Effect of low-frequency electric field on microbial community structure of Pacific white shrimp (Penaeus vannamei) during ice-temperature storage

doi:10.21203/rs.3.rs-3196453/v1

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Effect of low-frequency electric field on microbial community structure of Pacific white shrimp (Penaeus vannamei) during ice-temperature storage

https://doi.org/10.21203/rs.3.rs-3196453/v1

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Low-frequency electric field (LFEF) is a potential adjuvant technology for low-temperature preservation that can stabilize the supercooling state while delaying corruption and extending the shelf life. Changes in microbial community composition was investigated in Penaeus vannamei treated with LFEF in combination with ice temperature for 11 days. LFEF treatment increased the abundance and diversity of microbial communities in Penaeus vannamei and better maintained the relative content of original bacteria (Ralstonia), which indicates that LFEF contributes to the improvement in the species diversity. The spoilage microorganisms of shripm samples stored at ice temperature for 11 days included Aliivibrio, Pseudoalteromonas, Photobacterium, Moritella and Psychrobacter, which were also positively correlated with TVB-N and pH. However, Pseudoalteromonas and Psychrobacter were dominant in LFEF-treated samples. According to the difference analysis, LFEF treatment leaded to smaller changes in microbial structure, and the results showed that LFEF can effectively inhibit Vibrionaceae. A Spearman correlation analysis showed that Ralstonia and Faecalibacterium were positively correlated with spoilage bacteria, which may provide a reference for the improvement of LFEF assisted preservation. In conclusion, LFEF as an auxiliary technology for low temperature preservation is a reliable and potential method for preserving the quality of aquatic products.

Ice-temperature storage

Low-frequency electric field

Penaeus vannamei

Microbial community

High-throughput sequencing

Ice-temperature preservation refers to the technique of storing fresh food at a temperature below 0℃ and above the freezing point in order to keep the food at a low temperature without freezing, and its temperature range is typically − 0.5℃ to 2.8℃ (Duun & Rustad, 2007). After refrigeration and gas preservation, it is considered the third-generation preservation technology in the field of low temperature preservation. Ice temperature preservation can play a low-temperature stress effect on biological cells, some nutrient substances in the cells are not easy to freeze at low temperatures, effectively preventing cell freezing (Gallart-Jornet et al., 2006), while low temperatures can inhibit most enzymes and microbial metabolic levels (Chong et al., 2014; ANDO et al., 2004), so it can better maintain the quality of fresh products. Although ice temperature preservation methods have been discovered and studied by food scientists since the 1960s, they have not been applied industrially for food storage and preservation ( Wu et al., 2014). The main reason for this phenomenon may be the extremely strict temperature control requirements for ice temperature conditions. If the temperature fluctuation of the equipment does not meet the requirements, it may lead to the formation of ice crystals, resulting in a decrease in food quality. In addition, extremely precise temperature control can lead to a significant increase in equipment operating costs (Haoyan et al., 2021; Stonehouse & Evans, 2015), which may also be a factor limiting the application of ice-temperature preservation technology in production. Therefore, the development of methods to control ice crystal formation may be a key breakthrough point for the industrial application of ice temperature preservation. Interestingly, our team's preliminary study found that pure water does not freeze at -6°C under the influence of low-frequency electric field (LFEF) (frequency range ≤ 300 kHz). Therefore, LFEF may be a potential technology to overcome the technical challenges of industrializing ice-temperature preservation.

An electric field is an electromagnetic field effect created by the interaction between charges. The LFEF enters the cell through electromagnetic waves and resonates with the water molecules in the food, which interfere with the formation and stability of hydrogen bonds between water molecules, thus inhibiting the freezing of water in the food (James et al., 2015).Cells themselves, with ions, electrolytes, etc., have a certain electrical property, the case of an external electric field is bound to produce specific biological effects on life (Macrì et al., 2002). In a previous study, our group demonstrated that the shelf life of ice-temperature preserved Penaeus vannamei under LFEF intervention was extended from 11 to 16 days and the shelf life index (TVB-N) of shrimp was significantly reduced compared to conventional ice temperature preservation (Ming et al., 2022). However, the mechanism of shrimp meat quality improvement under LFEF intervention is unclear. Studies have shown that microorganisms are a key factor influencing the shelf life of ice-temperature preserved-foods and that total volatile basic nitrogen (TVB-N) is also a metabolite of microorganisms (Parlapani et al., 2020). It is well known that the formation of large ice crystals leads to the disruption of cell walls and the exudation of cell contents, which provides good conditions for microbial colonization. However, the physiological metabolism and reproduction of microorganisms can also be significantly altered by the intervention of electromagnetic fields (Ma et al., 2008; Feihong et al., 2022; Lukáš et al., 2010). Therefore, LFEF may prolong the shelf-life of Pacific white shrimp meat mainly by interfering with microbial physiological metabolism and reproduction, but the microbial evolution of shrimp meat under LFEF intervention and the effect of LFEF on microorganisms are not clear.

