Metagenomic and metabolomic profiling of dried shrimp (Litopenaeus Vannamei) prepared by a procedure traditional to the south China coastal area

doi:10.21203/rs.3.rs-3206216/v1

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Metagenomic and metabolomic profiling of dried shrimp (Litopenaeus Vannamei) prepared by a procedure traditional to the south China coastal area

https://doi.org/10.21203/rs.3.rs-3206216/v1

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published 09 Jan, 2024

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Sun-drying is a traditional process for preparing dried shrimp in coastal area of South China, but its impacts on nutrition and the formation of flavor-contributory substances in dried shrimp remain largely unknown. This study aimed to examine the effects of the production process on the microbiota and metabolites in dried shrimp. 16S rDNA amplicon sequencing was employed to identify 170 operational taxonomic units (OTUs), with Vibrio, Photobacterium, and Shewanella emerging as the primary pathogenic bacteria in shrimp samples. Lactococcus lactis was identified as the principal potential probiotic to accrue during the dried shrimp production process, and found to contribute significantly to the development of desirable shrimp flavors. LC-MS-based analyses of dried shrimp sample metabolomes revealed a notable increase in compounds associated with unsaturated fatty acid biosynthesis, arachidonic acid metabolism, amino acid biosynthesis, and flavonoid and flavanol biosynthesis throughout the drying process. Subsequent exploration of the relationship between metabolites and bacterial flora highlighted the predominant coexistence of Bifidobacterium, Clostridium, and Photobacterium contributing heterocyclic compounds and metabolites of organic acids and their derivatives. Conversely, Arthrobacter and Staphylococcus were found to inhibit each other, primarily in the presence of heterocyclic compounds. This comprehensive investigation provides valuable insights into the dynamic changes in the microbiota and metabolites of dried shrimps spanning different drying periods, which we expect to contribute to enhancing production techniques and safety measures for dried shrimp processing.

sun-drying shrimp

microbiota

metabolites

pathogens

nutrition

Litopenaeus Vannamei is one of the most widely farmed shrimp species in China, and one of the world's major aquatic products (Liao and Chien, 2011). It is favored by consumers for its taste and nutritional value. The drying of fresh shrimp can extend its shelf-life and effectively reduce its transport cost (Lin et al., 2022). The main drying methods include vacuum freeze-drying, heat-pump-drying, hot-air drying, and sun-drying (Hernández Becerra et al., 2014; Sun et al., 2022). Different drying methods may yield the different flavors in dried shrimp. Sun-drying is the most popular and most economical drying method in South China (Akintola, 2015; Deng et al., 2015; El Hage et al., 2018).

Raw shrimp of the L. vannamei species represent a complex ecosystem that harbors diverse, active microorganisms. Vibrio, Pseudomonas, Photobacterium, and Acinetobacter were the most abundant microorganisms genera in shrimp (Cornejo-Granados et al., 2017). These genera include multiple pathogens and probiotics. Vibrio parahaemolyticus expresses PirA/B toxins and that can lead to acute hepatopancreatic necrosis disease (AHPND), is one of the most common pathogens among shrimps (Joshi et al., 2014; Silvia et al., 2014). At present, changes in the probiotic flora and associated metabolites of shrimp in response to sun-drying remain unknown.

The presence of the main nutritional and flavor components of dried shrimp, including unsaturated fatty acids, arachidonic acids, amino acids, flavonoids and flavanols, and to what extent these components are influenced by retained microorganisms are unknown. To understand changes in the microbial community and what microbial species are primarily retained during shrimp drying, and their relationship with the nutritional and flavor components of dried shrimp, we analyzed the microbiota and metabolites changes in dried shrimp during three periods. The results provide valuable information with application to the food industry.

Dried shrimp preparation

Freshwater shrimp purchased from a local market were prepared by removing the internal organs and peeling off the shells. The shrimp meat was soaked in a mild saline solution for ten minutes (salt: water = 3:1000). Excess moisture was then drained from the shrimp meat, which was placed under the sun for three consecutive days. Samples were collected at three different time points: before sun drying (ME0), after one day of sun drying (ME1), and after 3 days of sun drying (ME3). Collected samples were stored at -80°C for further microbiological analysis, 16s rDNA sequencing, and metabolite detection.

Microbiological analysis

Ten grams of each shrimp sample was homogenized for 2.5 minutes in a sterile bag containing 90 mL of physiological saline solution. A one mL aliquot of the homogenized solution was used to prepare a series of 10-fold dilutions in physiological saline. Plate streaking was carried out using a volume of 100 µL from each sample dilution, followed by incubation at 36 ± 1°C for 48 h for subsequent counting.

