Heat Diffusion Kernel Algorithm-Based Interpretation of the Disease Intervention Mechanism for DHA.

Docosahexaenoic acid (DHA) is effective in the prevention and treatment of cancer, congenital disorders, and various chronic diseases. According to the omnigenic hypothesis, these complex diseases are caused by disordered gene regulatory networks comprising dozens to hundreds of core genes and a mass of peripheral genes. However, conventional research on the disease intervention mechanism of DHA only focused on specific types of genes or pathways instead of examining genes at the network level, resulting in conflicting conclusions. In this study, we used HotNet2, a heat diffusion kernel algorithm, to calculate the gene regulatory networks of connectivity map (cMap)-derived agents (including DHA) based on gene expression profiles, aiming to interpret the disease intervention mechanism of DHA at the network level. As a result, significant gene regulatory networks for DHA and 676 cMap-derived agents were identified respectively. The biological functions of the DHA-regulated gene network provide preliminary insights into the mechanism by which DHA intervenes in disease. In addition, we compared the gene regulatory networks of DHA with those of cMap-derived agents, which allowed us to predict the pharmacological effects and disease intervention mechanism of DHA by analogy with similar agents with clear indications and mechanisms. Some of our analysis results were supported by experimental observations. Therefore, this study makes a significant contribution to research on the disease intervention mechanism of DHA at the regulatory network level, demonstrating the potential application value of this methodology in clarifying the mechanisms about nutrients influencing health.

Identifying cancer prognostic modules by module network analysis.

BACKGROUND: The identification of prognostic genes that can distinguish the prognostic risks of cancer patients remains a significant challenge. Previous works have proven that functional gene sets were more reliable for this task than the gene signature. However, few works have considered the cross-talk among functional gene sets, which may result in neglecting important prognostic gene sets for cancer. RESULTS: Here, we proposed a new method that considers both the interactions among modules and the prognostic correlation of the modules to identify prognostic modules in cancers. First, dense sub-networks in the gene co-expression network of cancer patients were detected. Second, cross-talk between every two modules was identified by a permutation test, thus generating the module network. Third, the prognostic correlation of each module was evaluated by the resampling method. Then, the GeneRank algorithm, which takes the module network and the prognostic correlations of all the modules as input, was applied to prioritize the prognostic modules. Finally, the selected modules were validated by survival analysis in various data sets. Our method was applied in three kinds of cancers, and the results show that our method succeeded in identifying prognostic modules in all the three cancers. In addition, our method outperformed state-of-the-art methods. Furthermore, the selected modules were significantly enriched with known cancer-related genes and drug targets of cancer, which may indicate that the genes involved in the modules may be drug targets for therapy. CONCLUSIONS: We proposed a useful method to identify key modules in cancer prognosis and our prognostic genes may be good candidates for drug targets.

The effects of secretion factors from umbilical cord derived mesenchymal stem cells on osteogenic differentiation of mesenchymal stem cells.

Factors synthesized by mesenchymal stem cells (MSCs) contain various growth factors, cytokines, exosomes and microRNAs, which may affect the differentiation abilities of MSCs. In the present study, we investigated the effects of secretion factors of human umbilical cord derived mesenchymal stem cells (hUCMSCs) on osteogenesis of human bone marrow derived MSCs (hBMSCs). The results showed that 20 µg/ml hUCMSCs secretion factors could initiate osteogenic differentiation of hBMSCs without osteogenic induction medium (OIM), and the amount of calcium deposit (stained by Alizarin Red) was significantly increased after the hUCMSCs secretion factors treatment. Real time quantitative reverse transcription-polymerase chain reaction (real time qRT-PCR) demonstrated that the expression of osteogenesis-related genes including ALP, BMP2, OCN, Osterix, Col1α and Runx2 were significantly up-regulated following hUCMSCs secretion factors treatment. In addition, we found that 10 µg hUCMSCs secretion factors together with 2×10(5) hBMSCs in the HA/TCP scaffolds promoted ectopic bone formation in nude mice. Local application of 10 µg hUCMSCs secretion factors with 50 µl 2% hyaluronic acid hydrogel and 1×10(5) rat bone marrow derived MSCs (rBMSCs) also significantly enhanced the bone repair of rat calvarial bone critical defect model at both 4 weeks and 8 weeks. Moreover, the group that received the hUCMSCs secretion factors treatment had more cartilage and bone regeneration in the defect areas than those in the control group. Taken together, these findings suggested that hUCMSCs secretion factors can initiate osteogenesis of bone marrow MSCs and promote bone repair. Our study indicates that hUCMSCs secretion factors may be potential sources for promoting bone regeneration.

MDAS: an integrated system for metabonomic data analysis.