Pacific white shrimp is an important aquatic product due to its delicious meat, rich nutrients and high economic value (Shengnan et al., 2021). However, due to the high enzyme activity in the cephalothorax of shrimp and the high moisture content of shrimp meat, post-harvest shrimp meat is susceptible to spoilage due to the multiplication of spoilage microorganisms (Chuang et al., 2019; He et al., 2023). Through the action of endogenous enzymes and microorganisms (Cen et al., 2021), proteins and nitrogenous compounds in shrimp are degraded to form TVB-N. European Commission regulations indicate that the maximum concentration of TVB-N in fish products acceptable to consumers is 25–35 mgN/100 g. TVB-N in shrimp meat above 30 mgN/100 g is generally considered spoilage (Okpala et al., 2014). Recently, 16S rRNA amplicon sequencing has been used to study the microbial changes in seafood during storage (Edit et al., 2021), which can identify most bacteria and correlate microbial abundance information with other relevant variables (De Filippis et al., 2018). Therefore, in this study, 16S rRNA amplicon sequencing of Penaeus vannamei shrimp meat from fresh, conventional ice temperature storage for 11 days (TVB-N reached 29.77 ± 2.84 mg N/100g) and LEFE combined with ice-temperature storage for 11 days was performed to determine the microbiota. The aim was to evaluate the effect of LFEF on the microbial community of shrimp under ice-temperature storage conditions and the correlation between colonies and physicochemical indicators (TVB and pH), with the aim of elucidating the mechanism of the quality change of Penaeus vannamei in ice-temperature preservation assisted by LFEF and to provide theoretical support for the industrial application of LFEF preservation technology in the field of ice-temperature preservation.

2.1 Sample preparation

Penaeus vannamei (mariculture) was purchased from Munai market (Yangjiang, China), and was transported to the laboratory alive in an oxygenated aquarium within 1 h. Penaeus vannamei was washed with sterile water after sudden death with crushed ice. Shrimps (12 ~ 16 g) were pre-cooled at 4°C for 1 h and randomly divided into two groups: LFEF-assisted ice temperature preservation (A group) and conventional ice temperature preservation (B group). Shrimps were placed in sterile plastic boxes on the clean bench, and then group A samples were stored in a test chamber (-1 ± 1°C) (LHS-250 CL, Shanghai Qixin Scientific Instrument Co., Ltd.) with an LFEF generator, and group B samples were stored in a test chamber at the same temperature conditions but without an LFEF generator. The frequency and voltage of the LFEF generator were set to 50Hz and 3000V, respectively. Samples were stored at -1 ± 1°C for 17 days and pH, TVB-N, and total viable count (TVC) were measured every two days (data not shown). Based on the TVB-N results, we selected fresh Penaeus vannamei (XY group), Penaeus vannamei (A group) stored at the LFEF combined with ice temperature for 11 days (-1 ± 1°C), and Penaeus vannamei (B group) stored at ice temperature (-1 ± 1°C) for 11 days (TVB-N reached 29.77 ± 2.84 mg N/100g) for 16S rRNA amplicon sequence analysis.

2.2 LFEF experiment apparatus

As shown in Fig. 1, two transmitting electrodes were placed evenly at the upper and lower ends of the test chamber. The LFEF-generating device outside the test chamber was connected to the transmitting electrode inside the tube by the transmission wire. The LFEF generating device was assembled according to the authorized patent of our team. The device was consisted of an input control module, an electric field signal generation module, an output control module, an electric field generation module, and a safety protection module.

