ALBERT

Description: Background: Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, protein/gene interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. Results: atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied protein/gene list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. Conclusion: atBioNet is a free web-based network analysis tool that provides a systematic insight into protein/gene interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm

Description: Rapid and specific detection of avian influenza virus (AIV) is urgently needed due to the concerns over the potential outbreaks of highly pathogenic H5N1 influenza in animals and humans. Aptamers are artificial oligonucleic acids that can bind specific target molecules, and show comparable affinity for target viruses and better thermal stability than monoclonal antibodies. The objective of this research was to use a DNA-aptamer as the specific recognition element in a portable Surface Plasmon Resonance (SPR) biosensor for rapid detection of AIV H5N1 in poultry swab samples. A SPR biosensor was fabricated using selected aptamers that were biotinylated and then immobilized on the sensor gold surface coated with streptavidin via streptavidin-biotin binding. The immobilized aptamers captured AIV H5N1 in a sample solution, which caused an increase in the refraction index (RI). After optimizing the streptavidin and aptamer parameters, the results showed that the RI value was linearly related (R2 = 0.99) to the concentration of AIV in the range of 0.128 to 1.28 HAU. Negligible signal ( 〈 4% of H5N1) was observed from six non-target AIV subtypes. The AIV H5N1 in poultry swab samples with concentrations of 0.128 to 12.8 HAU could be detected using this aptasensor in 1.5 h.

Description: Background: Rhodosporidium toruloides is a beta-carotenoid accumulating, oleaginous yeast that has great biotechnological potential. The lack of reliable and efficient genetic manipulation tools have been a major hurdle blocking its adoption as a biotechnology platform. Results: We report for the first time the development of a highly efficient targeted gene deletion method in R. toruloides ATCC 10657 via Agrobacterium tumefaciens-mediated transformation. To further improve targeting frequency, the KU70 and KU80 homologs in R. toruloides were isolated and characterized in detail. A KU70-deficient mutant ([increment]ku70e) generated with the hygromycin selection cassette removed by the Cre-loxP recombination system showed a dramatically improved targeted gene deletion frequency, with over 90% of the transformants being true knockouts when homology sequence length of at least 1 kb was used. Successful gene targeting could be made with homologous flanking sequences as short as 100 bp in the [increment]ku70e strain. KU70 deficiency did not perturb cell growth although an elevated sensitivity to DNA mutagenic agents was observed. Compared to the other well-known oleaginous yeast, Yarrowia lipolytica, R. toruloides KU70/KU80 genes contain much higher density of introns and are the most GC-rich KU70/KU80 genes reported. Conclusions: The KU70-deficient mutant generated herein was effective in improving gene deletion frequency and allowed shorter homology sequences to be used for gene targeting. It retained the key oleaginous and fast growing features of R. toruloides. The strain should facilitate both fundamental and applied studies in this important yeast, with the approaches taken here likely to be applicable in other species in subphylum Pucciniomycotina.

Description: Background: Enzymes of the cellulose synthase (CesA) family and CesA-like (Csl) families are responsible for the synthesis of celluloses and hemicelluloses, and thus are of great interest to bioenergy research. We studied the occurrences and phylogenies of CesA/Csl families in diverse plants and algae by comprehensive data mining of 82 genomes and transcriptomes. Results: We found that 1) charophytic green algae (CGA) have orthologous genes in CesA, CslC and CslD families; 2) liverwort genes are found in the CesA, CslA, CslC and CslD families; 3) The fern Pteridium aquilinum not only has orthologs in these conserved families but also in the CslB, CslH and CslE families; 4) basal angiosperms, e.g. Aristolochia fimbriata, have orthologs in these families too; 5) gymnosperms have genes forming clusters ancestral to CslB/H and to CslE/J/G respectively; 6) CslG is found in switchgrass and basal angiosperms; 7) CslJ is widely present in dicots and monocots; 8) CesA subfamilies have already diversified in ferns. Conclusions: We speculate that: (i) ferns and horsetails might both have CslH enzymes, responsible for the synthesis of mixed-linkage glucans and (ii) CslD and similar genes might be responsible for the synthesis of mannans in CGA. Our findings led to a more detailed model of cell wall evolution and suggested that gene loss played an important role in the evolution of Csl families. We also demonstrated the usefulness of transcriptome data in the study of plant cell wall evolution and diversity.

