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台灣鳥類誌 讀者回饋

學術演講

2017-09-01
9/1 15:45 施廷翰博士演講_ What’s Galling On? -Progress of the Studies on Insect Galls in Taiwan-

2017-09-29
9/29 10:30 Dr. Karine Olu-Le Roy 演講_ Baseline Studies of Cold-Seep Ecosystems Sustained by Gas Hydrates and other Methane Stocks

中心消息

徵人

2017-08-31
江殷儒助研究員研究室誠徵研究助理1名

2017-08-31
洪志銘助研究員研究室誠徵研究助理1名

2017-08-31
蔡怡陞助研究員研究室誠徵研究助理1名

2017-09-15
Tenure-Track Position in Microbial Diversity - Effective 03 June 2017

2017-09-15
Tenure-Track Position in Evolutionary Biology - Effective 03 June 2017

活動公告

2017-08-28
國立中山大學海洋科學學院公開徵求院長啟事

2017-09-08
9/8 106年度「醫學研究倫理教育訓練課程III」

2017-09-15
9/15 9:30 農委會林聰賢主委_ 農產品安全的展望

2017-09-25
林秋榮教授植物科學創新研究獎自即日起至9月25日止受理申請

2017-10-01
「2018年中央研究院年輕學者研究著作獎」自8/16起至10/1止受理線上申請

王達益 副主任/
副研究員

回研究人員

Dr. Daryi Wang

[ email ]