DNA extraction and 16s rDNA sequencing

A one mL aliquot of each homogenized solution was used for 16s rDNA sequencing. Five biological replicates were sequenced in each experimental group. Total microbial genomic DNA was extracted from shrimp samples using a FastDNA® Spin kit for soil (MP Biomedicals, Irvine, CA, USA) according to the manufacturer’s instructions. DNA quality and concentration were determined by 1.0% agarose gel electrophoresis and NanoDrop2000 spectrophotometry (Thermo Fisher Scientific, Waltham, MA, USA). Samples were kept at -80 ℃ prior to further use. The bacterial 16S rDNA gene was amplified with primer pairs 341F (5'-CCTAYGGGRBGCASCAG-3') and 806R (5'-GGACTACNNGGGTATCTAAT-3') using a T100 Thermal Cycler PCR thermocycler (BIO-RAD, Hercules, CA, USA)(Guo et al., 2017). The PCR reaction mixture included 10 µL 2 × mix, 0.8 µL of each primer (5 µM), 10 ng/µL of template DNA, and ddH₂O to a final volume of 20 µL. PCR amplification cycling conditions were as follows: initial denaturation at 95 ℃ for 3 min, followed by 27 cycles of denaturing at 95 ℃ for 30 s, annealing at 55 ℃ for 30 s and extension at 72 ℃ for 45 s, followed by a single extension at 72 ℃ for 10 min, followed by indefinite holding at 10 ℃. PCR products were extracted from 2% agarose gels purified using a PCR Clean-Up Kit (YuHua, Shanghai, China) according to manufacturer’s instructions, and quantified using Qubit 4.0 (Thermo Fisher Scientific, Waltham, MA, USA).

Purified amplicons were pooled in equimolar amounts and paired-end sequenced on an Illumina PE250 platform (Illumina, San Diego, CA, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China).

Bioinformatics analysis

Paired-end reads were quality filtered using FastP (v0.18.0) (Chen et al., 2018), and merged using FLASH (v1.2.11) (Magoč and Salzberg, 2011). Operational taxonomic units (OTUs) were clustered with a 97% similarity cut-off using UPARSE (Edgar, 2013), and chimeric sequences were removed using UCHIME (v9.2.64) (Edgar et al., 2011). Bacterial annotation of OTUs was performed using the SILVA database v132 (Pruesse et al., 2007). Estimation of alpha diversity including richness and shannon indices, principal coordinate analysis (PCoA), and analysis of similarities (ANOSIM) were performed using vegan (v 2.5.3) (Stevens and Wagner, 2010). Indicator taxa were identified at the genus level using the Labdsv package (v2.0-1) (Roberts, 2013). BugBase as used to predict the potential functions of microorganisms in samples from the three periods (Ward et al., 2017).

Metabolites extraction and LC-MS analysis

Untargeted metabolomics was used to conduct metabolic profiling of the samples, with five biological replicates per group. Samples were stored in a -80 ℃ freezer thawed on ice, and ground with liquid nitrogen. A 400 µL solution (Methanol: Water = 7:3, v/v) containing an internal standard was added to 20 mg of ground sample, shaken at 1500 rpm for 5 min, and centrifuged at 12000 rpm for 10 min at 4 ℃. Supernatants (300 µL) were collected and stored at -20 ℃ for 30 minutes. Samples were then centrifuged at 12000 rpm for 3 min at 4 ℃. Finally, 200 µL of each supernatant was collected for LC-MS analysis.

An ACQUITY UPLC BEH system supplied with a C18 1.8 µm column (2.1 mm × 100 mm; Waters) was utilized. The extracts were eluted with a gradient starting from 5–90% mobile phase B (0.1% formic acid in acetonitrile) linearly over 11 minutes, held for 1 minute, then returned to 5% mobile phase B within 0.1 min and held for 1.9 min before rapidly returning to starting conditions. Mass spectrometry was performed using a TripleTOF 6600 mass spectrometer (AB SCIEX, Foster City, CA, USA) interfaced with a heated electrospray ionization source. Data acquisition was performed using the information-dependent acquisition (IDA) mode with Analyst TF 1.7.1 Software (AB SCIEX).

The LC-MS data file was converted to mzML format using ProteoWizard software (Chambers et al., 2012). Subsequently, the XCMS program (v3.6.1) was employed for peak extraction, peak alignment, and retention time correction (Smith et al., 2006). Peak areas were corrected using the "SVR" method. Any peaks with a detection rate lower than 50% in each sample group were excluded. Metabolic identification information was obtained by searching a combination of the laboratory's self-built database, integrated public databases, an AI database, and metDNA.

Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was performed using the OPLSR.Anal function from the MetaboAnalystR package (v1.0.1) in R software (Chong et al., 2019). Differential analysis among the three sample groups (ME1_vs_ME0, ME3_vs_ME0, and ME3_vs_ME1) was performed, and metabolites with Variable Importance in Projection (VIP) > 1 and a P < 0.05 (determined by Student's t-test) were considered statistically significant. Differential metabolites were annotated using the KEGG database (Ogata et al., 1999) (http://www.kegg.jp/kegg/compound/), and mapped to KEGG pathways (http://www.kegg.jp/kegg/pathway.html). Differential abundance skeys (DA Skey) were calculated for each KEGG pathway. The Short Time-series Expression Miner (STEM) tool (Ernst and Bar-Joseph, 2006) was used for temporal metabolomic analysis. Metabolite profiles were selected based on a significance level of P < 0.05. KEGG pathway enrichment analysis was performed using clusterProfiler (v4.7.1) (Yu et al., 2012).

