Levistilide A Exerts a Neuroprotective Effect by Suppressing Glucose Metabolism Reprogramming and Preventing Microglia Polarization Shift: Implications for Parkinson's Disease.

The microglia, displaying diverse phenotypes, play a significant regulatory role in the development, progression, and prognosis of Parkinson's disease. Research has established that glycolytic reprogramming serves as a critical regulator of inflammation initiation in pro-inflammatory macrophages. Furthermore, the modulation of glycolytic reprogramming has the potential to reverse the polarized state of these macrophages. Previous studies have shown that Levistilide A (LA), a phthalide component derived from Angelica sinensis, possesses a range of pharmacological effects, including anti-inflammatory, antioxidant, and neuroprotective properties. In our study, we have examined the impact of LA on inflammatory cytokines and glucose metabolism in microglia induced by lipopolysaccharide (LPS). Furthermore, we explored the effects of LA on the AMPK/mTOR pathway and assessed its neuroprotective potential both in vitro and in vivo. The findings revealed that LA notably diminished the expression of M1 pro-inflammatory factors induced by LPS in microglia, while leaving M2 anti-inflammatory factor expression unaltered. Additionally, it reduced ROS production and suppressed IκB-α phosphorylation levels as well as NF-κB p65 nuclear translocation. Notably, LA exhibited the ability to reverse microglial glucose metabolism reprogramming and modulate the phosphorylation levels of AMPK/mTOR. In vivo experiments further corroborated these findings, demonstrating that LA mitigated the death of TH-positive dopaminergic neurons and reduced microglia activation in the ventral SNpc brain region of the midbrain and the striatum. In summary, LA exhibited neuroprotective benefits by modulating the polarization state of microglia and altering glucose metabolism, highlighting its therapeutic potential.

A Food-Energy-Water Nexus approach for land use optimization.

Allocation and management of agricultural land is of emergent concern due to land scarcity, diminishing supply of energy and water, and the increasing demand of food globally. To achieve social, economic and environmental goals in a specific agricultural land area, people and society must make decisions subject to the demand and supply of food, energy and water (FEW). Interdependence among these three elements, the Food-Energy-Water Nexus (FEW-N), requires that they be addressed concertedly. Despite global efforts on data, models and techniques, studies navigating the multi-faceted FEW-N space, identifying opportunities for synergistic benefits, and exploring interactions and trade-offs in agricultural land use system are still limited. Taking an experimental station in China as a model system, we present the foundations of a systematic engineering framework and quantitative decision-making tools for the trade-off analysis and optimization of stressed interconnected FEW-N networks. The framework combines data analytics and mixed-integer nonlinear modeling and optimization methods establishing the interdependencies and potentially competing interests among the FEW elements in the system, along with policy, sustainability, and feedback from various stakeholders. A multi-objective optimization strategy is followed for the trade-off analysis empowered by the introduction of composite FEW-N metrics as means to facilitate decision-making and compare alternative process and technological options. We found the framework works effectively to balance multiple objectives and benchmark the competitions for systematic decisions. The optimal solutions tend to promote the food production with reduced consumption of water and energy, and have a robust performance with alternative pathways under different climate scenarios.

Fusing literature and full network data improves disease similarity computation.

BACKGROUND: Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature. RESULTS: Among function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively. CONCLUSIONS: Integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http:// www.digintelli.com:8000/ .

An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases.

Groups of distinct but related diseases often share common symptoms, which suggest likely overlaps in underlying pathogenic mechanisms. Identifying the shared pathways and common factors among those disorders can be expected to deepen our understanding for them and help designing new treatment strategies effected on those diseases. Neurodegeneration diseases, including Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD), were taken as a case study in this research. Reported susceptibility genes for AD, PD and HD were collected and human protein-protein interaction network (hPPIN) was used to identify biological pathways related to neurodegeneration. 81 KEGG pathways were found to be correlated with neurodegenerative disorders. 36 out of the 81 are human disease pathways, and the remaining ones are involved in miscellaneous human functional pathways. Cancers and infectious diseases are two major subclasses within the disease group. Apoptosis is one of the most significant functional pathways. Most of those pathways found here are actually consistent with prior knowledge of neurodegenerative diseases except two cell communication pathways: adherens and tight junctions. Gene expression analysis showed a high probability that the two pathways were related to neurodegenerative diseases. A combination of common susceptibility genes and hPPIN is an effective method to study shared pathways involved in a group of closely related disorders. Common modules, which might play a bridging role in linking neurodegenerative disorders and the enriched pathways, were identified by clustering analysis. The identified shared pathways and common modules can be expected to yield clues for effective target discovery efforts on neurodegeneration.

