Systematic identification and characterization of lncRNAs and lncRNA-miRNA-mRNA networks in the liver of turbot (Scophthalmus maximus L.) induced with Vibrio anguillarum

Screened of long non-coding RNA related to wool development and fineness in Gansu alpine fine-wool sheep

Wool growth and fineness regulation is influenced by some factors such as genetics and environment. At the same time, lncRNA participates in numerous biological processes in animal production. In this research, we conducted a thorough analysis and characterization of the microstructure of wool, along with long non-coding RNAs (lncRNAs), their target genes, associated pathways, and Gene Ontology terms pertinent to the wool fineness development. The investigation utilized scanning electron microscopy and transcriptomic technology, focusing on two distinct types in Gansu alpine fine-wool sheep: coarse type (group C, MFD = 22.26 ± 0.69 μm, n = 6) and fine type (group F, MFD = 16.91 ± 0.29 μm, n = 6), which exhibit differing wool fiber diameters. The results showed that fine type wool fiber scales were more regularly distributed in rings with large scale spacing and smooth edges, while coarse type wool fiber scales were more irregularly arranged in tiles with relatively rougher edges, and the density of wool scales was greater than that of fine type wool. Furthermore, a comprehensive analysis revealed 164 differentially expressed lncRNAs along with 146 potential target genes linked to these lncRNAs in the skin tissues from groups C and F. Utilizing functional enrichment analysis on the target genes, we successfully identified a number of target genes might be associated with the improvement of wool fineness, such as FOXN1, LIPK, LOC101116068, LOC101106296, KRTAP5.4, KRT71, KRT82, DNASE1L2, which are related to hair follicle development, histidine metabolism, epidermal cell differentiation, oxidative phosphorylation and hair cycle process. Additionally, the interoperability network involving lncRNAs-mRNAs indicated lncRNAs (MSTRG.17445.2, XR_006060725.1, MSTRG.871.1, MSTRG.10907.4) might play a significant role in the wool growth development and fineness improvement process. In conclusion, the research enlarges the current lncRNAs database, providing a new insight for the investigation of wool fineness development in fine-wool sheep.

Exploring Disparities in Gill Physiological Responses to NaHCO3-Induced Habitat Stress in Triploid and Diploid Crucian Carp (Carassius auratus): A Comprehensive Investigation Through Multi-Omics and Biochemical Analyses

Background: Owing to the progressive rise in saline waters globally, resulting in detrimental impacts on freshwater aquaculture, the underlying molecular distinctions governing the response to alkaline stress between diploid and triploid crucian carp remain unknown. Methods: This investigation explores the effects of 20 and 60 mmol NaHCO3 stress over 30 days on the gills of diploid and triploid crucian carp, employing histological, biochemical, and multi-omic analyses. Results: Findings reveal structural damage to gill lamellas in the examined tissue. Diploid crucian carp exhibit heightened activities of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), and alkaline phosphatase (AKP), alongside lower malondialdehyde (MDA) and urea nitrogen (BUN) levels compared to triploid counterparts. Metabolomic investigations suggest alterations in purine metabolism, lipid metabolism, sphingolipid metabolism, and aminoglycan and nucleotide sugar metabolism following NaHCO3 exposure. Transcriptomic data indicate differential expression of genes associated with nitrogen metabolism, complement and coagulation cascades, IL-17 signaling pathways, and Toll-like receptor signaling pathways. Conclusions: Overall, NaHCO3-induced stress leads to significant gill tissue damage, accompanied by reactive oxygen species (ROS) production causing oxidative stress and disruptions in lipid metabolism in crucian carp. Furthermore, an inflammatory response in gill cells triggers an immune response. Diploid crucian carp exhibit superior antioxidant and immune capacities compared to triploid counterparts, while also displaying reduced inflammatory responses in vivo. Notably, diploid carp efficiently excrete excess BUN through purine metabolism, mitigating protein metabolism and amino acid imbalances caused by BUN accumulation. This enables them to allocate less energy for coping with external environmental stress, redirecting surplus energy toward growth and development. The above results indicate that diploid organisms can better adapt to saline-alkaline environments. Overall, this study provides novel perspectives into species selection of crucian carp of different ploidy in saline-alkaline waters.

Transcriptomic analysis of turbot (Scophthalmus maximus) treated with zymosan a reveals that lncRNAs and inflammation-related genes mediate the protection conferred against Aeromonas salmonicida

Aeromonas salmonicida is one of the most harmful pathogens in finfish aquaculture worldwide. Immunostimulants such as β-glucans are used to enhance the immunity of cultured fish. However, their effects on fish physiology are not completely understood. In the present work, we evaluated the effect of a single intraperitoneal (ip) injection of zymosan A on fish survival against A. salmonicida infection. A single administration of this compound protected fish against A. salmonicida challenge and reduce the bacterial load in the head kidney one week after its administration. Transcriptome analyses of head kidney samples revealed several molecular mechanisms involved in the protection conferred by zymosan A and their regulation by long noncoding RNAs. The transcriptome profile of turbot exposed only to zymosan A was practically unaltered one week after ip injection. However, the administration of this immunostimulant induced significant transcriptomic changes once the fish were in contact with the bacteria and increased the survival of the infected turbot. Our results suggest that the restraint of the infection-induced inflammatory response, the management of apoptotic cell death, cell plasticity and cellular processes involving cytoskeleton dynamics support the protective effects of zymosan A. All this information provides insights on the cellular and molecular mechanisms involved in the protective effects of this widely used immunostimulant.

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA-IncRNA Based on Artificial Gorilla Troops Algorithm

MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective alteration might result in abnormal gene manifestation in the downstream pathway of their target. miRNA gene variants can impact miRNA transcription, maturation, or target selectivity, impairing their usefulness in plant growth and stress responses. Simple Sequence Repeat (SSR) based on miRNA is a newly introduced functional marker that has recently been used in plant breeding. MicroRNA and long non-coding RNA (lncRNA) are two examples of non-coding RNA (ncRNA) that play a vital role in controlling the biological processes of animals and plants. According to recent studies, the major objective for decoding their functional activities is predicting the relationship between lncRNA and miRNA. Traditional feature-based classification systems' prediction accuracy and reliability are frequently harmed because of the small data size, human factors' limits, and huge quantity of noise. This paper proposes an optimized deep learning model built with Independently Recurrent Neural Networks (IndRNNs) and Convolutional Neural Networks (CNNs) to predict the interaction in plants between lncRNA and miRNA. The deep learning ensemble model automatically investigates the function characteristics of genetic sequences. The proposed model's main advantage is the enhanced accuracy in plant miRNA-IncRNA prediction due to optimal hyperparameter tuning, which is performed by the artificial Gorilla Troops Algorithm and the proposed intelligent preying algorithm. IndRNN is adapted to derive the representation of learned sequence dependencies and sequence features by overcoming the inaccuracies of natural factors in traditional feature architecture. Working with large-scale data, the suggested model outperforms the current deep learning model and shallow machine learning, notably for extended sequences, according to the findings of the experiments, where we obtained an accuracy of 97.7% in the proposed method.