Comparison of molecular and antibiotic resistance profile methods for the population analysis of Bradyrhizobium spp. (TGx) isolates that nodulate the new TGx soybean cultivars in Africa

AIMS: Comparison of molecular and antibiotic resistance profile methods to identify an easy method that can differentiate between strains of introduced Bradyrhizobium japonicum and the indigenous Bradyrhizobium spp. (TGx) isolates which nodulate the newly developed TGx soybean cultivars in Africa. METHODS AND RESULTS: Restriction fragment length polymorphism (RFLP) of 16S rDNA generated by five restriction enzymes, banding patterns in Southern hybridization using nod and nif genes as probes, and resistance patterns of the isolates to nine antibiotics, were used to group 26 Bradyrhizobium spp. (TGx) isolates and four other Bradyrhizobium strains. The clusters of isolates obtained from the four grouping methods were all different, although all methods revealed large genetic diversity among the isolates. CONCLUSIONS: Results indicate that the antibiotic resistance profile method is as good as the three molecular methods used in this study for phylogenetic grouping of the Bradyrhizobium spp. (TGx) isolates, which may serve as a basis for further characterization of selected isolates from each group. SIGNIFICANCE AND IMPACT OF THE STUDY: The antibiotic resistance profile method can be used as a simple means of assessing genetic variability and grouping of a large number of Bradyrhizobium spp. (TGx) isolates. Representative isolates from each group can then be selected for further characterization.     

Disease pathways at the Rat Genome Database Pathway Portal: genes in context-a network approach to understanding the molecular mechanisms of disease

Background

Biological systems are exquisitely poised to respond and adjust to challenges, including damage. However, sustained damage can overcome the ability of the system to adjust and result in a disease phenotype, its underpinnings many times elusive. Unraveling the molecular mechanisms of systems biology, of how and why it falters, is essential for delineating the details of the path(s) leading to the diseased state and for designing strategies to revert its progression. An important aspect of this process is not only to define the function of a gene but to identify the context within which gene functions act. It is within the network, or pathway context, that the function of a gene fulfills its ultimate biological role. Resolving the extent to which defective function(s) affect the proceedings of pathway(s) and how altered pathways merge into overpowering the system's defense machinery are key to understanding the molecular aspects of disease and envisioning ways to counteract it. A network-centric approach to diseases is increasingly being considered in current research. It also underlies the deployment of disease pathways at the Rat Genome Database Pathway Portal. The portal is presented with an emphasis on disease and altered pathways, associated drug pathways, pathway suites, and suite networks.

Results

The Pathway Portal at the Rat Genome Database (RGD) provides an ever-increasing collection of interactive pathway diagrams and associated annotations for metabolic, signaling, regulatory, and drug pathways, including disease and altered pathways. A disease pathway is viewed from the perspective of networks whose alterations are manifested in the affected phenotype. The Pathway Ontology (PW), built and maintained at RGD, facilitates the annotations of genes, the deployment of pathway diagrams, and provides an overall navigational tool. Pathways that revolve around a common concept and are globally connected are presented within pathway suites; a suite network combines two or more pathway suites.

Conclusions

The Pathway Portal is a rich resource that offers a range of pathway data and visualization, including disease pathways and related pathway suites. Viewing a disease pathway from the perspective of underlying altered pathways is an aid for dissecting the molecular mechanisms of disease.

     

Top-down approaches to the study of natural variation in complex physiological pathways using the white-footed mouse (Peromyscus leucopus) as a model

Variation in complex physiological pathways has important effects on human function and medical treatment. Complex pathways involve cells at multiple locations, which serve different functions regulated by many genes and include complex neuroendocrine pathways that regulate physiological function. One of two competing hypotheses regarding the effects of selection on complex pathways predicts that variability should be common within complex pathways. If this hypothesis is correct, then we should expect wide variation in neuroendocrine function to be typical within natural populations. To test this hypothesis, a complex neuroendocrine pathway that regulates photoperiod-dependent changes in fertility in a natural population of white-footed mice (Peromyscus leucopus) was used to test for natural genetic variability in multiple components of the pathway. After testing only six elements in the photoperiod pathway in P. leucopus, genetic variation in the following four of these elements was evident: the circadian clock, melatonin receptor abundance or affinity, sensitivity of the reproductive axis to steroid negative feedback, and gonadotropin-releasing hormone neuronal activity. If this result can be extended to humans, the prediction would be that significant variation at multiple loci in complex neuroendocrine pathways is common among humans, and that variation would exist even in human populations from a common genetic background. This finding could only be drawn from an "exotic" animal model derived from a natural source population, confirming the continuing importance of nontraditional models alongside the standard laboratory species.     

