Annotation and characterization of Cd-responsive metal transporter genes in rapeseed (<Emphasis Type="Italic">Brassica napus</Emphasis>)

In higher plants, heavy metal transporters are responsible for metal uptake, translocation and homeostasis. These metals include essential metals such as zinc (Zn) or manganese (Mn) and non-essential metals like cadmium (Cd) or lead (Pb). Although a few heavy metal transporters have been well identified in model plants (e.g. Arabidopsis and rice), little is known about their functionality in rapeseed (Brassica napus). B. napus is an important oil crop ranking the third largest sources of vegetable oil over the world. Importantly, B. napus has long been considered as a desirable candidate for phytoremediation owning to its massive dry weight productivity and moderate to high Cd accumulation. In this study, 270 metal transporter genes (MTGs) from B. napus genome were identified and annotated using bioinformatics and high-throughput sequencing. Most of the MTGs (74.8%, 202/270) were validated by RNA-sequencing (RNA-seq) the seedling libraries. Based on the sequence identity, nine superfamilies including YSL, OPT, NRAMP, COPT, ZIP, CDF/MTP, HMA, MRP and PDR have been classified. RNA-sequencing profiled 202 non-redundant MTGs from B. napus seedlings, of which, 108 MTGs were differentially expressed and 62 genes were significantly induced under Cd stress. These differentially expressed genes (DEGs) are dispersed in the rapeseed genome. Some of the genes were well confirmed by qRT-PCR. Analysis of the genomic distribution of MTGs on B. napus chromosomes revealed that their evolutional expansion was probably through localized allele duplications.     

固醇调节元件结合蛋白1及其靶基因网络

固醇调节元件结合蛋白1(Sterol regulatory element-binding protein 1, SREBP-1)是重要的核转录因子之一, 能调控内源性胆固醇、脂肪酸、甘油三酯和磷脂合成所需酶的表达, 以维持血脂动态平衡。研究表明, SREBP-1及其靶基因网络的异常可引起胰岛素抵抗、Ⅱ型糖尿病、心功能紊乱、血管并发症和肝脂肪变等一系列代谢性疾病。近年高通量组学技术的发展极大扩展了对SREBP-1靶基因及其转录调控模式的了解。文章对SREBP-1蛋白结构、活化过程、DNA结合位点及其调控的靶基因等方面的研究进展进行了综述, 并着重介绍了基于组学数据的转录调控网络的构建, 这将有助于更好的认识SREBP-1在脂类代谢中的作用, 为深入探讨脂质代谢性疾病的治疗提供新线索。     

Identification of differentially expressed genes in individual bovine preimplantation embryos produced by nuclear transfer: improper reprogramming of genes required for development

Using an interwoven-loop experimental design in conjunction with highly conservative linear mixed model methodology using estimated variance components, 18 genes differentially expressed between nuclear transfer (NT)- and in vitro fertilization (IVF)-produced embryos were identified. The set is comprised of three intermediate-filament protein genes (cytokeratin 8, cytokeratin 19, and vimentin), three metabolic genes (phosphoribosyl pyrophosphate synthetase 1, mitochondrial acetoacetyl-coenzyme A thiolase, and alpha-glucosidase), two lysosomal-related genes (prosaposin and lysosomal-associated membrane protein 2), and a gene associated with stress responses (heat shock protein 27) along with major histocompatibility complex class I, nidogen 2, a putative transport protein, heterogeneous nuclear ribonuclear protein K, mitochondrial 16S rRNA, and ES1 (a zebrafish orthologue of unknown function). The three remaining genes are novel. To our knowledge, this is the first report comparing individual embryos produced by NT and IVF using cDNA microarray technology for any species, and it uses a rigorous experimental design that emphasizes statistical significance to identify differentially expressed genes between NT and IVF embryos in cattle.     

Inferring gene-phenotype associations via global protein complex network propagation

Background

Phenotypically similar diseases have been found to be caused by functionally related genes, suggesting a modular organization of the genetic landscape of human diseases that mirrors the modularity observed in biological interaction networks. Protein complexes, as molecular machines that integrate multiple gene products to perform biological functions, express the underlying modular organization of protein-protein interaction networks. As such, protein complexes can be useful for interrogating the networks of phenome and interactome to elucidate gene-phenotype associations of diseases.

