Effect of starvation on global gene expression and proteolysis in rainbow trout (Oncorhynchus mykiss)

<p>Abstract</p> <p>Background</p> <p>Fast, efficiently growing animals have increased protein synthesis and/or reduced protein degradation relative to slow, inefficiently growing animals. Consequently, minimizing the energetic cost of protein turnover is a strategic goal for enhancing animal growth. Characterization of gene expression profiles associated with protein turnover would allow us to identify genes that could potentially be used as molecular biomarkers to select for germplasm with improved protein accretion.</p> <p>Results</p> <p>We evaluated changes in hepatic global gene expression in response to 3-week starvation in rainbow trout (<it>Oncorhynchus mykiss</it>). Microarray analysis revealed a coordinated, down-regulated expression of protein biosynthesis genes in starved fish. In addition, the expression of genes involved in lipid metabolism/transport, aerobic respiration, blood functions and immune response were decreased in response to starvation. However, the microarray approach did not show a significant increase of gene expression in protein catabolic pathways. Further studies, using real-time PCR and enzyme activity assays, were performed to investigate the expression of genes involved in the major proteolytic pathways including calpains, the multi-catalytic proteasome and cathepsins. Starvation reduced mRNA expression of the calpain inhibitor, calpastatin long isoform (CAST-L), with a subsequent increase in the calpain catalytic activity. In addition, starvation caused a slight but significant increase in 20S proteasome activity without affecting mRNA levels of the proteasome genes. Neither the mRNA levels nor the activities of cathepsin D and L were affected by starvation.</p> <p>Conclusion</p> <p>These results suggest a significant role of calpain and 20S proteasome pathways in protein mobilization as a source of energy during fasting and a potential association of the CAST-L gene with fish protein accretion.</p

Expression of cocaine- and amphetamine-regulated transcript (CART) in hen ovary

Cocaine- and amphetamine-regulated transcript (CART), discovered initially by via differential display RT-PCR analysis of brains of rats administered cocaine, is expressed mainly in central nervous system or neuronal origin cells, and is involved in a wide range of behaviors, such as regulation of food intake, energy homeostasis, and reproduction. The hens egg-laying rate mainly depends on the developmental status of follicles, expression of CART have not been identified from hen follicles, the regulatory mechanisms of CART biological activities are still unknown. The objective of this study was to characterize the mRNA expression of CART in hen follicular granulosa cells and determine CART peptide localization and regulatory role during follicular development

Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation is receiving great attention due to its low human annotation cost. In this paper, we aim to tackle bounding box supervised semantic segmentation, i.e., training accurate semantic segmentation models using bounding box annotations as supervision. To this end, we propose Affinity Attention Graph Neural Network ($A^2$GNN). Following previous practices, we first generate pseudo semantic-aware seeds, which are then formed into semantic graphs based on our newly proposed affinity Convolutional Neural Network (CNN). Then the built graphs are input to our $A^2$GNN, in which an affinity attention layer is designed to acquire the short- and long- distance information from soft graph edges to accurately propagate semantic labels from the confident seeds to the unlabeled pixels. However, to guarantee the precision of the seeds, we only adopt a limited number of confident pixel seed labels for $A^2$GNN, which may lead to insufficient supervision for training. To alleviate this issue, we further introduce a new loss function and a consistency-checking mechanism to leverage the bounding box constraint, so that more reliable guidance can be included for the model optimization. Experiments show that our approach achieves new state-of-the-art performances on Pascal VOC 2012 datasets (val: 76.5\%, test: 75.2\%). More importantly, our approach can be readily applied to bounding box supervised instance segmentation task or other weakly supervised semantic segmentation tasks, with state-of-the-art or comparable performance among almot all weakly supervised tasks on PASCAL VOC or COCO dataset. Our source code will be available at https://github.com/zbf1991/A2GNN.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TAPMI 2021

Comprehensive analysis of lncRNAs and mRNAs in skeletal muscle of rainbow trout (Oncorhynchus mykiss) exposed to estradiol