Interaction analysis of key microbiota and metabolites

Spearman’s correlation coefficient was calculated between key microbial communities, and between key microbial communities and differential metabolites. Significant correlations (P < 0.05) were used to construct an interaction network and were visualized using Cytoscape (v3.9) (Shannon et al., 2003).

Statistical analysis

The pH level and total viable count (TVC) during production of dried shrimp were statistically examined based on Duncan’s test, and P < 0.05 indicated a significant difference.

Bacterial enumeration

During dried shrimp production, as the shrimp dehydrated, their volume gradually shrank (Fig. 1A). The pH level significantly decreased at ME1 stage and slightly increased thereafter (Fig. 1B). TVC significantly increased at ME3 stage (Fig. 1C).

Diversity of the bacterial community in dried shrimp

An average of 79,985 paired-end reads per sample were obtained, and the numbers of taxon tags ranged from 45,685 to 134,259 among the samples with 73,329 taxon tags per sample (Table S1). These tags were grouped into 270 microbial Operational Taxonomic Units (OTUs) (Table S1).

The number of OTUs in ME0 period samples was the highest, and a total of 170 OTUs was shared by all three periods (Fig. 2A). The Richness (Fig. 2B) and Shannon (Fig. 2C) indices indicated higher α-diversity in the ME0 period than in the latter two periods. PCoA analysis also suggested that samples within groups tended to cluster together, while the ME0 period was distinct from the latter two stages (Fig. 2D). During dried shrimp production, the similarity between samples within groups gradually decreased, indicating a trend towards unstable community structures, and significant differences were observed in the community structures among different sample groups (Fig. 2E). A previous study demonstrated the importance of the initial microbial load found on ready-to-eat foods. However, it should be noted that the microbiological load of these foods at the point of sale can be influenced by several factors, including processing, storage, and display conditions (Angelidis et al., 2006; Beuchat and Ryu, 1997).

Microbial community composition and changes during preparation

In the ME0 period, the predominant bacterial phyla in the samples were Bacteroidetes, Actinobacteria, and Proteobacteria, while in the latter two periods, Proteobacteria was the predominant phylum, and the relative abundance of Firmicutes was increased (Fig. 3A). At the genus level, the ME0 samples showed a high abundance of Psychrobacter, while the ME1 samples mainly included Vibrio and Photobacterium genera, and the genera Vibrio, Shewanella, Lactococcus, and Photobacterium were more abundant in the ME3 samples (Fig. 3B). A previous study showed that Firmicutes and Proteobacteria were the predominant phyla during the drying process in dry sausages, consistent with our results (Hu et al., 2020). Lactobacillus sakei is the predominant species in Croatian dry fermented sausages (Zgomba Maksimovic et al., 2018).

Indicator species analysis indicated that the abundance of Rhodobacter, Flavobacterium, Pseudoalteromonas, Psychrobacter, and Loktanella genera was significantly higher in the ME0 period than in the latter two stages, while Aliivibrio and Vibrio were more abundant in the latter two periods (Fig. 3C). Photobacterium was significantly more abundant in the ME1 period than in the other two stages, whereas Shewanella was significantly less abundant in the ME1 period than in the other two periods (Fig. 3C). In particular, Lactococcus gradually increased during shrimp drying process (Fig. 3C). Vibrio, Photobacterium, and Shewanella are known marine animal pathogens and potential opportunistic human pathogens, whereas Lactococcus are potential beneficial bacteria. A previous study showed that Aliivibrio and Vibrio are important pathogens among seafood and pose a threat to human health (Huehn et al., 2014; Neetoo et al., 2022; Toranzo et al., 2023). Another study showed that Photobacterium and Shewanella were the most prevalent and abundant pathogens among bluespotted seabreams, making up 30.2% and 11.3% of detected pathogens (Itay et al., 2022). Lactococcus is the most important beneficial bacteria in dried seafood. One study found that Lactococcus lactis 69, isolated from sun-dried meat, could secrete a heat-stable bacteriocin that exhibited good inhibitory effects againast various pathogens (Biscola et al., 2013). Another Lactococcus lactis KTH0-1S isolated from dried Thai shrimp could produce a heat-stable bacteriocin that also inhibits food-borne pathogens and reduces tyramine accumulation (Saelao et al., 2018, 2017). The contribution of Lactococcus to flavor development in fermented meat products was found to be relatively insignificant, primarily owing to its low lipolytic activity (Flores and Toldrá, 2011). By contrast, the accumulation of Lactococcus during the drying process in shrimp plays an important role in changes in the shrimp microbiome and flavor.

