Functional category analysis based on Gene Ontology (GO) annotations of CED-Trinity (upper panel) and ESI-Trinity (lower panel) unigenes.

<p>Results for ESI-Velvet are found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119701#pone.0119701.s006" target="_blank">S2 Fig</a>.</p

<i>Ephedra sinica</i> unigenes representing enzymes putatively involved in ephedrine alkaloid biosynthesis.

<p>Each unigene is assigned an identifier, which corresponds to a database ID in the ESI-Velvet library. Percent amino acid identity between unigenes and queries is provided. Abbreviations: AAO4, aromatic aldehyde oxidase 4; Ab, <i>Atropa belladonna</i>; Am, <i>Antirrhinum majus</i>; AHAS, acetohydroxyacid synthase; ArAT, aromatic amino acid transaminase; At, <i>Arabidopsis thaliana</i>; BALDH, benzaldehyde dehydrogenase; BDH, benzaldehyde dehydrogenase; BL; benzoate CoA ligase; BZO, benzoyloxyglucosinolate; Ca, <i>Caffea arabica</i>; Ce, <i>Catha edulis</i>; CHD, cinnamoyl-CoA hydratase-dehydrogenase; 4CL, 4-coumaroyl-CoA ligase; Cm, <i>Cucumis melo</i>; CS, caffeine synthase; COR, codeinone reductase; Ds, <i>Datura stramonium</i>; Ec, <i>Eschscholzia californica</i>; Es, <i>Ephedra sinica</i>; KAT, 3-ketoacyl-CoA thiolasae; NMT, <i>N</i>-methyltransferase; PEANMT, phosphoethanolamine <i>N</i>-methyltransferase; PMT, putrescine <i>N</i>-methyltransferase; Ps, <i>Papaver somniferum</i>; PAL, L-phenylalanine ammonia lyase; PDC, pyruvate decarboxylase; Ph, <i>Petunia x hybrida</i>; PPA-AT, prephenate aminotransferase; PRMT, protein arginine <i>N</i>-methyltransferase; Pt, <i>Pinus taeda</i>; RED, reductase; SanR, sanguinarine reductase; Sl, <i>Solanum lycopersicon</i>; SUVH, histone lysine <i>N</i>-methyltransferase, H3L9-specific; TA, transaminase; ThDPC, thiamin diphosphate-dependent carboligase; TNMT, (<i>S</i>)-tetrahydroprotoberberine <i>N</i>-methyltransferase; TR, tropinone reducase.</p><p><i>Ephedra sinica</i> unigenes representing enzymes putatively involved in ephedrine alkaloid biosynthesis.</p

Relative expression of gene candidates identified in khat (CED-Trinity).

<p>FPKM (fragments mapped per kilobase of exon per million reads mapped) is a normalizing statistic measuring gene expression while accounting for variation in gene length [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119701#pone.0119701.ref023" target="_blank">23</a>]. Abbreviations are defined in Tables <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119701#pone.0119701.t002" target="_blank">2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119701#pone.0119701.t003" target="_blank">3</a>.</p

Summary of the construction and assembly for three Illumina NGS libraries.

<p>Abbreviations: CED, <i>Catha edulis</i>; ESI, <i>Ephedra sinica</i>; SRA, short-read archive; CDS, coding sequence.</p><p>Summary of the construction and assembly for three Illumina NGS libraries.</p

Proposed biosynthetic routes leading from L-phenylalanine to amphetamine-type alkaloids in khat and <i>Ephedra sinica</i>.

<p>A CoA-independent, non-β-oxidative pathway of L-phenylalanine side chain-shortening is shown in blue, whereas a CoA-dependent, β-oxidative route is shown in purple. Benzaldehyde, benzoic acid and/or benzoyl-CoA undergo condensation with pyruvate, a reaction putatively catalyzed by a ThDP-dependent carboligase. 1-Phenylpropane-1,2-dione undergoes transamination to yield (<i>S</i>)-cathinone, which is reduced to cathine and (1<i>R</i>,2<i>S</i>)-norephedrine. <i>N</i>-Methylation is restricted to <i>Ephedra</i> spp. and does not occur in khat. Activity has been detected for enzymes highlighted in yellow, and corresponding genes are available for enzymes highlighted in green. Enzymes highlighted in red have not been isolated, although database mining revealed numerous potential candidates (Tables <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119701#pone.0119701.t002" target="_blank">2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119701#pone.0119701.t003" target="_blank">3</a>). Abbreviations: CoA, Coenzyme A; NAD(H), nicotinamide adenine dinucleotide; NADP(H), nicotinamide adenine dinucleotide phosphate. PAL, phenylalanine ammonia lyase; ThDP, thiamine diphosphate.</p