Description: With the development of high-performance aircraft, precise air data are necessary to complete challenging tasks such as flight maneuvering with large angles of attack and high speed. As a result, the flush air data sensing system (FADS) was developed to satisfy the stricter control demands. In this paper, comparative stuides on the solving model and algorithm for FADS are conducted. First, the basic principles of FADS are given to elucidate the nonlinear relations between the inputs and the outputs. Then, several different solving models and algorithms of FADS are provided to compute the air data, including the angle of attck, sideslip angle, dynamic pressure and static pressure. Afterwards, the evaluation criteria of the resulting models and algorithms are discussed to satisfy the real design demands. Futhermore, a simulation using these algorithms is performed to identify the properites of the distinct models and algorithms such as the measuring precision and real-time features. The advantages of these models and algorithms corresponding to the different flight conditions are also analyzed, furthermore, some suggestions on their engineering applications are proposed to help future research.

Description: Background: The zygomycete fungi like Rhizomucor miehei have been extensively exploited for the production of various enzymes. As a thermophilic fungus, R. miehei is capable of growing at temperatures that approach the upper limits for all eukaryotes. To date, over hundreds of fungal genomes are publicly available. However, Zygomycetes have been rarely investigated both genetically and genomically. Results: Here, we report the genome of R. miehei CAU432 to explore the thermostable enzymatic repertoire of this fungus. The assembled genome size is 27.6-million-base (Mb) with 10,345 predicted protein-coding genes. Even being thermophilic, the G + C contents of fungal whole genome (43.8%) and coding genes (47.4%) are less than 50%. Phylogenetically, R. miehei is more closerly related to Phycomyces blakesleeanus than to Mucor circinelloides and Rhizopus oryzae. The genome of R. miehei harbors a large number of genes encoding secreted proteases, which is consistent with the characteristics of R. miehei being a rich producer of proteases. The transcriptome profile of R. miehei showed that the genes responsible for degrading starch, glucan, protein and lipid were highly expressed. Conclusions: The genome information of R. miehei will facilitate future studies to better understand the mechanisms of fungal thermophilic adaptation and the exploring of the potential of R. miehei in industrial-scale production of thermostable enzymes. Based on the existence of a large repertoire of amylolytic, proteolytic and lipolytic genes in the genome, R. miehei has potential in the production of a variety of such enzymes.

Description: Background: Identification of the novel genes relevant to plant cell-wall (PCW) synthesis represents a highly important and challenging problem. Although substantial efforts have been invested into studying this problem, the vast majority of the PCW related genes remain unknown. Results: Here we present a computational study focused on identification of the novel PCW genes in Arabidopsis based on the co-expression analyses of transcriptomic data collected under 351 conditions, using a bi-clustering technique. Our analysis identified 217 highly co-expressed gene clusters (modules) under some experimental conditions, each containing at least one gene annotated as PCW related according to the Purdue Cell Wall Gene Families database. These co-expression modules cover 349 known/annotated PCW genes and 2,438 new candidates. For each candidate gene, we annotated the specific PCW synthesis stages in which it is involved and predicted the detailed function. In addition, for the co-expressed genes in each module, we predicted and analyzed their cis regulatory motifs in the promoters using our motif discovery pipeline, providing strong evidence that the genes in each co-expression module are transcriptionally co-regulated. From the all co-expression modules, we infer that 108 modules are related to four major PCW synthesis components, using three complementary methods. Conclusions: We believe our approach and data presented here will be useful for further identification and characterization of PCW genes. All the predicted PCW genes, co-expression modules, motifs and their annotations are available at a web-based database: http://csbl.bmb.uga.edu/publications/materials/shanwang/CWRPdb/index.html.