GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection.

Genomic selection (GS) has emerged as an effective technology to accelerate crop hybrid breeding by enabling early selection prior to phenotype collection. Genomic best linear unbiased prediction (GBLUP) is a robust method that has been routinely used in GS breeding programs. However, GBLUP assumes that markers contribute equally to the total genetic variance, which may not be the case. In this study, we developed a novel GS method called GA-GBLUP that leverages the genetic algorithm (GA) to select markers related to the target trait. We defined four fitness functions for optimization, including AIC, BIC, R2, and HAT, to improve the predictability and bin adjacent markers based on the principle of linkage disequilibrium to reduce model dimension. The results demonstrate that the GA-GBLUP model, equipped with R2 and HAT fitness function, produces much higher predictability than GBLUP for most traits in rice and maize datasets, particularly for traits with low heritability. Moreover, we have developed a user-friendly R package, GAGBLUP, for GS, and the package is freely available on CRAN (https://CRAN.R-project.org/package=GAGBLUP).

PLAU promotes cell proliferation and migration of head and neck cancer via STAT3 signaling pathway.

It was reported that within the head and neck cancer (HNC) cell line CAL21 the epithelial-mesenchymal transition (EMT) and cell proliferation were promoted by Urokinase-Type Plasminogen Activator (PLAU) proteinase through TNFRSF12A. Additionally, in this paper HNC cell lines refer to Fadu and Tu686. A novel PLAU-STAT3 axis was found to be involved in HNC cell line proliferation and metastasis. PLAU expression in HNC samples was upregulated, besides, the elevated expression of PLAU was linked to the lower overall survival (OS) and disease-free survival (DFS). Ectopic PLAU expression promoted cell proliferation and migration, while PLAU knockdown exhibited opposite results. RNA-seq data identified the JAK-STAT signaling pathway, confirmed by western blotting. A recovery assay using S3I-201, a selective inhibitor of signal transducer and activator of transcription 3 (STAT3), indicated that PLAU promoted HNC cell line progression via STAT3 signaling in vitro. The oncogenic role of PLAU in HNC tumor growth in vivo was confirmed using xenograft models. In summary, we identified the tumorigenic PLAU function in the HNC progress. PLAU may represent a potential prognostic biomarker of HNC and the PLAU-STAT3 pathway might be considered a therapeutic target of HNC.

Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning.

Bioavailability assessment of heavy metals in compost products is crucial for evaluating associated environmental risks. However, existing experimental methods are time-consuming and inefficient. The machine learning (ML) method has demonstrated excellent performance in predicting heavy metal fractions. In this study, based on the conventional physicochemical properties of 260 compost samples, including compost time, temperature, electrical conductivity (EC), pH, organic matter (OM), total phosphorus (TP), total nitrogen, and total heavy metal contents, back propagation neural network, gradient boosting regression, and random forest (RF) models were used to predict the dynamic changes in bioavailable fractions of Cu and Zn during composting. All three models could be used for effective prediction of the variation trend in bioavailable fractions of Cu and Zn; the RF model showed the best prediction performance, with the prediction level higher than that reported in related studies. Although the key factors affecting changes among fractions were different, OM, EC, and TP were important for the accurate prediction of bioavailable fractions of Cu and Zn. This study provides simple and efficient ML models for predicting bioavailable fractions of Cu and Zn during composting, and offers a rapid evaluation method for the safe application of compost products.

Manual acupuncture ameliorates inflammatory pain by upregulating adenosine A3 receptor in complete Freund's adjuvant-induced arthritis rats.

BACKGROUND: Adenosine A3 receptor (A3R) exerts analgesic, anti-inflammatory, and anti-nociceptive effects. In this study, we determined the analgesic mechanism of manual acupuncture (MA) in rats with complete Freund's adjuvant (CFA)-induced arthritis and explored whether MA ameliorates inflammation in these rats by upregulating A3R. METHODS: Sixty Sprague Dawley (SD) rats were randomly divided into the following groups: Control, CFA, CFA + MA, CFA + sham MA, CFA + MA + DMSO, CFA + MA + IB-MECA, and CFA + MA + Reversine groups. The arthritis rat model was induced by injecting CFA into the left ankle joints. Thereafter, the rats were subjected to MA (ST36 acupoint) for 3 days. The clinical indicators paw withdrawal latency (PWL), paw withdrawal threshold (PWT), and open field test (OFT) were used to determine the analgesic effect of MA. In addition, to explore the effect of A3R on inflammation after subjecting arthritis rats to MA, IB-MECA (A3R agonist) and Reversine (A3R antagonist) were injected into ST36 before MA. RESULTS: MA ameliorated the pathological symptoms of CFA-induced arthritis, including the pain indicators PWL and PWT, number of rearing, total ambulatory distance, and activity trajectory. Furthermore, after MA, the mRNA and protein expression of A3R was upregulated in CFA-induced arthritis rats. In contrast, the protein levels of TNF-α, IL-1ß, Rap1, and p-p65 were downregulated after MA. Interestingly, the A3R agonist and antagonist further downregulated and upregulated inflammatory cytokine expression, respectively, after MA. Furthermore, the A3R antagonist increased the degree of ankle swelling after MA. CONCLUSION: MA can alleviate inflammatory pain by inhibiting the NF-κB signaling pathway via upregulating A3R expression of the superficial fascia of the ST36 acupoint site in CFA-induced arthritis rats.