Potential functions of the bacterial community

Predicted functions results showed that the aerobic category was significantly higher in the ME0 period than in the latter two periods (Fig. 4A), and the aerobic category decreased during the shrimp drying process. Facultatively anaerobic (Fig. 4B), potentially pathogenic (Fig. 4C), and stress tolerant (Fig. 4D) categories were significantly higher in the latter two periods, while anaerobic (Fig. 4E) and biofilm formation (Fig. 4F) categories did not show significant differences among the three periods. One study showed that aerobic microorganisms such as E. coli O157:H7 are reduced by three logarithms during the process of drying apple slices (Derrickson-Tharrington et al., 2005). Another study proved that potential pathogens undergo osmotic stress-tolerances increases to adapt to environmental changes during drying in the food chain (Burgess et al., 2016; Sleator and Hill, 2002).

Metabolomic changes in dried shrimp during preparation

A total of 13,713 metabolites were identified in the samples during shrimp drying, with 7,571 metabolites identified at the secondary level (Table S2). Among the differentially accumulated metabolites (DAM), the majority increased during shrimp drying (Fig. 5A). Pairwise comparison of differentially regulated metabolites was performed across the three periods, and 717 metabolites were found to be differentially regulated (Fig. 5B). Compared to the ME1 period, the arachidonic acid metabolism, purine metabolism, the sphingolipid signaling pathway, biosynthesis of nucleotide sugars, drug metabolism and other enzymes, the apelin signaling pathway, the pentose phosphate pathway, nicotinate and nicotinamide metabolism, arginine and proline metabolism, the cAMP signaling pathway, and calcium signaling pathway were significantly upregulated in ME0 samples (Fig. 5C). Compared with the ME1 period, the biosynthesis of unsaturated fatty acids, arachidonic acid metabolism, lysine degradation, necroptosis, phenylalanine metabolism, lysine biosynthesis, fatty acid elongation, propanoate metabolism, glycerophospholipid metabolism, linoleic acid metabolism, valine, leucine and isoleucine biosynthesis, sulfur metabolism, aminoacyl-tRNA biosynthesis, and the prolactin signaling pathways were significantly upregulated in ME3 samples (Fig. 5D). Biosynthesis of unsaturated fatty acids is an important change during the sun-drying of shrimp. A previous study showed that unsaturated fatty acids (including monounsaturated fatty acids and polyunsaturated fatty acids) were released by Saccharomyces cerevisiae 31 during the fermentation process of fish paste (Chen et al., 2017). One study found 43.90% monounsaturated fatty acids, 28.61% polyunsaturated fatty acids, and 27.48% saturated fatty acids among dried shrimps (Sampaio et al., 2006). The arachidonic cis-5, 8, 11, 14-Eicosatetraenoic (C20:4 n-6) value in the raw shrimps was increased significantly by smoking and sun-drying (Akintola, 2015). Amino acid metabolism also plays a vital role in the drying of shrimp. A previous study showed that tyrosine values increased approximately 70% and 80% respectively in smoked and sun-dried shrimp, which was associated with phenylalanine metabolism and lysine biosynthesis (Akintola, 2015; Omolola, 2013).

Temporal analysis of DAM was performed by STEM, which identified three significant profiles with an overall increasing trend (Fig. 6A). The DAM in these significant profiles were mainly enriched in oxidative phosphorylation, cGMP-PKG signaling, flavones and flavonols biosynthesis, biosynthesis of various antibiotics, novobiocin biosynthesis, purine metabolism, AMPK signaling, Type I polyketide structures, riboflavin metabolism, flavonoid biosynthesis, chlorocyclohexane and chlorobenzene degradation, nicotinate and nicotinamide metabolism, and longevity regulating pathway, etc (Fig. 6B). Flavones and flavonols are important nutriments impacted by food among the fermentation and drying proceses. A previous study showed that the total flavonoid content of green tea was highest after drying at 38 ℃ (Roshanak et al., 2016).

Interaction of key microbiota and metabolites in dried shrimp

Spearman’s correlation analysis was performed on the key indicator microbial community and on differential metabolites (Figures S1 and S2). The majority of key microbial taxa, including Psychrobacter, Shewanella, Lactococcus, Flavobacterium, Rhodobacter, Acinetobacter, Ulvibacter, Tenacibaculum, Leadbetterella, Aequorivita, Staphylococcus, Lactobacillus, Arthrobacter, Paracoccus, Aeromonas, Fluviicola, Olleya, Thiothrix, and Leucothrix, showed positive correlations with each other, and exhibited positive correlations with differentially accumulated metabolites (Figures S1 and S2). By contrast, Vibrio, Allivibrio, Photobacterium, and Bifidobacterium were positively correlated with each other, but their abundances were predominantly negatively correlated with differentially accumulated metabolites (Figures S1 and S2). Based on the results of these correlation analysis, a network depicting microbial interactions and microbial-metabolite interactions was constructed (Fig. 7). The results indicate that Bifidobacterium, Clostridium, and Photobacterium are mainly associated with heterocyclic compounds and organic acids and their derivatives metabolites in a co-occurring manner, while Arthrobacter and Staphylococcus were predominantly mutually exclusive with heterocyclic compounds (Fig. 7). Previous studies have shown that Bifidobacterium and Photobacterium are associated with heterocyclic compounds (Faridnia et al., 2010; Xu et al., 2000). Meanwhile, the immobilization of Clostridium was the main effect of organic acids (Dolejš et al., 2014; Stinson and Naftulin, 1991). One study showed that Arthrobacter mainly degrades heterocyclic compounds (Guo et al., 2019). Another study showed that Staphylococcus is primarily exclusive to heterocyclic compounds (Paudel et al., 2017).