2.3 Sequencing sample preparation

Total genomic DNA was extracted from samples using the CTAB method (Brandfass & Karlovsky, 2008). DNA concentration and purity were monitored on a 1% agarose gel. Depending on the concentration, DNA was diluted to 1ng/µL with sterile water. The 16S rRNA genes of 16S V4 were amplified using 515F-806R(5' GTGCCAGCMGCCGCGGTAA-3' and 5'-GGACTACHVGGGTWTCTAAT-3') with the barcode. All PCR reactions were performed with 15 µL of Phusion® High-Fidelity PCR Master Mix (New England Biolabs), and the same volume of 1X loading buffer (containing SYB green) was mixed with the PCR products, and then the mixture was operated electrophoresed on a 2% agarose gel for detection. PCR products were mixed in equidensity ratios. Finally, the mixture of PCR products was purified using Qiagen Gel Extraction Kit (Qiagen, Germany). Tag-encoded high throughput sequencing was carried out by the Illumina HiSeq platform (Beijing Novogene Bio-information Science and Technology Co., Ltd., China) and 250 bp paired-end reads were generated.

2.4 Paired-end reads assembly and Species annotation

Paired-end reads were assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence. Paired-end reads were merged using FLASH (V1.2.7). Sequence analysis was performed using Uparse software (Uparse v7.0.1001), and sequences with ≥ 97% similarity were assigned to the same OTUs. The Silva database was used for each representative sequence, based on the Mothur algorithm to annotate taxonomic information.

2.5 Bioinformatics analysis

Alpha diversity was used to analyze the complexity of species diversity for a sample through 6 indices, including Observed-species, Chao1, Shannon, Simpson, ACE, and Good coverage. All these indices in our samples were calculated using QIIME (Version 1.7.0) and displayed with R software (Version 2.15.3). Based on the results of species annotation, clustering heat-map, Venn diagram, PCoA analysis, and UPGMA cluster analysis were used to evaluate the similarity and difference in species composition between groups or samples. Relative abundance histograms were used to analyze species composition in samples. Linear discriminant analysis (LDA) effect size (LEfSe) (Nicola et al., 2011b)was further used for pairwise comparison based on species composition and comparative analysis to screen species with significant differences between different groups. Finally, the relationship between flora and physicochemical factors was preliminarily explained by correlation analysis.

2.6 Statistical analysis

Data were processed using SPSS 17 with a one-way analysis of variance and Duncan's test at 5% significance level and plotted using Origin 2022b.

3.1 Sequencing Data Analysis

Based on the Illumina Nova sequencing platform, the Paired-End sequence data (Fig. 2) were obtained. By splicing reads, an average of 97,472 tags were measured per sample. The splicing data were filtered by QIIME software, and an average of 83,043 valid data were obtained. The effective quality control data were 60,698, and the effective quality control rate was 62.33%. Sequences were clustered into operational taxonomic units (OTUs) with 97% identity, and a total of 2,332 OTUs were obtained. Of these, 1,053 (45.15%) OTUs were annotated to the genus level in the SILVA 138 database. Although both the Chao1 and ACE indices were higher than the number of OTUs obtained in all the sample groups, the Goods coverage of all the groups was greater than 0.9(Table 1), which means that almost all the bacteria in the samples were detected, and some other bacterial phylotypes were present. In addition, the rarefaction curve is used to reflect the reasonableness of the amount of sequencing data (Fig. 3༉, which indirectly reflects the richness of the species in the sample. As shown in Fig. 3(a), as the sequencing depth increased, the rarefaction curve for all samples gradually became flattened, indicating that the depth and data volume the sequencing results were sufficient for subsequent analysis. Furthermore, the rank abundance curve is also used to reflect the richness and evenness of the species in the sample (Lundberg et al., 2013). The higher the species richness, the winder the span of the curve on the horizontal axis, the smoother the curve, and the more uniform the species distribution (Whittaker, 1965). As shown in the rank abundance curve (Fig. 3(b)), the horizontal width decreases in order from A to XY to B, indicating that LFEF has some effect on the species richness. In addition, there was no significant difference in the smoothness of the vertical direction of the sample rank abundance curve (except for B3), indicating that there was no significant difference in the evenness of the microbial distribution.

