Uncovering the microbiome landscape in sashimi delicacies.

It is widely believed that a significant portion of the gut microbiota, which play crucial roles in overall health and disease, originates from the food we consume. Sashimi is a type of popular raw seafood cuisine. Its microbiome, however, remained to be thoroughly explored. The objective of this study is to explore the microbiome composition in sashimi at the time when it is served and ready to be eaten. Specifically, our tasks include investigating the diversity and characteristics of microbial profiles in sashimi with respect to the fish types. We utilized the Sanger-sequencing based DNA barcoding technology for fish species authentication and next-generation sequencing for sashimi microbiome profiling. We investigated the microbiome profiles of amberjack, cobia, salmon, tuna and tilapia sashimi, which were all identified using the MT-CO1 DNA sequences regardless of their menu offering names. Chao1 and Shannon indexes, as well as Bray-Curtis dissimilarity index were used to evaluate the alpha and beta diversities of sashimi microbiome. We successfully validated our previous observation that tilapia sashimi has a significantly higher proportions of Pseudomonas compared to other fish sashimi, using independent samples (P = 0.0010). Salmon sashimi exhibited a notably higher Chao1 index in its microbiome in contrast to other fish species (P = 0.0031), indicating a richer and more diverse microbial ecosystem. Non-Metric Multidimensional Scaling (NMDS) based on Bray-Curtis dissimilarity index revealed distinct clusters of microbiome profiles with respect to fish types. Microbiome similarity was notably observed between amberjack and tuna, as well as cobia and salmon. The relationship of microbiome similarity can be depicted as a tree which resembles partly the phylogenetic tree of host species, emphasizing the close relationship between host evolution and microbial composition. Moreover, salmon exhibited a pronounced relative abundance of the Photobacterium genus, significantly surpassing tuna (P = 0.0079), observed consistently across various restaurant sources. In conclusion, microbiome composition of Pseudomonas is significantly higher in tilapia sashimi than in other fish sashimi. Salmon sashimi has the highest diversity of microbiome among all fish sashimi that we analyzed. The level of Photobacterium is significantly higher in salmon than in tuna across all the restaurants we surveyed. These findings provide critical insights into the intricate relationship between the host evolution and the microbial composition. These discoveries deepen our understanding of sashimi microbiota, facilitating our decision in selecting raw seafood.

Investigating seafood substitution problems and consequences in Taiwan using molecular barcoding and deep microbiome profiling.

Seafood is commonly seen in cuisines of the Asia-Pacific regions. The rates and consequences of seafood substitution frauds in Taiwan were elusive. To address this, we conducted a consumer-centered study, collecting seafood dishes and cooking materials from restaurants and markets easily accessible to the residents in Taiwan. Seafood substitutions were evaluated using DNA barcodes in the mitochondrial MT-CO1 gene. Among the 127 samples collected, 24 samples were mislabeled (18.9%, 95% Confidence interval [CI] = [12.5-26.8%]). The mislabel rates vary in different fish and product types (snapper [84.6%, 54.6-98.1%], cod [25%, 5.5-57.2%], swordfish [16.7%, 2.1-48.4%], cobia [16.7%, 0.4-64.1%], surimi products [100.0%]). A deep microbiome profiling was performed in 8 correctly-labeled conventional sushi and 2 tilapia sashimi mislabeled as snapper, with sequencing depths greater than 100,000 reads for every sample. The relative abundance of Pseudomonas genus is significantly higher in tilapia sashimi than in conventional sushi (P = 0.044). In conclusion, the gross seafood mislabel rate in Taiwan is 18.9% (12.5-26.8%). Snapper, cod and surimi products are particularly vulnerable to fraudulent substitutions. The high abundance of Pseudomonas in tilapia sashimi mislabeled as snapper unveils a potential health issue pertaining to the consumption of raw mislabeled seafood.

KStable: A Computational Method for Predicting Protein Thermal Stability Changes by K-Star with Regular-mRMR Feature Selection.

Thermostability is a protein property that impacts many types of studies, including protein activity enhancement, protein structure determination, and drug development. However, most computational tools designed to predict protein thermostability require tertiary structure data as input. The few tools that are dependent only on the primary structure of a protein to predict its thermostability have one or more of the following problems: a slow execution speed, an inability to make large-scale mutation predictions, and the absence of temperature and pH as input parameters. Therefore, we developed a computational tool, named KStable, that is sequence-based, computationally rapid, and includes temperature and pH values to predict changes in the thermostability of a protein upon the introduction of a mutation at a single site. KStable was trained using basis features and minimal redundancy-maximal relevance (mRMR) features, and 58 classifiers were subsequently tested. To find the representative features, a regular-mRMR method was developed. When KStable was evaluated with an independent test set, it achieved an accuracy of 0.708.

Production, IMAC purification, and molecular modeling of N-carbamoyl-D-amino acid amidohydrolase C-terminally fused with a six-his peptide.

A six-His peptide was genetically engineered to the C-terminus of Agrobacterium radiobacter N-carbamoyl-D-amino acid amidohydrolase monomer to facilitate the protein purification with immobilized metal affinity chromatography (IMAC). The fusion enzyme, named as DCaseH, was overexpressed in Escherichia coli and one-step IMAC-purified. The production study showed that DCaseH was optimally produced at 15 degrees C for 25 h by the induction of 0.05 mM IPTG. Both Co(2+)-chelated TANOL gels and Ni(2+)-chelated nitriloacetic acid agarose gels efficiently purified DCaseH, with the former yielding purer enzyme than the latter. Highly pure DCaseH was obtained in the former purification with the addition of 5 mM imidazole in the washing buffer, and the specific enzyme activity was increased more than 11-fold. Denaturing IMAC purification successfully purified DCaseH from inclusion bodies that were mostly composed of the overexpressed DCaseH, while the attempt to refold the purified enzyme by either dialysis or solid-state refolding was not achieved. The purified native enzyme was optimally active at pH 6.5 and 50 degrees C, and the presence of 10% glycerol increased the activity. The molecular modeling of dimeric DCaseH indicated that the six-His tags were freely exposed to the protein surface, resulting in the selective and effective IMAC purification of DCaseH.