Quantitative trait locus analysis for obesity reveals multiple networks of interacting loci

Obesity is a highly heritable and genetically complex trait with hundreds of potential loci identified. An intercross of 513 F₂ progeny between the SM/J × NZB/BINJ inbred mouse strains was generated to identify quantitative trait loci (QTL) that are involved in the weight of four fat pads: mesenteric, inguinal, gonadal, and retroperitoneal. Sex and lean body weight were treated as covariates in the analysis of these fat pads. This analysis uncoupled genetic effects related to overall body size from those influencing the adiposity of a mouse. We identified multiple significant QTL. QTL alleles associated with increased lean body weight and individual fat pad weights are contributed by the NZB background. Adiposity loci are distinct from these body size QTLs and high-adiposity alleles are contributed by the SM background. An extended network of epistatic QTL is also observed. A QTL on Chr 19 is the center of a network of eight interacting QTL, Chr 4 is the center of six, and Chr 17 the center of four interacting QTL. We conclude that interacting networks of multiple genes characterize the regulation of fat pad depots and body weight. Haplotype patterns and a literature-driven approach were used to generate hypotheses regarding the identity of the genes and pathways underlying the QTL.     

Functional genomics reveals diverse cellular processes that modulate tumor cell response to oxaliplatin

Oxaliplatin is widely used to treat colorectal cancer, as both adjuvant therapy for resected disease and palliative treatment of metastatic disease. However, a significant number of patients experience serious side effects, including prolonged neurotoxicity, from oxaliplatin treatment creating an urgent need for biomarkers of oxaliplatin response or resistance to direct therapy to those most likely to benefit. As a first step to improve selection of patients for oxaliplatin-based chemotherapy, we have conducted an in vitro cell-based small interfering RNA (siRNA) screen of 500 genes aimed at identifying genes whose loss of expression alters tumor cell response to oxaliplatin. The siRNA screen identified twenty-seven genes, which when silenced, significantly altered colon tumor cell line sensitivity to oxaliplatin. Silencing of a group of putative resistance genes increased the extent of oxaliplatin-mediated DNA damage and inhibited cell-cycle progression in oxaliplatin-treated cells. The activity of several signaling nodes, including AKT1 and MEK1, was also altered. We used cDNA transfection to overexpress two genes (LTBR and TMEM30A) that were identified in the siRNA screen as mediators of oxaliplatin sensitivity. In both instances, overexpression conferred resistance to oxaliplatin. In summary, this study identified numerous putative predictive biomarkers of response to oxaliplatin that should be studied further in patient specimens for potential clinical application. Diverse gene networks seem to influence tumor survival in response to DNA damage by oxaliplatin. Finally, those genes whose loss of expression (or function) is related to oxaliplatin sensitivity may be promising therapeutic targets to increase patient response to oxaliplatin.     

Integrated analysis of multiple data sources reveals modular structure of biological networks

It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks.     

Mitochondrial VDAC and hexokinase together modulate plant programmed cell death

The voltage-dependent anion channel (VDAC) and mitochondrially located hexokinase have been implicated both in pathways leading to cell death on the one hand, and immortalization in tumor formation on the other. While both proteins have also been implicated in death processes in plants, their interaction has not been explored. We have examined cell death following heterologous expression of a rice VDAC in the tobacco cell line BY2 and in leaves of tobacco plants and show that it is ameliorated by co-expression of hexokinase. Hexokinase also abrogates death induced by H₂O₂. We conclude that the ratio of expression of the two proteins and their interaction play a major role in modulating death pathways in plants.     

Genomic,transcriptomic, and metabolomic analysis of Oldenlandia corymbosa reveals the biosynthesis and mode of action of anti-cancer metabolites

Plants accumulate a vast array of secondary metabolites,which constitute a natural resource for pharmaceuticals.Oldenlandia corymbosa belongs to the Rubiaceae family,and has been used in traditional medicine to treat different diseases,including cancer.However,the active metabolites of the plant,their biosynthetic pathway and mode of action in cancer are unknown.To fill these gaps,we exposed this plant to eight different stress conditions and combined different omics data capturing gene expressi...     

Comparative transcriptomic analysis reveals that multiple hormone signal transduction and carbohydrate metabolic pathways are affected by Bacillus cereus in Nicotiana tabacum

Bacillus cereus is thought to be a beneficial bacterium for plants in several aspects, such as promoting plant growth and inducing plant disease resistance. However, there is no detailed report on the effect of Bacillus cereus acting on Nicotiana tabacum. In the present study, RNA-based sequencing (RNA-seq) was used to identify the molecular mechanisms of the interaction between B. cereus CGMCC 5977 and N. tabacum. A total of 7345 and 5604 differentially expressed genes (DEGs) were identified from leaves inoculated with Bacillus cereus at 6 and 24 hpi, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that the most DEGs could be significantly enriched in hormone signal transduction, the MAPK signaling pathway, photosynthesis, oxidative stress, and amino sugar, and nucleotide sugar metabolism. Furthermore, glycolysis/gluconeogenesis was severely affected by inoculation with Bacillus cereus. In the hormone signal pathway, multiple DEGs were involved in plant defense-related major hormones, including activation of jasmonic acid (JA), salicylic acid (SA), and ethylene (Eth). Further analyses showed that other hormone-related genes involved in abscisic acid (ABA), gibberellin (GA), auxin (AUX), and cytokinin (CK) also showed changes. Notably, a large number of genes associated with glycolysis/gluconeogenesis, catabolism of starch and oxidative stress were induced. In addition, the majority of DEGs related to nucleic acid sugar metabolism were also significantly upregulated. Biochemical assays showed that the starch content of B. cereus-treated leaves was reduced to 2.51 mg/g and 2.38 mg/g at 6 and 24 hpi, respectively, while that of the control sample was 5.42 mg/g. Overall, our results demonstrated that multiple hormone signal transduction and carbohydrate metabolic pathways are involved in the interaction of tobacco and B. cereus.     

Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining

Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC–MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling.     

An integrated transcriptomic and meta-analysis of hepatoma cells reveals factors that influence susceptibility to HCV infection

Hepatitis C virus (HCV) is a global problem. To better understand HCV infection researchers employ in vitro HCV cell-culture (HCVcc) systems that use Huh-7 derived hepatoma cells that are particularly permissive to HCV infection. A variety of hyper-permissive cells have been subcloned for this purpose. In addition, subclones of Huh-7 which have evolved resistance to HCV are available. However, the mechanisms of susceptibility or resistance to infection among these cells have not been fully determined. In order to elucidate mechanisms by which hepatoma cells are susceptible or resistant to HCV infection we performed genome-wide expression analyses of six Huh-7 derived cell cultures that have different levels of permissiveness to infection. A great number of genes, representing a wide spectrum of functions are differentially expressed between cells. To focus our investigation, we identify host proteins from HCV replicase complexes, perform gene expression analysis of three HCV infected cells and conduct a detailed analysis of differentially expressed host factors by integrating a variety of data sources. Our results demonstrate that changes relating to susceptibility to HCV infection in hepatoma cells are linked to the innate immune response, secreted signal peptides and host factors that have a role in virus entry and replication. This work identifies both known and novel host factors that may influence HCV infection. Our findings build upon current knowledge of the complex interplay between HCV and the host cell, which could aid development of new antiviral strategies.