We undertook a comprehensive analysis of the variations in the microbiota and metabolites of dried shrimp at three stages of drying. The predominant pathogenic bacteria identified in shrimp samples were Vibrio, Photobacterium, and Shewanella, while Lactococcus lactis, a potential probiotic produced during shrimp drying, played a significant role in the development of shrimp flavors. Subsequent examination of the metabolites present in dried shrimp samples revealed a substantial increase in compounds associated with unsaturated fatty acid biosynthesis, arachidonic acid metabolism, amino acid biosynthesis, and flavonoid and flavanol biosynthesis during the drying process. Finally, an exploration of the interplay between metabolites and bacterial flora demonstrated that Bifidobacterium, Clostridium, and Photobacterium predominantly coexisted with heterocyclic compounds and metabolites of organic acids and their derivatives, whereas Arthrobacter and Staphylococcus primarily inhibited each other, particularly in the presence of heterocyclic compounds. These findings provide valuable insights into the dynamic alterations in the microbiota and metabolites of dried shrimp throughout various stages of drying, contributing to a deeper understanding of shrimp processing and paving the way for improved safety practices in the production of dried shrimp.

Author contribution

Conception and design of the work, MY, JL; analysis, MY, JL, JC and MW; Drafting the manuscript, MY, SD and CL; Editing and revising the manuscript, MY and JL. All authors have read and approved the final manuscript.

Funding

This work was supported by Funds for Guangdong University Innovation Team Project (2021KCXTD081), the Innovation Projects of Colleges and Universities in Guangdong Province (2019GKTSCX120) and Foundation of Foshan Polytechnic (KY201902, KY201903).

Availability of data and material

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Additional information

Supplementary information is available for this paper.

