Soil organic matter dynamics after C3–C4 vegetation change of red soil in Southern, China: Evidence from natural 13C abundance

Soil samples from natural forests and adjacent farmland were analyzed to investigate the dynamics of soil organic matter of red soil in Southern, China. Based on the δ¹³C values and the content of soil organic matter, the data indicated that the turnover of soil organic matter under the virgin forest was slower than that under cultivation. Soil organic matter is fresh in coarse sand and oldest in fine silt and clay. Also, the soil light fraction contained the younger organic matter than soil heavy fraction and bulk soil. Deforestation has accelerated the decomposition rate of soil organic matter and reduced the proportion of active components in SOM and thus soil fertility.     

<Emphasis Type="Italic">Sorting nexin 24</Emphasis> genetic variation associates with coronary artery aneurysm severity in Kawasaki disease patients

Background

The sorting nexin (SNX) family is involved in endocytosis and protein trafficking and plays multiple roles in various diseases. The role of SNX proteins in Kawasaki disease (KD) is not known. We attempted to test whether genetic SNX variation associates with the risk of coronary artery aneurysm (CAA) formation in KD.

Methods and results

Chi-square tests were used to identify SNX24 genetic variants associated with KD susceptibility and CAA formation in KD; models were adjusted for fever duration and time of first administration of intravenous immunoglobulin. We obtained clinical characteristics and genotypes from KD patients (76 with CAA and 186 without CAA) in a population-based retrospective KD cohort study (n?=?262). Clinical and genetic factors were associated with CAA formation in KD. In addition, endothelial cell inflammation was evaluated. Significant correlation was observed between KD with CAA complications and the rs28891 single-nucleotide polymorphism in SNX24. Patients with CC?+?CT genotypes had lesser CAA complications. In lipopolysaccharide-treated human umbilical vein endothelial cells, siRNA knockdown of SNX24 significantly decreased gene expression of the proinflammatory cytokines IL-1 beta, IL-6, and IL-8.

Conclusions

Polymorphisms in SNX24 may be used as genetic markers for the diagnosis and prognosis of CAA formation in KD.

     

Cancer-derived immunoglobulin G promotes tumor cell growth and proliferation through inducing production of reactive oxygen species

Cancer cells have been found to express immunoglobulin G (IgG), but the exact functions and underlying mechanisms of cancer-derived IgG remain elusive. In this study, we first confirmed that downregulation of IgG restrained the growth and proliferation of cancer cells in vitro and in vivo. To elucidate its mechanism, we carried out a co-immunoprecipitation assay in HeLa cells and identified 27 potential IgG-interacting proteins. Among them, receptor of activated protein kinase C 1 (RACK1), ras-related nuclear protein (RAN) and peroxiredoxin 1 (PRDX1) are closely related to cell growth and oxidative stress, which prompted us to investigate the mechanism of action of IgG in the above phenomena. Upon confirmation of the interactions between IgG and the three proteins, further experiments revealed that downregulation of cancer-derived IgG lowered levels of intracellular reactive oxygen species (ROS) by enhancing cellular total antioxidant capacity. In addition, a few ROS scavengers, including catalase (CAT), dimethylsulfoxide (DMSO), n-acetylcysteine (NAC) and superoxide dismutase (SOD), further inhibited the growth of IgG-deficient cancer cells through suppressing mitogen-activated protein kinase/extracellular-regulated kinase (MAPK/ERK) signaling pathway induced by a low level of intracellular ROS, whereas exogenous hydrogen peroxide (H₂O₂) at low concentration promoted their survival via increasing intracellular ROS levels. Similar results were obtained in an animal model and human tissues. Taken together, our results demonstrate that cancer-derived IgG can enhance the growth and proliferation of cancer cells via inducing the production of ROS at low level. These findings provide new clues for understanding tumor proliferation and designing cancer therapy.     

A Role of RIP3-Mediated Macrophage Necrosis in Atherosclerosis Development

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Highlights? RIP3 deficiency in macrophages reduces advanced atherosclerotic lesions ? RIP3 deficiency reduces primary necrosis of macrophages in advanced lesions ? RIP3-dependent macrophage necrosis is not postapoptotic cell death ? Increased RIP3 converts apoptosis to necrosis in advanced lesional macrophages     

Differential Phosphorylation of Smad1 Integrates BMP and Neurotrophin Pathways through Erk/Dusp in Axon Development

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Comprehensive analysis of the overall codon usage patterns in equine infectious anemia virus

Background

Equine infectious anemia virus (EIAV) is an important animal model for understanding the relationship between viral persistence and the host immune response during lentiviral infections. Comparison and analysis of the codon usage model between EIAV and its hosts is important for the comprehension of viral evolution. In our study, the codon usage pattern of EIAV was analyzed from the available 29 full-length EIAV genomes through multivariate statistical methods.

Finding

Effective number of codons (ENC) suggests that the codon usage among EIAV strains is slightly biased. The ENC-plot analysis demonstrates that mutation pressure plays a substantial role in the codon usage pattern of EIAV, whereas other factors such as geographic distribution and host translation selection also take part in the process of EIAV evolution. Comparative analysis of codon adaptation index (CAI) values among EIAV and its hosts suggests that EIAV utilize the translational resources of horse more efficiently than that of donkey.

Conclusion

The codon usage bias in EIAV is slight and mutation pressure is the main factor that affects codon usage variation in EIAV. These results suggest that EIAV genomic biases are the result of the co-evolution of genome composition and the ability to evade the host’s immune response.

     

The arbuscular mycorrhizal fungus Diversispora spurca ameliorates effects of waterlogging on growth,root system architecture and antioxidant enzyme activities of citrus seedlings

Citrus plants are often exposed to heavy rain and subsequent periods of soil waterlogging which severely restrict tree growth. We assessed the effect of one arbuscular mycorrhizal fungus species (Diversispora spurca) on growth, root system architecture (RSA), and antioxidant enzyme activities of young citrus (Citrus junos) seedlings. Waterlogging for 37 d significantly restricted mycorrhizal colonization but increased the number of entry points and vesicles. Compared with non-mycorrhizal controls, mycorrhizal seedlings had significantly greater plant height, fresh mass, total root and taproot lengths, projected and surface root areas, root volume, and numbers of lst, 2nd and 3rd order lateral roots regardless of waterlogging treatment. D. spurca significantly increased root catalase (CAT) activity in non-stressed seedlings and increased root soluble protein concentration and leaf CAT activity in waterlogged seedlings, thereby inducing lower oxidative damage. These results suggest that D. spurca ameliorates effects of waterlogging on growth, RSA and antioxidant enzyme activities.     

Application of Artificial Bee Colony Algorithm to Maximum Likelihood DOA Estimation

Maximum Likelihood (ML) method has an excellent performance for Direction-Of-Arrival (DOA) estimation, but a multidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use. To reduce the high computational burden of ML method and make it more suitable to engineering applications, we apply the Artificial Bee Colony (ABC) algorithm to maximize the likelihood function for DOA estimation. As a recently proposed bio-inspired computing algorithm, ABC algorithm is originally used to optimize multivariable functions by imitating the behavior of bee colony finding excellent nectar sources in the nature environment. It offers an excellent alternative to the conventional methods in ML-DOA estimation. The performance of ABC-based ML and other popular meta-heuristic-based ML methods for DOA estimation are compared for various scenarios of convergence, Signal-to-Noise Ratio (SNR), and number of iterations. The computation loads of ABC-based ML and the conventional ML methods for DOA estimation are also investigated. Simulation results demonstrate that the proposed ABC based method is more efficient in computation and statistical performance than other ML-based DOA estimation methods.