Examinando por Materia "genomics"

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    Ceratocystis cacaofunesta genome analysis reveals a large expansion of extracellular phosphatidylinositol-specific phospholipase-C genes (PI-PLC)
    (BioMed Central Ltd., 2018-01-17) Molano, E.P.L.; Cabrera, O.G.; Jose, J.; do Nascimento, L.C.; Carazzolle, M.F.; Teixeira, P.J.P.L.; Alvarez, J.C.; Tiburcio, R.A.; Tokimatu Filho, P.M.; de Lima, G.M.A.; Guido, R.V.C.; Corrêa, T.L.R.; Leme, A.F.P.; Mieczkowski, P.; Pereira, G.A.G.; Universidad EAFIT. Departamento de Ciencias; Biodiversidad, Evolución y Conservación
    Background: The Ceratocystis genus harbors a large number of phytopathogenic fungi that cause xylem parenchyma degradation and vascular destruction on a broad range of economically important plants. Ceratocystis cacaofunesta is a necrotrophic fungus responsible for lethal wilt disease in cacao. The aim of this work is to analyze the genome of C. cacaofunesta through a comparative approach with genomes of other Sordariomycetes in order to better understand the molecular basis of pathogenicity in the Ceratocystis genus. Results: We present an analysis of the C. cacaofunesta genome focusing on secreted proteins that might constitute pathogenicity factors. Comparative genome analyses among five Ceratocystidaceae species and 23 other Sordariomycetes fungi showed a strong reduction in gene content of the Ceratocystis genus. However, some gene families displayed a remarkable expansion, in particular, the Phosphatidylinositol specific phospholipases-C (PI-PLC) family. Also, evolutionary rate calculations suggest that the evolution process of this family was guided by positive selection. Interestingly, among the 82 PI-PLCs genes identified in the C. cacaofunesta genome, 70 genes encoding extracellular PI-PLCs are grouped in eight small scaffolds surrounded by transposon fragments and scars that could be involved in the rapid evolution of the PI-PLC family. Experimental secretome using LC-MS/MS validated 24% (86 proteins) of the total predicted secretome (342 proteins), including four PI-PLCs and other important pathogenicity factors. Conclusion: Analysis of the Ceratocystis cacaofunesta genome provides evidence that PI-PLCs may play a role in pathogenicity. Subsequent functional studies will be aimed at evaluating this hypothesis. The observed genetic arsenals, together with the analysis of the PI-PLC family shown in this work, reveal significant differences in the Ceratocystis genome compared to the classical vascular fungi, Verticillium and Fusarium. Altogether, our analyses provide new insights into the evolution and the molecular basis of plant pathogenicity. © 2018 The Author(s).
  • No hay miniatura disponible
    Ítem
    Ceratocystis cacaofunesta genome analysis reveals a large expansion of extracellular phosphatidylinositol-specific phospholipase-C genes (PI-PLC)
    (BioMed Central Ltd., 2018-01-17) Molano, E.P.L.; Cabrera, O.G.; Jose, J.; do Nascimento, L.C.; Carazzolle, M.F.; Teixeira, P.J.P.L.; Alvarez, J.C.; Tiburcio, R.A.; Tokimatu Filho, P.M.; de Lima, G.M.A.; Guido, R.V.C.; Corrêa, T.L.R.; Leme, A.F.P.; Mieczkowski, P.; Pereira, G.A.G.; Universidad EAFIT. Departamento de Ciencias; Ciencias Biológicas y Bioprocesos (CIBIOP)
    Background: The Ceratocystis genus harbors a large number of phytopathogenic fungi that cause xylem parenchyma degradation and vascular destruction on a broad range of economically important plants. Ceratocystis cacaofunesta is a necrotrophic fungus responsible for lethal wilt disease in cacao. The aim of this work is to analyze the genome of C. cacaofunesta through a comparative approach with genomes of other Sordariomycetes in order to better understand the molecular basis of pathogenicity in the Ceratocystis genus. Results: We present an analysis of the C. cacaofunesta genome focusing on secreted proteins that might constitute pathogenicity factors. Comparative genome analyses among five Ceratocystidaceae species and 23 other Sordariomycetes fungi showed a strong reduction in gene content of the Ceratocystis genus. However, some gene families displayed a remarkable expansion, in particular, the Phosphatidylinositol specific phospholipases-C (PI-PLC) family. Also, evolutionary rate calculations suggest that the evolution process of this family was guided by positive selection. Interestingly, among the 82 PI-PLCs genes identified in the C. cacaofunesta genome, 70 genes encoding extracellular PI-PLCs are grouped in eight small scaffolds surrounded by transposon fragments and scars that could be involved in the rapid evolution of the PI-PLC family. Experimental secretome using LC-MS/MS validated 24% (86 proteins) of the total predicted secretome (342 proteins), including four PI-PLCs and other important pathogenicity factors. Conclusion: Analysis of the Ceratocystis cacaofunesta genome provides evidence that PI-PLCs may play a role in pathogenicity. Subsequent functional studies will be aimed at evaluating this hypothesis. The observed genetic arsenals, together with the analysis of the PI-PLC family shown in this work, reveal significant differences in the Ceratocystis genome compared to the classical vascular fungi, Verticillium and Fusarium. Altogether, our analyses provide new insights into the evolution and the molecular basis of plant pathogenicity. © 2018 The Author(s).
