Dataset Information

Functional prediction based on 16S rRNA metagenome data from bacterial microbiota associated with macroalgae from the Peruvian coast.

ABSTRACT: Macroalgae are vital reservoirs for essential epibiotic microorganisms. Among these are growth-promoting bacteria that support the growth and healthy development of their host macroalgae, and these macroalgae can be utilized in agriculture as biostimulants, offering an alternative to traditional agrochemicals. However, to date, no comparative studies have been conducted on the functional profile and bacterial diversity associated with coastal macroalgae of Peru. In this study, we employed amplicon sequencing of the V3-V4 region of 16S rRNA gene in twelve host macroalgae collected from two rocky shores in central Peru to compare their bacterial communities. The results revealed high bacterial diversity across both sites, but differences in microbial composition were noted. The phyla Bacteroidota and Pseudomonadota were predominant. The functional prediction highlighted 44 significant metabolic pathways associated with the bacterial microbiota when comparing host macroalgae. These active pathways are related to metabolism and genetic and cellular information processing. No direct association was detected between the macroalgal genera and the associated microbiota, suggesting that the bacterial community is largely influenced by their genetic functions than the taxonomic composition of their hosts. Furthermore, some species of Chlorophyta and Rhodophyta were observed to host growth-promoting bacteria, such as Maribacter sp. and Sulfitobacter sp.

SUBMITTER: Vigil BE

PROVIDER: S-EPMC11316746 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Functional prediction based on 16S rRNA metagenome data from bacterial microbiota associated with macroalgae from the Peruvian coast.

Vigil Bianca E BE Ascue Francisco F Ayala Rosmery Y RY Murúa Pedro P Calderon Martha S MS Bustamante Danilo E DE

Scientific reports 20240810 1

Macroalgae are vital reservoirs for essential epibiotic microorganisms. Among these are growth-promoting bacteria that support the growth and healthy development of their host macroalgae, and these macroalgae can be utilized in agriculture as biostimulants, offering an alternative to traditional agrochemicals. However, to date, no comparative studies have been conducted on the functional profile and bacterial diversity associated with coastal macroalgae of Peru. In this study, we employed amplic ...[more]

PMID: 39127849

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Project description:Fermented fruits and vegetables play an important role in safeguarding food security world-wide. Recently, robust sequencing-based microbial community analysis platforms have improved microbial safety assessment. This study aimed to examine the composition of bacteria and evaluate the bacterial safety of fermented fruit products using high-throughput 16S-rRNA metagenomic analysis. The operational taxonomic unit-based taxonomic classification of DNA sequences revealed 53 bacterial genera. However, the amplicon sequencing variant (ASV)-based clustering revealed 43 classifiable bacterial genera. Taxonomic classifications revealed that the abundance of Sphingomonas, which was the predominant genus in the majority of tested samples, was more than 85-90% among the total identified bacterial community in most samples. Among these identified genera, 13 low abundance genera were potential opportunistic pathogens, including Acinetobacter, Bacillus, Staphylococcus, Clostridium, Klebsiella, Mycobacterium, Ochrobactrum, Chryseobacterium, Stenotrophomonas, and Streptococcus. Of these 13 genera, 13 major opportunistic pathogenic species were validated using polymerase chain reaction. The pathogens were not detected in the samples of different stages and the final products of fermentation, except in one sample from the first stage of fermentation in which S. aureus was detected. This finding was consistent with that of ASV-based taxonomic classification according to which S. aureus was detected only in the sample from the first stage of fermentation. However, S. aureus was not significantly correlated with the human disease pathways. These results indicated that fermentation is a reliable and safe process as pathogenic bacteria were not detected in the fermentation products. The hybrid method reported in this study can be used simultaneously to evaluate the bacterial diversity, their functional predictions and safety assessment of novel fermentation products. Additionally, this hybrid method does not involve the random detection of pathogens, which can markedly decrease the time of detection and food safety verification. Furthermore, this hybrid method can be used for the quality control of products and the identification of external contamination.

Project description:BackgroundSequencing of 16S rRNA genes has become a powerful technique to study microbial communities and their responses towards changing environmental conditions in various ecosystems. Several tools have been developed for the prediction of functional profiles from 16S rRNA gene sequencing data, because numerous questions in ecosystem ecology require knowledge of community functions in addition to taxonomic composition. However, the accuracy of these tools relies on functional information derived from genomes available in public databases, which are often not representative of the microorganisms present in the studied ecosystem. In addition, there is also a lack of tools to predict functional gene redundancy in microbial communities.ResultsTo address these challenges, we developed Tax4Fun2, an R package for the prediction of functional profiles and functional gene redundancies of prokaryotic communities from 16S rRNA gene sequences. We demonstrate that functional profiles predicted by Tax4Fun2 are highly correlated to functional profiles derived from metagenomes of the same samples. We further show that Tax4Fun2 has higher accuracies than PICRUSt and Tax4Fun. By incorporating user-defined, habitat-specific genomic information, the accuracy and robustness of predicted functional profiles is substantially enhanced. In addition, functional gene redundancies predicted with Tax4Fun2 are highly correlated to functional gene redundancies determined for simulated microbial communities.ConclusionsTax4Fun2 provides researchers with a unique tool to predict and investigate functional profiles of prokaryotic communities based on 16S rRNA gene sequencing data. It is easy-to-use, platform-independent and highly memory-efficient, thus enabling researchers without extensive bioinformatics knowledge or access to high-performance clusters to predict functional profiles. Another unique feature of Tax4Fun2 is that it allows researchers to calculate the redundancy of specific functions, which is a potentially important measure of how resilient a community will be to environmental perturbation. Tax4Fun2 is implemented in R and freely available at https://github.com/bwemheu/Tax4Fun2.

Dataset Information

Functional prediction based on 16S rRNA metagenome data from bacterial microbiota associated with macroalgae from the Peruvian coast.

Publications

Functional prediction based on 16S rRNA metagenome data from bacterial microbiota associated with macroalgae from the Peruvian coast.

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