Ethics approval Not applicable

Competing interests The authors declare no competing interests

Akintola, S.L., 2015. Effects of smoking and sun-drying on proximate, fatty and amino acids compositions of Southern pink shrimp (Penaeus notialis). J. Food Sci. Technol. 52, 2646–2656. https://doi.org/10.1007/s13197-014-1303-0
Angelidis, A.S., Chronis, E.N., Papageorgiou, D.K., Kazakis, I.I., Arsenoglou, K.C., Stathopoulos, G.A., 2006. Non-lactic acid, contaminating microbial flora in ready-to-eat foods: A potential food-quality index. Food Microbiol. 23, 95–100. https://doi.org/https://doi.org/10.1016/j.fm.2005.01.015
Beuchat, L.R., Ryu, J.H., 1997. Produce handling and processing practices. Emerg. Infect. Dis. 3, 459–465. https://doi.org/10.3201/eid0304.970407
Biscola, V., Todorov, S.D., Capuano, V.S.C., Abriouel, H., Gálvez, A., Franco, B.D.G.M., 2013. Isolation and characterization of a nisin-like bacteriocin produced by a Lactococcus lactis strain isolated from charqui, a Brazilian fermented, salted and dried meat product. Meat Sci. 93, 607–613. https://doi.org/https://doi.org/10.1016/j.meatsci.2012.11.021
Burgess, C.M., Gianotti, A., Gruzdev, N., Holah, J., Knøchel, S., Lehner, A., Margas, E., Esser, S.S., Sela (Saldinger), S., Tresse, O., 2016. The response of foodborne pathogens to osmotic and desiccation stresses in the food chain. Int. J. Food Microbiol. 221, 37–53. https://doi.org/https://doi.org/10.1016/j.ijfoodmicro.2015.12.014
Chambers, M.C., Maclean, B., Burke, R., Amodei, D., Ruderman, D.L., Neumann, S., Gatto, L., Fischer, B., Pratt, B., Egertson, J., Hoff, K., Kessner, D., Tasman, N., Shulman, N., Frewen, B., Baker, T.A., Brusniak, M.-Y., Paulse, C., Creasy, D., Flashner, L., Kani, K., Moulding, C., Seymour, S.L., Nuwaysir, L.M., Lefebvre, B., Kuhlmann, F., Roark, J., Rainer, P., Detlev, S., Hemenway, T., Huhmer, A., Langridge, J., Connolly, B., Chadick, T., Holly, K., Eckels, J., Deutsch, E.W., Moritz, R.L., Katz, J.E., Agus, D.B., MacCoss, M., Tabb, D.L., Mallick, P., 2012. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920. https://doi.org/10.1038/nbt.2377
Chen, Q., Kong, B., Han, Q., Xia, X., Xu, L., 2017. The role of bacterial fermentation in lipolysis and lipid oxidation in Harbin dry sausages and its flavour development. LWT 77, 389–396. https://doi.org/https://doi.org/10.1016/j.lwt.2016.11.075
Chen, S., Zhou, Y., Chen, Y., Gu, J., 2018. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890. https://doi.org/10.1093/bioinformatics/bty560
Chong, J., Yamamoto, M., Xia, J., 2019. MetaboAnalystR 2.0: From Raw Spectra to Biological Insights. Metabolites 9. https://doi.org/10.3390/metabo9030057
Cornejo-Granados, F., Lopez-Zavala, A.A., Gallardo-Becerra, L., Mendoza-Vargas, A., Sánchez, F., Vichido, R., Brieba, L.G., Viana, M.T., Sotelo-Mundo, R.R., Ochoa-Leyva, A., 2017. Microbiome of Pacific Whiteleg shrimp reveals differential bacterial community composition between Wild, Aquacultured and AHPND/EMS outbreak conditions. Sci. Rep. 7, 11783. https://doi.org/10.1038/s41598-017-11805-w
Deng, Y., Luo, Y., Wang, Y., Zhao, Y., 2015. Effect of different drying methods on the myosin structure, amino acid composition, protein digestibility and volatile profile of squid fillets. Food Chem. 171, 168–176. https://doi.org/https://doi.org/10.1016/j.foodchem.2014.09.002
Derrickson-Tharrington, E., Kendall, P.A., Sofos, J.N., 2005. Inactivation of Escherichia coli O157:H7 during storage or drying of apple slices pretreated with acidic solutions. Int. J. Food Microbiol. 99, 79–89. https://doi.org/https://doi.org/10.1016/j.ijfoodmicro.2004.07.015
Dolejš, I., Rebroš, M., Rosenberg, M., 2014. Immobilisation of Clostridium spp. for production of solvents and organic acids. Chem. Pap. 68, 1–14. https://doi.org/10.2478/s11696-013-0414-9
Edgar, R.C., 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998. https://doi.org/10.1038/nmeth.2604
Edgar, R.C., Haas, B.J., Clemente, J.C., Quince, C., Knight, R., 2011. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200. https://doi.org/10.1093/bioinformatics/btr381
El Hage, H., Herez, A., Ramadan, M., Bazzi, H., Khaled, M., 2018. An investigation on solar drying: A review with economic and environmental assessment. Energy 157, 815–829. https://doi.org/https://doi.org/10.1016/j.energy.2018.05.197
Ernst, J., Bar-Joseph, Z., 2006. STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 7, 191. https://doi.org/10.1186/1471-2105-7-191
Faridnia, F., Hussin, A.S.M., Saari, N., Mustafa, S., Yee, L.Y., Manap, M.Y.A., 2010. In vitro binding of mutagenic heterocyclic aromatic amines by Bifidobacterium pseudocatenulatum G4. Benef. Microbes 1, 149–154. https://doi.org/10.3920/BM2009.0035
Flores, M., Toldrá, F., 2011. Microbial enzymatic activities for improved fermented meats. Trends Food Sci. Technol. 22, 81–90. https://doi.org/https://doi.org/10.1016/j.tifs.2010.09.007
