Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1086354 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR28286600_1.fastq.gz | Fastqsanger.gz | |||
| SRR28286600_2.fastq.gz | Fastqsanger.gz | |||
| SRR28286601_1.fastq.gz | Fastqsanger.gz | |||
| SRR28286601_2.fastq.gz | Fastqsanger.gz | |||
| SRR28286602_1.fastq.gz | Fastqsanger.gz |
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Project description:Understanding the bacterial community structure, and their functional analysis for active bioremediation process is essential to design better and cost effective strategies. Microarray analysis enables us to simultaneously study the functional and phylogenetic markers of hundreds of microorganisms which are involved in active bioremediation process in an environment. We have previously described development of a hybrid 60-mer multibacterial microarray platform (BiodegPhyloChip) for profiling the bacterial communities and functional genes simultaneously in environments undergoing active bioremediation process (Pathak et al; Appl Microbiol Biotechnol,Vol. 90, 1739-1754). The present study involved profiling the status of bacterial communities and functional (biodegradation) genes using the developed 60-mer oligonucleotide microarray BiodegPhyloChip at five contaminated hotspots in the state of Gujarat, in western India. The expression pattern of functional genes (coding for key enzymes in active bioremediation process) at these sites was studied to understand the dynamics of biodegradation in the presence of diverse group of chemicals. The results indicated that the nature of pollutants and their abundance greatly influence the structure of bacterial communities and the extent of expression of genes involved in various biodegradation pathways. In addition, site specific factors also play a pivotal role to affect the microbial community structure as was evident from results of 16S rRNA gene profiling of the five contaminated sites, where the community structure varied from one site to another drastically.
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Project description:Genome scale metabolic model of Drosophila gut microbe Acetobacter fabarum
Abstract -
An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in complex microbial communities and their impact on host physiology. This research can be confounded by poorly understood effects of community composition and host diet on the metabolic traits of individual taxa. Here, we investigated these multiway interactions by constructing and analyzing metabolic models comprising every combination of five bacterial members of the Drosophila gut microbiome (from single taxa to the five-member community of Acetobacter and Lactobacillus species) under three nutrient regimes. We show that the metabolic function of Drosophila gut bacteria is dynamic, influenced by community composition, and responsive to dietary modulation. Furthermore, we show that ecological interactions such as competition and mutualism identified from the growth patterns of gut bacteria are underlain by a diversity of metabolic interactions, and show that the bacteria tend to compete for amino acids and B vitamins more frequently than for carbon sources. Our results reveal that, in addition to fermentation products such as acetate, intermediates of the tricarboxylic acid (TCA) cycle, including 2-oxoglutarate and succinate, are produced at high flux and cross-fed between bacterial taxa, suggesting important roles for TCA cycle intermediates in modulating Drosophila gut microbe interactions and the potential to influence host traits. These metabolic models provide specific predictions of the patterns of ecological and metabolic interactions among gut bacteria under different nutrient regimes, with potentially important consequences for overall community metabolic function and nutritional interactions with the host.IMPORTANCE Drosophila is an important model for microbiome research partly because of the low complexity of its mostly culturable gut microbiota. Our current understanding of how Drosophila interacts with its gut microbes and how these interactions influence host traits derives almost entirely from empirical studies that focus on individual microbial taxa or classes of metabolites. These studies have failed to capture fully the complexity of metabolic interactions that occur between host and microbe. To overcome this limitation, we reconstructed and analyzed 31 metabolic models for every combination of the five principal bacterial taxa in the gut microbiome of Drosophila This revealed that metabolic interactions between Drosophila gut bacterial taxa are highly dynamic and influenced by cooccurring bacteria and nutrient availability. Our results generate testable hypotheses about among-microbe ecological interactions in the Drosophila gut and the diversity of metabolites available to influence host traits.
2021-07-01 | MODEL2002040002 | BioModels