeline to Infer and Compare Biogeochemical Pathways From Metagenomic and Metatranscriptomic Data

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