Dataset Information

The evolutionary signal in metagenome phyletic profiles predicts many gene functions.

ABSTRACT:

Background

The function of many genes is still not known even in model organisms. An increasing availability of microbiome DNA sequencing data provides an opportunity to infer gene function in a systematic manner.

Results

We evaluated if the evolutionary signal contained in metagenome phyletic profiles (MPP) is predictive of a broad array of gene functions. The MPPs are an encoding of environmental DNA sequencing data that consists of relative abundances of gene families across metagenomes. We find that such MPPs can accurately predict 826 Gene Ontology functional categories, while drawing on human gut microbiomes, ocean metagenomes, and DNA sequences from various other engineered and natural environments. Overall, in this task, the MPPs are highly accurate, and moreover they provide coverage for a set of Gene Ontology terms largely complementary to standard phylogenetic profiles, derived from fully sequenced genomes. We also find that metagenomes approximated from taxon relative abundance obtained via 16S rRNA gene sequencing may provide surprisingly useful predictive models. Crucially, the MPPs derived from different types of environments can infer distinct, non-overlapping sets of gene functions and therefore complement each other. Consistently, simulations on >?5000 metagenomes indicate that the amount of data is not in itself critical for maximizing predictive accuracy, while the diversity of sampled environments appears to be the critical factor for obtaining robust models.

Conclusions

In past work, metagenomics has provided invaluable insight into ecology of various habitats, into diversity of microbial life and also into human health and disease mechanisms. We propose that environmental DNA sequencing additionally constitutes a useful tool to predict biological roles of genes, yielding inferences out of reach for existing comparative genomics approaches.

SUBMITTER: Vidulin V

PROVIDER: S-EPMC6040064 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Publications

The evolutionary signal in metagenome phyletic profiles predicts many gene functions.

Vidulin Vedrana V Šmuc Tomislav T Džeroski Sašo S Supek Fran F

Microbiome 20180710 1

<h4>Background</h4>The function of many genes is still not known even in model organisms. An increasing availability of microbiome DNA sequencing data provides an opportunity to infer gene function in a systematic manner.<h4>Results</h4>We evaluated if the evolutionary signal contained in metagenome phyletic profiles (MPP) is predictive of a broad array of gene functions. The MPPs are an encoding of environmental DNA sequencing data that consists of relative abundances of gene families across me ...[more]

PMID: 29991352

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Project description:The two-component signal transduction (TCS) machinery is a key mechanism of sensing environmental changes in the prokaryotic world. TCS systems have been characterized thoroughly in bacteria but to a much lesser extent in archaea. Here, we provide an updated census of more than 2,000 histidine kinases and response regulators encoded in 218 complete archaeal genomes, as well as unfinished genomes available from metagenomic data. We describe the domain architectures of the archaeal TCS components, including several novel output domains, and discuss the evolution of the archaeal TCS machinery. The distribution of TCS systems in archaea is strongly biased, with high levels of abundance in haloarchaea and thaumarchaea but none detected in the sequenced genomes from the phyla Crenarchaeota, Nanoarchaeota, and Korarchaeota The archaeal sensor histidine kinases are generally similar to their well-studied bacterial counterparts but are often located in the cytoplasm and carry multiple PAS and/or GAF domains. In contrast, archaeal response regulators differ dramatically from the bacterial ones. Most archaeal genomes do not encode any of the major classes of bacterial response regulators, such as the DNA-binding transcriptional regulators of the OmpR/PhoB, NarL/FixJ, NtrC, AgrA/LytR, and ActR/PrrA families and the response regulators with GGDEF and/or EAL output domains. Instead, archaea encode multiple copies of response regulators containing either the stand-alone receiver (REC) domain or combinations of REC with PAS and/or GAF domains. Therefore, the prevailing mechanism of archaeal TCS signaling appears to be via a variety of protein-protein interactions, rather than direct transcriptional regulation.IMPORTANCE Although the Archaea represent a separate domain of life, their signaling systems have been assumed to be closely similar to the bacterial ones. A study of the domain architectures of the archaeal two-component signal transduction (TCS) machinery revealed an overall similarity of archaeal and bacterial sensory modules but substantial differences in the signal output modules. The prevailing mechanism of archaeal TCS signaling appears to involve various protein-protein interactions rather than direct transcription regulation. The complete list of histidine kinases and response regulators encoded in the analyzed archaeal genomes is available online at http://www.ncbi.nlm.nih.gov/Complete_Genomes/TCSarchaea.html.

Project description:BackgroundStudies 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.ResultsWe 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.ConclusionWe 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 . Video abstract.

Project description:Analyses of metagenome data (MG) and metatranscriptome data (MT) are often challenged by a paucity of complete reference genome sequences and the uneven/low sequencing depth of the constituent organisms in the microbial community, which respectively limit the power of reference-based alignment and de novo sequence assembly. These limitations make accurate protein family classification and abundance estimation challenging, which in turn hamper downstream analyses such as abundance profiling of metabolic pathways, identification of differentially encoded/expressed genes, and de novo reconstruction of complete gene and protein sequences from the protein family of interest. The profile hidden Markov model (HMM) framework enables the construction of very useful probabilistic models for protein families that allow for accurate modeling of position specific matches, insertions, and deletions. We present a novel homology detection algorithm that integrates banded Viterbi algorithm for profile HMM parsing with an iterative simultaneous alignment and assembly computational framework. The algorithm searches a given profile HMM of a protein family against a database of fragmentary MG/MT sequencing data and simultaneously assembles complete or near-complete gene and protein sequences of the protein family. The resulting program, HMM-GRASPx, demonstrates superior performance in aligning and assembling homologs when benchmarked on both simulated marine MG and real human saliva MG datasets. On real supragingival plaque and stool MG datasets that were generated from healthy individuals, HMM-GRASPx accurately estimates the abundances of the antimicrobial resistance (AMR) gene families and enables accurate characterization of the resistome profiles of these microbial communities. For real human oral microbiome MT datasets, using the HMM-GRASPx estimated transcript abundances significantly improves detection of differentially expressed (DE) genes. Finally, HMM-GRASPx was used to reconstruct comprehensive sets of complete or near-complete protein and nucleotide sequences for the query protein families. HMM-GRASPx is freely available online from http://sourceforge.net/projects/hmm-graspx.

Dataset Information

The evolutionary signal in metagenome phyletic profiles predicts many gene functions.

Background

Results

Conclusions

Publications

The evolutionary signal in metagenome phyletic profiles predicts many gene functions.

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