Explore the vast range of titles available.

Macrophages comprise the front line of defense against various pathogens. Classically activated macrophages (M1), induced by IFN-γ and LPS, highly express inflammatory cytokines and contribute to inflammatory processes. By contrast, alternatively activated macrophages (M2) are induced by IL-4 and IL-13, produce IL-10, and display anti-inflammatory activity. Adenylate kinase 4 (Ak4), an enzyme that transfers phosphate group among ATP/GTP, AMP, and ADP, is a key modulator of ATP and maintains the homeostasis of cellular nucleotides which is essential for cell functions. However, its role in regulating the function of macrophages is not fully understood. Here we report that Ak4 expression is induced in M1 but not M2 macrophages. Suppressing the expression of Ak4 in M1 macrophages with shRNA or siRNA enhances ATP production and decreases ROS production, bactericidal ability and glycolysis in M1 cells. Moreover, Ak4 regulates the expression of inflammation genes, including Il1b, Il6, Tnfa, Nos2, Nox2 , and Hif1a , in M1 macrophages. We further demonstrate that Ak4 inhibits the activation of AMPK and forms a positive feedback loop with Hif1α to promote the expression of inflammation-related genes in M1 cells. Furthermore, RNA-seq analysis demonstrates that Ak4 also regulates other biological processes in addition to the expression of inflammation-related genes in M1 cells. Interestingly, Ak4 does not regulate M1/M2 polarization. Taken together, our study uncovers a potential mechanism linking energy consumption and inflammation in macrophages.

Background Current methods for gene-set or pathway analysis are usually designed to test the enrichment of a single gene-set. Once the analysis is carried out for each of the sets under study, a list of significant sets can be obtained. However, if one wishes to further prioritize the importance or strength of association of these sets, no such quantitative measure is available. Using the magnitude of p -value to rank the pathways may not be appropriate because p -value is not a measure for strength of significance. In addition, when testing each pathway, these analyses are often implicitly affected by the number of differentially expressed genes included in the set and/or affected by the dependence among genes. Results Here we propose a two-stage procedure to prioritize the pathways/gene-sets. In the first stage we develop a pathway-level measure with three properties. First, it contains all genes (differentially expressed or not) in the same set, and summarizes the collective effect of all genes per sample. Second, this pathway score accounts for the correlation between genes by synchronizing their correlation directions. Third, the score includes a rank transformation to enhance the variation among samples as well as to avoid the influence of extreme heterogeneity among genes. In the second stage, all scores are included simultaneously in a Bayesian logistic regression model which can evaluate the strength of association for each set and rank the sets based on posterior probabilities. Simulations from Gaussian distributions and human microarray data, and a breast cancer study with RNA-Seq are considered for demonstration and comparison with other existing methods. Conclusions The proposed summary pathway score provides for each sample an overall evaluation of gene expression in a gene-set. It demonstrates the advantages of including all genes in the set and the synchronization of correlation direction. The simultaneous utilization of all pathway-level scores in a Bayesian model not only offers a probabilistic evaluation and ranking of the pathway association but also presents good accuracy in identifying the top-ranking pathways. The resulting recommendation list of ranked pathways can be a reference for potential target therapy or for future allocation of research resources.

Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage and length. CNVs comprise a large proportion of variation in human genomes and impact health conditions. To detect rare CNV associations, kernel-based methods have been shown to be a powerful tool due to their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their accommodation of effect heterogeneity. To perform a kernel association test, a CNV locus needs to be defined so that locus-specific effects can be retained during aggregation. However, CNV loci are arbitrarily defined and different locus definitions can lead to different performance depending on the underlying effect patterns. In this work, we develop a new kernel-based test called CONCUR (i.e., copy number profile curve-based association test) that is free from a definition of locus and evaluates CNV-phenotype associations by comparing individuals' copy number profiles across the genomic regions. CONCUR is built on the proposed concepts of \"copy number profile curves\" to describe the CNV profile of an individual, and the \"common area under the curve (cAUC) kernel\" to model the multi-feature CNV effects. The proposed method captures the effects of CNV dosage and length, accounts for the numerical nature of copy numbers, and accommodates between- and within-locus etiological heterogeneity without the need to define artificial CNV loci as required in current kernel methods. In a variety of simulation settings, CONCUR shows comparable or improved power over existing approaches. Real data analyses suggest that CONCUR is well powered to detect CNV effects in the Swedish Schizophrenia Study and the Taiwan Biobank.

Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage and length. CNVs comprise a large proportion of variation in human genomes and impact health conditions. To detect rare CNV association, kernel-based methods have been shown to be a powerful tool because their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their ability to accommodate effect heterogeneity. To perform a kernel association test, a CNV locus needs to be defined so that locus-specific effects can be retained during aggregation. However, CNV loci are arbitrarily defined and different locus definitions can lead to different performance depending on the underlying effect patterns. In this work, we develop a new kernel-based test called CONCUR (i.e., COpy Number profile CURve-based association test) that is free from a definition of locus and evaluates CNV-phenotype association by comparing individuals' copy number profiles across the genomic regions. CONCUR is built on the proposed concepts of ``copy number profile curves\" to describe the CNV profile of an individual, and the ``common area under the curve (cAUC) kernel\" to model the multi-feature CNV effects. Compared to existing methods, CONCUR captures the effects of CNV dosage and length, accounts for the continuous nature of copy number values, and accommodates between- and within-locus etiological heterogeneities without the need to define artificial CNV loci as required in current kernel methods. In a variety of simulation settings, CONCUR shows comparable and improved power over existing approaches. Real data analyses suggest that CONCUR is well powered to detect CNV effects in gene pathways associated with phenotypes using data from the Swedish Schizophrenia Study and the Taiwan Biobank.

