A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes
Publication Date
October 10, 2013
Journal
PLOS Computational Biology
Authors
Daniel B. Larremore, Aaron Clauset & Caroline O. Buckee
Volume
9
Issue
10
Pages
e1003268
DOI
http://doi.org/10.1371/journal.pcbi.1003268
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1003268
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/24130474
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3794903
Europe PMC
http://europepmc.org/abstract/MED/24130474
Web of Science
000330355300030
Scopus
84887267765
Mendeley
http://www.mendeley.com/research/network-approach-analyzing-highly-recombinant-malaria-parasite-genes
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Mendeley | Further Information

{"title"=>"A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes", "type"=>"journal", "authors"=>[{"first_name"=>"Daniel B.", "last_name"=>"Larremore", "scopus_author_id"=>"36925731100"}, {"first_name"=>"Aaron", "last_name"=>"Clauset", "scopus_author_id"=>"8298347800"}, {"first_name"=>"Caroline O.", "last_name"=>"Buckee", "scopus_author_id"=>"20733408400"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-84887267765", "sgr"=>"84887267765", "issn"=>"1553734X", "doi"=>"10.1371/journal.pcbi.1003268", "pmid"=>"24130474", "arxiv"=>"1308.5254", "pui"=>"370217854", "isbn"=>"1553-7358 (Electronic)\\n1553-734X (Linking)"}, "id"=>"d2fc04c7-79d5-379e-82af-f923c265cade", "abstract"=>"The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-{\\alpha} (DBL{\\alpha}) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBL{\\alpha} classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences.", "link"=>"http://www.mendeley.com/research/network-approach-analyzing-highly-recombinant-malaria-parasite-genes", "reader_count"=>72, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Student > Doctoral Student"=>4, "Researcher"=>19, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>4, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Student > Doctoral Student"=>4, "Researcher"=>19, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>4, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Agricultural and Biological Sciences"=>37, "Business, Management and Accounting"=>1, "Computer Science"=>8, "Economics, Econometrics and Finance"=>1, "Engineering"=>2, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>5, "Mathematics"=>6, "Medicine and Dentistry"=>6, "Physics and Astronomy"=>2, "Psychology"=>1, "Social Sciences"=>1, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>6}, "Environmental Science"=>{"Environmental Science"=>1}, "Engineering"=>{"Engineering"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>37}, "Computer Science"=>{"Computer Science"=>8}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}}, "reader_count_by_country"=>{"United States"=>6, "United Kingdom"=>2, "Kenya"=>1, "Switzerland"=>1, "India"=>1, "Spain"=>1, "Belgium"=>1, "Panama"=>1, "Denmark"=>2, "Brazil"=>1, "Israel"=>1, "Australia"=>1, "France"=>1, "Germany"=>1}, "group_count"=>2}

CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1231651"], "description"=>"<p>Summary statistics for DBLα HVR networks.</p>", "links"=>[], "tags"=>["hvr"], "article_id"=>819145, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Ecology"], "users"=>["Daniel B. Larremore", "Aaron Clauset", "Caroline O. Buckee"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003268.t002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_statistics_for_DBL_945_HVR_networks_/819145", "title"=>"Summary statistics for DBLα HVR networks.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-10-10 06:24:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1231650"], "description"=>"<p>Summary statistics for DBLα HVRs.</p>", "links"=>[], "tags"=>[], "article_id"=>819144, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Ecology"], "users"=>["Daniel B. Larremore", "Aaron Clauset", "Caroline O. Buckee"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003268.t001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_statistics_for_DBL_945_HVRs_/819144", "title"=>"Summary statistics for DBLα HVRs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-10-10 06:24:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1231648"], "description"=>"<p>Each HVR of DBLα is highly recombinant, yet recombinational constraints, as revealed by network community structure, vary by HVR. We find that some are strongly related (HVRs 1 and 6) while others are independent (HVRs 5 and 9). This suggests that those HVRs that diversify and recombine under constraints that are independent of each other may have the primary purpose of immune evasion by diversity generation (red stripes). Those HVRs whose recombinational constraints are related to each other may recombine only under functional constraints (blue). Independent or related constraints may also be found in other domains throughout the larger PfEMP1 molecule.</p>", "links"=>[], "tags"=>["hvr"], "article_id"=>819142, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Ecology"], "users"=>["Daniel B. Larremore", "Aaron Clauset", "Caroline O. Buckee"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003268.g008", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_of_HVR_hypothesis_/819142", "title"=>"Schematic of HVR hypothesis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-10 06:24:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1231645"], "description"=>"<p>(A) Variation of information (VI) measures the distance between two different partitions on the same set of nodes (two different sets of community assignments). For a given HVR we compute pairwise VI distances to recovered communities of other HVRs (colored symbols), cys/PoLV (+), UPS classifications (x), and to those in a null model (grey open symbols). Uncertainty in VI measurements is discussed in detail in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003268#pcbi.1003268.s010\" target=\"_blank\">Text S4</a>. (B) HVRs 1 and 6 are close to each other, indicating that their communities strongly match each other. Histograms show the distributions of VI distances for one partition and 10,000 randomizations of the other. Top, the measured distance between HVRs 5 and 9 falls within the distribution of randomizations, indicated by the arrow. Bottom, the measured distance between HVRs 1 and 6 falls well outside the distribution of randomizations, indicated by the left arrow. For contrast, the silhouette of the top histogram is reproduced. (C) Most HVRs show moderate but positive levels of assortativity <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003268#pcbi.1003268-Newman2\" target=\"_blank\">[45]</a>, the tendency for nodes with similar labels or values to be connected. Assortativity varies by HVR and by label (symbols). For all cases except DBLα classification (DBLα0, DBLα1, DBLα2), assortativity was significantly higher than expected by chance. Solid lines with whiskers show mean assortativity ± one standard deviation for 10,000 randomizations of labels. Z-scores may be found in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003268#pcbi.1003268.s005\" target=\"_blank\">Figure S5A</a>.