tel: +886-2-2787-2271

Deputy Director/ Associate Research Fellow
Ph.D.-National Taiwan Ocean University, 1999
Research Fields
Gut microbiota, Microbial interaction, Analyzing tools for metagenome,
Major Research Achievements (2013-2017)
  • MetaMIS: a metagenomic microbial interaction simulator based on a microbial community profiles
  • The complex and dynamics of microbial community are major factors in ecology system. With NGS technique, metagenomics data provides a new source to explore microbial interactions. Lotka-Volterra models have been widely used to infer animal interaction of dynamic systems and recently been applied to analyze metagenomic data. In this paper, we presented the first Lotka-Volterra model based tool, Metagenomic Microbial Interaction Simulator (MetaMIS), to analyze time series data of microbial community profiles. MetaMIS firstly infers the underlying microbial interactions from operational taxonomic units (OTU) abundance tables and interprets interaction models by the use of Lotka-Volterra model. We also embedded Bray-Curtis dissimilarity method in MetaMIS to evaluate the resemblance of biological reality. MetaMIS was designed to tolerate a high level of missing data, it can estimate the interaction information without the influence from rare microbes. For each interaction model, MetaMIS systematically examines interaction patterns (such as mutualism (+/+), competition (-/-), parasitism or predation (+/-), commensalism (+/0), amensalism (-/0), and no effect (0/0)) and refines the biotic role inside microbes. The output of MetaMIS can be exported as Gephi or Cytoscape format for advanced analysis. In a test case, we collected the human female gut microbiota which contained 124 time points of 88 OTUs at the family level. Through the test, MetaMIS generated 55 interactions in around 5 minutes on a standard desktop computer, the results also revealed that rare species may play important roles in the microbial dynamic system. MetaMIS provides an efficiency and user-friendly platform and may reveal new insights from metagenomics data.
  • Construction of microbial interaction network using rule-based approach
  • 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. 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 characterize the regulatory relationship composed of microbial pairs with low correlation coefficient in microbial networks. Finally, our results show that Bifidobacterium, Streptococcus, Clostridium XI, and Bacteroides are essential for causing abundance changes of Veillonella in gut microbiome. Furthermore, we found new possible microbial interactions, including the competitive relationship between Veillonella and Bacteroides, and the cooperative relationship between Veillonella and Clostridium XI. In conclusion, 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.
  • Effects of host phylogeny and habitats on gut microbiomes of freshwater shrimp
  • The gut microbial community is one of the richest and most complex ecosystems on earth, and the intestinal microbes play an important role in host development and health. Next generation sequencing approaches, which rapidly produce millions of short reads that enable the investigation on a culture independent basis, are now popular for exploring microbial community. Currently, the gut microbiome in fresh water shrimp is unexplored. To explore gut microbiomes of the oriental river prawn (Macrobrachium nipponense) and investigate the effects of host genetics and habitats on the microbial composition, 454 pyrosequencing based on the 16S rRNA gene were performed. We collected six groups of samples, including M. nipponense shrimp from two populations, rivers and lakes, and one sister species (M. asperulum) as an out group. We found that Proteobacteria is the major phylum in oriental river prawn, followed by Firmicutes and Actinobacteria. Compositional analysis showed microbial divergence between the two shrimp species is higher than that between the two populations of one shrimp species collected from river and lake. Hierarchical clustering also showed that host genetics had a greater impact on the divergence of gut microbiome than host habitats. This finding was also congruent with the functional prediction from the metagenomic data implying that the two shrimp species still shared the same type of biological functions, reflecting a similar metabolic profile in their gut environments. In conclusion, this study provides the first investigation of the gut microbiome of fresh water shrimp, and supports the hypothesis of host species-specific signatures of bacterial community composition.