Table 1

Alpha diversity profiles of three groups of samples of shrimps.
Alpha diversity	ACE	Chao1	Shannon	Simpson	Observed species	Goods coverage	PD_whole_tree
XY	1162.905	1113.229	4.003	0.709	983	0.995	125.648
B	1034.974	997.573	4.243	0.835	845	0.995	83.566
A	1207.065	1162.186	4.390	0.765	1018	0.995	96.578

3.2 Diversity of bacterial communities

Alpha diversity indices of different samples are shown in Table 1. Chao 1 and Ace indices are used to assess the abundance of microbial communities, with higher values indicating a higher abundance of community species. Shannon and Simpson (1-D) indices reflect species diversity, which are comprehensive indices of species richness and evenness (Liang et al., 2022). Theoretically, they are positively correlated with community diversity. Compared to the XY group, the Observed species, ACE and Chao1 indices decreased and the Shannon and Simpson indices increased in the B group. These indicated a decrease in microbial abundance and an increase in homogeneity in shrimp stored at -1 ± 1°C for 11 days compared to fresh samples. This was due to the fact that the inhibition of the growth of microorganisms intolerant to low temperatures, resulting in the proliferation of psychrophilic dominant spoilage bacteria, which inhibited the growth of other bacteria and formed a simpler community structure. The PD whole tree value of B was 83,566, which was the lowest of all groups, indicating that it had the simplest genetic relationship. In addition, the shrimp samples were already spoiled and the microbial growth environment system may have reached stability (Chaillou et al., 2015). Interestingly, in this study, group A had the highest indices except for the Simpson index, which was very different from some previous studies (Huang et al., 2020; Menghua et al., 2022; Tiantian et al., 2022), indicating that the shrimp samples in group A had the most diverse bacterial species. The possible reason for this is that the LFEF inhibited the growth and reproduction of some dominant bacteria and produced bacteria that constantly competed with them for the limited substrate and nutrients, leading to an increase in microbial diversity in the LFEF treatment group. Fojt et al. (Fojt et al., 2004) found that low-frequency alternating electromagnetic fields at 50 Hz were able to inhibit the proliferation of E. coli and rod-shaped bacteria. External electromagnetic fields can alter the permeability of the cell membranes, affect the transmembrane potential of the cell membranes, and influence the metabolism of microorganisms to achieve an inhibitory effect (Rego et al., 2015). Several studies have demonstrated that exposure of plants and animals to electric fields increases the probability of gene mutations. Song et al. (Song & Liang, 2010) treated the E. coli K12 strain with LFEF at 50 Hz with different field strengths and found that LFEF could induce mutations in the strain. In previous experiments, it was found that the total number of colonies in group A was significantly lower than group B on the 11th day of shrimp meat samples (Ming et al., 2022). However, in this study, group A had the highest community richness, which might be another reason for the increased diversity due to the mutagenic effect of LFEF on some bacteria, leading to an increase in unknown species. Meanwhile, according to the observed species and Shannon index difference between groups in Fig. 4, it was found that the number of species measured was significantly different (p < 0.05) in A&B and B&XY, with significant p values of 0.0190 and 0.0254, respectively, indicating that there were differences in the richness between XY and B, and between A and B. While there was no significant difference in the richness of the bacterial community between XY and A. The Shannon index was not significantly different (p > 0.05) in groups A&XY, A&B, and B&XY, with p values of 0.5265, 0.6101, and 0.8975, respectively, indicating that the difference in the evenness of the microbial community of the samples in each group was not significant. This is consistent with the results of the rank abundance curve above.

3.3 Species Annotation and Composition Analysis

Changes in the bacterial composition and abundance within Penaeus vannamei samples under different treatment conditions were revealed using the 16S rRNA amplicon sequencing approach. Figure 5 annotates the top 10 flora species information in three sample groups at phylum and family, and top at genus level in the form of histograms. The microbial composition of aquatic products undergoes major changes in the early stages of storage, usually reflected in a decrease in microbial abundance and diversity. With increasing storage time, only a few bacteria become the major spoilage bacteria in the late stages of spoilage (Gram et al., 2002; Shengping et al., 2018). Currently, the most common specific spoilage organisms (SSOs) in aquatic products are mainly Vibrionaceae, Pseudomonas spp, Shewanella, Photobacterium phosphoreum, Lactobacillus, Bacillus, etc (Bekaert et al., 2015; Chaillou et al., 2015; Gram & Dalgaard, 2002).

