Comparative profiling of microbial community of three economically important fishes reared in sea cages under tropical offshore environment

Rasheeda, M.K.; Rangamaran, Vijaya Raghavan; Srinivasan, Senthilkumar; Ramaiah, Sendhil Kumar; Gunasekaran, Rajaprabhu; Jaypal, Santhanakumar; Dharani, G.; Kirubagaran, Ramalingam

doi:10.1016/j.margen.2017.04.003

Cited by 29 publications

(25 citation statements)

References 34 publications

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“…According to the literature, in the case of temperature abuse, Pseudomonas and Acinetobacter are predominant organisms in the spoilage microbiota (Gill & Newton, ). The specific spoilage organisms in low‐temperature storage are Acinetobacter , Pseudomonas , Psychrobacter and Shewanella , while potential pathogens associated with fish include Pelomonas and Cupriavidus (Doulgeraki, Ercolini, Villani, & Nychas, ; Doulgeraki & Nychas, ; Gram et al, ; Rasheeda et al, ). With increasing storage time, the relative abundances of Acinetobacter and Pseudomonas significantly decreased from 24.3 to 7.8% and from 8.7 to 5.3% (Group D), respectively, while Pelomonas , Psychrobacter , and Shewanella increased steadily from 8.4, 3.0, and 4.5% to 14.4, 8.8, and 6.1%, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…and Shewanella, while potential pathogens associated with fish include Pelomonas and Cupriavidus (Doulgeraki, Ercolini, Villani, & Nychas, 2012;Doulgeraki & Nychas, 2013;Gram et al, 2002;Rasheeda et al, 2017). With increasing storage time, the relative abundances of Acinetobacter and Pseudomonas significantly decreased from 24.3 to 7.8% and from 8.7 to 5.3% (Group D), respectively, while…”

Section: Bacterial Composition In Bigeye Tunamentioning

confidence: 99%

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Water dynamics and microbial communities of bigeye tuna (Thunnus obesus) during simulated cold chain logistics

Wang

Xie

2020

Journal of Food Safety

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This study investigated changes in the physicochemical indexes, water dynamics, and microbial communities of bigeye tuna (Thunnus obesus) during simulated cold chain logistics. With increasing storage time, the color and water activity decreased gradually, while drip loss gradually increased, and pH increased with a fluctuating pattern. The trend of bound water content was not obvious, trapped water declined, and free water increased with fluctuations over time, according to low‐field nuclear magnetic resonance (LF‐1H NMR). High‐throughput sequencing results indicated that fluctuating temperatures inhibited Acinetobacter and Pseudomonas and promoted Pelomonas, Psychrobacter, and Shewanella. Principal component analysis and redundancy analysis revealed significantly different microbial community interactions among the four groups, and environmental factors had distinct effects on bacterial community assemblages. LF‐NMR and high‐throughput sequencing can be used to monitor dynamic quality changes in bigeye tuna during simulated cold chain logistics with fluctuating temperatures.

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Section: Resultsmentioning

confidence: 99%

Section: Bacterial Composition In Bigeye Tunamentioning

confidence: 99%

Water dynamics and microbial communities of bigeye tuna (Thunnus obesus) during simulated cold chain logistics

Wang

Xie

2020

Journal of Food Safety

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“…One way of understanding how fertilizers, pesticides and environmental chemicals affect the baseline pond environment is to study the impact on microbial communities [14][15][16][17]. The concept that microorganisms are ubiquitous in distribution and can proliferate in any habitat that supports their growth has been a long-standing notion in microbial ecology.…”

Section: Introductionmentioning

confidence: 99%

“…An analysis of the 16S rRNA gene is conducted through: (1) isolation & extraction of genetic material from the source, (2) manipulation of the genetic material, such as through the amplification of the 16S rRNA gene, (3) library construction and (4) the analysis of genetic material in the metagenomic library. Several genomic approaches have greatly advanced understanding of the ecology and diversity of microbial communities in aquatic environments [14][15][16][17]. Together with polymerase chain reaction (PCR), fingerprinting methods like denaturing-gradient gel electrophoresis (DGGE), obtain a qualitative representation of the presence and abundance of different phylotypes in a sample.…”