</p>", "links"=>[], "tags"=>["structures"], "article_id"=>819139, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Ecology"], "users"=>["Daniel B. Larremore", "Aaron Clauset", "Caroline O. Buckee"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003268.g006", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Community_structures_vary_across_HVRs_/819139", "title"=>"Community structures vary across HVRs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-10 06:24:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1231659", "https://ndownloader.figshare.com/files/1231660", "https://ndownloader.figshare.com/files/1231661", "https://ndownloader.figshare.com/files/1231662", "https://ndownloader.figshare.com/files/1231663", "https://ndownloader.figshare.com/files/1231664", "https://ndownloader.figshare.com/files/1231665", "https://ndownloader.figshare.com/files/1231666", "https://ndownloader.figshare.com/files/1231667", "https://ndownloader.figshare.com/files/1231668"], "description"=>"<div><p>The <i>var</i> genes of the human malaria parasite <i>Plasmodium falciparum</i> present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. <i>Var</i> gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of <i>var</i> genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα) domain of <i>var</i> genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in <i>var</i> genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences.</p></div>", "links"=>[], "tags"=>["analyzing", "recombinant", "malaria", "parasite"], "article_id"=>819153, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Ecology"], "users"=>["Daniel B. Larremore", "Aaron Clauset", "Caroline O. Buckee"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003268.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003268.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003268.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003268.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003268.s005", "https://dx.doi.org/10.1371/journal.pcbi.1003268.s006", "https://dx.doi.org/10.1371/journal.pcbi.1003268.s007", "https://dx.doi.org/10.1371/journal.pcbi.1003268.s008", "https://dx.doi.org/10.1371/journal.pcbi.1003268.s009", "https://dx.doi.org/10.1371/journal.pcbi.1003268.s010"], "stats"=>{"downloads"=>33, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Network_Approach_to_Analyzing_Highly_Recombinant_Malaria_Parasite_Genes_/819153", "title"=>"A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-10-10 06:24:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1231644"], "description"=>"<p>HVR 6 is shown in two forms, colored according to the best partition into three communities. (A) A force-directed visualization of the network with the identified communities labeled by color. (B) Adjacency matrix in which the ordering of rows and columns has been permuted to match the inferred communities. Diagonal colored blocks are within-community links, and off-diagonal blocks are between-community links. The matrix is shown symmetrically to aid the eye.</p>", "links"=>[], "tags"=>["modeling", "identifies"], "article_id"=>819138, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Ecology"], "users"=>["Daniel B. Larremore", "Aaron Clauset", "Caroline O. Buckee"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003268.g005", "stats"=>{"downloads"=>2, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Stochastic_block_modeling_identifies_network_communities_/819138", "title"=>"Stochastic block modeling identifies network communities.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-10 06:24:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1231639"], "description"=>"<p>Choosing a noise threshold requires balance between two competing requirements for correctly identifying network communities: minimize the number of incorrectly placed links, yet retain as many correctly placed links as possible to satisfy the network connectivity requirements of the community detection method. (A) The probability of two sequences sharing a block while not actually being related decreases as block length increases, modeled in S1. Each HVR's length and composition are taken into account separately (colored lines). Choosing a tolerance for false positives (grey line) specifies a minimum retained block length; since blocks are of integer length, the next largest integer is the minimum retained block length (squares). Curves for HVRs 3 and 5 are plotted, for which we would select thresholds of five and seven, respectively. Curves for all nine HVRs are shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003268#pcbi.1003268.s006\" target=\"_blank\">Fig. S6A</a>. (B) For a choice of threshold 6 for HVR 1, the histogram of HVR 1 block lengths shows that a vast majority of the blocks are below the threshold (white bars) and that the retained blocks are widely distributed (green bars, inset). (C) Networks are fragmented as the block length threshold is increased and more links are discarded. The relationship between the size of the largest component and block length threshold is shown for the least-connected (HVR3) and most-connected (HVR7) networks. Some thresholds allow too many false positives, as described in panel A (grey lines), yet other thresholds fragment the network too much for reliable community detection (shaded region). Those points that are plotted in color above the shaded region are both sufficiently error-free and well connected that we may reliably infer network communities. For HVRs 2–4, even the most permissive false positive threshold results in a network that is too fragmented for community detection (red circle). Curves for all nine HVRs are shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003268#pcbi.1003268.s006\" target=\"_blank\">Figure S6B</a>.