Mining breast cancer genes with a network based noise-tolerant approach.

BACKGROUND: Mining novel breast cancer genes is an important task in breast cancer research. Many approaches prioritize candidate genes based on their similarity to known cancer genes, usually by integrating multiple data sources. However, different types of data often contain varying degrees of noise. For effective data integration, it's important to design methods that work robustly with respect to noise. RESULTS: Gene Ontology (GO) annotations were often utilized in cancer gene mining works. However, the vast majority of GO annotations were computationally derived, thus not completely accurate. A set of genes annotated with breast cancer enriched GO terms was adopted here as a set of source data with realistic noise. A novel noise tolerant approach was proposed to rank candidate breast cancer genes using noisy source data within the framework of a comprehensive human Protein-Protein Interaction (PPI) network. Performance of the proposed method was quantitatively evaluated by comparing it with the more established random walk approach. Results showed that the proposed method exhibited better performance in ranking known breast cancer genes and higher robustness against data noise than the random walk approach. When noise started to increase, the proposed method was able to maintained relatively stable performance, while the random walk approach showed drastic performance decline; when noise increased to a large extent, the proposed method was still able to achieve better performance than random walk did. CONCLUSIONS: A novel noise tolerant method was proposed to mine breast cancer genes. Compared to the well established random walk approach, it showed better performance in correctly ranking cancer genes and worked robustly with respect to noise within source data. To the best of our knowledge, it's the first such effort to quantitatively analyze noise tolerance between different breast cancer gene mining methods. The sorted gene list can be valuable for breast cancer research. The proposed quantitative noise analysis method may also prove useful for other data integration efforts. It is hoped that the current work can lead to more discussions about influence of data noise on different computational methods for mining disease genes.

Quantitative assessment of the association between the GSTM1-null genotype and the risk of childhood asthma.

BACKGROUND: Many studies investigated the association between the glutathione S-transferase M 1 (GSTM1)-null genotype and childhood asthma risk, but there was obvious inconsistence among those studies. The aim of this meta-analysis was to quantify the strength of association between the GSTM1-null genotype and risk of childhood asthma. METHODS: We searched the PubMed, Embase, and Wangfang databases for studies relating the association between the GSTM1-null genotype and risk of childhood asthma. We estimated the pooled odds ratio (OR) with its 95% confidence interval (95% CI) to assess the association. RESULTS: Nineteen case-control studies with 4,543 childhood asthma cases and 19,394 controls were included into this meta-analysis. Meta-analysis of all 19 studies showed that the GSTM1-null genotype was associated with increased risk of childhood asthma (OR=1.17, 95% CI 1.03-1.34, p=0.017). Subgroup analyses by ethnicity suggested that the GSTM1-null genotype was associated with an increased risk of childhood asthma in Caucasians and Africans (for Caucasians, fixed-effects OR=1.16, 95% CI 1.07-1.27, p=0.001; for Africans, fixed-effects OR=1.92, 95% CI 1.35-2.74, p<0.001). The cumulative meta-analyses showed a trend of obvious association between the GSTM1-null genotype and risk of childhood asthma as information accumulated in the analyses of both total studies and Caucasians. No evidence of publication bias was observed. CONCLUSION: Meta-analyses of available data suggest a significant association between the GSTM1-null genotype and the risk of childhood asthma, and the GSTM1-null genotype contributes to increased risk of childhood asthma, especially in Caucasians and Africans.