Aim： To investigate whether CPU86017, a berberine derivative, attenuates heart failure by blocking calcium influx and exerting its antioxidant activity. Methods： Myocardial infarction was induced in male Sprague-Dawley rats for 17 d followed by isoproterenol （ISO） （5 mg/kg, sc） treatment for 5 d to reduce cardiac function. The rats were divided into 5 groups： sham operation, myocardial infarction （MI）, MI plus ISO, and co-treated （in mg/kg, po） with either propranolol （PRO, 10） or CPU86017 （80）. Hemodynamic measurements were conducted, and measurements of the redox system, calcium handling proteins and endothelin （ET） system in vivo were done. Furthermore, calcium flux studies and PLB immunocytochemistry were conducted in vitro. Results： Compared to sham operation, HF was evident following MI and further worsened by ISO treatment. This occurred in parallel with downregulated mRNA and protein production of SERCA2a, PLB, and FKBP12.6, and was associated with upregulation of preproET-1, endothelin converting enzyme, and PKA mRNA production in the myocardium in vivo. Calcium leakage was induced by ISO treatment of isolated beating myocytes in vitro. These changes were attenuated by treatment with either PRO or CPU86017. PLB fluorescence in myocytes was downregulated by ISO treatment, and was relieved significantly by treatment with antioxidant aminoguanidine, ascorbic acid or CPU86017 in vitro. Conclusion： HF, calcium leakage, downregulated PLB, FKBP12.6, SERCA2a production, and upregulated PKA were caused by ISO treatment, and were abolished by CPU86017 treatment. The beneficial effects of CPU86017 are attributable to its antioxidant and calcium influx blocking effects.

Aim： To investigate the endogenous signaling pathways associated with high proliferation potential of breast cancer cells. Methods： Breast cancer cell lines LM-MCF-7 and MCF-7 with high and low proliferation capability were used. The promoter activity of fatty acid synthase （FASN） was examined using luciferase reporter gene assay. The expression level of FASN mRNA was measured using RT-PCR and real time PCR, respectively. The level of leukotriene B4 （LTB4） was determined with ELISA. The expression levels of 5-1ipoxygenase （5-LOX） was analyzed using RT-PCR and Western blot, respectively. 5-Bromo-20-deoxyuridine （BrdU） incorporation assay was used to study the proliferation of LM-MCF-7 and MCF-7 cells. Results： The promoter activity of FASN was significantly higher in LM-MCF-7 cells than MCF-7 cells. Treatment of LM-MCF-7 cells with ERK1/2 inhibitor PD98059 （30-50 pmol/L） or LOX inhibitor NDGA （25 pmol/L） abolished the activation of FASN. Moreover, treatment of LM-MCF-7 cells with the specific 5-LOX inhibitor MK-886 （20-40 pmol/L） or 5-LOX siRNA （50-100 nmol/L） decreased the promoter activity of FASN. The level of LTB4, the final metabolite produced by 5-LOX, was significantly higher in LM-MCF-7 cells than MCF-7 cells. Administration of exogenous LTB4 （1-10 nmol/L） was able to stimulate the promoter activity of FASN in MCF-7 cells. Treatment of LMMCF-7 cells with the FASN inhibitor cerulenin （10 pmol/L） reduced all the levels of p-ERK1/2, 5-LOX, and LTB4. Treatment of LM-MCF-7 cells with cerulenin, PD98059, or MK-886 abolished the proliferation. Administration of exogenous LTB4 （10 nmol/L） significantly increased BrdU incorporation in MCF-7cells. Conclusion： These results suggest a novel positive feedback loop involving FASN/p-ERK1/2/5-LOX/LTB4/FASN contributes to the sustaining growth of breast cancer LM-MCF-7 cells.

Quantum tricriticality, a unique form of high-order criticality, is expected to exhibit fascinating features including unconventional critical exponents and universal scaling laws. However, a quantum tricritical point (QTCP) is much harder to access, and the corresponding phenomena at tricriticality have rarely been investigated. In this study, we explore a tricritical quantum Rabi model, which incorporates a non-trivial parameter to adjust the coupling ratio between a cavity and a three-level atom. The QTCP emerges at the intersection of first- and second-order superradiant phase transitions according to Landau theory. By using finite-frequency scaling analysis on quantum fluctuations and the average photon number, universal critical exponents differentiate the QTCP from the second-order critical point. Our results indicate that the phase transition at the tricritical point goes beyond the conventional second-order phase transition. Our work explores an interesting direction in the generalization of the well-known Rabi model for the study of higher-order critical points due to its high control and tunability.

A more common and noninvasive predicting biomarker for programmed cell death 1 (PD-1) antibody remains to be explored. We assessed 46 patients with advanced gastric cancer who received PD-1 antibody immunotherapy and 425-genes next-generation sequencing (NGS) testing. Patients who had a > 25% decline in maximal somatic variant allelic frequency (maxVAF) had a longer progression free survival (PFS) and higher response rate than those who did not (7.3 months vs 3.6 months, p  = 0.0011; 53.3% vs 13.3%, p  = 0.06). The median PFS of patients with undetectable and detectable post-treatment circulating tumor DNA (ctDNA) was 7.4 months vs. 4.9 months ( p  = 0.025). Mutation status of TGFBR2, RHOA, and PREX2 in baseline ctDNA influenced the PFS of immunotherapy ( p  < 0.05). Patients with alterations in CEBPA, FGFR4, MET or KMT2B ( p  = 0.09) gene had greater likelihood of immune-related adverse events (irAEs). ctDNA can serve as a potential biomarker of the response to immunotherapy in advanced gastric cancers, and its potential role in predicting irAEs worth further exploration.