Inferring microbial interaction network from microbiome data using RMN algorithm.

BACKGROUND: Microbial interactions are ubiquitous in nature. Recently, many similarity-based approaches have been developed to study the interaction in microbial ecosystems. These approaches can only explain the non-directional interactions yet a more complete view on how microbes regulate each other remains elusive. In addition, the strength of microbial interactions is difficult to be quantified by only using correlation analysis. RESULTS: In this study, a rule-based microbial network (RMN) algorithm, which integrates regulatory OTU-triplet model with parametric weighting function, is being developed to construct microbial regulatory networks. The RMN algorithm not only can extrapolate the cooperative and competitive relationships between microbes, but also can infer the direction of such interactions. In addition, RMN algorithm can theoretically characterize the regulatory relationship composed of microbial pairs with low correlation coefficient in microbial networks. Our results suggested that Bifidobacterium, Streptococcus, Clostridium XI, and Bacteroides are essential for causing abundance changes of Veillonella in gut microbiome. Furthermore, we inferred some possible microbial interactions, including the competitive relationship between Veillonella and Bacteroides, and the cooperative relationship between Veillonella and Clostridium XI. CONCLUSIONS: The RMN algorithm provides the reconstruction of gut microbe networks, and can shed light on the dynamical interactions of microbes in the infant intestinal tract.

Crossing borders for science.

Exchanging ideas with like-minded, enthusiastic people interested in the same topic is crucial for the advancement of a scientist's career. Several Regional Student Groups (RSGs) of the International Society for Computational Biology (ISCB) Student Council have cooperated in the last six years to organize scientific workshops and conferences. With motivated students, it is possible to create a memorable event for fellow scientists; in doing so, the organizers gain valuable experiences. While collaborating across borders and time zones can be difficult, feedback from event organizers was always positive. When limited resources are juxtaposed with great ideas and a network of contacts, the outcome is always an amazing experience, despite organizers being separated geographically across different countries.

Metabolomics of ginger essential oil against alcoholic fatty liver in mice.

Fatty liver is significantly associated with hepatic cirrhosis and liver cancer. Excessive alcohol consumption causes alcoholic fatty liver disease (AFLD). Ginger has been reported to exhibit antioxidant potential and hepatoprotective activity. In the present study, a mouse model for AFLD was developed by employing male C57BL/6 mice that were fed an alcohol-containing liquid diet (Lieber-DeCarli diet) ad libitum. In the treatment groups, ginger essential oil (GEO) and citral were orally administered every day for 4 weeks. Serum biochemical analysis, antioxidant enzyme activity analysis, and histopathological evaluation revealed that GEO and citral exhibited hepatoprotective activity against AFLD. Metabolites in serum samples were profiled by high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS). Metabolomic data indicated the amounts of metabolites such as d-glucurono-6,3-lactone, glycerol-3-phosphate, pyruvic acid, lithocholic acid, 2-pyrocatechuic acid, and prostaglandin E1 were increased after alcohol administration, but the levels were recovered in treatment groups. The analysis indicated that ginger possesses hepatoprotective properties against AFLD. Furthermore, these metabolites can serve as early noninvasive candidate biomarkers in the clinical application of AFLD for health management.

Reconstruction of phyletic trees by global alignment of multiple metabolic networks.

BACKGROUND: In the last decade, a considerable amount of research has been devoted to investigating the phylogenetic properties of organisms from a systems-level perspective. Most studies have focused on the classification of organisms based on structural comparison and local alignment of metabolic pathways. In contrast, global alignment of multiple metabolic networks complements sequence-based phylogenetic analyses and provides more comprehensive information. RESULTS: We explored the phylogenetic relationships between microorganisms through global alignment of multiple metabolic networks. The proposed approach integrates sequence homology data with topological information of metabolic networks. In general, compared to recent studies, the resulting trees reflect the living style of organisms as well as classical taxa. Moreover, for phylogenetically closely related organisms, the classification results are consistent with specific metabolic characteristics, such as the light-harvesting systems, fermentation types, and sources of electrons in photosynthesis. CONCLUSIONS: We demonstrate the usefulness of global alignment of multiple metabolic networks to infer phylogenetic relationships between species. In addition, our exhaustive analysis of microbial metabolic pathways reveals differences in metabolic features between phylogenetically closely related organisms. With the ongoing increase in the number of genomic sequences and metabolic annotations, the proposed approach will help identify phenotypic variations that may not be apparent based solely on sequence-based classification.

Autophagy therapeutic potential of garlic in human cancer therapy.

Cancer is one of the deadliest diseases against humans. To tackle this menace, humans have developed several high-technology therapies, such as chemotherapy, tomotherapy, targeted therapy, and antibody therapy. However, all these therapies have their own adverse side effects. Therefore, recent years have seen increased attention being given to the natural food for complementary therapy, which have less side effects. Garlic (Dà Suàn; Allium sativum), is one of most powerful food used in many of the civilizations for both culinary and medicinal purpose. In general, these foods induce cancer cell death by apoptosis, autophagy, or necrosis. Studies have discussed how natural food factors regulate cell survival or death by autophagy in cancer cells. From many literature reviews, garlic could not only induce apoptosis but also autophagy in cancer cells. Autophagy, which is called type-II programmed cell death, provides new strategy in cancer therapy. In conclusion, we wish that garlic could be the pioneer food of complementary therapy in clinical cancer treatment and increase the life quality of cancer patients.

Feature Identification of Compensatory Gene Pairs without Sequence Homology in Yeast.

Genetic robustness refers to a compensatory mechanism for buffering deleterious mutations or environmental variations. Gene duplication has been shown to provide such functional backups. However, the overall contribution of duplication-based buffering for genetic robustness is rather small. In this study, we investigated whether transcriptional compensation also exists among genes that share similar functions without sequence homology. A set of nonhomologous synthetic-lethal gene pairs was assessed by using a coexpression network, protein-protein interactions, and other types of genetic interactions in yeast. Our results are notably different from those of previous studies on buffering paralogs. The low expression similarity and the conditional coexpression alone do not play roles in identifying the functionally compensatory genes. Additional properties such as synthetic-lethal interaction, the ratio of shared common interacting partners, and the degree of coregulation were, at least in part, necessary to extract functional compensatory genes. Our network-based approach is applicable to select several well-documented cases of compensatory gene pairs and a set of new pairs. The results suggest that transcriptional reprogramming plays a limited role in functional compensation among nonhomologous genes. Our study aids in understanding the mechanism and features of functional compensation more in detail.

Phylogenomic and domain analysis of iterative polyketide synthases in Aspergillus species.

Aspergillus species are industrially and agriculturally important as fermentors and as producers of various secondary metabolites. Among them, fungal polyketides such as lovastatin and melanin are considered a gold mine for bioactive compounds. We used a phylogenomic approach to investigate the distribution of iterative polyketide synthases (PKS) in eight sequenced Aspergilli and classified over 250 fungal genes. Their genealogy by the conserved ketosynthase (KS) domain revealed three large groups of nonreducing PKS, one group inside bacterial PKS, and more than 9 small groups of reducing PKS. Polyphyly of nonribosomal peptide synthase (NRPS)-PKS genes raised questions regarding the recruitment of the elegant conjugation machinery. High rates of gene duplication and divergence were frequent. All data are accessible through our web database at http://metabolomics.jp/wiki/Category:PK.