Estradiol (E2) is a steroid hormone that negatively affects muscle growth in rainbow trout (Oncorhynchus mykiss), but the mechanisms directing with this response are not fully understood. To better characterize the effects of E2 in muscle, we identified differentially regulated mRNAs and lncRNAs in juvenile rainbow trout exposed to E2. Here, we performed next-generation RNA sequencing and comprehensive bioinformatics analyses to characterize the transcriptome profiles, including mRNAs and long noncoding RNAs (lncRNAs), in skeletal muscle of rainbow trout injected with E2. A total of 226 lncRNAs and 253 mRNAs were identified as differentially regulated. We identified crucial pathways, including several signal transduction pathways, hormone response, oxidative response and protein, carbon and fatty acid metabolism pathways. Subsequently, a functional lncRNA-mRNA co- expression network was constructed, which consisted of 681 co-expression relationships between 164 lncRNAs and 201 mRNAs. Moreover, a lncRNA-pathway network was constructed. A total of 65 key lncRNAs were identified that regulate 20 significantly enriched pathways. Overall, our analysis provides insights into mRNA and lncRNA networks in rainbow trout skeletal muscle and their regulation by E2 while understanding the molecular mechanism of lncRNAs

Characterization of the Rainbow Trout Egg MicroRNA Transcriptome

MicroRNAs (miRNAs) are a class of endogenous small non-coding RNA molecules that regulate post-transcriptional expression of target genes and play important roles in animal development. The objectives of this study were to characterize the egg miRNA transcriptome and identify novel egg-predominant miRNAs in rainbow trout. Small RNAs isolated from mature unfertilized rainbow trout eggs were subjected to deep sequencing using an Illumina Genome Analyzer. The massive sequencing produced 24,621,741 quality reads, among which, 266 known miRNAs were identified and 230 putatively novel miRNAs were predicted. The most abundantly known miRNAs are let-7 and miR-21, accounting for 24.06% and 18.71% of the known miRNAs, respectively. Other known miRNAs which are abundantly present in eggs include miR-24, miR-202, miR-148, miR-30, miR-10, miR-146, miR-25, and miR-143. Real time PCR analysis using cDNAs derived from 10 tissues validated 87 out of 90 selected putative miRNAs and identified three novel miRNAs predominantly expressed in rainbow trout eggs. Each of these novel egg-predominant miRNAs is predicted to target a significant number of genes, most of which are significantly down-regulated in naturally ovulated rainbow trout eggs based on analysis of publicly available microarray data sets. Quantitative real time PCR analysis also demonstrated low expression of a selected number of target genes in eggs relative to liver and muscle tissues. This study represents the first complete survey of miRNAs in fish eggs and provides a starting point for future studies aimed at understanding the roles of miRNAs in controlling egg quality and early embryogenesis in rainbow trout

Bovine Lhx8, a Germ Cell-SpecificNuclear Factor, Interacts with Figla

LIM homeobox 8 (Lhx8) is a germ cell-specific transcription factor essential for the development of oocytes during early oogenesis. In mice, Lhx8 deficiency causes postnatal oocyte loss and affects the expression of many oocyte-specific genes. The aims of this study were to characterize the bovine Lhx8 gene, determine its mRNA expression during oocyte development and early embryogenesis, and evaluate its interactions with other oocyte-specific transcription factors. The bovine Lhx8 gene encodes a protein of 377 amino acids. A splice variant of Lhx8 (Lhx8_v1) was also identified. The predicted bovine Lhx8 protein contains two LIM domains and one homeobox domain. However, one of the LIM domains in Lhx8_v1 is incomplete due to deletion of 83 amino acids near the N terminus. Both Lhx8 and Lhx8_v1 transcripts were only detected in the gonads but none of the somatic tissues examined. The expression of Lhx8 and Lhx8_v1 appears to be restricted to oocytes as none of the transcripts was detectable in granulosa or theca cells. The maternal Lhx8 transcript is abundant in GV and MII stage oocytes as well as in early embryos but disappear by morula stage. A nuclear localization signal that is required for the import of Lhx8 into nucleus was identified, and Lhx8 is predominantly localized in the nucleus when ectopically expressed in mammalian cells. Finally, a novel interaction between Lhx8 and Figla, another transcription factor essential for oogenesis, was detected. The results provide new information for studying the mechanisms of action for Lhx8 in oocyte development and early embryogenesis

DNA Methylation and miRNA-1296 Act in Concert to Mediate Spatiotemporal Expression of KPNA7 During Bovine Oocyte and Early Embryonic Development

