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Sixty-One Volatiles Have Phylogenetic Signals Across Bacterial Domain and Fungal Kingdom.

ABSTRACT: Microorganisms are diverse in their genome sequences and subsequently in their encoded metabolic pathways, which enabled them to adapt to numerous environmental conditions. They produce thousands of small molecules, many of which are volatiles in nature and play important roles in signaling in intra- and inter-species to kingdom and domain interactions, survival, or virulence. Many of these compounds have been studied, characterized, and organized in the mVOC 2.0 database. However, such dataset has not been investigated comprehensively in terms of its phylogeny to determine key volatile markers for certain taxa. It was hypothesized that some of the volatiles described in the mVOC 2.0 database could function as a phylogenetic signal since their production is conserved among certain taxa within the microbial evolutionary tree. Our meta-analysis revealed that some volatiles were produced by a large number of bacteria but not in fungal genera such as dimethyl disulfide, acetic acid, 2-nonanone, dimethyl trisulfide, 2-undecanone, isovaleric acid, 2-tridecanone, propanoic acid, and indole (common bacterial compounds). In contrast, 1-octen-3-ol, 3-octanone, and 2-pentylfuran (common fungal compounds) were produced primarily by fungal genera. Such chemical information was further confirmed by investigating genomic data of publicly available databases revealing that bacteria or fungi harbor gene families involved in these volatiles' biosynthesis. Our phylogenetic signal testing identified 61 volatiles with a significant phylogenetic signal as demonstrated by phylogenetic D statistic P-value < 0.05. Thirty-three volatiles were phylogenetically conserved in the bacterial domain (e.g., cyclocitral) compared to 17 volatiles phylogenetically conserved in the fungal kingdom (e.g., aristolochene), whereas 11 volatiles were phylogenetically conserved in genera from both bacteria and fungi (e.g., geosmin). These volatiles belong to different chemical classes such as heterocyclic compounds, long-chain fatty acids, sesquiterpenoids, and aromatics. The performed approaches serve as a starting point to investigate less explored volatiles with potential roles in signaling, antimicrobial therapy, or diagnostics.

SUBMITTER: Elmassry MM

PROVIDER: S-EPMC7554305 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Sixty-One Volatiles Have Phylogenetic Signals Across Bacterial Domain and Fungal Kingdom.

Elmassry Moamen M MM Farag Mohamed A MA Preissner Robert R Gohlke Björn-Oliver BO Piechulla Birgit B Lemfack Marie C MC

Frontiers in microbiology 20200930

Microorganisms are diverse in their genome sequences and subsequently in their encoded metabolic pathways, which enabled them to adapt to numerous environmental conditions. They produce thousands of small molecules, many of which are volatiles in nature and play important roles in signaling in intra- and inter-species to kingdom and domain interactions, survival, or virulence. Many of these compounds have been studied, characterized, and organized in the mVOC 2.0 database. However, such dataset ...[more]

