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Developing Bioprospecting Strategies for Bioplastics Through the Large-Scale Mining of Microbial Genomes.

ABSTRACT: The accumulation of petroleum-based plastic waste has become a major issue for the environment. A sustainable and biodegradable solution can be found in Polyhydroxyalkanoates (PHAs), a microbially produced biopolymer. An analysis of the global phylogenetic and ecological distribution of potential PHA producing bacteria and archaea was carried out by mining a global genome repository for PHA synthase (PhaC), a key enzyme involved in PHA biosynthesis. Bacteria from the phylum Actinobacteria were found to contain the PhaC Class II genotype which produces medium-chain length PHAs, a physiology until now only found within a few Pseudomonas species. Further, several PhaC genotypes were discovered within Thaumarchaeota, an archaeal phylum with poly-extremophiles and the ability to efficiently use CO₂ as a carbon source, a significant ecological group which have thus far been little studied for PHA production. Bacterial and archaeal PhaC genotypes were also observed in high salinity and alkalinity conditions, as well as high-temperature geothermal ecosystems. These genome mining efforts uncovered previously unknown candidate taxa for biopolymer production, as well as microbes from environmental niches with properties that could potentially improve PHA production. This in silico study provides valuable insights into unique PHA producing candidates, supporting future bioprospecting efforts toward better targeted and relevant taxa to further enhance the diversity of exploitable PHA production systems.

SUBMITTER: Vuong P

PROVIDER: S-EPMC8312272 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: The Pubchem Database is a large-scale resource for chemical information, containing millions of chemical compound activities derived by high-throughput screening (HTS). The ability to extract characteristic substructures from such enormous amounts of data is steadily growing in importance. Compounds with shared basic active structures (BASs) exhibiting G-protein coupled receptor (GPCR) activity and repeated dose toxicity have been mined from small datasets. However, the mining process employed was not applicable to large datasets owing to a large imbalance between the numbers of active and inactive compounds. In most datasets, one active compound will appear for every 1000 inactive compounds. Most mining techniques work well only when these numbers are similar. RESULTS: This difficulty was overcome by sampling an equal number of active and inactive compounds. The sampling process was repeated to maintain the structural diversity of the inactive compounds. An interactive KNIME workflow that enabled effective sampling and data cleaning processes was created. The application of the cascade model and subsequent structural refinement yielded the BAS candidates. Repeated sampling increased the ratio of active compounds containing these substructures. Three samplings were deemed adequate to identify all of the meaningful BASs. BASs expressing similar structures were grouped to give the final set of BASs. This method was applied to HIV integrase and protease inhibitor activities in the MDL Drug Data Report (MDDR) database and to procaspase-3 activators in the PubChem BioAssay database, yielding 14, 12, and 18 BASs, respectively. CONCLUSIONS: The proposed mining scheme successfully extracted meaningful substructures from large datasets of chemical structures. The resulting BASs were deemed reasonable by an experienced medicinal chemist. The mining itself requires about 3 days to extract BASs with a given physiological activity. Thus, the method described herein is an effective way to analyze large HTS databases.

Project description:Large-scale metagenome assemblies of human microbiomes have produced a vast catalogue of previously unseen microbial genomes; however, comparatively few microbial genomes derive from other vertebrates. Here, we generated 5,596 metagenome-assembled genomes (MAGs) from the gut metagenomes of 180 predominantly wild animal species representing 5 classes, in addition to 14 existing animal gut metagenome data sets. The MAGs comprised 1,522 species-level genome bins (SGBs), most of which were novel at the species, genus, or family level, and the majority were enriched in host versus environment metagenomes. Many traits distinguished SGBs enriched in host or environmental biomes, including the number of antimicrobial resistance genes. We identified 1,986 diverse biosynthetic gene clusters; only 23 clustered with any MIBiG database references. Gene-based assembly revealed tremendous gene diversity, much of it host or environment specific. Our MAG and gene data sets greatly expand the microbial genome repertoire and provide a broad view of microbial adaptations to the vertebrate gut.IMPORTANCE Microbiome studies on a select few mammalian species (e.g., humans, mice, and cattle) have revealed a great deal of novel genomic diversity in the gut microbiome. However, little is known of the microbial diversity in the gut of other vertebrates. We studied the gut microbiomes of a large set of mostly wild animal species consisting of mammals, birds, reptiles, amphibians, and fish. Unfortunately, we found that existing reference databases commonly used for metagenomic analyses failed to capture the microbiome diversity among vertebrates. To increase database representation, we applied advanced metagenome assembly methods to our animal gut data and to many public gut metagenome data sets that had not been used to obtain microbial genomes. Our resulting genome and gene cluster collections comprised a great deal of novel taxonomic and genomic diversity, which we extensively characterized. Our findings substantially expand what is known of microbial genomic diversity in the vertebrate gut.

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Developing Bioprospecting Strategies for Bioplastics Through the Large-Scale Mining of Microbial Genomes.

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