Genes, RNAs, and proteins play important roles during germline development. However, the functions of non-coding RNAs (ncRNAs) on germline development remain unclear in avian species. Recent high-throughput techniques have identified several classes of ncRNAs, including micro RNAs (miRNAs), small-interfering RNAs (siRNAs), and PIWI-interacting RNAs (piRNAs). These ncRNAs are functionally important in the genome, however, the identification and annotation of ncRNAs in a genome is challenging. The aim of this study was to identify different types of small ncRNAs particularly piRNAs, and the role of piRNA pathway genes in the protection of chicken primordial germ cells (PGCs).

Whales have captivated the human imagination for millennia. These incredible cetaceans are the only mammals that have adapted to life in the open oceans and have been a source of human food, fuel and tools around the globe. The transition from land to water has led to various aquatic specializations related to hairless skin and ability to regulate their body temperature in cold water. We present four common minke whale (Balaenoptera acutorostrata) genomes with depth of ×13 ~ ×17 coverage and perform resequencing technology without a reference sequence. Our results indicated the time to the most recent common ancestors of common minke whales to be about 2.3574 (95% HPD, 1.1521 – 3.9212) million years ago. Further, we found that genes associated with epilation and tooth-development showed signatures of positive selection, supporting the morphological uniqueness of whales. This whole-genome sequencing offers a chance to better understand the evolutionary journey of one of the largest mammals on earth.

Extracts from the plants Phlomis umbrosa and Dipsacus asperoides—which are widely used in Korean and Chinese traditional medicine to treat osteoarthritis and other bone diseases—were used to treat experimental osteoarthritis (OA) rats. Genome-wide differential methylation regions (DMRs) of these medicinal-plant-treated rats were profiled as therapeutic evidence associated with traditional medicine, and they need to be investigated further using detailed molecular research to extrapolate traditional practices to modern medicine. In total, 49 protein-encoding genes whose expression is differentially regulated during disease progression and recovery have been discovered via systematic bioinformatic analysis and have been approved/proposed as druggable targets for various bone diseases by the US food and drug administration. Genes encoding proteins involved in the PI3K/AKT pathway were found to be enriched, likely as this pathway plays a crucial role during OA progression as well as during the recovery process after treatment with the aforementioned plant extracts. The four sub-networks of PI3K/AKT were highly regulated by these plant extracts. Overall, 29 genes were seen in level 2 (51–75%) DMRs and were correlated highly with OA pathogenesis. Here, we propose that these genes could serve as targets to study OA; moreover, the iridoid and triterpenoid phytochemicals obtained from these two plants may serve as potential therapeutic agents.

The cultivation of abalone, a species with high economic value, faces significant challenges due to its slow growth rate and sensitivity to environmental conditions, resulting in prolonged cultivation periods and increased mortality risks. To address these challenges, we propose a novel probabilistic machine learning approach based on a Bayesian framework to predict abalone growth by modeling key environmental factors, including water temperature, pH, salinity, nutrient supply, and dissolved oxygen levels. The proposed method employs a weighted Bayesian kernel machine regression model, integrating Gaussian processes with a spike-and-slab prior to identify influential variables. This approach accommodates heteroscedasticity, capturing varying levels of variance across observations, and models complex, non-linear relationships between environmental factors and abalone growth. Our analysis reveals that time, dissolved oxygen, salinity, and nutrient supply are the most critical factors influencing growth, while water temperature and pH play relatively minor roles under controlled indoor farming conditions. Interaction analysis highlights the non-linear dependencies among factors, such as the combined effects of salinity and nutrient supply. The proposed model not only improves prediction accuracy compared to baseline methods, but also provides actionable insights into the environmental dynamics that optimize abalone growth. These findings underscore the potential of advanced machine learning techniques in enhancing aquaculture practices and offer a robust framework for managing complex, multi-variable systems in sustainable farming.

Olive flounder (Paralichthys olivaceus) is a key aquaculture species in South Korea, but its production has been challenged by rising mortality under environmental stress from key environmental factors such as water temperature, dissolved oxygen, and feeding conditions. To support adaptive management, this study proposes a Bayesian Deep Kernel Machine Regression (BDKMR) model that integrates Gaussian process regression with neural network-based feature learning. Using longitudinal data from commercial farms, we model fish growth as a function of water temperature, dissolved oxygen, and feed quantity. Model performance is assessed via Leave-One-Out Cross-Validation and compared against kernel ridge regression and Bayesian kernel machine regression. Results show that BDKMR achieves substantially lower prediction errors, indicating superior accuracy and robustness. These findings suggest that BDKMR offers a flexible and effective framework for predictive modeling in aquaculture systems.

Growth variation is one of the most economically important traits in oyster aquaculture, yet the transcriptional programs associated with divergent growth performance remain incompletely understood. In this study, three growth cohorts of the Pacific oyster (Crassostrea gigas) originating from the same production batch were examined to characterize coordinated differences in tissue-associated transcriptional programs across contrasting growth cohorts. A total of 25 RNA-seq libraries were generated from slow-growth (SG) whole-body samples and mantle and gill tissues of medium-growth (MG) and fast-growth (FG) oysters. RNA-seq analysis identified 28,773 expressed genes and 9373 differentially expressed genes (|log₂FC| ≥ 2, FDR ≤ 0.05). Tissue-specific analysis further identified 1407 mantle-specific and 773 gill-specific genes, reflecting distinct functional specialization of the two tissues. Weighted gene co-expression network analysis (WGCNA) resolved six biologically interpretable modules associated with growth state and tissue identity. When module eigengene patterns were examined across the ordered SG–MG–FG continuum, the modules showed sequential shifts from SG-associated basal metabolic and RNA-processing programs to MG-associated tissue-specialization programs and FG-associated mature mantle and gill functional programs. The FG mantle-associated module was enriched for chitin remodeling, shell matrix-related functions, and structural organization, whereas the FG gill-associated module showed enrichment of Toll-like receptor signaling, epithelial organization, and innate immune functions. Expression patterns of representative genes from the six modules were independently validated by quantitative RT-PCR and showed strong concordance with the RNA-seq data. Collectively, these results identify coordinated differences in basal and tissue-associated transcriptional programs across contrasting growth cohorts. This framework provides a transcriptomic basis for interpreting growth-associated tissue maturation and identifies candidate molecular indicators for future validation in oyster selective breeding.