Maintaining stabilization of the intestinal microbiota is important in preventing bacterial diseases in cultured fish. At present, there have been no reports on the composition and functional analysis of intestinal microbiota in Yunlong Grouper (Epinephelus moara♀ × Epinephelus lanceolatus♂). In this study we analyzed and compared the intestinal microbiota composition of healthy and diseased pond-reared fish to discern the functional profile of a healthy status. The richness and diversity of the intestinal microbiota did not differ significantly between diseased and healthy fish, yet the abundance of predominant phyla like the Proteobacteria were upregulated in the diseased Yunlong Grouper. At the genus level, a significant reduction of Cetobacterium was observed in the intestinal tracts of diseased fish, as Pseudomonas became the most dominant bacterium. To compare the intestinal microorganism abundances between the two health groups of fish, we first screened the gut bacteria and discerned 4 phyla and 12 genera to designate a healthy status in Yunlong Grouper. The environmental bacterial community influenced composition of the intestinal microbiota in Yunlong Grouper, and the intestinal microbiota of diseased fish was more susceptible to the influence of the culture water. In addition, the prediction of functional genes by phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) indicated that the intestinal microbiota of Yunlong Grouper is related mainly to the terms “metabolism, environmental information processing, genetic information processing, human diseases, and cellular processing; moreover, the functions of the intestinal microbiota differed between the different health states of this fish. The overall results indicate that the occurrence of disease can affect the composition and function of the intestinal microbiota in a cultured fish.

Acyl-CoA: diacylglycerol acyltransferase (DGAT) is a key enzyme responsible for triacylglycerol (TAG) synthesis in eukaryotic organisms. The present work reported DGAT genes in the green alga Myrmecia incisa, a promising candidate for arachidonic acid (ArA) production. According to the results of homology search against a transcriptome database, we cloned three cDNAs encoding putative DGAT1 and DGAT2. The 2238-bp, 1056-bp and 1068-bp of open reading frame (ORF) of these three cDNAs, designated as MiDGAT1, MiDGAT2A and MiDGAT2B, were predicted to encode proteins composed of 745, 351 and 355 amino acids. They were separated by 14, 6 and 6 introns, respectively, as revealed by comparing their corresponding cDNA and DNA sequences. Multiple sequence alignment of amino acids indicated that MiDGAT1 had a pleckstrin homology (PH) domain, whilst MiDGAT2s contained a highly conserved HPHG, a characteristic motif of DGAT2 family. To determine the function, they were expressed heterologously in a Saccharomyces cerevisiae mutant strain with impaired TAG metabolism. Results of thin-layer chromatography and BODIPY staining indicated that both MiDGAT1 and MiDGAT2s were able to restore TAG synthesis and lipid body formation. GC-MS analysis indicated that palmitic acid and stearic acid were the major components of TAGs in yeast cells, and their ratio between wild type and the transformed yeasts was not significantly different. Quantitative RT-PCR results showed that the transcript level of MiDGAT2A was regulated by nitrogen starvation, which was consistent with TAG accumulation in M. incisa.

Messenger RNA (mRNA) vaccines are being used to combat the spread of COVID-19 (refs. 1–3), but they still exhibit critical limitations caused by mRNA instability and degradation, which are major obstacles for the storage, distribution and efficacy of the vaccine products4. Increasing secondary structure lengthens mRNA half-life, which, together with optimal codons, improves protein expression5. Therefore, a principled mRNA design algorithm must optimize both structural stability and codon usage. However, owing to synonymous codons, the mRNA design space is prohibitively large—for example, there are around 2.4 × 10632 candidate mRNA sequences for the SARS-CoV-2 spike protein. This poses insurmountable computational challenges. Here we provide a simple and unexpected solution using the classical concept of lattice parsing in computational linguistics, where finding the optimal mRNA sequence is analogous to identifying the most likely sentence among similar-sounding alternatives6. Our algorithm LinearDesign finds an optimal mRNA design for the spike protein in just 11 minutes, and can concurrently optimize stability and codon usage. LinearDesign substantially improves mRNA half-life and protein expression, and profoundly increases antibody titre by up to 128 times in mice compared to the codon-optimization benchmark on mRNA vaccines for COVID-19 and varicella-zoster virus. This result reveals the great potential of principled mRNA design and enables the exploration of previously unreachable but highly stable and efficient designs. Our work is a timely tool for vaccines and other mRNA-based medicines encoding therapeutic proteins such as monoclonal antibodies and anti-cancer drugs7,8.

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