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Exploration of noncoding sequences in metagenomes.

ABSTRACT: Environment-dependent genomic features have been defined for different metagenomes, whose genes and their associated processes are related to specific environments. Identification of ORFs and their functional categories are the most common methods for association between functional and environmental features. However, this analysis based on finding ORFs misses noncoding sequences and, therefore, some metagenome regulatory or structural information could be discarded. In this work we analyzed 23 whole metagenomes, including coding and noncoding sequences using the following sequence patterns: (G+C) content, Codon Usage (Cd), Trinucleotide Usage (Tn), and functional assignments for ORF prediction. Herein, we present evidence of a high proportion of noncoding sequences discarded in common similarity-based methods in metagenomics, and the kind of relevant information present in those. We found a high density of trinucleotide repeat sequences (TRS) in noncoding sequences, with a regulatory and adaptive function for metagenome communities. We present associations between trinucleotide values and gene function, where metagenome clustering correlate with microorganism adaptations and kinds of metagenomes. We propose here that noncoding sequences have relevant information to describe metagenomes that could be considered in a whole metagenome analysis in order to improve their organization, classification protocols, and their relation with the environment.

SUBMITTER: Tobar-Tosse F

PROVIDER: S-EPMC3607601 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Exploration of noncoding sequences in metagenomes.

Tobar-Tosse Fabián F Rodríguez Adrián C AC Vélez Patricia E PE Zambrano María M MM Moreno Pedro A PA

PloS one 20130325 3

Environment-dependent genomic features have been defined for different metagenomes, whose genes and their associated processes are related to specific environments. Identification of ORFs and their functional categories are the most common methods for association between functional and environmental features. However, this analysis based on finding ORFs misses noncoding sequences and, therefore, some metagenome regulatory or structural information could be discarded. In this work we analyzed 23 ...[more]

PMID: 23536879

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Project description:BACKGROUND: The proportion of conserved DNA sequences with no clear function is steadily growing in bioinformatics databases. Studies of sequence and structural homology have indicated that many uncharacterized protein domain sequences are variants of functionally described domains. If these variants promote an organism's ecological fitness, they are likely to be conserved in the genome of its progeny and the population at large. The genetic composition of microbial communities in their native ecosystems is accessible through metagenomics. We hypothesize the co-variation of protein domain sequences across metagenomes from similar ecosystems will provide insights into their potential roles and aid further investigation. METHODOLOGY/PRINCIPAL FINDINGS: We calculated the correlation of Pfam protein domain sequences across the Global Ocean Sampling metagenome collection, employing conservative detection and correlation thresholds to limit results to well-supported hits and associations. We then examined intercorrelations between domains of unknown function (DUFs) and domains involved in known metabolic pathways using network visualization and cluster-detection tools. We used a cautious "guilty-by-association" approach, referencing knowledge-level resources to identify and discuss associations that offer insight into DUF function. We observed numerous DUFs associated to photobiologically active domains and prevalent in the Cyanobacteria. Other clusters included DUFs associated with DNA maintenance and repair, inorganic nutrient metabolism, and sodium-translocating transport domains. We also observed a number of clusters reflecting known metabolic associations and cases that predicted functional reclassification of DUFs. CONCLUSION/SIGNIFICANCE: Critically examining domain covariation across metagenomic datasets can grant new perspectives on the roles and associations of DUFs in an ecological setting. Targeted attempts at DUF characterization in the laboratory or in silico may draw from these insights and opportunities to discover new associations and corroborate existing ones will arise as more large-scale metagenomic datasets emerge.

Project description:Here we focus on identifying conserved elements that are required for Chaserr function, a lncRNA that we recently characterized as being essential for mouse viability (https://doi.org/10.1038/s41467-019-13075-8). Chaserr is highly conserved in different species, and is located in close proximity (<2kb) to the transcription start site of CHD2. It acts in concert with the CHD2 protein to maintain proper Chd2 expression levels. Using the LncLOOM framework we identified AUGG enriched conserved elements which are located in the last exon of mouse Chaserr. In order to identify proteins that potentially bind to these conserved regions, we used in vitro transcription to generate the following biotinylated RNAs; along with their antisense controls: 1) The WT sequence, containing the first 322 nt of the last exon of Chaserr (bases 764–1086 of the NR_037601 isoform). An antisense control fragment was also prepared, labelled as AWT 2) The mutant conserved (MC) sequence, that contained AUGG→UACC mutations in four conserved motifs that were found using LncLOOM. An antisense control fragment was also prepared, labelled as AMC 3) The muatant all (MA) sequence in which all seven of the AUGG sites in the last exon were mutated to UACC. An antisense control fragment was also prepared, labelled as AMA 4) We also included a No Probe Control, which did not contain an RNA fragment. The fragments were incubated with N2a cell lysates followed by RNA-pulldown and Mass Spec analysis. The experiment was performed in triplicate. A total of 938 proteins were found to associate with the WT sequence: 74 of these were enriched ≥3-fold compared to the antisense sequence.We identified 9 proteins that had ≥2-fold higher recovery when using the WT sequence compared to both mutants. Included in these proteins was DHX36, which showed the highest enrichment compared to the mutated sequences. We validated the association of DHX36 with Chaserr through RNA immunoprecipitation experiments.

Project description:The number of metagenomic studies conducted each year is growing dramatically. Storage and analysis of such big data is difficult and time-consuming. Interestingly, analysis shows that environmental and human metagenomes include a significant amount of non-annotated sequences, representing a 'dark matter.' We established a bioinformatics pipeline that automatically detects metagenome reads matching query sequences from a given set and applied this tool to the detection of sequences matching large and giant DNA viral members of the proposed order Megavirales or virophages. A total of 1,045 environmental and human metagenomes (? 1 Terabase) were collected, processed, and stored on our bioinformatics server. In addition, nucleotide and protein sequences from 93 Megavirales representatives, including 19 giant viruses of amoeba, and 5 virophages, were collected. The pipeline was generated by scripts written in Python language and entitled MG-Digger. Metagenomes previously found to contain megavirus-like sequences were tested as controls. MG-Digger was able to annotate 100s of metagenome sequences as best matching those of giant viruses. These sequences were most often found to be similar to phycodnavirus or mimivirus sequences, but included reads related to recently available pandoraviruses, Pithovirus sibericum, and faustoviruses. Compared to other tools, MG-Digger combined stand-alone use on Linux or Windows operating systems through a user-friendly interface, implementation of ready-to-use customized metagenome databases and query sequence databases, adjustable parameters for BLAST searches, and creation of output files containing selected reads with best match identification. Compared to Metavir 2, a reference tool in viral metagenome analysis, MG-Digger detected 8% more true positive Megavirales-related reads in a control metagenome. The present work shows that massive, automated and recurrent analyses of metagenomes are effective in improving knowledge about the presence and prevalence of giant viruses in the environment and the human body.

Dataset Information

Exploration of noncoding sequences in metagenomes.

Publications

Exploration of noncoding sequences in metagenomes.

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