Research Interests
  • Intestinal microbiome. Tools for metagenomic analysis.
    Ongoing projects
    • Exploring the microbial interaction network in the tree frog intestinal tract
    • There has been a long interaction history between gut microbes and their hosts. Indeed, the microbes contribute important functions to their hosts such as fermenting unused energy substrates, training the immune system, preventing growth of pathogenic bacteria, regulating the development of the gut, producing vitamins for the host. Note that the number of microbes living in the hosts largely exceeds total number of host cell, and the number of genes the microbes encode largely exceeds that of host genes. One of the most concerned topics is the association between the gut microbes and the host diseases. A few lines of evidence have shown that microorganisms contribute to human diseases, such as obesity, vaginosis, and inflammatory bowel disease. Moreover, recent studies showed that a certain types of cancer and coronary heart disease are also associated with gut microbes. For clarifying the association of disease and the microbes, international projects, such as HMP (human metagenome project) supported by NIH, MetaHit (human intestinal tract) project supported by the European Commission, have been generating related big data, and promote a series of downstream bioiformatic studies.
    • One of the major challenge in studying microbe-host interaction is monitoring the microbial dynamic change. As most studies only investigated the microbiomes between health individuals and patients, it is difficult to record the realtime change of microbial community. We proposed to make the use of frog hibernation when the microbes are in low activity and low nutrient supply, from which we may have the chance to detect the initiation of microbial interaction. The second challenge in this project is inferring the microbial interaction network from metagenomic data, which was notoriously biased. We proposed to utilize generalized Lotka–Volterra equations to simulate metagenome time series data which may improve the prediction of microbial interaction. Then, we will test the data on Bayesian network inference algorithms and predict its temporal dynamics.
      Publications (2009-2017)
      1. Weng, F.C.H., Shaw, G.T.W., Weng, C.Y., Yang, Y.J., Wang, D.*, 2017, “Inferring Microbial Interactions in the Gut of the Hong Kong Whipping Frog (Polypedates megacephalus) and a Validation Using Probiotics”, FRONTIERS IN MICROBIOLOGY, 8, 525-526. (SCI) (IF: 4.165; SCI ranking: 18.7%)
      2. Chen CY, Chen PC, Weng FC, Shaw GT, Wang D, 2017, “Habitat and indigenous gut microbes contribute to the plasticity of gut microbiome in oriental river prawn during rapid environmental change.”, PloS one, 12(7), e0181427. (SCI) (IF: 3.057; SCI ranking: 17.5%)
      3. Shaw GT, Liu AC, Weng CY, Chou CY, Wang D, 2017, “Inferring microbial interactions in thermophilic and mesophilic anaerobic digestion of hog waste.”, PloS one, 12(7), e0181395. (SCI) (IF: 3.057; SCI ranking: 17.5%)
      4. Weng Francis Cheng-Hsuan, Yang Yi-Ju, Wang Daryi*, 2016, “Functional analysis for gut microbes of the brown tree frog (Polypedates megacephalus) in artificial hibernation”, BMC Genomics, 17(S13), 31-42. (SCI) (IF: 3.867; SCI ranking: 25.9%,19.9%)
      5. Shaw Grace Tzun-Wen, Pao Yueh-Yang, Wang Daryi*, 2016, “MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles”, BMC Bioinformatics, 17(1), 2-6. (SCI) (IF: 2.435; SCI ranking: 39.8%,50.6%,17.9%)
      6. Tzeng, T.D., Pao, Y.Y., Chen, P.C., Weng, F.C.H., Jean, W.D., Wang, D.*, 2015, “Effects of host phylogeny and habitats on gut microbiomes of oriental river prawn (Macrobrachium nipponense).”, PLoS One, 10(7), e0132860. (SCI) (IF: 3.057; SCI ranking: 17.5%)
      7. Tsai, K.N., Lin, S.H., Liu, W.C., Wang, D.*, 2015, “Inferring microbial interaction network from microbiomic data using RMN algorithm”, BMC System biology, 9, 54. (SCI) (IF: 2.208; SCI ranking: 19.6%)
      8. Chen PC, Shih CH, Chu TJ, Wang D, Lee YC, Tzeng TD, 2015, “Population Structure and Historical Demography of the Oriental River Prawn (Macrobrachium nipponense) in Taiwan.”, PloS one, 10(12), e0145927. (SCI) (IF: 3.057; SCI ranking: 17.5%)
      9. Swamy, K.B.S., Lin, C.H., Yen, M.R., Wang, C.Y., Wang, D.*, 2014, “Examining the condition-specific antisense transcription in S. cerevisiae and S. paradoxus”, BMC GENOMICS, 15, 521. (SCI) (IF: 3.867; SCI ranking: 25.9%,19.9%)
      10. Lin, CH., Tsai, ZT., Wang D.*, 2013, “Role of antisense RNAs in evolution of yeast regulatory complexity”, Genomics, 102(5-6), 484-490. (SCI) (IF: 2.386; SCI ranking: 41.6%,53.6%)
      11. Tsai ZT, Tsai HK*, Cheng JH, Lin CH, Tsai YF, Wang D*, 2012, “Evolution of cis-regulatory elements in yeast de novo and duplicated new genes”, BMC GENOMICS, 13, 717. (SCI) (IF: 3.867; SCI ranking: 25.9%,19.9%)
      12. Chiang, S.F., Swamy, K.B.S., Ysai, Z.T.Y., Hsy, L.T.W., Lu, H.H.S., Wang, D.*, Tsai, H.K.*, 2012, “Analysis of the association between transcription factor binding site variants and distinct accompanying regulatory motifs in yeast”, GENE, 491(2), 237-245. (SCI) (IF: 2.319; SCI ranking: 55.4%)
      13. Tsai, K.N., Wang, D.*, 2012, “Identification of Activated Cryptic 5’ Splice Sites using Structure Profiles and Odds Measure”, NUCLEIC ACIDS RESEARCH, 40(10), e73. (SCI) (IF: 9.202; SCI ranking: 6.2%)
      14. Chu, T.J., Wang, D., Haung, H.L., Lin, F.J. Tzeng, T.D.*, 2012, “Population Structure and Historical Demography of the Whiskered Velvet Shrimp (Metapenaeopsis barbata) off China and Taiwan Inferred from the Mitochondrial Control Region”, ZOOLOGICAL STUDIES, 51(1), 99-107. (SCI) (IF: 0.885; SCI ranking: 56.5%)
      15. Su, C.H. Wang, T.Y., Hsu, M.T., Weng, F.C.H., Kao, C.Y., Wang, D.*, Tsai, H.K.*, 2012, “The impact of normalization and phylogenetic information on estimating the distance for metagenomes.”, IEEE-ACM Transactions on Computational Biology and Bioinformatics, 9(2), 619-628. (SCI) (IF: 1.609; SCI ranking: 46.2%,72.7%,21.1%,31.7%)
      16. Chien, Y.I., Nonaka, I., Wang, D., 2011, “Autosomal dominant late-onset quadriceps myopathy. Internal medicine”, INTERNAL MEDICINE, 50, 1175-1181. (SCI) (IF: 0.832; SCI ranking: 66.5%)
      17. Swamy, K.B.S, Chu, W.Y., Wang ,C.Y., Tsai, H.K.*, Wang, D.*, 2011, “Evidence of association between Nucleosome Occupancy and the Evolution of Transcription Factor Binding Sites in Yeast”, BMC EVOLUTIONARY BIOLOGY, 11, 150. (SCI) (IF: 3.406; SCI ranking: 32.6%,31.3%)
      18. Chu, T.J., Wang, D., Haung, H.L., Lin, F.J., Tzeng, T.D.*, 2011, “Genetic variations and expansion of Whiskered velvet shrimp (Metapenaeopsis barbata) off China and Taiwan inferred from intron sequence”, BIOCHEMICAL SYSTEMATICS AND ECOLOGY, 39, 520–525. (SCI) (IF: 0.988; SCI ranking: 89.1%,89.3%,77.3%)
      19. Su, C.H., Hsu, M.T., Wang, T.Y., Chiang, S.F., Cheng, J.H., Weng, F.C., Kao, C.Y., Wang, D.*, Tsai, H.K.*, 2011, “MetaABC – an integrated Metagenomics platform for data Adjustment, Binning, and Clustering.”, BIOINFORMATICS, 27(16), 2298-2299. (SCI) (IF: 5.766; SCI ranking: 9.1%,5.4%,9.3%)
      20. Weng, F.C.H., Su, C.H., Hsu, M.T., Wang, T.Y., Tsai, H.K.*, Wang, D.*, 2010, “Reanalyze unassigned reads in Sanger based metagenomic data using conserved gene adjacency”, BMC Bioinformatics, 11(1), 565. (SCI) (IF: 2.435; SCI ranking: 39.8%,50.6%,17.9%)
      21. Sung, H.M., T.Y. Wang, D. Wang, Y.S. Huang, J.P. Wu, H.K. Tsai, J.Tseng, C,J, Huang, Y.C. Lee, P. Yang, J.H.T. Chang, C.Y. Cho, L.C. Weng, T.C. Lee, T.H. Chang, W.H. Li, M.C. Shih, 2009, “Roles of Trans and Cis Variation in Yeast Intra-species Evolution of Gene Expression”, Mol. Biol. Evol, 26(11), 2533-2538. (SCI) (IF: 13.649; SCI ranking: 1.7%,2.4%,4.3%)
      22. Wang, F.Y., H. Y. Yan, J.S.C. Chen, T.Y. Wang, D. Wang, 2009, “Adaptation of visual spectra and opsin genes in seabreams”, Vision Research, 49(14), 1860-1868. (SCI) (IF: 1.776; SCI ranking: 74.2%,48.2%)
      23. Tsai, H.K., P.Y., Huang, C.U. Kao, D. Wang, 2009, “Co-expression of Neighboring Genes in the genome of Zebrafish”, Int. J. Mol. Sci., 10: 3658-3670. (SCI) (IF: 3.257; SCI ranking: 38.1%,31.9%)
      24. Wang, D.*, T.Y. Wang, L.C. Weng, Y. Emori, C.S. Tzeng, W.H. Li, 2009, “Evolution of Olfactory Receptor Genes in East Asian Loaches”, Zool Stud, 48, 223-237.. (SCI) (IF: 0.885; SCI ranking: 56.5%)