Methodology/Principal Findings

We proposed a technique called RWPCN (Random Walker on Protein Complex Network) for predicting and prioritizing disease genes. The basis of RWPCN is a protein complex network constructed using existing human protein complexes and protein interaction network. To prioritize candidate disease genes for the query disease phenotypes, we compute the associations between the protein complexes and the query phenotypes in their respective protein complex and phenotype networks. We tested RWPCN on predicting gene-phenotype associations using leave-one-out cross-validation; our method was observed to outperform existing approaches. We also applied RWPCN to predict novel disease genes for two representative diseases, namely, Breast Cancer and Diabetes.

Conclusions/Significance

Guilt-by-association prediction and prioritization of disease genes can be enhanced by fully exploiting the underlying modular organizations of both the disease phenome and the protein interactome. Our RWPCN uses a novel protein complex network as a basis for interrogating the human phenome-interactome network. As the protein complex network can capture the underlying modularity in the biological interaction networks better than simple protein interaction networks, RWPCN was found to be able to detect and prioritize disease genes better than traditional approaches that used only protein-phenotype associations.     

Time-dependent network analysis reveals molecular targets underlying the development of diet-induced obesity and non-alcoholic steatohepatitis

Prolonged high-fat diet leads to the development of obesity and multiple comorbidities including non-alcoholic steatohepatitis (NASH), but the underlying molecular basis is not fully understood. We combine molecular networks and time course gene expression profiles to reveal the dynamic changes in molecular networks underlying diet-induced obesity and NASH. We also identify hub genes associated with the development of NASH. Core diet-induced obesity networks were constructed using Ingenuity pathway analysis (IPA) based on 332 high-fat diet responsive genes identified in liver by time course microarray analysis (8 time points over 24 weeks) of high-fat diet-fed mice compared to normal diet-fed mice. IPA identified five core diet-induced obesity networks with time-dependent gene expression changes in liver. These networks were associated with cell-to-cell signaling and interaction (Network 1), lipid metabolism (Network 2), hepatic system disease (Network 3 and 5), and inflammatory response (Network 4). When we merged these core diet-induced obesity networks, Tlr2, Cd14, and Ccnd1 emerged as hub genes associated with both liver steatosis and inflammation and were altered in a time-dependent manner. Further, protein–protein interaction network analysis revealed Tlr2, Cd14, and Ccnd1 were interrelated through the ErbB/insulin signaling pathway. Dynamic changes occur in molecular networks underlying diet-induced obesity. Tlr2, Cd14, and Ccnd1 appear to be hub genes integrating molecular interactions associated with the development of NASH. Therapeutics targeting hub genes and core diet-induced obesity networks may help ameliorate diet-induced obesity and NASH.     

Species status and phylogeography of two closely related Coptolabrus species (Coleoptera: Carabidae) in South Korea inferred from mitochondrial and nuclear gene sequences

We investigated the species status and intraspecific phylogeography in South Korea of two ground beetle species, Coptolabrus jankowskii and Coptolabrus smaragdinus (Coleoptera: Carabidae), using statistical parsimony networks and nested clade analyses based on sequences from the mitochondrial cytochrome oxidase subunit I (COI) and nuclear phosphoenolpyruvate carboxykinase (PepCK) and wingless (Wg) genes. Although traditional parsimony tree construction generally failed to resolve interspecific relationships and construct biologically meaningful genealogies, analysis using statistical parsimony networks yielded statistically significant inter- and intraspecific genealogical structures. We found that although these two species represent a notable case of trans-species polymorphisms in both mitochondrial and nuclear gene sequences, their status as separate species was evidenced by the nonrandom association between species and nested clades at various nesting levels. The exceptional occurrence of shared identical or very similar COI sequences was considered to be the result of introgressive hybridization. In addition, range expansion and fragmentation events across the Korean Peninsula and adjacent islands were inferred from nested clade phylogeographical analyses. The COI gene revealed the geographical divergence of major eastern and western clades and historical biogeographical events within each major clade, whereas the nuclear PepCK gene, which did not reveal corresponding east-west clades, indicated past fragmentation and range expansion across wide areas that may have been the result of older biogeographical events. Thus, phylogeographical inferences drawn from analyses of mitochondrial and nuclear genes can reveal different and potentially complementary information about phylogeographical processes.     