Integrating metabolomics and network pharmacology analysis to explore mechanism of Pueraria lobata against pulmonary fibrosis: Involvement of arginine metabolism pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Pueraria lobata (Willd.) Ohwi is a typical medicinal and edible plant with a long application history in China and Southeast Asia. As a widely used traditional medicine, P. lobata exhibits the properties of anti-inflammatory, antipyretic, antioxidant, relieving cough and asthma. Particularly, the increasing evidence indicates that the P. lobata has the therapeutic effect on fibrotic-related diseases in terms of metabolic regulation. However, the mechanisms of P. lobata on pulmonary fibrosis (PF) has not been thoroughly explored. AIM OF THE STUDY: This study aimed to explore the effect of arginine metabolites of P. lobata against PF model by integrating metabolomics and network pharmacology analysis. It might provide a new idea for the target finding of P. lobata anti-pulmonary fibrosis. MATERIALS AND METHODS: In this study, the Sprague Dawley (SD) rats were randomly divided into five experimental groups: saline-treated control group, bleomycin-induced fibrosis group, prednisolone acetate group, P. lobata 3.2 g/kg group and P. lobata 6.4 g/kg group. The therapeutic effect of P. lobata on bleomycin-induced PF in rats was evaluated by clinical symptoms such as lung function, body weight, hematoxylin eosin staining (HE), Masson staining and hydroxyproline assay. Next, the plasma metabolomics analysis was carried out by LC-MS to explore the pathological differences between the group of control, PF and P. lobata-treated rats. Then, the network pharmacology study coupled with experimental validation was conducted to analysis the results of metabolic research. We constructed the "component-target-disease" network of P. lobata in the treatment of PF. In addition, the molecular docking method was used to verify the interaction between potential active ingredients and core targets of P. lobata. Finally, we tested NOS2 and L-OT in arginine-related metabolic pathway in plasma of the rats by enzyme-linked immunosorbent assay (ELISA). Real-time PCR was performed to observe the level of TNF-α mRNA and MMP9 mRNA. And we tested the expression of TNF-α and MMP9 by Western blot analysis. RESULTS: Our findings revealed that P. lobata improved lung function and ameliorated the pathological symptoms, such as pathological damage, collagen deposition, and body weight loss in PF rats. Otherwise, the plasma metabolomics were employed to screen the differential metabolites of amino acids, lipids, flavonoids, arachidonic acid metabolites, glycoside, etc. Finally, we found that the arginine metabolism signaling mainly involved in the regulating of P. lobata on the treatment of PF rats. Furtherly, the network pharmacology predicted that the arginine metabolism pathway was contained in the top 20 pathways. Next, we integrated metabolomics and network pharmacology that identified NOS2, MMP9 and TNF-α as the P. lobata regulated hub genes by molecular docking. Importantly, it indicated a strong affinity between the puerarin and the NOS2. P. lobata attenuated TNF-α, MMP-9 and NOS2 levels, suppressed TNF-α and MMP-9 protein expression, and decreased L-OT and NOS2 content in PF rats. These results indicated that the effects of P. lobata may ameliorated PF via the arginine metabolism pathway in rats. Therefore, P. lobata may be a potential therapeutic agent to ameliorated PF. CONCLUSION: In this work, we used metabolomics and network pharmacology to explore the mechanisms of P. lobata in the treatment of PF. Finally, we confirmed that P. lobata alleviated BLM-induced PF in rats by regulating arginine metabolism pathway based on reducing the L-OT and NOS2-related signal molecular. The search for the biomarkers finding of arginine metabolism pathway revealed a new strategy for P. lobata in the treatment of PF.