</p>", "links"=>[], "tags"=>[], "article_id"=>819133, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Ecology"], "users"=>["Daniel B. Larremore", "Aaron Clauset", "Caroline O. Buckee"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003268.g002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Choice_of_link_noise_threshold_/819133", "title"=>"Choice of link noise threshold.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-10 06:24:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1231638"], "description"=>"<p>(A) Starting from a multiple alignment of the domain of interest, four steps are taken to identify highly variable regions (HVRs), as described in the text. We show the HVR identification process for the 307 DBLα sequences from <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003268#pcbi.1003268-Rask1\" target=\"_blank\">[18]</a>. (B) For each HVR, a network is made in which each sequence is a node, and a link connecting two nodes corresponds to a shared sequence block. (C) The set of pairwise connections above the noise threshold defines a complex network representing recent recombination events. (D) Communities are inferred directly from this network using a probabilistic generative model. Steps B,C, and D are repeated for each of the HVRs identified in step A.</p>", "links"=>[], "tags"=>["overview"], "article_id"=>819132, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Ecology"], "users"=>["Daniel B. Larremore", "Aaron Clauset", "Caroline O. Buckee"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003268.g001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pictorial_overview_of_sequence_analysis_method_/819132", "title"=>"Pictorial overview of sequence analysis method.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-10 06:24:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1231647"], "description"=>"<p>Bars show the UPS group (A) and cys/PoLV group (B) composition of each of the three recovered communities. HVRs 1 and 6–8 show one community each in which a vast majority of UPS A sequences are found. HVR 6 shows one community in which a vast majority of group 1, 2, and 3 sequences are found, which are all characterized by having only two cysteines in HVRs 5 and 6.</p>", "links"=>[], "tags"=>["communities"], "article_id"=>819141, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Ecology"], "users"=>["Daniel B. Larremore", "Aaron Clauset", "Caroline O. Buckee"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003268.g007", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correspondence_of_network_communities_to_existing_classifications_/819141", "title"=>"Correspondence of network communities to existing classifications.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-10 06:24:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1231643"], "description"=>"<p>DBLα HVR networks show a wide range of characteristics, including community size, number of components, and number of links. Nodes in each HVR are colored according to the best three-community partition identified by the inference algorithm (see text). (Identical networks colored by upstream promotor are found in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003268#pcbi.1003268.s002\" target=\"_blank\">Figure S2</a>.) HVRs 2–4 are sufficiently fragmented that block model inference cannot be trusted. An interactive version of this figure with varying communities and node labels may be found at <a href=\"http://danlarremore.com/var\" target=\"_blank\">http://danlarremore.com/var</a>. Networks are displayed using a force-directed algorithm that allows a system of repelling point-charges (nodes) and linear springs (links) to relax to a low-energy two dimensional configuration, allowing for visualization of network communities.</p>", "links"=>[], "tags"=>["hvr", "networks", "colored", "inferred"], "article_id"=>819137, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Ecology"], "users"=>["Daniel B. Larremore", "Aaron Clauset", "Caroline O. Buckee"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003268.g004", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Nine_HVR_networks_colored_by_inferred_communities_/819137", "title"=>"Nine HVR networks colored by inferred communities.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-10 06:24:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1231641"], "description"=>"<p>We validate our method's ability to detect constraints on recombination by testing it on synthetic data with known structure. Sequences were generated at random and divided into three communities, after which 1000 recombination events were simulated, described fully in S2. For each recombination event, the two sequences were forced to be chosen from the same community with probability <i>p</i> or were selected uniformly at random with probability (1<i>-p</i>). As the probability that recombination is constrained to within-community is varied from no constraint (<i>p</i> = 0) to strict constraint (<i>p</i> = 1), the ability of our method to correctly classify sequences into one of three communities increases from very poor to perfect. The connected line shows the mean of 25 replicates, with whiskers indicating ± one standard deviation. Two example networks are shown for <i>p</i> = 0.1 and <i>p</i> = 0.9. The dashed line indicates the accuracy of guessing communities uniformly at random, which is slightly larger than 1/3 as explained in S2. Networks are displayed using a force-directed algorithm that allows a system of repelling point-charges (nodes) and linear springs (links) to relax to a low-energy two dimensional configuration, allowing for visualization of network communities.</p>", "links"=>[], "tags"=>["synthetic"], "article_id"=>819135, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Ecology"], "users"=>["Daniel B. Larremore", "Aaron Clauset", "Caroline O. Buckee"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003268.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_on_synthetic_data_/819135", "title"=>"Performance on synthetic data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-10 06:24:22"}

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