A Comparison of Aggregate P-Value Methods and Multivariate Statistics for Self-Contained Tests of Metabolic Pathway Analysis

For pathway analysis of genomic data, the most common methods involve combining p-values from individual statistical tests. However, there are several multivariate statistical methods that can be used to test whether a pathway has changed. Because of the large number of variables and pathway sizes in genomics data, some of these statistics cannot be computed. However, in metabolomics data, the number of variables and pathway sizes are typically much smaller, making such computations feasible. Of particular interest is being able to detect changes in pathways that may not be detected for the individual variables. We compare the performance of both the p-value methods and multivariate statistics for self-contained tests with an extensive simulation study and a human metabolomics study. Permutation tests, rather than asymptotic results are used to assess the statistical significance of the pathways. Furthermore, both one and two-sided alternatives hypotheses are examined. From the human metabolomic study, many pathways were statistically significant, although the majority of the individual variables in the pathway were not. Overall, the p-value methods perform at least as well as the multivariate statistics for these scenarios.     

Encapsulation of gold nanoparticles into self-assembling protein nanoparticles

Background

Since silver-nanoparticles (NPs) possess an antibacterial activity, they were commonly used in medical products and devices, food storage materials, cosmetics, various health care products, and industrial products. Various silver-NP based medical devices are available for clinical uses, such as silver-NP based dressing and silver-NP based hydrogel (silver-NP-hydrogel) for medical applications. Although the previous data have suggested silver-NPs induced toxicity in vivo and in vitro, there is lack information about the mechanisms of biological response and potential toxicity of silver-NP-hydrogel.

Methods

In this study, the genotoxicity of silver-NP-hydrogel was assayed using cytokinesis-block micronucleus (CBMN). The molecular response was studied using DNA microarray and GO pathway analysis.

Results and discussion

The results of global gene expression analysis in HeLa cells showed that thousands of genes were up- or down-regulated at 48?h of silver-NP-hydrogel exposure. Further GO pathway analysis suggested that fourteen theoretical activating signaling pathways were attributed to up-regulated genes; and three signal pathways were attributed to down-regulated genes. It was discussed that the cells protect themselves against silver NP-mediated toxicity through up-regulating metallothionein genes and anti-oxidative stress genes. The changes in DNA damage, apoptosis and mitosis pathway were closely related to silver-NP-induced cytotoxicity and chromosome damage. The down-regulation of CDC14A via mitosis pathway might play a role in potential genotoxicity induced by silver-NPs.

Conclusions

The silver-NP-hydrogel induced micronuclei formation in cellular level and broad spectrum molecular responses in gene expression level. The results of signal pathway analysis suggested that the balances between anti-ROS response and DNA damage, chromosome instability, mitosis inhibition might play important roles in silver-NP induced toxicity. The inflammatory factors were likely involved in silver-NP-hydrogel complex-induced toxic effects via JAK-STAT signal transduction pathway and immune response pathway. These biological responses eventually decide the future of the cells, survival or apoptosis.     

Molecular signatures from omics data: From chaos to consensus

In the past 15 years, new "omics" technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported "molecular signatures". However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice.     

Signaling pathway networks mined from human pituitary adenoma proteomics data

Background

We obtained a series of pituitary adenoma proteomic expression data, including protein-mapping data (111 proteins), comparative proteomic data (56 differentially expressed proteins), and nitroproteomic data (17 nitroproteins). There is a pressing need to clarify the significant signaling pathway networks that derive from those proteins in order to clarify and to better understand the molecular basis of pituitary adenoma pathogenesis and to discover biomarkers. Here, we describe the significant signaling pathway networks that were mined from human pituitary adenoma proteomic data with the Ingenuity pathway analysis system.