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    Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic Neighbor Embeddings
    (Institute of Electrical and Electronics Engineers Inc., 2018-01-01) Ariza-Jimenez L.; Quintero O.L.; Pinel N.; Universidad EAFIT. Departamento de Ciencias; Ciencias Biológicas y Bioprocesos (CIBIOP)
    Shotgun metagenomic studies attempt to reconstruct population genome sequences from complex microbial communities. In some traditional genome demarcation approaches, high-dimensional sequence data are embedded into two-dimensional spaces and subsequently binned into candidate genomic populations. One such approach uses a combination of the Barnes-Hut approximation and the t -Stochastic Neighbor Embedding (BH-SNE) algorithm for dimensionality reduction of DNA sequence data pentamer profiles; and demarcation of groups based on Gaussian mixture models within humanimposed boundaries. We found that genome demarcation from three-dimensional BH-SNE embeddings consistently results in more accurate binnings than 2-D embeddings. We further addressed the lack of a priori population number information by developing an unsupervised binning approach based on the Subtractive and Fuzzy c-means (FCM) clustering algorithms combined with internal clustering validity indices. Lastly, we addressed the subject of shared membership of individual data objects in a mixed community by assigning a degree of membership to individual objects using the FCM algorithm, and discriminated between confidently binned and uncertain sequence data objects from the community for subsequent biological interpretation. The binning of metagenome sequence fragments according to thresholds in the degree of membership opens the door for the identification of horizontally transferred elements and other genomic regions of uncertain assignment in which biologically meaningful information resides. The reported approach improves the unsupervised genome demarcation of populations within complex communities, increases the confidence in the coherence of the binned elements, and enables the identification of evolutionary processes ignored in hard-binning approaches in shotgun metagenomic studies. © 2018 IEEE.
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    Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic Neighbor Embeddings
    (Institute of Electrical and Electronics Engineers Inc., 2018-01-01) Ariza-Jimenez L.; Quintero O.L.; Pinel N.; Universidad EAFIT. Departamento de Ciencias; Biodiversidad, Evolución y Conservación
    Shotgun metagenomic studies attempt to reconstruct population genome sequences from complex microbial communities. In some traditional genome demarcation approaches, high-dimensional sequence data are embedded into two-dimensional spaces and subsequently binned into candidate genomic populations. One such approach uses a combination of the Barnes-Hut approximation and the t -Stochastic Neighbor Embedding (BH-SNE) algorithm for dimensionality reduction of DNA sequence data pentamer profiles; and demarcation of groups based on Gaussian mixture models within humanimposed boundaries. We found that genome demarcation from three-dimensional BH-SNE embeddings consistently results in more accurate binnings than 2-D embeddings. We further addressed the lack of a priori population number information by developing an unsupervised binning approach based on the Subtractive and Fuzzy c-means (FCM) clustering algorithms combined with internal clustering validity indices. Lastly, we addressed the subject of shared membership of individual data objects in a mixed community by assigning a degree of membership to individual objects using the FCM algorithm, and discriminated between confidently binned and uncertain sequence data objects from the community for subsequent biological interpretation. The binning of metagenome sequence fragments according to thresholds in the degree of membership opens the door for the identification of horizontally transferred elements and other genomic regions of uncertain assignment in which biologically meaningful information resides. The reported approach improves the unsupervised genome demarcation of populations within complex communities, increases the confidence in the coherence of the binned elements, and enables the identification of evolutionary processes ignored in hard-binning approaches in shotgun metagenomic studies. © 2018 IEEE.
  • No hay miniatura disponible
    Ítem
    Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic Neighbor Embeddings
    (Institute of Electrical and Electronics Engineers Inc., 2018-01-01) Ariza-Jimenez L.; Quintero O.L.; Pinel N.; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    Shotgun metagenomic studies attempt to reconstruct population genome sequences from complex microbial communities. In some traditional genome demarcation approaches, high-dimensional sequence data are embedded into two-dimensional spaces and subsequently binned into candidate genomic populations. One such approach uses a combination of the Barnes-Hut approximation and the t -Stochastic Neighbor Embedding (BH-SNE) algorithm for dimensionality reduction of DNA sequence data pentamer profiles; and demarcation of groups based on Gaussian mixture models within humanimposed boundaries. We found that genome demarcation from three-dimensional BH-SNE embeddings consistently results in more accurate binnings than 2-D embeddings. We further addressed the lack of a priori population number information by developing an unsupervised binning approach based on the Subtractive and Fuzzy c-means (FCM) clustering algorithms combined with internal clustering validity indices. Lastly, we addressed the subject of shared membership of individual data objects in a mixed community by assigning a degree of membership to individual objects using the FCM algorithm, and discriminated between confidently binned and uncertain sequence data objects from the community for subsequent biological interpretation. The binning of metagenome sequence fragments according to thresholds in the degree of membership opens the door for the identification of horizontally transferred elements and other genomic regions of uncertain assignment in which biologically meaningful information resides. The reported approach improves the unsupervised genome demarcation of populations within complex communities, increases the confidence in the coherence of the binned elements, and enables the identification of evolutionary processes ignored in hard-binning approaches in shotgun metagenomic studies. © 2018 IEEE.