Metabonomics, the latest 'omics' research field, shows great promise as a tool in biomarker discovery, drug efficacy and toxicity analysis, disease diagnosis and prognosis. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system, e.g., the mechanism of diseases. Traditional methods employed in metabonomic data analysis use multivariate analysis methods developed independently in chemometrics research. Additionally, with the development of machine learning approaches, some methods such as SVMs also show promise for use in metabonomic data analysis. Aside from the application of general multivariate analysis and machine learning methods to this problem, there is also a need for an integrated tool customized for metabonomic data analysis which can be easily used by biologists to reveal interesting patterns in metabonomic data.In this paper, we present a novel software tool MDAS (Metabonomic Data Analysis System) for metabonomic data analysis which integrates traditional chemometrics methods and newly introduced machine learning approaches. MDAS contains a suite of functional models for metabonomic data analysis and optimizes the flow of data analysis. Several file formats can be accepted as input. The input data can be optionally preprocessed and can then be processed with operations such as feature analysis and dimensionality reduction. The data with reduced dimensionalities can be used for training or testing through machine learning models. The system supplies proper visualization for data preprocessing, feature analysis, and classification which can be a powerful function for users to extract knowledge from the data. MDAS is an integrated platform for metabonomic data analysis, which transforms a complex analysis procedure into a more formalized and simplified one. The software package can be obtained from the authors.

[Detection of small molecules based on surface plasmon (correction of plasman) resonance technology].

OBJECTIVE: To develop a detective method applied in online assaying of astronauts' humours using the portable online bio-molecules analyzer (POBA) based on surface plasmon resonance biosensor. METHOD: An assay format was developed based on the detection of 2, 4-Dinitrophenyl-hydrazine. The bio-molecule slide was made by DNP-BSA. Range of detection and standard curve were obtained using inhibition assay. Reliability and specificity of the assay were also tested. RESULT: 1) The linear range of the assay was 7.8 ng/ml-2 micrograms/ml with lower detection limit of 2.5 ng/ml; 2) Preparation of the bio-molecule slide and regeneration of the biosensor ensured detections for many samples. CONCLUSION: This assay method can be used to detect small molecules sensitively, rapidly and easily. It can be repeated with good reliability, and has a good application in space medicine.

Gray characteristic analysis of microtubules in cardiac myocytes.

OBJECTIVE: To quantify the images of the microtubules in fetal rat cardiac myocytes under simulated microgravity by utilizing the characteristic parameters of image gray, and to study their morphological change. METHOD: Gray characteristic of the microtubules in fetal rat cardiac myocytes was quantified in both simulated microgravity and control conditions by variance, skewness, and kurtosis. RESULT: From feature analysis of 24 images, the characteristic parameters selected here were proved to be effective. Good result was obtained when discrimination between simulated microgravity group and control group was made by multivariate analysis with these parameters. The total false verdict rate even reached 16.7% when using multivariate analysis with these parameters. CONCLUSION: The morphology of the microtubules in cardiac myocytes cytoskeleton became diffused under simulated microgravity, and the quantitative analysis of gray parameters (variance, skewness, kurtosis) described the variation satisfactorily.

[Image analysis of cardiac muscle cytoskeleton under simulated microgravity based on gray-level co-occurrence matrix (GLCM)].

OBJECTIVE: To study morphological changes of the cytoskeleton-microtubule (MT) of the fetal rat cardiac myocytes under simulated microgravity, and to quantify its image by utilizing the gray level co-occurrence matrix (GLCM) parameters of the image. METHOD: Cytoskeleton images, including cellular microphotographs taken under normal or microgravity (clinostat) conditions, were quantified by gray level co-occurrence matrix parameters, and the pharmacological counter effect of quercetin against the influences of microgravity was estimated with these parameters. RESULT: The results showed that the texture of microtubules in the image became worse under simulated microgravity environment. It also showed that quercetin has certain counter effect against the influence of microgravity. CONCLUSION: The microtubule of the cardiac myocytes cytoskeleton becomes diffused under microgravity, and the GLCM parameters can well describe these variation. Quercetin has certain counter-effect against the influence of microgravity.

[Effects of nitric oxide on myocardial contraction function].

Nitric oxide (NO) produced by myocardial nitric oxide synthase has been implicated as a modulator of myocardial contraction [correction of contracion]. This paper reviewed the reports on myocardial contraction modulated by NO, its mechanism, and regulation of expression and activity of iNOS. NO was recently shown to produce biphasic contractile [correction of contratile] effects on myocardium: augmentation at low levels and depression at high levels. The up-regulation of inducible nitric oxide synthase (iNOS) often negatively modulates myocardial function.