PMID: 33101231

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Project description:BackgroundThe efficiencies of the stop codons TAA, TAG, and TGA in protein synthesis termination are not the same. These variations could allow many genes to be regulated. There are many similar nucleotide trimers found on the second and third reading-frames of a gene. They are called premature stop codons (PSC). Like stop codons, the PSC in bacterial genomes are also highly bias in terms of their quantities and qualities on the genes. Phylogenetically related species often share a similar PSC profile. We want to know whether the selective forces that influence the stop codons and the PSC usage biases in a genome are related. We also wish to know how strong these trimers in a genome are related to the natural history of the bacterium. Knowing these relations may provide better knowledge in the phylogeny of bacteriaResultsA 16SrRNA-alignment tree of 19 well-studied α-, β- and γ-Proteobacteria Type species is used as standard reference for bacterial phylogeny. The genomes of sixty-one bacteria, belonging to the α-, β- and γ-Proteobacteria subphyla, are used for this study. The stop codons and PSC are collectively termed "Translation Stop Signals" (TSS). A gene is represented by nine scalars corresponding to the numbers of counts of TAA, TAG, and TGA on each of the three reading-frames of that gene. "Translation Stop Signals Ratio" (TSSR) is the ratio between the TSS counts. Four types of TSSR are investigated. The TSSR-1, TSSR-2 and TSSR-3 are each a 3-scalar series corresponding respectively to the average ratio of TAA: TAG: TGA on the first, second, and third reading-frames of all genes in a genome. The Genomic-TSSR is a 9-scalar series representing the ratio of distribution of all TSS on the three reading-frames of all genes in a genome. Results show that bacteria grouped by their similarities based on TSSR-1, TSSR-2, or TSSR-3 values could only partially resolve the phylogeny of the species. However, grouping bacteria based on thier Genomic-TSSR values resulted in clusters of bacteria identical to those bacterial clusters of the reference tree. Unlike the 16SrRNA method, the Genomic-TSSR tree is also able to separate closely related species/strains at high resolution. Species and strains separated by the Genomic-TSSR grouping method are often in good agreement with those classified by other taxonomic methods. Correspondence analysis of individual genes shows that most genes in a bacterial genome share a similar TSSR value. However, within a chromosome, the Genic-TSSR values of genes near the replication origin region (Ori) are more similar to each other than those genes near the terminus region (Ter).ConclusionThe translation stop signals on the three reading-frames of the genes on a bacterial genome are interrelated, possibly due to frequent off-frame recombination facilitated by translational-associated recombination (TSR). However, TSR may not occur randomly in a bacterial chromosome. Genes near the Ori region are often highly expressed and a bacterium always maintains multiple copies of Ori. Frequent collisions between DNA- polymerase and RNA-polymerase would create many DNA strand-breaks on the genes; whereas DNA strand-break induced homologues-recombination is more likely to take place between genes with similar sequence. Thus, localized recombination could explain why the TSSR of genes near the Ori region are more similar to each other. The quantity and quality of these TSS in a genome strongly reflect the natural history of a bacterium. We propose that the Genomic- TSSR can be used as a subjective biomarker to represent the phyletic status of a bacterium.

Project description:BackgroundPfam is a general-purpose database of protein domain alignments and profile Hidden Markov Models (HMMs), which is very popular for the annotation of sequence data produced by genome sequencing projects. Pfam provides models that are often very general in terms of the taxa that they cover and it has previously been suggested that such general models may lack some of the specificity or selectivity that would be provided by kingdom-specific models.ResultsHere we present a general approach to create domain libraries of HMMs for sub-taxa of a kingdom. Taking fungal species as an example, we construct a domain library of HMMs (called Fungal Pfam or FPfam) using sequences from 30 genomes, consisting of 24 species from the ascomycetes group and two basidiomycetes, Ustilago maydis, a fungal pathogen of maize, and the white rot fungus Phanerochaete chrysosporium. In addition, we include the Microsporidion Encephalitozoon cuniculi, an obligate intracellular parasite, and two non-fungal species, the oomycetes Phytophthora sojae and Phytophthora ramorum, both plant pathogens. We evaluate the performance in terms of coverage against the original 30 genomes used in training FPfam and against five more recently sequenced fungal genomes that can be considered as an independent test set. We show that kingdom-specific models such as FPfam can find instances of both novel and well characterized domains, increases overall coverage and detects more domains per sequence with typically higher bitscores than Pfam for the same domain families. An evaluation of the effect of changing E-values on the coverage shows that the performance of FPfam is consistent over the range of E-values applied.ConclusionKingdom-specific models are shown to provide improved coverage. However, as the models become more specific, some sequences found by Pfam may be missed by the models in FPfam and some of the families represented in the test set are not present in FPfam. Therefore, we recommend that both general and specific libraries are used together for annotation and we find that a significant improvement in coverage is achieved by using both Pfam and FPfam.

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Sixty-One Volatiles Have Phylogenetic Signals Across Bacterial Domain and Fungal Kingdom.

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Sixty-One Volatiles Have Phylogenetic Signals Across Bacterial Domain and Fungal Kingdom.

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