The Eucalyptus grandis NBS-LRR gene family : physical clustering and expression hotspots

Eucalyptus grandis is a commercially important hardwood species and is known to be susceptible to a number of pests and pathogens. Determining mechanisms of defense is therefore a research priority. The published genome for E. grandis has aided the identification of one important class of resistance (R) genes that incorporate nucleotide binding sites and leucine-rich repeat domains (NBS-LRR). Using an iterative search process we identified NBS-LRR gene models within the E. grandis genome. We characterized the gene models and identified their genomic arrangement. The gene expression patterns were examined in E. grandis clones, challenged with a fungal pathogen (Chrysoporthe austroafricana) and insect pest (Leptocybe invasa). One thousand two hundred and fifteen putative NBS-LRR coding sequences were located which aligned into two large classes, Toll or interleukin-1 receptor (TIR) and coiled-coil (CC) based on NB-ARC domains. NBS-LRR gene-rich regions were identified with 76% organized in clusters of three or more genes. A further 272 putative incomplete resistance genes were also identified. We determined that E. grandis has a higher ratio of TIR to CC classed genes compared to other woody plant species as well as a smaller percentage of single NBS-LRR genes. Transcriptome profiles indicated expression hotspots, within physical clusters, including expression of many incomplete genes. The clustering of putative NBS-LRR genes correlates with differential expression responses in resistant and susceptible plants indicating functional relevance for the physical arrangement of this gene family. This analysis of the repertoire and expression of E. grandis putative NBS-LRR genes provides an important resource for the identification of novel and functional R-genes; a key objective for strategies to enhance resilience.Table S1 Full list of Eucalyptus grandis putative NBS-LRR genes sorted by position on the genome. Information per gene includes the chromosomal position, class, physical cluster and phylogeny clade membership, identification method, raw expression data, log2 fold change values and ANOVA results (p-values). S_F_C, susceptible, fungal treatment, control; S_F_I, susceptible, fungal treatment, inoculated; R_F_C, resistant, fungal treatment, control; R_F_I, resistant, fungal treatment, inoculated; S_I_C, susceptible, insect treatment, control; S_I_I, susceptible, insect treatment, infested; R_I_C, resistant, insect treatment, control; R_I_I, resistant, insect treatment, infested.Table S2 Conserved amino acid sequences for NB-ARC and TIR motifs from MEME analysis with CNL-like and TNL-like gene models in Eucalyptus grandis (Eg) and Arabidopsis thaliana (At; Meyers et al., 2003). The expected amino acid tryptophan (W) is identified in the Kinase 2 subdomain for CNL sequences–underlined.Figure S1 Neighbor joining tree of 480 Eucalyptus grandis NB-ARC domains from complete NBS-LRR genes. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the p-distance method and are in the units of the number of amino acid differences per site. The analysis involved 495 amino acid sequences (480 E. grandis). All ambiguous positions were removed for each sequence pair.Figure S2 Neighbor joining tree of 616 Eucalyptus grandis NB-ARC domains from all non-TIR NBS-LRR-like genes. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the p-distance method and are in the units of the number of amino acid differences per site. The analysis involved 631 amino acid sequences (616 E. grandis). All ambiguous positions were removed for each sequence pair.Figure S3 Neighbor joining tree of 396 Eucalyptus grandis NB-ARC domains from all TIR NBS-LRR-like genes. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the p-distance method and are in the units of the number of amino acid differences per site. The analysis involved 411 amino acid sequences (396 E. grandis). All ambiguous positions were removed for each sequence pair.Figure S4 The definition of a (A) cluster and a (B) supercluster is illustrated using a region (starting at 13 Mb and ending at 18 Mb) on chromosome 4.Figure S5 Physical locations for all complete, partial, and incomplete NBS-LRR gene models that were expressed under challenge of Chrysoporthe austroafricana and Leptocybe invasa on Eucalyptus grandis chromosomes (Mapchart). Variation in means from treatment (ANOVA) were identified based on significance *p < 0.01, **p < 0.001, ***p < 0.0001 (*** are also underlined) and log2 gene expression ratios greater than 1 or smaller than −1 for resistant and susceptible plants. Color distinguishes between different classes (TNL = pink, CNL = green, NL = red, incomplete NL = black, BLAST homolog non-NL = black). Scale bar = Mb. Cluster and supercluster regions are indicated and E. grandis gene IDs are provided.Figure S6 NB-ARC-LRR fused domains (A) and TIR-NB-ARC-LRR fused domains (B). Conserved amino acid sequences are indicated with lines (top). The GKT (Kinase 1) conserved motif is recognized as a P-loop structure important in ATP hydrolysis while the hDD is also well conserved in NB-ARC domains (Kinase 2) as important in co-ordinating Mg2+ as a co-factor (Tameling et al., 2006). These two important sub-domains of NB-ARC are sometimes termed the Walker A and Walker B motifs (Walker et al., 1982) and are identified as A and B, respectively, within the I-Tasser protein structures (bottom) for a representative CNL (Eucgr.L01363) and TNL (Eucgr.C00020) sequence from the Eucalyptus grandis genome.Top up scholarships were generously provided for PT from the University of Sydney and Rural Industries Research and Development Corporation, Australiahttp://www.frontiersin.orgam2016Genetic

