HRM and 16S rRNA gene sequencing reveal the cultivable microbiota of the European sea bass during ice storage

doi:10.1016/j.ijfoodmicro.2020.108658

International Journal of Food Microbiology

Volume 327, 16 August 2020, 108658

https://doi.org/10.1016/j.ijfoodmicro.2020.108658 Get rights and content

Highlights

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HRM differentiated the unknown cultivable microbiota during fish ice storage.
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16S rRNA gene sequence analysis identified sea bass microbiota.
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Psychrobacter dominated the flesh of ice-stored sea bass.
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Isolates with HRM profiles >91% similar could be used as reference strains.

Abstract

The total cultivable microbiota of the ice-stored European sea bass (Dicentrarchus labrax), the most important commercial fish species of the Mediterranean aquaculture, was determined using 16S rRNA gene sequence analysis. High Resolution Melting (HRM) curve profiles and sequencing analysis (V3–V4 region of the 16S rRNA gene) were used respectively for the differentiation and identification of the collected isolates from six time intervals (day 0, 4, 8, 12, 14 and 16) while fish were stored in ice. HRM analysis differentiated the unknown microbiota in ten groups (208 isolates) and in two single isolates based on their HRM curve profiles. The isolates with HRM profiles which were >91% similar within each group were found to belong to the same species using sequencing analysis. Thus, the ten groups consist of representatives of Psychrobacter glacincola, Ps. alimentarius, Ps. cryohalolentis, Ps. maritimus, Ps. fozii, Pseudomonas sp., Paeniglutamicibacter sp., Carnobacterium sp., Leucobacter aridicolis and Bacillus thuringiensis. Based on this approach, Ps. cryohalolentis was found to be the most dominant phylotype at the beginning of fish shelf-life compared to other species. The abundance of this bacterium decreased throughout storage, while Ps. glacincola increased and dominated at the time of the sensory minimum acceptability (day 14) and rejection (day 16). To conclude, HRM could be used for the rapid determination of sea bass microbiota, using the representatives of each group as reference bacterial strains, in order for scientists to solve rapidly stakeholders problems related with microbial quality or safety of fish.

Introduction

The vigorous growth of aquaculture has changed the status of fish supply for human consumption during the last years (FAO, 2018). Greece produces over 110 K tonnes of farmed fish annually and estimates to double its production by 2030 (FAO, 2019). The European sea bass (Dicentrarchus labrax) is the most important commercial fish farmed in the Mediterranean, with Greece being the biggest producer in the EU (FAO, 2019). It is well documented that fresh fish spoils due to the metabolic activity of a bacterial consortium that prevails under the applied storage conditions, the so-called Specific Spoilage Organisms (SSOs). Meanwhile, the composition of the potential SSOs consortium differs among aquaculture products due to a plethora of abiotic and biotic factors before (pre-harvest) and after catch (post-harvest, during handling, processing, transportation, marketing), which makes spoilage a complex phenomenon in such products (Boziaris and Parlapani, 2016; Parlapani et al., 2018a, Parlapani et al., 2018b, Parlapani et al., 2019). SSOs, even those of a high degree of homology of the 16S rRNA gene sequence, can present different growth rates under the same storage conditions and their metabolism might not be similar (Parlapani et al., 2017). Therefore, the dominance of particular microorganisms can determine the shelf-life of the product.

The 16S rRNA gene sequence analysis is the most common approach for studying fish bacterial communities grown on plates (Broekaert et al., 2011; Parlapani et al., 2015a, Parlapani et al., 2015b). However, the amplification of the target gene is followed by sequencing which is expensive for a large number of PCR products. Alternatively, several electrophoresis approaches such as the denaturing gradient gel electrophoresis (DGGE) or thermal gradient gel electrophoresis (TGGE) have been applied in the past to differentiate microorganisms present in fish tissue (Nisiotou et al., 2014). However, these approaches are not simple to use in order for scientists to solve rapidly problems of stakeholders e.g. aquaculture sector, fish processing industry, retailers, inspection authorities, on microbial quality or safety of fish. On the other hand, High-Resolution Melting (HRM) analysis is a simple, accurate, closed-tube and low cost approach (Vossen et al., 2009), which has been used for identification of bacterial species or molecular typing in several research fields such as in epidemiology (Tamburro and Ripabelli, 2017) and food science (Liu et al., 2018; Omiccioli et al., 2009; Parlapani et al., 2020).