Guo, M., Wu, F., Hao, G., Qi, Q., Li, R., Li, N., Wei, L., Chai, T., 2017. Bacillus subtilis Improves Immunity and Disease Resistance in Rabbits. Front. Immunol. 8. https://doi.org/10.3389/fimmu.2017.00354
Guo, X., Xie, C., Wang, L., Li, Q., Wang, Y., 2019. Biodegradation of persistent environmental pollutants by Arthrobacter sp. Environ. Sci. Pollut. Res. 26, 8429–8443. https://doi.org/10.1007/s11356-019-04358-0
Hernández Becerra, J.A., Ochoa Flores, A.A., Valerio-Alfaro, G., Soto-Rodriguez, I., Rodríguez-Estrada, M.T., García, H.S., 2014. Cholesterol oxidation and astaxanthin degradation in shrimp during sun drying and storage. Food Chem. 145, 832–839. https://doi.org/https://doi.org/10.1016/j.foodchem.2013.08.098
Hu, Y., Zhang, L., Liu, Q., Wang, Y., Chen, Q., Kong, B., 2020. The potential correlation between bacterial diversity and the characteristic volatile flavour of traditional dry sausages from Northeast China. Food Microbiol. 91, 103505. https://doi.org/https://doi.org/10.1016/j.fm.2020.103505
Huehn, S., Eichhorn, C., Urmersbach, S., Breidenbach, J., Bechlars, S., Bier, N., Alter, T., Bartelt, E., Frank, C., Oberheitmann, B., Gunzer, F., Brennholt, N., Böer, S., Appel, B., Dieckmann, R., Strauch, E., 2014. Pathogenic vibrios in environmental, seafood and clinical sources in Germany. Int. J. Med. Microbiol. 304, 843–850. https://doi.org/https://doi.org/10.1016/j.ijmm.2014.07.010
Itay, P., Shemesh, E., Ofek-Lalzar, M., Davidovich, N., Kroin, Y., Zrihan, S., Stern, N., Diamant, A., Wosnick, N., Meron, D., Tchernov, D., Morick, D., 2022. An insight into gill microbiome of Eastern Mediterranean wild fish by applying next generation sequencing. Front. Mar. Sci. 9. https://doi.org/10.3389/fmars.2022.1008103
Joshi, J., Srisala, J., Truong, V.H., Chen, I.-T., Nuangsaeng, B., Suthienkul, O., Lo, C.F., Flegel, T.W., Sritunyalucksana, K., Thitamadee, S., 2014. Variation in Vibrio parahaemolyticus isolates from a single Thai shrimp farm experiencing an outbreak of acute hepatopancreatic necrosis disease (AHPND). Aquaculture 428–429, 297–302. https://doi.org/https://doi.org/10.1016/j.aquaculture.2014.03.030
Liao, I.C., Chien, Y.-H., 2011. The Pacific White Shrimp, Litopenaeus vannamei, in Asia: The World’s Most Widely Cultured Alien Crustacean BT - In the Wrong Place - Alien Marine Crustaceans: Distribution, Biology and Impacts, in: Galil, B.S., Clark, P.F., Carlton, J.T. (Eds.), . Springer Netherlands, Dordrecht, pp. 489–519. https://doi.org/10.1007/978-94-007-0591-3_17
Lin, Y., Gao, Y., Li, A., Wang, L., Ai, Z., Xiao, H., Li, J., Li, X., 2022. Improvement of Pacific White Shrimp (Litopenaeus vannamei) Drying Characteristics and Quality Attributes by a Combination of Salting Pretreatment and Microwave. Foods. https://doi.org/10.3390/foods11142066
Magoč, T., Salzberg, S.L., 2011. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963. https://doi.org/10.1093/bioinformatics/btr507
Neetoo, H., Reega, K., Manoga, Z.S., Nazurally, N., Bhoyroo, V., Allam, M., Jaufeerally-Fakim, Y., Ghoorah, A.W., Jaumdally, W., Hossen, A.M., Mayghun, F., Ismail, A., Hosenally, M., 2022. Prevalence, Genomic Characterization, and Risk Assessment of Human Pathogenic Vibrio Species in Seafood. J. Food Prot. 85, 1553–1565. https://doi.org/https://doi.org/10.4315/JFP-22-064
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., Kanehisa, M., 1999. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 27, 29–34. https://doi.org/10.1093/nar/27.1.29
Omolola, S.L.A.A.-S.L.A.A.-A.B.A.-A.B.A.-O.D.O.A.-B., 2013. Effects of Hot Smoking and Sun Drying Processes on Nutritional Composition of Giant Tiger Shrimp (Penaeus monodon, Fabricius, 1798). Polish J. food Nutr. Sci. v. 63, 227-237–2013 v.63 no.4. https://doi.org/10.2478/v10222-012-0093-1
Paudel, A., Hamamoto, H., Panthee, S., Kaneko, K., Matsunaga, S., Kanai, M., Suzuki, Y., Sekimizu, K., 2017. A Novel Spiro-Heterocyclic Compound Identified by the Silkworm Infection Model Inhibits Transcription in Staphylococcus aureus. Front. Microbiol. 8. https://doi.org/10.3389/fmicb.2017.00712
Pruesse, E., Quast, C., Knittel, K., Fuchs, B.M., Ludwig, W., Peplies, J., Glöckner, F.O., 2007. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196. https://doi.org/10.1093/nar/gkm864
Roberts, D., 2013. labdsv: Ordination and Multivariate Analysis for Ecology.
Roshanak, S., Rahimmalek, M., Goli, S.A.H., 2016. Evaluation of seven different drying treatments in respect to total flavonoid, phenolic, vitamin C content, chlorophyll, antioxidant activity and color of green tea (Camellia sinensis or C. assamica) leaves. J. Food Sci. Technol. 53, 721–729. https://doi.org/10.1007/s13197-015-2030-x