The Eucalyptus grandis NBS-LRR gene family : physical clustering and expression hotspots

Eucalyptus grandis is a commercially important hardwood species and is known to be susceptible to a number of pests and pathogens. Determining mechanisms of defense is therefore a research priority. The published genome for E. grandis has aided the identification of one important class of resistance (R) genes that incorporate nucleotide binding sites and leucine-rich repeat domains (NBS-LRR). Using an iterative search process we identified NBS-LRR gene models within the E. grandis genome. We characterized the gene models and identified their genomic arrangement. The gene expression patterns were examined in E. grandis clones, challenged with a fungal pathogen (Chrysoporthe austroafricana) and insect pest (Leptocybe invasa). One thousand two hundred and fifteen putative NBS-LRR coding sequences were located which aligned into two large classes, Toll or interleukin-1 receptor (TIR) and coiled-coil (CC) based on NB-ARC domains. NBS-LRR gene-rich regions were identified with 76% organized in clusters of three or more genes. A further 272 putative incomplete resistance genes were also identified. We determined that E. grandis has a higher ratio of TIR to CC classed genes compared to other woody plant species as well as a smaller percentage of single NBS-LRR genes. Transcriptome profiles indicated expression hotspots, within physical clusters, including expression of many incomplete genes. The clustering of putative NBS-LRR genes correlates with differential expression responses in resistant and susceptible plants indicating functional relevance for the physical arrangement of this gene family. This analysis of the repertoire and expression of E. grandis putative NBS-LRR genes provides an important resource for the identification of novel and functional R-genes; a key objective for strategies to enhance resilience.Table S1 Full list of Eucalyptus grandis putative NBS-LRR genes sorted by position on the genome. Information per gene includes the chromosomal position, class, physical cluster and phylogeny clade membership, identification method, raw expression data, log2 fold change values and ANOVA results (p-values). S_F_C, susceptible, fungal treatment, control; S_F_I, susceptible, fungal treatment, inoculated; R_F_C, resistant, fungal treatment, control; R_F_I, resistant, fungal treatment, inoculated; S_I_C, susceptible, insect treatment, control; S_I_I, susceptible, insect treatment, infested; R_I_C, resistant, insect treatment, control; R_I_I, resistant, insect treatment, infested.Table S2 Conserved amino acid sequences for NB-ARC and TIR motifs from MEME analysis with CNL-like and TNL-like gene models in Eucalyptus grandis (Eg) and Arabidopsis thaliana (At; Meyers et al., 2003). The expected amino acid tryptophan (W) is identified in the Kinase 2 subdomain for CNL sequences–underlined.Figure S1 Neighbor joining tree of 480 Eucalyptus grandis NB-ARC domains from complete NBS-LRR genes. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the p-distance method and are in the units of the number of amino acid differences per site. The analysis involved 495 amino acid sequences (480 E. grandis). All ambiguous positions were removed for each sequence pair.Figure S2 Neighbor joining tree of 616 Eucalyptus grandis NB-ARC domains from all non-TIR NBS-LRR-like genes. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the p-distance method and are in the units of the number of amino acid differences per site. The analysis involved 631 amino acid sequences (616 E. grandis). All ambiguous positions were removed for each sequence pair.Figure S3 Neighbor joining tree of 396 Eucalyptus grandis NB-ARC domains from all TIR NBS-LRR-like genes. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the p-distance method and are in the units of the number of amino acid differences per site. The analysis involved 411 amino acid sequences (396 E. grandis). All ambiguous positions were removed for each sequence pair.Figure S4 The definition of a (A) cluster and a (B) supercluster is illustrated using a region (starting at 13 Mb and ending at 18 Mb) on chromosome 4.Figure S5 Physical locations for all complete, partial, and incomplete NBS-LRR gene models that were expressed under challenge of Chrysoporthe austroafricana and Leptocybe invasa on Eucalyptus grandis chromosomes (Mapchart). Variation in means from treatment (ANOVA) were identified based on significance *p < 0.01, **p < 0.001, ***p < 0.0001 (*** are also underlined) and log2 gene expression ratios greater than 1 or smaller than −1 for resistant and susceptible plants. Color distinguishes between different classes (TNL = pink, CNL = green, NL = red, incomplete NL = black, BLAST homolog non-NL = black). Scale bar = Mb. Cluster and supercluster regions are indicated and E. grandis gene IDs are provided.Figure S6 NB-ARC-LRR fused domains (A) and TIR-NB-ARC-LRR fused domains (B). Conserved amino acid sequences are indicated with lines (top). The GKT (Kinase 1) conserved motif is recognized as a P-loop structure important in ATP hydrolysis while the hDD is also well conserved in NB-ARC domains (Kinase 2) as important in co-ordinating Mg2+ as a co-factor (Tameling et al., 2006). These two important sub-domains of NB-ARC are sometimes termed the Walker A and Walker B motifs (Walker et al., 1982) and are identified as A and B, respectively, within the I-Tasser protein structures (bottom) for a representative CNL (Eucgr.L01363) and TNL (Eucgr.C00020) sequence from the Eucalyptus grandis genome.Top up scholarships were generously provided for PT from the University of Sydney and Rural Industries Research and Development Corporation, Australiahttp://www.frontiersin.orgam2016Genetic