HRM analysis has already been proposed as a tool for the rapid differentiation of bacterial species isolated from mussels during chill storage (Parlapani et al., 2020). The rapid determination of fish microbiota including the potential SSOs could allow aquaculture and fish industry to take the preventive measures to control the parameters involved in the aquaculture environment (sources of microbial contamination, aquaculture practices), handling (temperature, hygiene practices), processing (temperature, atmosphere and packaging e.g. gas concentration, film permeability and headspace), transportation (trade management, storage requirements) or marketing (storage facilities, temperature) in order to provide products of the longest shelf-life and highest safety in commerce. This scenario might minimize food and economic losses for aquaculture, fish industry, retailers and consumers contributing to food security, competitiveness and sustainability in general.

Despite the massive production of the European sea bass and its high commercialization around the world, there is no study, according to our knowledge, that determines the total microbiota grown on plates including the potential SSOs based on 16S rRNA gene sequence analysis. The aim of this study was a) to determine the cultivable microbiota, including the potential SSOs, isolated from sea bass during ice storage and b) to highlight the isolates which could be used as reference strains for the rapid determination of the microbiota in this kind of product in future works. To achieve our goals, the differentiation of the cultivable microbiota was performed by using HRM analysis, while the bacterial identification and the quest of reference strains was performed by sequencing analysis of V3–V4 region of the 16S rRNA gene.

Section snippets

Provision and storage of sea bass

Two different batches of whole sea bass of approximately 500 g were provided from a Greek aquaculture company in February 2017. Fish were packaged in insulated boxes with melted ice and transferred to the laboratory of Marketing and Technology of Aquatic Products and Foods (University of Thessaly, Volos). The insulated boxes were stored in an incubator operating at 0 °C until the end of the experiment, while the ice was replaced every 2 days.

Rejection time

The evaluation of fish (two fish per batch,

Enumeration of APC

At the beginning of fish shelf-life (day 0), APC was 3.42 ± 0.39 log cfu/g. APC increased during the storage time in ice reaching a population of 7.44 ± 0.29 and 7.72 ± 0.64 log cfu/g at day 14 (minimum acceptability level, shelf-life) and day 16 (rejection time point), respectively (Table 1).

Differentiation and identification of microbiota grown on TSA

Two hundred ten (210) isolates from six time intervals (day 0, 4, 8, 12, 14 and 16) were used for the differentiation and identification of the microbiota of ice-stored sea bass (Table 2). The isolates

Discussion

In the last years, there is an increasing tendency for the development of novel DNA-based approaches and management strategies to maximize the shelf-life of fresh fish by implementing the corrective actions to control the parameters that affect the microbial growth or are involved in microbial contamination along the production chain, handling, processing continuum, transportation and marketing (Parlapani et al., 2018b, Parlapani et al., 2020). The rapid determination of the microorganisms

Conclusions

Based on the HRM curve profiles, the unknown microbiota was differentiated in ten groups and in two single isolates. Ps. cryohalolentis was the most dominant phylotype at the beginning of fish shelf-life, while Ps. glacincola increased and dominated at the time of the sensory minimum acceptability (day 14) and rejection (day 16). HRM could be used as a simple and rapid method to differentiate or identify (using representatives of each group as reference bacterial strains) the sea bass

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HRM and 16S rRNA gene sequencing reveal the cultivable microbiota of the European sea bass during ice storage

Highlights

Abstract

Introduction

Section snippets

Provision and storage of sea bass

Rejection time

Enumeration of APC

Differentiation and identification of microbiota grown on TSA

Discussion

Conclusions

Food Microbiol.

Food Microbiol.

Food Microbiol.

Int. J. Food Microbiol.

Food Control

Meat Sci.

Food Microbiol.

LWT Food Sci. Technol.

Food Microbiol.

LWT Food Sci. Technol.

Food Microbiol.

Int. J. Food Microbiol.

Food Microbiol.

Food Microbiol.

Poult. Sci.