Saelao, S., Maneerat, S., Kaewsuwan, S., Rabesona, H., Choiset, Y., Haertlé, T., Chobert, J.-M., 2017. Inhibition of Staphylococcus aureus in vitro by bacteriocinogenic Lactococcus lactis KTH0-1S isolated from Thai fermented shrimp (Kung-som) and safety evaluation. Arch. Microbiol. 199, 551–562. https://doi.org/10.1007/s00203-016-1324-3
Saelao, S., Maneerat, S., Thongruck, K., Watthanasakphuban, N., Wiriyagulopas, S., Chobert, J.-M., Haertlé, T., 2018. Reduction of tyramine accumulation in Thai fermented shrimp (kung-som) by nisin Z-producing Lactococcus lactis KTH0-1S as starter culture. Food Control 90, 249–258. https://doi.org/https://doi.org/10.1016/j.foodcont.2018.03.003
Sampaio, G.R., Bastos, D.H.M., Soares, R.A.M., Queiroz, Y.S., Torres, E.A.F.S., 2006. Fatty acids and cholesterol oxidation in salted and dried shrimp. Food Chem. 95, 344–351. https://doi.org/https://doi.org/10.1016/j.foodchem.2005.02.030
Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T., 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504. https://doi.org/10.1101/gr.1239303
Silvia, G.-J., Lorena, N.-O., R., S.-M.R., A., C.-R.V., G., C.-G.A., G., C.-V.R., A., G.-A.L., Luis, del P.-Y., Eduardo, G.-H., D., G.-O.K., A., L.-Z.A., Adrián, O.-L., 2014. High-Quality Draft Genomes of Two Vibrio parahaemolyticus Strains Aid in Understanding Acute Hepatopancreatic Necrosis Disease of Cultured Shrimps in Mexico. Genome Announc. 2, 10.1128/genomea.00800-14. https://doi.org/10.1128/genomea.00800-14
Sleator, R.D., Hill, C., 2002. Bacterial osmoadaptation: the role of osmolytes in bacterial stress and virulence. FEMS Microbiol. Rev. 26, 49–71. https://doi.org/10.1111/j.1574-6976.2002.tb00598.x
Smith, C.A., Want, E.J., O’Maille, G., Abagyan, R., Siuzdak, G., 2006. XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. Anal. Chem. 78, 779–787. https://doi.org/10.1021/ac051437y
Stevens, M.H.H., Wagner, H., 2010. Vegan: community ecology package. R package version 1.17-4.
Stinson, E.E., Naftulin, K.A., 1991. Effect of pH on organic acid production byClostridium propionicum in test tube and fermentor cultures. J. Ind. Microbiol. 8, 59–63. https://doi.org/10.1007/BF01575592
Sun, W., Ji, H., Zhang, D., Zhang, Z., Liu, S., Song, W., 2022. Evaluation of Aroma Characteristics of Dried Shrimp (Litopenaeus vannamei) Prepared by Five Different Procedures. Foods. https://doi.org/10.3390/foods11213532
Toranzo, A.E., Magariños, B., Avendaño-Herrera, R., 2023. Vibriosis: Vibrio anguillarum, V. ordalii and Aliivibrio salmonicida. CABI Books. https://doi.org/10.1079/9781780647784.0314
Ward, T., Larson, J., Meulemans, J., Hillmann, B., Lynch, J., Sidiropoulos, D., Spear, J.R., Caporaso, G., Blekhman, R., Knight, R., Fink, R., Knights, D., 2017. BugBase predicts organism-level microbiome phenotypes. bioRxiv 133462. https://doi.org/10.1101/133462
Xu, S., Li, L., Tan, Y., Feng, J., Wei, Z., Wang, L., 2000. Prediction and QSAR Analysis of Toxicity to Photobacterium phosphoreum for a Group of Heterocyclic Nitrogen Compounds. Bull. Environ. Contam. Toxicol. 64, 316–322. https://doi.org/10.1007/s001280000002
Yu, G., Wang, L.-G., Han, Y., He, Q.-Y., 2012. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287. https://doi.org/10.1089/omi.2011.0118
Zgomba Maksimovic, A., Zunabovic-Pichler, M., Kos, I., Mayrhofer, S., Hulak, N., Domig, K.J., Mrkonjic Fuka, M., 2018. Microbiological hazards and potential of spontaneously fermented game meat sausages: A focus on lactic acid bacteria diversity. LWT 89, 418–426. https://doi.org/https://doi.org/10.1016/j.lwt.2017.11.017

No competing interests reported.

s1.jpg
Figure S1. Heatmap representing Spearman correlations among key microbiota. * and ** indicate P< 0.05 and P< 0.01, respectively.
s2.jpg
Figure S2. Heatmap representing Spearman correlations between key microbiota and differential metabolites. * and ** indicate P< 0.05 and P< 0.01, respectively.

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Metagenomic and metabolomic profiling of dried shrimp (Litopenaeus Vannamei) prepared by a procedure traditional to the south China coastal area

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Journal Publication

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Abstract

Figures

Introduction

Materials and methods

Dried shrimp preparation

Microbiological analysis

DNA extraction and 16s rDNA sequencing

Bioinformatics analysis

Metabolites extraction and LC-MS analysis

Interaction analysis of key microbiota and metabolites

Statistical analysis

Results and discussion

Bacterial enumeration

Diversity of the bacterial community in dried shrimp

Microbial community composition and changes during preparation

Potential functions of the bacterial community

Metabolomic changes in dried shrimp during preparation

Interaction of key microbiota and metabolites in dried shrimp

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

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