ALBERT

Description: Background: Liver is an important metabolic organ that plays a critical role in lipid synthesis, degradation, and transport; however, the molecular regulatory mechanisms of lipid metabolism remain unclear in chicken. In this study, RNA-Seq technology was used to investigate differences in expression profiles of hepatic lipid metabolism-related genes and associated pathways between juvenile and laying hens. The study aimed to broaden the understanding of liver lipid metabolism in chicken, and thereby to help improve laying performance in the poultry industry. Results: RNA-Seq analysis was carried out on total RNA harvested from the liver of juvenile (n = 3) and laying (n = 3) hens. Compared with juvenile hens, 2567 differentially expressed genes (1082 up-regulated and 1485 down-regulated) with P ≤ 0.05 were obtained in laying hens, and 960 of these genes were significantly differentially expressed (SDE) at a false discovery rate (FDR) of ≤0.05 and fold-change ≥2 or ≤0.5. In addition, most of the 198 SDE novel genes (91 up-regulated and 107 down-regulated) were discovered highly expressed, and 332 SDE isoforms were identified. Gene ontology (GO) enrichment and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis showed that the SDE genes were most enrichment in steroid biosynthesis, PPAR signaling pathway, biosynthesis of unsaturated fatty acids, glycerophospholipid metabolism, three amino acid pathways, and pyruvate metabolism (P ≤ 0.05). The top significantly enriched GO terms among the SDE genes included lipid biosynthesis, cholesterol and sterol metabolic, and oxidation reduction, indicating that principal lipogenesis occurred in the liver of laying hens. Conclusions: This study suggests that the majority of changes at the transcriptome level in laying hen liver were closely related to fat metabolism. Some of the SDE uncharacterized novel genes and alternative splicing isoforms that were detected might also take part in lipid metabolism, although this needs further investigation. This study provides valuable information about the expression profiles of mRNAs from chicken liver, and in-depth functional investigations of these mRNAs could provide new insights into the molecular networks of lipid metabolism in chicken liver.

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: 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: 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: In the near future, the bio-nutrient-removal (BNR) system is no longer confined to meet the nitrogen and phosphorus removal requirement, but operated for sequestering and recovering carbon, nitrogen and phosphorus from wastewaters and generate more high-value-added by-products. Polyhydroxyalkanoate (PHA) is a metabolite of microorganisms under unfavourable growth conditions for their own energy storage and utilization. More and more researches have gradually uncovered the impacting factors on the microbial synthesis of PHA in the BNR and none-BNR system. This mini-review summarizes the impacting factors of carbon source types and dosage, nitrogen concentration and phosphorus recovery and temperature variation on the enrichment of PHA by microbial populations. Furthermore, the potential genomic reasons for promoting PHA synthesis are introduced. It provides a new perspective to stimulate the PHA synthesis by the functional microorganisms and its potential benefit for bio-nutrient...

Description: Nitrification is defined as conversion of the ammonium(NH 4 + -N) to nitrate and it is generally divided into two categories: autotrophic nitrification and heterotrophic nitrification. With the metagenomes retrieved from the Nitrospira highly enriched biosamples, both the ammonium oxidation gens (full set of AMO and hydroxylamine dehydrogenase (HAO) genes) and nitrite oxidoreductase (NXR) coding genes necessary for nitrite oxidation were found in a completely nitrifying bacterium from the genus Nitrospira . Various species of Nitrospira (containing phylogenetically distinct ammonia monooxygenase and hydroxylamine dehydrogenase genes) had been found in many engineered systems and were proved to be comammox bacterium. Some anammox bacteria were found to use mono or dimethylamine and even methanol as electron donors, but the enzymes the employed in the special metabolism were not known. Many heterotrophic nitrifying bacteria have been isolate...

Description: As the carrying network of electric energy, the power grid is the foundation of the national industry. For the implementation of the “Internet plus” strategy, there is an urgent need for a new network communication technology to improve the traditional power communication network, and to make up for the original defects. This paper mainly analyzes the new demands of new intelligent power business of communication network. Then, the limitations of existing power communication network and power distribution equipment are explored. At last, It put forward the basic scheme of using SDN technology to improve the existing power communication network, which provides a train of thought for the design of intelligent power distribution system based on SDN Technology.

Description: The extraction precision of digital network in watershed depends on drainage area threshold. Due to the multi-scale effect of DEM, different scales of DEM would provide different terrain information. Taking Qingjiang watershed as the subject and the DEM under 30m*30m and 90m*90m resolution as the foundation, this research extracts the digital river network and studies the optimal drainage area threshold from DEM with different resolutions through ATRIC algorithm. It’s concluded that under the same resolution, the river network becomes sparser with the increase of threshold; the optimal drainage area threshold under higher resolution is larger than that under lower one. Compared with DEM data for 90m resolution, the river network extracted from DEM for 30m resolution enjoys a better goodness of fit with the real river network, with an overlapping rate of 68.8%. ATRIC algorithm is more suitable for DEM data under high resolution.

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.