Abstract Background: Epigenetic regulation of oocyte-specific maternal factors is essential for oocyte and early embryonic development. KPNA7 is an oocyte-specific maternal factor, which controls transportation of nuclear proteins important for early embryonic development. To elucidate the epigenetic mechanisms involved in the controlled expression of KPNA7, both DNA methylation associated transcriptional silencing and microRNA (miRNA)-mediated mRNA degradation of KPNA7 were examined. Results: Comparison of DNA methylation profiles at the proximal promoter of KPNA7 gene between oocyte and 6 different somatic tissues identified 3 oocyte-specific differentially methylated CpG sites. Expression of KPNA7 mRNA was reintroduced in bovine kidney-derived CCL2 cells after treatment with the methylation inhibitor, 5-aza-2′- deoxycytidine (5-Aza-CdR). Analysis of the promoter region of KPNA7 gene in CCL2 cells treated with 5-Aza-CdR showed a lighter methylation rate in all the CpG sites. Bioinformatic analysis predicted 4 miRNA-1296 binding sites in the coding region of KPNA7 mRNA. Ectopic co-expression of miRNA-1296 and KPNA7 in HEK293 cells led to reduced expression of KPNA7 protein. Quantitative real time PCR (RT-qPCR) analysis revealed that miRNA-1296 is expressed in oocytes and early stage embryos, and the expression reaches a peak level in 8-cell stage embryos, coincident with the time of embryonic genome activation and the start of declining of KPNA7 expression. Conclusions: These results suggest that DNA methylation may account for oocyte-specific expression of KPNA7, and miRNA-1296 targeting the coding region of KPNA7 is a potential mechanism for KPNA7 transcript degradation during the maternal-to-zygotic transition

Cloning and characterization of a novel oocyte-specific gene encoding an F-Box protein in rainbow trout (Oncorhynchus mykiss)

Background Oocyte-specific genes play critical roles in oogenesis, folliculogenesis and early embryonic development. The objectives of this study were to characterize the expression of a novel oocyte-specific gene encoding an F-box protein during ovarian development in rainbow trout, and identify its potential interacting partners in rainbow trout oocytes. Methods Through analysis of expressed sequence tags (ESTs) from a rainbow trout oocyte cDNA library, a novel transcript represented by ESTs only from the oocyte library was identified. The complete cDNA sequence for the novel gene (named fbxoo) was obtained by assembling sequences from an EST clone and a 5′RACE product. The expression and localization of fbxoo mRNA and protein in ovaries of different developmental stages were analyzed by quantitative real time PCR, immunoblotting, in situ hybridization and immunohistochemistry. Identification of Fbxoo binding proteins was performed by yeast two-hybrid screening. Results fbxoo mRNA is specifically expressed in mature oocytes as revealed by tissue distribution analysis. The fbxoo cDNA sequence is 1,996 bp in length containing an open reading frame, which encodes a predicted protein of 514 amino acids. The novel protein sequence does not match any known protein sequences in the NCBI database. However, a search of the Pfam protein database revealed that the protein contains an F-box motif at the N-terminus, indicating that Fbxoo is a new member of the F-box protein family. The expression of fbxoomRNA and protein is high in ovaries at early pre-vitellogenesis stage, and both fbxoo mRNA and protein are predominantly expressed in early pre-vitellogenic oocytes. Several proteins including tissue inhibitor of metalloproteinase 2 (Timp2) were identified as potential Fbxoo protein binding partners. Conclusions Results suggest that the novel oocyte-specific F-box protein may play an important role in early oocyte development by regulating other critical proteins involved in oogenesis in rainbow trout

Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoning

How neural networks in the human brain represent commonsense knowledge, and complete related reasoning tasks is an important research topic in neuroscience, cognitive science, psychology, and artificial intelligence. Although the traditional artificial neural network using fixed-length vectors to represent symbols has gained good performance in some specific tasks, it is still a black box that lacks interpretability, far from how humans perceive the world. Inspired by the grandmother-cell hypothesis in neuroscience, this work investigates how population encoding and spiking timing-dependent plasticity (STDP) mechanisms can be integrated into the learning of spiking neural networks, and how a population of neurons can represent a symbol via guiding the completion of sequential firing between different neuron populations. The neuron populations of different communities together constitute the entire commonsense knowledge graph, forming a giant graph spiking neural network. Moreover, we introduced the Reward-modulated spiking timing-dependent plasticity (R-STDP) mechanism to simulate the biological reinforcement learning process and completed the related reasoning tasks accordingly, achieving comparable accuracy and faster convergence speed than the graph convolutional artificial neural networks. For the fields of neuroscience and cognitive science, the work in this paper provided the foundation of computational modeling for further exploration of the way the human brain represents commonsense knowledge. For the field of artificial intelligence, this paper indicated the exploration direction for realizing a more robust and interpretable neural network by constructing a commonsense knowledge representation and reasoning spiking neural networks with solid biological plausibility