At the phylum level (Fig. 5(a)), each group (XY, A, B) was dominated by Proteobacteria (83.32%, 82.09%, 88.20%) and Firmicutes (11.55%, 11.77%, 7.94%), similar to some previous studies (Jia et al., 2019; Shengping et al., 2018). Here, Proteobacteria was the dominant phylum in the three sample groups, but more abundant in group B. In addition, the relative abundance of Firmicutes decreased in group B compared to group XY, while Proteobacteria and Firmicutes showed no significant changes in the LFEF-treated samples. Proteobacteria include the common spoilage bacteria of aquatic products, and most of the microorganisms associated with the spoilage of aquatic products belong to Proteobacteria (Tingting et al., 2020; Kergourlay et al., 2015). Their high presence in non-LFEF-treated samples may be attributed to the nutrient-rich nature of spoiled shrimp, which facilitates their growth. As expected, the LFEF-treated shrimp (A) had a similar bacterial community profile to the fresh shrimp (XY). This might be due to the ability of LFEF to inhibit the growth of Proteobacteria and Firmicutes growth during storage.

At the family level (Fig. 5(b)), the initial dominant bacteria of fresh shrimp samples were Burkholderiaceae, Vibrionaceae, and Moraxellaceae, accounting for 51.93%, 15.32%, and 3.31%, respectively. For the B, Burkholderiaceae (34.19%), Vibrionaceae (28.09%), Moritellaceae (8.29%), Pseudoalteromonadaceae (7.74%) and Moraxellaceae (3,84%) were identified as the predominant bacteria during 11 days storage. In contrast, Burkholderiaceae (43.07%), Vibrionaceae (10.62%), Pseudoalteromonadaceae (8.37%), Moraxellaceae (6,51%) and Moritellaceae (5.84%) were the predominant species in the A during storage. Burkholderiaceae are widespread in different environments, including pathogenic bacteria (Coenye, 2014). The growth environment was mainly responsible for the presence of a large number of Burkholderiaceae in fresh shrimp. Although Burkholderiaceae still accounted for the largest proportion in B, Vibrionaceae tended to replace Burkholderiaceae as the absolute dominant strain, suggesting that Vibrionaceae was the main spoilage bacterium causing shrimp spoilage under ice temperature storage conditions of -1 ± 1°C. The Vibrionaceae family is mainly distributed in the marine environments and contains a variety of bacteria that are commonly found as specific spoilage bacteria in aquatic products (Bekaert et al., 2015). Interestingly, the relative abundance of Vibrionaceae in A decreased significantly in this study, even less than in the fresh shrimp samples (XY), indicating that the LFEF had a good inactivation effect on Vibrionaceae.

The top 30 relative abundance levels of bacterial community proportions at the genus level of each group are shown in Fig. 5(c). High bacterial community diversity was observed in the A. At the beginning of storage, the primary dominant bacterial species in shrimp samoles was Ralstonia with a relative abundance of 51.91%, followed by Vibrio and Aliivibrio with 5.38% and 5.08%, respectively. Ralstonia is a newly discovered opportunistic pathogen genus in recent years, often found in soil or water supply systems (P & C, 2014). With an absolute percentage advantage, it became the dominant bacterium in fresh shrimp, which was influenced by the environment in which the shrimp were grown (Lacerda et al., 2022; Ryan et al., 2007). Liang et al. (Liang et al., 2021) found that Ralstonia was one of the major spoilage bacteria at the genus level when studying the effects of different storage temperatures on fresh lamb flora. Several studies (Liang et al., 2021; Zhang, 2018; Zhang et al., 2022) have found an increase or minimal decrease in Ralstonia content at low temperatures, suggesting that Ralstonia is not incapable of adapting to low temperature storage environments. Its significant reduction in B might be attributed to interspecific competition among bacteria and changes in the microenvironment. Interestingly, the relative abundance of Ralstonia was significantly higher in A than in B, suggesting that its activity might be stimulated in the electric field environment, resulting in a more tenacious survival. Li et al. found that an appropriate electric field could increase the relative abundance of denitrifying bacteria through experimental studies (Li et al., 2018). Vibrio is also considered to be an opportunistic pathogen associated with shrimp mortality and is generally present in live shrimp (Gram & Huss, 1996; Domínguez-Borbor et al., 2019). Low temperatures inhibit its growth and reproduction, resulting in a decrease in relative abundance during storage.