Section: Introductionmentioning

confidence: 99%

Microbial Diversity Assessment in Milkfish Culture Ponds

Dalmacio

Ramírez

Estacio

et al. 2020

AJFAR

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Aims: To determine bacterial diversity in milkfish culture ponds that contain different life-cycle stages of the milkfish (pond A: fry, pond B: juveniles and pond C: adults) by DNA sequence analysis of organisms and compare that microbial diversity to organisms found in soil adjacent to the ponds. Study Design: Comparative metagenomic study of aquatic and terrestrial biodiversity based on DNA sequence analysis of water and soil DNA. Place and Duration of Study: SEADEC milkfish ponds in Tingnauan, Iloilo. Philippines. All water and soil samples were collected over a three-day period. Methodology: DNA sequence analysis of nucleic acids extracted from water samples collected from the three types of milkfish ponds along with soil adjacent to the ponds. DNA was extracted and PCR was performed using the 11F-1492R primer pair to amplify 16S rRNA gene. Purified 16S rDNA amplicons were cloned in using the TOPO-TA cloning kit for DNA sequencing. 16s rRNA gene sequences were analyzed with the use of software tools at the National Center for Biotechnology Information website and imported into the ARB phylogenetic analysis software. Distance matrices were exported using the neighbor-joining algorithm in ARB, in the form of PHYLIP-formatted lower triangular matrices. The distance matrices were then used to calculate Shannon-Weaver and Simpson diversity indices to evaluate the richness and evenness of the sampled populations. Rarefaction curves were determined to evaluate sampling efficiency. Results: Rarefaction curves indicated that the sampling effort was sufficient to reveal the majority of phyla present in the sample. Shannon-Weaver and Simpson indices suggested that the diversities of all the groups were statistically different from each other. It was observed that pond A was least diverse, followed by pond C and pond B. The soil was most diverse. DNA sequence analysis identified the various species of bacteria in soil and water. Conclusion: All three pond communities were significantly different in diversity. This study did not identify any significant human pathogens such as Vibrios, Salmonella or Shigella. Bacterial diversity of sites decreased in the following order: soil > fry pond > fingerling pond > adult pond.

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“…Many cellulosedecomposing bacteria were harbored in the intestine of grass carp (Ctenopharyngodon idellus), such as Anoxybacillus, Actinomyces, and Citrobacter (Wu et al, 2012). Pompano (Trachinotus blochii) showed high abundance of Clostridia which was associated with polysaccharides degradation, when fed with commercial pellet (Rasheeda et al, 2017). The alpha diversity and the dominant bacterial taxa significantly changed with the development of Siniperca chuatsi .…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Systematic evaluation of the gut microbiome of swamp eel (Monopterus albus) by 16S rRNA gene sequencing (v0.2)"

2019

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Swamp eel (Monopterus albus) is a commercially important farmed species in China. T he dysbiosis and homeostasis of gut microbiota might be associated with swamp eel's diseases pathogenesis and food digestion. The contribution of gut microbiome in fish growth and health is being increasingly recognized. However, little was known about the microbial community in the intestine of swamp eel (Monopterus albus). Methods The intestinal microbiomes of the five distinct gut sections (midgut content and mucosa, hindgut content and mucosa, and stools) of swamp eel were compared by the Illumina MiSeq sequencing of the bacterial 16S rRNA gene sequencing and statistical analysis. Results The results showed that the number of observed OTUs decreased from proximal to distal of intestine. PCoA analysis revealed significant separations among samples from different gut sections. Nevertheless, there were 54 core OTUs shared by all gut sections and 36 out of 54 core OTUs significantly varied in the abundances in different gut sections. Furthermore, we discovered 66 section-specific enriched KEGG pathways. These section-specific enriched microbial taxa (e.g., Bacillus, Lactobacillus) and potential function capacities (e.g., amino acid metabolism, carbohydrate metabolism) might play vital roles in nutrients metabolism, immune modulation, and host-microbe interactions of swamp eel. Conclusions Our results showed that the microbial diversity, composition, and function capacity were varied substantially in different gut sections. The gut section-specific enriched core microbial taxa and function capacities may exert an important role in swamp eel's nutrients metabolism, immune modulation, and host-microbe interactions. This study should provide basic insights into the gut microbiome of swamp eel.

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Comparative profiling of microbial community of three economically important fishes reared in sea cages under tropical offshore environment

Cited by 29 publications

References 34 publications

Water dynamics and microbial communities of bigeye tuna (Thunnus obesus) during simulated cold chain logistics

Water dynamics and microbial communities of bigeye tuna (Thunnus obesus) during simulated cold chain logistics

Microbial Diversity Assessment in Milkfish Culture Ponds

Peer Review #1 of "Systematic evaluation of the gut microbiome of swamp eel (Monopterus albus) by 16S rRNA gene sequencing (v0.2)"

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