Metabolomic profiling of Marek's disease virus infection in host cell based on untargeted LC-MS.

Marek's disease (MD) caused by Marek's disease virus (MDV), poses a serious threat to the poultry industry by inducing neurological disease and malignant lymphoma in infected chickens. However, the underlying mechanisms how MDV disrupts host cells and causes damage still remain elusive. Recently, the application of metabolomics has shown great potential for uncovering the complex mechanisms during virus-host interactions. In this study, chicken embryo fibroblasts (CEFs) infected with MDV were subjected to ultrahigh-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS) and multivariate statistical analysis. The results showed that 261 metabolites were significantly altered upon MDV infection, with most changes occurring in amino acid metabolism, energy metabolism, nucleotide metabolism, and lipid metabolism. Notably, MDV infection induces an up-regulation of amino acids in host cells during the early stages of infection to provide the energy and intermediary metabolites necessary for efficient multiplication of its own replication. Taken together, these data not only hold promise in identifying the biochemical molecules utilized by MDV replication in host cells, but also provides a new insight into understanding MDV-host interactions.

Identification of Cancer Biomarkers in Human Body Fluids by Using Enhanced Physicochemical-incorporated Evolutionary Conservation Scheme.

OBJECTIVE: Cancer is one of the most serious diseases affecting human health. Among all current cancer treatments, early diagnosis and control significantly help increase the chances of cure. Detecting cancer biomarkers in body fluids now is attracting more attention within oncologists. In-silico predictions of body fluid-related proteins, which can be served as cancer biomarkers, open a door for labor-intensive and time-consuming biochemical experiments. METHODS: In this work, we propose a novel method for high-throughput identification of cancer biomarkers in human body fluids. We incorporate physicochemical properties into the weighted observed percentages (WOP) and position-specific scoring matrices (PSSM) profiles to enhance their attributes that reflect the evolutionary conservation of the body fluid-related proteins. The least absolute selection and shrinkage operator (LASSO) feature selection strategy is introduced to generate the optimal feature subset. RESULTS: The ten-fold cross-validation results on training datasets demonstrate the accuracy of the proposed model. We also test our proposed method on independent testing datasets and apply it to the identification of potential cancer biomarkers in human body fluids. CONCLUSION: The testing results promise a good generalization capability of our approach.

A portable fast neutron irradiation system for tumor therapy.

A portable neutron tube was introduced as a small-sized (weight≤14.4 kg, power consumption ≤50W and cost≤ $100,000) neutron accelerator and applied for irradiation therapy on cancer. The effect of growth-inhibiting in vitro by neutrons irradiation on HeLa cells (human cervical cancer cells) was evaluated by colony formation assays, and cell apoptosis was evaluated by Flow Cytometry. A polyethylene protection device as the neutron moderator was designed and connected to the neutron tube to shield normal tissue and organs of the test animals from scatter radiation. Hematology and blood biochemistry were investigated to evaluate the protective effect of polyethylene. U14 (mice cervical cancer cell) tumor-bearing mice were further investigated to determine the tumor suppression effect of neutron irradiation. We found that cells showed a dose-dependent relationship after fast neutrons irradiation at different dose (1.11 Gy, 2.23 Gy, 3.34 Gy and 4.45Gy). Furthermore, in vivo experiments showed that the anti-tumor effect on U14 tumor-bearing mice greatly depended on the neutron irradiation dose. A high dose of fast neutron irradiation (26.73 Gy) could have tumor growth rate only 12.31% compared to 56.07% with control group. All the blood cell counts and blood biochemistry parameters were in the standard value ranges. Immunohistochemistry examinations clearly indicated the apoptosis cells in tumor tissues by the TUNEL assay. This work provides useful evidences on cancer irradiation therapy using fast neutron in pre-clinical study. And the neutron therapy system device has great potential to be a more convenient tool in clinical application with significantly lower power consumption, irradiation toxicity and cost.

Prediction of bioluminescent proteins by using sequence-derived features and lineage-specific scheme.

BACKGROUND: Bioluminescent proteins (BLPs) widely exist in many living organisms. As BLPs are featured by the capability of emitting lights, they can be served as biomarkers and easily detected in biomedical research, such as gene expression analysis and signal transduction pathways. Therefore, accurate identification of BLPs is important for disease diagnosis and biomedical engineering. In this paper, we propose a novel accurate sequence-based method named PredBLP (Prediction of BioLuminescent Proteins) to predict BLPs. RESULTS: We collect a series of sequence-derived features, which have been proved to be involved in the structure and function of BLPs. These features include amino acid composition, dipeptide composition, sequence motifs and physicochemical properties. We further prove that the combination of four types of features outperforms any other combinations or individual features. To remove potential irrelevant or redundant features, we also introduce Fisher Markov Selector together with Sequential Backward Selection strategy to select the optimal feature subsets. Additionally, we design a lineage-specific scheme, which is proved to be more effective than traditional universal approaches. CONCLUSION: Experiment on benchmark datasets proves the robustness of PredBLP. We demonstrate that lineage-specific models significantly outperform universal ones. We also test the generalization capability of PredBLP based on independent testing datasets as well as newly deposited BLPs in UniProt. PredBLP is proved to be able to exceed many state-of-art methods. A web server named PredBLP, which implements the proposed method, is free available for academic use.

Identification of S-glutathionylation sites in species-specific proteins by incorporating five sequence-derived features into the general pseudo-amino acid composition.

As a selective and reversible protein post-translational modification, S-glutathionylation generates mixed disulfides between glutathione (GSH) and cysteine residues, and plays an important role in regulating protein activity, stability, and redox regulation. To fully understand S-glutathionylation mechanisms, identification of substrates and specific S-Glutathionylated sites is crucial. Experimental identification of S-glutathionylated sites is labor-intensive and time consuming, so establishing an effective computational method is much desirable due to their convenient and fast speed. Therefore, in this study, a new bioinformatics tool named SSGlu (Species-Specific identification of Protein S-glutathionylation Sites) was developed to identify species-specific protein S-glutathionylated sites, utilizing support vector machines that combine multiple sequence-derived features with a two-step feature selection. By 5-fold cross validation, the performance of SSGlu was measured with an AUC of 0.8105 and 0.8041 for Homo sapiens and Mus musculus, respectively. Additionally, SSGlu was compared with the existing methods, and the higher MCC and AUC of SSGlu demonstrated that SSGlu was very promising to predict S-glutathionylated sites. Furthermore, a site-specific analysis showed that S-glutathionylation intimately correlated with the features derived from its surrounding sites. The conclusions derived from this study might help to understand more of the S-glutathionylation mechanism and guide the related experimental validation. For public access, SSGlu is freely accessible at http://59.73.198.144:8080/SSGlu/.

In vitro dynamic degradation behavior of new magnesium alloy for orthopedic applications.

We report on methodologies for use in the design of a biodegradable Mg alloy appropriate for load-bearing but temporary orthopedic implant applications. Comparative studies of Mg-5Ca and Mg-5Ca-1Zn were conducted to explore the effects of a combination of minor alloying and hot extrusion, on the alloy's mechanical properties and corrosion resistance. The extruded Mg-5Ca-1Zn exhibited high ultimate compressive strength of 385 MPa and suffered no significant structural degradation even after immersion in simulated body fluid for 30 days. Mg-5Ca-1Zn alloy showed the mechanical strength and controlled corrosion rate to be considered as an ideal candidate for biodegradable orthopedic implant material.