As shown in Fig. 5(c), the relative abundance of Aliivibrio, Pseudoalteromonas, Photobacterium, Moritella, and Psychrobacter increased to 14.77%, 7,74%, 9.87%, 8.30% and 3.60% in group B, respectively. These are common spoilage bacteria in low-temperature storage (Dong et al., 2022; Jia et al., 2019; Li et al., 2022). This was attributed to the decay and deterioration of shrimp in the later stages of storage, resulting in the gradual breakdown of proteins, sugars and lipids into small molecules that favor the continued growth and reproduction of microorganisms. Aliivibrio and Photobacterium are Gram-negative genera of the Vibrionaceae, which are closely related (Broekaert et al., 2011). Moritella is a cold-tolerant and pressure-loving bacterium commonly found in marine fish, seawater and marine sediments (Basak et al., 2020). Their relative abundances in A were significantly reduced to 5.50%, 3.08% and 5.84%, respectively. LFEF can act directly or indirectly on microorganisms, altering their cellular structure and affecting the permeability of microbial cells (Rego et al., 2015b), which in turn affects cellular metabolism (Qi et al., 2021) and alters the activity of microorganisms, thereby inhibiting their growth. TVB-N is produced when protein is degraded to basic nitrogen-containing compounds such as ammonia, dimethylamine and trimethylamine under the action of microorganisms and enzymes (Huang et al., 2017). Aliivibrio and Photobacterium are considered to be strong players in fish spoilage and are capable of producing large amounts of biogenic amines during storage, resulting in off-flavors (Antunes-Rohling et al., 2019). The spoilage potential of Moritella in shrimp spoilage has not been reported, but Moritella is closely related to the genus Shewanella (Jia et al., 2019). Shewanella can produce large amounts of sulphide and adhere to the surface of aquatic products to form a biofilm, breaking down proteins and causing further spoilage (Eyjólfur et al., 2008), so it is suspected that Moritella also has some spoilage ability. This matches the results of the previous TVB-N analysis. Pseudoalteromonas, the core genus of meat spoilage, produces large amounts of sulphides that hydrolyze proteins (Chen et al., 2017) and is closely associated with seafood spoilage. Psychrobacter includes low-temperature resistant species that are widely distributed in the marine environment. Although Psychrobacter has proteolytic activity, it has weak spoilage ability and contributes little contribution to the deterioration of aquatic products (Broekaert et al., 2013; Li et al., 2022). Their relative abundances in A group increased to 8.37% and 6.12%, respectively. Although their relative abundances are low, the subsequent application of LFEF preservation should be done with attention to the control of Pseudoalteromonas and Psychrobacter.

3.4 Sample difference analysis

A heat-map was generated to analyze and compare the relative abundances and changes in the microbial community at the genus level (top 35 genera are shown) in all samples (Fig. 6(A)). According to the clustering results, there were significant differences at the genus level in the microbiota of the three groups. Also, when comparing the XY groups, the species diversity of group A was significantly increased, while the opposite was true for group B, which was consistent with the results of the α-diversity analysis. In a Venn diagram (Fig. 6(B)), the common OTUs of all groups were 887, accounting for 37.52% of the total number of OTUs, indicating that some microorganisms were always present during storage, which had a significant impact on the quality of shrimp meat. The common OTUs in two comparisons, XY&A and XY&B, were 1135 and 1036, respectively. Furthermore, the unique OTUs in A and B were 308 and 206, respectively. Consistent with the provious results, LFEF treatment influenced the species composition of the bacterial communities. The Principal Coordinates Analysis (PCoA) reflected the differences in the microbial community of the three shrimp groups (Fig. 6(C)). The maximum variations in the microbiota of different shrimp samples were 60.84% (PC1) and 15.5% (PC2) to the total community representation, explaining 76.34% of the data variation, indicating that these two principal components can reflect the main factors of community structure variation in all samples. The cluster analysis showed that the species distribution between groups XY and B showed great isolation, but not between groups XY and A, which was consistent with the UPGMA cluster analysis results (Fig. 6(D)). These results suggested that groups XY and A showed higher microbial structural similarity compared to groups XY and B, possibly due to LFEF delaying sample spoilage during storage by maintaining the relative stability of the original colonies. LFEF has a better affect on microbial community structure, and further research into the detailed mechanism of LFEF action is urgently needed.