A chromosome-level genome resource for studying virulence mechanisms and evolution of the coffee rust pathogen Hemileia vastatrix

Recurrent epidemics of coffee leaf rust, caused by the fungal pathogen Hemileia vastatrix, have constrained the sustainable production of Arabica coffee for over 150 years. The ability of H. vastatrix to overcome resistance in coffee cultivars and evolve new races is inexplicable for a pathogen that supposedly only utilizes clonal reproduction. Understanding the evolutionary complexity between H. vastatrix and its only known host, including determining how the pathogen evolves virulence so rapidly is crucial for disease management. Achieving such goals relies on the availability of a comprehensive and high-quality genome reference assembly. To date, two reference genomes have been assembled and published for H. vastatrix that, while useful, remain fragmented and do not represent chromosomal scaffolds. Here, we present a complete scaffolded pseudochromosome-level genome resource for H. vastatrix strain 178a (Hv178a). Our initial assembly revealed an unusually high degree of gene duplication (over 50% BUSCO basidiomycota_odb10 genes). Upon inspection, this was predominantly due to a single scaffold that itself showed 91.9% BUSCO Completeness. Taxonomic analysis of predicted BUSCO genes placed this scaffold in Exobasidiomycetes and suggests it is a distinct genome, which we have named Hv178a associated fungal genome (Hv178a AFG). The high depth of coverage and close association with Hv178a raises the prospect of symbiosis, although we cannot completely rule out contamination at this time. The main Ca. 546 Mbp Hv178a genome was primarily (97.7%) localised to 11 pseudochromosomes (51.5 Mb N50), building the foundation for future advanced studies of genome structure and organization.info:eu-repo/semantics/publishedVersio

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis

<p>Abstract</p> <p>Background</p> <p>Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis.</p> <p>Methods</p> <p>We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation.</p> <p>Results</p> <p>On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL.</p> <p>Conclusion</p> <p>The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data.</p

TRY plant trait database - enhanced coverage and open access

This article has 730 authors, of which I have only listed the lead author and myself as a representative of University of HelsinkiPlant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.Peer reviewe

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Identification of the Eucalyptus grandis chitinase gene family and expression characterization under different biotic stress challenges