A gradient of nuclear localization of the dorsal protein determines dorsoventral pattern in the Drosophila embryo

The dorsoventral axis of the Drosophila embryo is determined by a morphogen gradient established by the action of 12 maternal-effect genes: the dorsal group genes and cactus. One of the dorsal group genes, dorsal (dl), encodes the putative morphogen. Although no overall asymmetry in the distribution of dorsal protein is observed, a gradient of nuclear concentration of dl protein is established during cleavage stages, with a maximum at the ventral side of the egg. At the dorsal side of the egg, the protein remains in the cytoplasm. Nuclear localization of the dl protein, and hence gradient formation, is blocked in dorsalizing alleles of all of the other dorsal group genes, while in ventralizing mutants nuclear localization extends to the dorsal side of the egg. A correlation between dl protein distribution and embryonic pattern in mutant embryos indicates that the nuclear concentration of the dl protein determines pattern along the dorsoventral axis.     

Global Genetic Determinants of Mitochondrial DNA Copy Number

Many human diseases including development of cancer is associated with depletion of mitochondrial DNA (mtDNA) content. These diseases are collectively described as mitochondrial DNA depletion syndrome (MDS). High similarity between yeast and human mitochondria allows genomic study of the budding yeast to be used to identify human disease genes. In this study, we systematically screened the pre-existing respiratory-deficient Saccharomyces cerevisiae yeast strains using fluorescent microscopy and identified 102 nuclear genes whose deletions result in a complete mtDNA loss, of which 52 are not reported previously. Strikingly, these genes mainly encode protein products involved in mitochondrial protein biosynthesis process (54.9%). The rest of these genes either encode protein products associated with nucleic acid metabolism (14.7%), oxidative phosphorylation (3.9%), or other protein products (13.7%) responsible for bud-site selection, mitochondrial intermembrane space protein import, assembly of cytochrome-c oxidase, vacuolar protein sorting, protein-nucleus import, calcium-mediated signaling, heme biosynthesis and iron homeostasis. Thirteen (12.7%) of the genes encode proteins of unknown function. We identified human orthologs of these genes, conducted the interaction between the gene products and linked them to human mitochondrial disorders and other pathologies. In addition, we screened for genes whose defects affect the nuclear genome integrity. Our data provide a systematic view of the nuclear genes involved in maintenance of mitochondrial DNA. Together, our studies i) provide a global view of the genes regulating mtDNA content; ii) provide compelling new evidence toward understanding novel mechanism involved in mitochondrial genome maintenance and iii) provide useful clues in understanding human diseases in which mitochondrial defect and in particular depletion of mitochondrial genome plays a critical role.     

Causal inference in biomolecular pathways using a Bayesian network approach and an Implicit method

We introduce here the concept of Implicit networks which provide, like Bayesian networks, a graphical modelling framework that encodes the joint probability distribution for a set of random variables within a directed acyclic graph. We show that Implicit networks, when used in conjunction with appropriate statistical techniques, are very attractive for their ability to understand and analyze biological data. Particularly, we consider here the use of Implicit networks for causal inference in biomolecular pathways. In such pathways, an Implicit network encodes dependencies among variables (proteins, genes), can be trained to learn causal relationships (regulation, interaction) between them and then used to predict the biological response given the status of some key proteins or genes in the network. We show that Implicit networks offer efficient methodologies for learning from observations without prior knowledge and thus provide a good alternative to classical inference in Bayesian networks when priors are missing. We illustrate our approach by an application to simulated data for a simplified signal transduction pathway of the epidermal growth factor receptor (EGFR) protein.