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

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.