Methods

The Ingenuity pathway analysis system was used to analyze signal pathway networks and canonical pathways from protein-mapping data, comparative proteomic data, adenoma nitroproteomic data, and control nitroproteomic data. A Fisher's exact test was used to test the statistical significance with a significance level of 0.05. Statistical significant results were rationalized within the pituitary adenoma biological system with literature-based bioinformatics analyses.

Results

For the protein-mapping data, the top pathway networks were related to cancer, cell death, and lipid metabolism; the top canonical toxicity pathways included acute-phase response, oxidative-stress response, oxidative stress, and cell-cycle G2/M transition regulation. For the comparative proteomic data, top pathway networks were related to cancer, endocrine system development and function, and lipid metabolism; the top canonical toxicity pathways included mitochondrial dysfunction, oxidative phosphorylation, oxidative-stress response, and ERK/MAPK signaling. The nitroproteomic data from a pituitary adenoma were related to cancer, cell death, lipid metabolism, and reproductive system disease, and the top canonical toxicity pathways mainly related to p38 MAPK signaling and cell-cycle G2/M transition regulation. Nitroproteins from a pituitary control related to gene expression and cellular development, and no canonical toxicity pathways were identified.

Conclusions

This pathway network analysis demonstrated that mitochondrial dysfunction, oxidative stress, cell-cycle dysregulation, and the MAPK-signaling abnormality are significantly associated with a pituitary adenoma. These pathway-network data provide new insights into the molecular mechanisms of human pituitary adenoma pathogenesis, and new clues for an in-depth investigation of pituitary adenoma and biomarker discovery.

     

Receptor protein tyrosine phosphatases and cancer

There is general agreement that many cancers are associated with aberrant phosphotyrosine signaling, which can be caused by the inappropriate activities of tyrosine kinases or tyrosine phosphatases. Furthermore, incorrect activation of signaling pathways has been often linked to changes in adhesion events mediated by cell surface receptors. Among these receptors, receptor protein tyrosine phosphatases (RPTPs) both antagonize tyrosine kinases as well as engage extracellular ligands. A recent wealth of data on this intriguing family indicates that its members can fulfill either tumor suppressing or oncogenic roles. The interpretation of these results at a molecular level has been greatly facilitated by the recent availability of structural information on the extra- and intracellular regions of RPTPs. These structures provide a molecular framework to understand how alterations in extracellular interactions can inactivate RPTPs in cancers or why the overexpression of certain RPTPs may also participate in tumor progression.     

Receptor protein tyrosine phosphatases and cancer: New insights from structural biology

There is general agreement that many cancers are associated with aberrant phosphotyrosine signaling, which can be caused by the inappropriate activities of tyrosine kinases or tyrosine phosphatases. Furthermore, incorrect activation of signaling pathways has been often linked to changes in adhesion events mediated by cell surface receptors. Among these receptors, receptor protein tyrosine phosphatases (RPTPs) both antagonize tyrosine kinases as well as engage extracellular ligands. A recent wealth of data on this intriguing family indicates that its members can fulfill either tumor suppressing or oncogenic roles. The interpretation of these results at a molecular level has been greatly facilitated by the recent availability of structural information on the extra- and intracellular regions of RPTPs. These structures provide a molecular framework to understand how alterations in extracellular interactions can inactivate RPTPs in cancers or why the overexpression of certain RPTPs may also participate in tumor progression.     

Metabolomics study on the toxicity of aconite root and its processed products using ultraperformance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry coupled with pattern recognition approach and ingenuity pathways analysis

The mother and lateral root of Aconitum carmichaelii Debx, named "Chuanwu" (CW) and "Fuzi", respectively, has been used to relieve joint pain and treat rheumatic diseases for over 2000 years. However, it has a very narrow therapeutic range, and the toxicological risk of its usage remains very high. The traditional Chinese processing approach, Paozhi (detoxifying measure),can decompose poisonous Aconitum alkaloids into less or nontoxic derivatives and plays an important role in detoxification. The difference in metabolomic characters among the crude and processed preparations is still unclear, limited by the lack of sensitive and reliable biomarkers. Therefore, this paper was designed to investigate comprehensive metabolomic characters of the crude and its processed products by UPLC-Q-TOF-HDMS combined with pattern recognition methods and ingenuity pathway analysis (IPA). The significant difference in metabolic profiles and changes of metabolite biomarkers of interest between the crude and processed preparations were well observed. The underlying regulations of Paozhi-perturbed metabolic pathways are discussed according to the identified metabolites, and four metabolic pathways are identified using IPA. The present study demonstrates that metabolomic analysis could greatly facilitate and provide useful information to further comprehensively understand the pharmacological activity and potential toxicity of processed Aconite roots in the clinic.     

