This research aimed to investigate the effects of dietary fructooligosaccharides (FOS) on attenuating the Aeromonas hydrophila (A. hydrophila)-induced oxidative stress and apoptosis in blunt snout bream Megalobrama amblycephala. Fish were divided into three groups as follows: C1 (Control), T1 (A. hydrophila), and T2 (A. hydrophila + 4 g/kg FOS). The results showed that the activities of antioxidant enzymes increased, the liver morphology had disorderly arrangement, and extensive cell necrosis occurred because of A. hydrophila-infection. While the dietary FOS improved the above-mentioned liver damage. Additionaly, FOS elevated mRNA levels of pro-apoptotic molecules, including caspase-8 and 9, and down-regulated mRNA levels of the anti-apoptotic molecule Bcl-2, which is triggered by A. hydrophila-infection. The transcriptome analysis showed that the oxidative stress-related DEGs pathways were activated in intestine of blunt snout bream by A. hydrophila-infection. The FOS-added group led to the enrichment of more pathways to health. Further WGCNA co-expression network analysis showed that the screened single genes were clustered into 49 modules. The two modules with the highest association to the five traits (10 hub genes) were chosen to build the network by combining the physiological and biochemical characteristic. In summary, this research offers a foundation for the exploring of A. hydrophila-restoration genes in dietary FOS, and also lays a theoretical foundation for aquaculture in the future.
An effective method to characterize full-length circRNA sequences from low-input RNA samples with rolling circular reverse transcription and nanopore sequencing.
RNA degradation can significantly affect the results of gene expression profiling, with subsequent analysis failing to faithfully represent the initial gene expression level. It is urgent to have an artificial intelligence approach to better utilize the limited data to obtain meaningful and reliable analysis results in the case of data with missing destination time. In this study, we propose a method based on the signal decomposition technique and deep learning, named Multi-LSTM. It is divided into two main modules: One decomposes the collected gene expression data by an empirical mode decomposition (EMD) algorithm to obtain a series of sub-modules with different frequencies to improve data stability and reduce modeling complexity. The other is based on long short-term memory (LSTM) as the core predictor, aiming to deeply explore the temporal nonlinear relationships embedded in the sub-modules. Finally, the prediction results of sub-modules are reconstructed to obtain the final prediction results of time-series transcriptomic gene expression. The results show that EMD can efficiently reduce the nonlinearity of the original data, which provides reliable theoretical support to reduce the complexity and improve the robustness of LSTM models. Overall, the decomposition-combination prediction framework can effectively predict gene expression levels at unknown time points.
The RNA-binding protein quaking-a (Qkia) was cloned from the liver of blunt snout bream Megalobrama amblycephala through the rapid amplification of cDNA ends method, with its potential role in glucose metabolism investigated. The full-length cDNA of qkia covered 1,718 bp, with an open reading frame of 1,572bp, which encodes 383 AA. Sequence alignment and phylogenetic analysis revealed a high degree of conservation (97-99%) among most fish and other higher vertebrates. The mRNA of qkia was detected in all examined organs/tissues. Then, the plasma glucose levels and tissue qkia expressions were determined in fish intraperitoneally injected with glucose (1.67 g per kg body weight (BW)), insulin (0.052 mg/kg BW) and glucagon (0.075 mg/kg BW) respectively as well as in fish fed two dietary carbohydrate levels (31% and 41%) for 12 weeks. Glucose administration induced a remarkable increase of plasma glucose with the highest value being recorded at 1 h. Thereafter, it reduced to the basal value. After glucose administration, qkia expressions significantly decreased with the lowest value being recorded at 1 h in liver and muscle and 8 h in brain, respectively. Then they gradually returned to the basal value. The insulin injection induced a significant decrease of plasma glucose with the lowest value being recorded at 1 h, whereas the opposite was true after glucagon load (the highest value was gained at 4 h). Subsequently, glucose levels gradually returned to the basal value. After insulin administration, the qkia expressions significantly decreased with the lowest value being attained at 2 h in brain and muscle and 1 h in liver, respectively. However, glucagon significantly stimulated the expressions of qkia in tissues with the highest value being gained at 6 h. Moreover, high dietary carbohydrate levels remarkably increased plasma glucose levels, but down-regulated the transcriptions of qkia in tissues. These results indicated that the qkia gene of blunt snout bream shared a high similarity with that of the other vertebrates. Glucose and insulin administration, as well as high-carbohydrate feeding, remarkably down-regulated its transcriptions in brain, muscle and liver, whereas the opposite was true after the glucagon load.
The circRNAs sequencing results vary due to the different enrichment methods and their performance is needed to systematic comparison. This study investigated the effects of different circRNA enrichment methods on sequencing results, including abundance and species of circRNAs, as well as the sensitivity and precision. This experiment was carried out by following four common circRNA enrichment methods: including ribosomal RNA depletion (rRNA-), polyadenylation and poly (A+) RNA depletion followed by RNase R treatment (polyA+RNase R), rRNA-+polyA+RNase R and polyA+RNase R+ rRNA-. The results showed that polyA+RNase R+ rRNA - enrichment method obtained more circRNA number, higher sensitivity and abundance among them; polyA+RNase R method obtained higher precision. The linear RNAs can be thoroughly removed in all enrichment methods except rRNA depletion method. Overall, our results helps researchers to quickly selection a circRNA enrichment of suitable for own study among many enrichment methods, and it provides a benchmark framework for future improvements circRNA enrichment methods.[Figure: see text].
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