Lipopolysaccharide (LPS), also known as endotoxin, is a component of the outer membrane of gram-negative bacteria. LPS is released into the surrounding environment during bacterial death and lysis. Due to its chemical and thermal stability, LPS can be detected anywhere and easily exposed to humans and animals. Previous studies have shown that LPS causes hormonal imbalances, ovarian failure, and infertility in mammals. However, the potential mechanisms remain unclear. In this study, we investigated the effects and mechanisms of LPS on tryptophan degradation, both in vivo and in vitro. The effects of kynurenine, a tryptophan derivative, on granulosa cell function and reproductive performance were explored. Results showed that p38, NF-κB, and JNK signaling pathways were involved in LPS-induced Ido1 expressions and kynurenine accumulation. Furthermore, the kynurenine decreased estradiol production, but increased granulosa cell proliferation. In vivo, experiments showed that kynurenine decreased estradiol and FSH production and inhibited ovulation and corpus luteum formation. Additionally, pregnancy and offspring survival rates decreased considerably after kynurenine treatment. Our findings suggest that kynurenine accumulation disrupts hormone secretion, ovulation, corpus luteal formation, and reproductive performance in mammals.

H1N1 and H3N2 are the two most common subtypes of swine influenza virus (SIV). They not only endanger the pig industry, but are also a huge risk of zoonotic diseases. However, the molecular mechanism and regulatory network of pigs (hosts) against influenza virus infection are still unclear. In this study, porcine alveolar macrophage cell (3D4/21) models infected by swine influenza virus (H1N1 and H3N2) were constructed. The expression profiles of miRNAs, mRNAs, lncRNAs and circRNAs after H1N1 and H3N2 infected 3D4/21 cells were revealed in this study. Then, two ceRNAs (TCONS_00166432-miR10391-MAN2A1 and novel_circ_0004733-miR10391-MAN2A1) that regulated H1N1 and H3N2 infection in 3D4/21 cells were verified by the methods of bioinformatics analysis, gene overexpression, gene interference, real-time quantitative PCR (qPCR), dual luciferase activity assay and RNA immunoprecipitation (RIP). In addition, the important candidate molecules (miR-10391, TCONS_00166432, and novel_circ_0004733) were identified by qPCR and enzyme linked immunosorbent assay (ELISA). Finally, the regulatory effect and possible molecular mechanism of the target gene MAN2A1 were identified by the methods of gene interference, qPCR, Western blot and ELISA. The results of this study suggested that TCONS_00166432 and novel_circ_0004733 could competitively bind miR-10391 to target the MAN2A1 gene to regulate swine influenza virus infecting 3D4/21 cells. This study reported for the first time the ceRNA networks involved in the regulation of the swine influenza virus infecting 3D4/21 cells, which provided a new insight into the molecular mechanism of 3D4/21 cells against swine influenza virus infection.

"A NEW SPECIES OF ALPINE FRESHWATER CRAB (BRACHYURA, POTAMIDAE) FROM SIANGYANG NATIONAL FOREST RECREATION AREA, TAIWAN" published on 01 Jan 2007 by Brill.

Background: Multibreed genomic prediction (MBGP) is crucial for improving prediction accuracy for breeds with small populations, for which limited data are often available. Recent studies have demonstrated that partitioning the genome into nonoverlapping blocks to model heterogeneous genetic (co)variance in multitrait models can achieve higher joint prediction accuracy. However, the block partitioning method, a key factor influencing model performance, has not been extensively explored. Results: We introduce mbBayesABLD, a novel Bayesian MBGP model that partitions each chromosome into nonoverlapping blocks on the basis of linkage disequilibrium (LD) patterns. In this model, marker effects within each block are assumed to follow normal distributions with block-specific parameters. We employ simulated data as well as empirical datasets from pigs and beans to assess genomic prediction accuracy across different models using cross-validation. The results demonstrate that mbBayesABLD significantly outperforms conventional MBGP models, such as GBLUP and BayesR. For the meat marbling score trait in pigs, compared with GBLUP, which does not account for heterogeneous genetic (co)variance, mbBayesABLD improves the prediction accuracy for the small-population breed Landrace by 15.6%. Furthermore, our findings indicate that a moderate level of similarity in LD patterns between breeds (with an average correlation of 0.6) is sufficient to improve the prediction accuracy of the target breed. Conclusions: This study presents a novel LD block-based approach for multibreed genomic prediction. Our work provides a practical tool for livestock breeding programs and offers new insights into leveraging genetic diversity across breeds for improved genomic prediction.