Linear discriminant analysis (LDA) effect size (LEfSe) classification helps to identify Biomarkers that are statistically different between groups (Nicola et al., 2011) (Fig. 7). Using an LDA score > 4 (p＜0.05) as the threshold for differential screening, and performing a two-way comparison between A and B, the results showed that several characteristics of the 11-days samples treated with different storage conditions showed a highly significant differences from order to genus. These included Vibrionales, Vibrionaceae, Photobacterium and Photobacterium phosphoreum, all of which were enriched in group B shrimp samples and belonged to the same evolutionary branch, further confirming the inhibitory effect of LFEF on Vibrionaceae .

3.4 Correlation Analysis

It is important to understand the correlation between physicochemical factors and microorganisms for the preservation of shrimp meat. As depicted in Fig. 8, the Spearman correlation analysis was employed to analyze the correlation of TVB-N, pH values with the top 10 abundant microbial compositions at the genus level as inputs. Notably, TVB-N and pH as indicators of deterioration showed a significant positive correlation. Pseudoalteromonas, Aliivibrio, Moritella, Shewanella, and Psychromonas showed a positive correlation with TVB-N and pH, and photobacterium also showed a positive correlation with TVB-N, confirming that these bacteria could have a major impact on the spoilage process of aquatic products. These genera were considered to be strong producers of TVB-N (Broekaert et al., 2013; Erke et al., 2015; Weipeng et al., 2018). In addition, as shown in Fig. 8, a significant positive correlation was found between these bacteria, implying that there is a synergistic relationship between these bacteria and can promote mutual growth (Danilo et al., 2006; Macé et al., 2014; Macé et al., 2013). On the other hand, Ralstonia and Faecalibacterium were negatively correlated with the above bacteria, especially Faecalibacterium, indicating that there might be an antagonistic relationship. Faecalibacterium is widely found in human and animal gut and has been considered to have the ability to positively regulate microbiota function and gut health (Howard, 2022; Sylvie et al., 2014). Whether Ralstonia is a beneficial bacterium is unknown and requires further investigation. Microbial interactions are one of the most important factors influencing the microbial composition of shrimp meat (Gram et al., 2002), so understanding the relationship between the main dominant strains is beneficial to help improve preservation methods.

The results of high-throughput sequencing analysis revealed the effect of LFEF on the microbial community of Penaeus vannamei stored at ice temperature. The results showed that LFEF treatment increased the diversity of the microbial communities. Ralstonia is the absolute original genus of Penaeus vannamei samples in this study, and LFEF can better maintain its relative abundance. In addition, the relative abundance of Vibrionaceae in LFEF-treated samples was significantly reduced, which was beneficial in delaying the spoilage of shrimp meat. Cluster analysis results showed that the community structure of LFEF-treated samples changed less compared to the untreated samples. Correlation analysis showed that TVB-N and pH were positively correlated with spoilage bacteria, and there were also correlations among the major genera. Overall, LFEF could be a potential tool for the preservation of Penaeus vannamei and other aquatic products stored at low temperatures.

Data Availability

The data related to this study has been presented in the article.

Credit Author Statement

Lijuan Xu: Writing - original draft, Software, Formal analysis. Haiqiang Chen: Conceptualization, Methodology, Visualization, Validation, Investigation. Zuanhao Liang: Validation, Investigation, Software. Shanshan Chen: Investigation. Yu Xia: Investigation, Supervision. Siming Zhu: Funding acquisition, Writing - review & editin, Data curation. Ming Yu: Project administration, Funding acquisition，Supervision.

Statements and Declarations

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding:

This project was supported by the Fundamental and Applied Basic Research Fund of Guangdong Province (2023A1515010763), the Special Project in Key Areas for General Universities in Guangdong Province (2022ZDZX4118), and the Project for Young Innovative Talents in General Universities in Guangdong Province (2022KQNC261).

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No competing interests reported.

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Effect of low-frequency electric field on microbial community structure of Pacific white shrimp (Penaeus vannamei) during ice-temperature storage

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Abstract

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1. Introduction

2. Materials and Methods

2.1 Sample preparation

2.2 LFEF experiment apparatus

2.3 Sequencing sample preparation

2.4 Paired-end reads assembly and Species annotation

2.5 Bioinformatics analysis

2.6 Statistical analysis

3. Results and Discussion

3.1 Sequencing Data Analysis

3.2 Diversity of bacterial communities

3.3 Species Annotation and Composition Analysis

3.4 Sample difference analysis

3.4 Correlation Analysis

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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