piRNAs initiate an epigenetic memory of nonself RNA in the C. elegans germline

Organisms employ a fascinating array of strategies to silence invasive nucleic acids such as transposons and viruses. Although evidence exists for several pathways that detect foreign sequences, including pathways that sense copy number, unpaired DNA, or aberrant RNA (e.g., dsRNA), in many cases, the mechanisms used to distinguish "self" from "nonself" nucleic acids remain mysterious. Here, we describe an RNA-induced epigenetic silencing pathway that permanently silences single-copy transgenes. We show that the Piwi Argonaute PRG-1 and its genomically encoded piRNA cofactors initiate permanent silencing, and maintenance depends on chromatin factors and the WAGO Argonaute pathway. Our findings support a model in which PRG-1 scans for foreign sequences and two other Argonaute pathways serve as epigenetic memories of "self" and "nonself" RNAs. These findings suggest how organisms can utilize RNAi-related mechanisms to detect foreign sequences not by any molecular signature, but by comparing the foreign sequence to a memory of previous gene expression.     

Metabolic network modeling of redox balancing and biohydrogen production in purple nonsulfur bacteria

Background

Synthetic biology is used to develop cell factories for production of chemicals by constructively importing heterologous pathways into industrial microorganisms. In this work we present a retrosynthetic approach to the production of therapeutics with the goal of developing an in situ drug delivery device in host cells. Retrosynthesis, a concept originally proposed for synthetic chemistry, iteratively applies reversed chemical transformations (reversed enzyme-catalyzed reactions in the metabolic space) starting from a target product to reach precursors that are endogenous to the chassis. So far, a wider adoption of retrosynthesis into the manufacturing pipeline has been hindered by the complexity of enumerating all feasible biosynthetic pathways for a given compound.

Results

In our method, we efficiently address the complexity problem by coding substrates, products and reactions into molecular signatures. Metabolic maps are represented using hypergraphs and the complexity is controlled by varying the specificity of the molecular signature. Furthermore, our method enables candidate pathways to be ranked to determine which ones are best to engineer. The proposed ranking function can integrate data from different sources such as host compatibility for inserted genes, the estimation of steady-state fluxes from the genome-wide reconstruction of the organism's metabolism, or the estimation of metabolite toxicity from experimental assays. We use several machine-learning tools in order to estimate enzyme activity and reaction efficiency at each step of the identified pathways. Examples of production in bacteria and yeast for two antibiotics and for one antitumor agent, as well as for several essential metabolites are outlined.

Conclusions

We present here a unified framework that integrates diverse techniques involved in the design of heterologous biosynthetic pathways through a retrosynthetic approach in the reaction signature space. Our engineering methodology enables the flexible design of industrial microorganisms for the efficient on-demand production of chemical compounds with therapeutic applications.     

基于基因表达变异性的通路富集方法研究

当前的通路富集方法主要是基于基因的表达差异,很少有方法从通路变异性(方差)角度对其富集分析．我们注意到用合适的统计量描述通路的变异性时,在疾病表型下一些通路的变异性有明显的上升或者下降．因此本研究假设：通路变异性程度在不同表型中存在差异．本文设计了14种描述通路变异性的统计量与检验方法,检测不同表型下变异性有差异的通路即富集通路,并将富集结果与文献检索结果进行比较,同时,分析不同芯片预处理方法对数据和结果的影响．研究结果表明：5种预处理方法中,多阵列对数健壮算法(RMA)是数据预处理的最优方法;不同表型下通路的变异性程度存在差异;根据文献检索的通路结果,14种基于变异性的通路富集方法中,以通路中各基因欧氏距离的方差做统计量进行permutation检验(方法11)能有效识别显著通路,其富集结果优于基因集富集分析(GSEA)．综上所述,基于通路变异性的通路富集策略具有可行性,不仅对通路富集分析有一定的理论指导意义,而且为人类疾病研究提供新的视角．     

Identification of single- and multiple-class specific signature genes from gene expression profiles by group marker index

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases.