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PLOS ONE  2012 

Similar Pathogen Targets in Arabidopsis thaliana and Homo sapiens Protein Networks

DOI: 10.1371/journal.pone.0045154

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We study the behavior of pathogens on host protein networks for humans and Arabidopsis - noting striking similarities. Specifically, we preform -shell decomposition analysis on these networks - which groups the proteins into various “shells” based on network structure. We observe that shells with a higher average degree are more highly targeted (with a power-law relationship) and that highly targeted nodes lie in shells closer to the inner-core of the network. Additionally, we also note that the inner core of the network is significantly under-targeted. We show that these core proteins may have a role in intra-cellular communication and hypothesize that they are less attacked to ensure survival of the host. This may explain why certain high-degree proteins are not significantly attacked.


[1]  Mukhtar MS, Carvunis AR, Dreze M, Epple P, Steinbrenner J, et al. (2011) Independently Evolved Virulence Effectors Converge onto Hubs in a Plant Immune System Network. Science 333: 596–601.
[2]  Consortium AIM (2011) Evidence for Network Evolution in an Arabidopsis Interactome Map. Science 333: 601–607.
[3]  Navratil V, de Chassey B, Combe CRR, Lotteau V (2011) When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases. BMC systems biology 5: 13+.
[4]  Seidman S (1983) Network structure and minimum degree. Social Networks 5: 269–287.
[5]  Carmi S, Havlin S, Kirkpatrick S, Shavitt Y, Shir E (2007) From the Cover: A model of Internet topology using k-shell decomposition. PNAS 104: 11150–11154.
[6]  Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, et al. (2010) Identification of inuential spreaders in complex networks. Nat Phys 6: 888–893.
[7]  Reshef D, Reshef Y, Finucane H, Grossman S, McVean G, et al. (2011) Detecting novel associations in large data sets. Science 334.
[8]  Jensen K, Little TJ, Skorping A, Ebert D (2006) Empirical support for optimal virulence in a castrating parasite. PLoS Biology 4.
[9]  Berenos C, Schmid-Hempel P, Wegner K (2011) Experimental coevolution leads to a decrease in parasite-induced host mortality. Journal of Evolutionary Biology 24.
[10]  Smith J (2007) A gene's-eye view of symbiont transmission. American Naturalist 170: 542–550.
[11]  Kover P, Clay K (1998) Trade-off between virulence and vertical transmission and the maintenance of a virulent plant pathogen. American Naturalist 152: 165.
[12]  Best A, White A, Boots M (2009) The implications of coevolutionary dynamics to host-parasite interactions. American Naturalist 173: 779.
[13]  Stephenson K, Zelen M (1989) Rethinking centrality: Methods and examples. Social Networks 11: 1–37.
[14]  Noh JD, Rieger H (2004) Random walks on complex networks. Physical Review Letters 92: 118701.
[15]  Brandes U, Fleischer D (2005) Centrality measures based on current ow. In: STACS. pp. 533–544.
[16]  Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40: 35–41.
[17]  Brandes U (2001) A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25.


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