Eucalyptus grandis (W. Hill ex Maiden) is an Australian Myrtaceae tree grown for timber in many parts of the world and for which the annotated genome sequence is available. Known to be susceptible to a number of pests and diseases, E. grandis is a useful study organism for investigating defense responses in woody plants. Chitinases are widespread in plants and cleave glycosidic bonds of chitin, the major structural component of fungal cell walls and arthropod exoskeletons. They are encoded by an important class of genes known to be up-regulated in plants in response to pathogens. The current study identified 67 chitinase gene models from two families known as glycosyl hydrolase 18 and 19 (36 GH18 and 31 GH19) within the E. grandis genome assembly (v1.1), indicating a recent gene expansion. Sequences were aligned and analyzed as conforming to currently recognized plant chitinase classes (I–V). Unlike other woody species investigated to date, E. grandis has a single gene encoding a putative vacuolar targeted Class I chitinase. In response to Leptocybe invasa (Fisher & La Salle) (the eucalypt gall wasp) and Chrysoporthe austroafricana (Gryzenhout & M.J. Wingf. 2004) (causal agent of fungal stem canker), this Class IA chitinase is strongly up-regulated in both resistant and susceptible plants. Resistant plants, however, indicate greater constitutive expression and increased up-regulation than susceptible plants following fungal challenge. Up-regulation within fungal resistant clones was further confirmed with protein data. Clusters of putative chitinase genes, particularly on chromosomes 3 and 8, are significantly up-regulated in response to fungal challenge, while a cluster on chromosome 1 is significantly down-regulated in response to gall wasp. The results of this study show that the E. grandis genome has an expanded group of chitinase genes, compared with other plants. Despite this expansion, only a single Class I chitinase is present and this gene is highly up-regulated within diverse biotic stress conditions. Our research provides insight into a major class of defense genes within E. grandis and indicates the importance of the Class I chitinase.S1 Fig. Domains and classes for glycoside hydrolase 18 and 19 (chitinases) from the Eucalyptus grandis sequences. S = signal sequence, H = hinge region (proline/threonine-rich in Class IV and glycine-rich in Class I), CBD = chitin binding domain, black box indicates C-terminal vacuolar extension, aa = amino acid residue with approximate sizes. Diagrammatic concept from Collinge, Kragh et al. (1993).S2 Fig. Quantitative reverse-transcriptase polymerase chain reaction relative expression change (A) of putative Class 1A chitinase (Eucgr. I01495) in Eucalyptus grandis in response to Chrysoporthe austroafricana inoculation (three days post-inoculation). Expression is relative to reference gene Elongation factor S-II (B). R = resistant, S = susceptible.S3 Fig. Predicted tertiary structure for (A) Eucalyptus grandis Class IA chitinase peptide with cleaved signal and vacuolar sequences had a C-score of 1.72 (with range being between 5 and 2) and estimated TM-score of 0.96 (structural similarity score between 0 and 1) (Roy et al. 2010). (B) The putative mature protein matched tertiary structure for Oryza sativa L. japonica Class IA chitinase crystal structure (Kezuka et al. 2010) with TM score of 0.95 (http://www.rcsb.org/pdb/explore/jmol.do?structureId=2DKV&bionumber=1&opt=3&jmolMod e=HTML5). Blue = GH19 and Chitin binding domains (linked by hinge region), white = hinge region (glycine-rich in E.grandis Class I but usually proline/threonine rich), red = catalytic regions as identified by 0.6nm of bound substrate, magenta = essential residues for catalytic activity determined with mutagenesis (Bishop et al. 2000).S1 Table. Full list of Eucalyptus grandis putative chitinase genes sorted by position on the genome. Information per gene includes the chromosomal position, class, physical cluster and localization. Log2 of normalized FPKM reads expression data, log2 fold change values and ANOVA results (p values). S_F_C = susceptible, fungal treatment, control. S_F_I = susceptible, fungal treatment, inoculated. R_F_C = resistant, fungal treatment, control. R_F_I = resistant, fungal treatment, inoculated. S_I_C = susceptible, insect treatment, control. S_I_I = susceptible, insect treatment, infested. R_I_C = resistant, insect treatment, control. R_I_I = resistant, insect treatment, infested.Top-up scholarships were generously provided for P.A.T. from the University of Sydney and the Australian Government, Rural Industries Research and Development Corporation.https://academic.oup.com/treephys2018-05-01hj2018Forestry and Agricultural Biotechnology Institute (FABI)Genetic