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Targeted Switchgrass BAC Library Screening and Sequence Analysis Identifies Predicted Biomass and Stress Response-Related Genes

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Abstract

To identify switchgrass homologs of rice genes, known/predicted to control biomass and stress response-related traits, we screened 96,000 clones from two switchgrass bacterial artificial chromosome (BAC) libraries. Full-length sequencing of 311 BAC clones revealed sequence for ∼3.2 % (51.7 Mb) of the switchgrass genome, coding for 3948 genes. A comparison with Arabidopsis and five grass genomes revealed that switchgrass genes share the highest number of homologs with rice (95.5 %) followed by foxtail millet (91.7 %) and Sorghum (91.5 %). One hundred eighteen of the annotated genes are unique to switchgrass. Gene annotation and ontology analysis revealed 695 genes belonging to gene families targeted in the screening. These include 350 kinase, 203 glycosyltransferase (GT), 109 glycoside hydrolase (GH), and 33 ethylene responsive transcription factor (ERF) family genes. Rice homologs of 65 genes, identified here, have demonstrated roles in bioenergy-relevant traits. These include 14 GT2 family genes involved in the synthesis of cellulose and hemicelluloses. Comparative expression analysis in six switchgrass organs revealed a conserved expression pattern for three cellulose synthase (CesA1, CesA2, and CesA9) and five cellulose-synthase-like genes (CslA2, CslA11, CslC1, CslD4, and CslE6). CslF genes that encode mixed linkage glucans are expressed in wider range of tissues in switchgrass compared with rice.

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Acknowledgments

This work was primarily supported by the Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 to US Department of Energy Joint Genome Institute and Office of Biological and Environmental Research of the US, Joint BioEnergy Institute, and to the BioEnergy Science Center (grant number DE-PS02-06ER64304). Partial funding for this research was provided by the NSF CREATE-IGERT program at UC Davis (Award Number DGE-0653984).

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    1. Department of Plant Pathology and the Genome Center, University of California, Davis, CA, USA

      Manoj K. Sharma, Rita Sharma, Mitch Harkenrider & Pamela C. Ronald

    2. Joint BioEnergy Institute, Emeryville, CA, USA

      Manoj K. Sharma, Rita Sharma & Pamela C. Ronald

    3. School of Biotechnology, Jawaharlal Nehru University, New Delhi, India

      Manoj K. Sharma

    4. School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, India

      Rita Sharma

    5. China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute, Zhengzhou, China

      Peijian Cao

    6. HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA

      Jerry Jenkins, Jane Grimwood & Jeremy Schmutz

    7. Department of Energy Joint Genome Institute, Walnut Creek, CA, USA

      Jerry Jenkins, Jane Grimwood & Jeremy Schmutz

    8. Plant Biology Division, Samuel Roberts Noble Foundation, Ardmore, OK, USA

      Jiyi Zhang & Michael K. Udvardi

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    Manoj K. Sharma and Rita Sharma contributed equally to this work.

    Electronic supplementary material

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    Supplementary Figure 1

    Flow chart showing the BAC library screening workflow used in this study. (PNG 406 kB)

    Supplementary File 1

    Genomic sequences of switchgrass genes annotated from full-length sequences of BAC clones. (TXT 14353 kB)

    Supplementary File 2

    cDNA sequences of switchgrass genes annotated from full-length BAC clones. (TXT 5759 kB)

    Supplementary Table 1

    BAC statistics (XLSX 34 kB)

    Supplementary Table 2

    List of genes annotated from switchgrass BAC clones. (XLSX 1432 kB)

    Supplementary Table 3

    Homologs of genes annotated from switchgrass full-length BAC sequences from five grass genomes and Arabidopsis. (XLSX 1014 kB)

    Supplementary Table 4

    List of primers used for qPCR analysis. (XLSX 10 kB)

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    Sharma, M.K., Sharma, R., Cao, P. et al. Targeted Switchgrass BAC Library Screening and Sequence Analysis Identifies Predicted Biomass and Stress Response-Related Genes. Bioenerg. Res. 9, 109–122 (2016). https://doi.org/10.1007/s12155-015-9667-1

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