Transcriptome Profiling of Khat (<i>Catha edulis</i>) and <i>Ephedra sinica</i> Reveals Gene Candidates Potentially Involved in Amphetamine-Type Alkaloid Biosynthesis

<div><p>Amphetamine analogues are produced by plants in the genus <i>Ephedra</i> and by khat (<i>Catha edulis</i>), and include the widely used decongestants and appetite suppressants (1<i>S</i>,2<i>S</i>)-pseudoephedrine and (1<i>R</i>,2<i>S</i>)-ephedrine. The production of these metabolites, which derive from L-phenylalanine, involves a multi-step pathway partially mapped out at the biochemical level using knowledge of benzoic acid metabolism established in other plants, and direct evidence using khat and <i>Ephedra</i> species as model systems. Despite the commercial importance of amphetamine-type alkaloids, only a single step in their biosynthesis has been elucidated at the molecular level. We have employed Illumina next-generation sequencing technology, paired with Trinity and Velvet-Oases assembly platforms, to establish data-mining frameworks for <i>Ephedra sinica</i> and khat plants. Sequence libraries representing a combined 200,000 unigenes were subjected to an annotation pipeline involving direct searches against public databases. Annotations included the assignment of Gene Ontology (GO) terms used to allocate unigenes to functional categories. As part of our functional genomics program aimed at novel gene discovery, the databases were mined for enzyme candidates putatively involved in alkaloid biosynthesis. Queries used for mining included enzymes with established roles in benzoic acid metabolism, as well as enzymes catalyzing reactions similar to those predicted for amphetamine alkaloid metabolism. Gene candidates were evaluated based on phylogenetic relationships, FPKM-based expression data, and mechanistic considerations. Establishment of expansive sequence resources is a critical step toward pathway characterization, a goal with both academic and industrial implications.</p></div

Table_4_Comparative genomic analysis reveals differential genomic characteristics and featured genes between rapid- and slow-growing non-tuberculous mycobacteria.XLSX

IntroductionNon-tuberculous mycobacteria (NTM) is a major category of environmental bacteria in nature that can be divided into rapidly growing mycobacteria (RGM) and slowly growing mycobacteria (SGM) based on their distinct growth rates. To explore differential molecular mechanisms between RGM and SGM is crucial to understand their survival state, environmental/host adaptation and pathogenicity. Comparative genomic analysis provides a powerful tool for deeply investigating differential molecular mechanisms between them. However, large-scale comparative genomic analysis between RGM and SGM is still uncovered.MethodsIn this study, we screened 335 high-quality, non-redundant NTM genome sequences covering 187 species from 3,478 online NTM genomes, and then performed a comprehensive comparative genomic analysis to identify differential genomic characteristics and featured genes/protein domains between RGM and SGM.ResultsOur findings reveal that RGM has a larger genome size, more genes, lower GC content, and more featured genes/protein domains in metabolism of some main substances (e.g. carbohydrates, amino acids, nucleotides, ions, and coenzymes), energy metabolism, signal transduction, replication, transcription, and translation processes, which are essential for its rapid growth requirements. On the other hand, SGM has a smaller genome size, fewer genes, higher GC content, and more featured genes/protein domains in lipid and secondary metabolite metabolisms and cellular defense mechanisms, which help enhance its genome stability and environmental adaptability. Additionally, orthogroup analysis revealed the important roles of bacterial division and bacteriophage associated genes in RGM and secretion system related genes for better environmental adaptation in SGM. Notably, PCoA analysis of the top 20 genes/protein domains showed precision classification between RGM and SGM, indicating the credibility of our screening/classification strategies.DiscussionOverall, our findings shed light on differential underlying molecular mechanisms in survival state, adaptation and pathogenicity between RGM and SGM, show the potential for our comparative genomic pipeline to investigate differential genes/protein domains at whole genomic level across different bacterial species on a large scale, and provide an important reference and improved understanding of NTM.</p

Table_9_Comparative genomic analysis reveals differential genomic characteristics and featured genes between rapid- and slow-growing non-tuberculous mycobacteria.XLSX

IntroductionNon-tuberculous mycobacteria (NTM) is a major category of environmental bacteria in nature that can be divided into rapidly growing mycobacteria (RGM) and slowly growing mycobacteria (SGM) based on their distinct growth rates. To explore differential molecular mechanisms between RGM and SGM is crucial to understand their survival state, environmental/host adaptation and pathogenicity. Comparative genomic analysis provides a powerful tool for deeply investigating differential molecular mechanisms between them. However, large-scale comparative genomic analysis between RGM and SGM is still uncovered.MethodsIn this study, we screened 335 high-quality, non-redundant NTM genome sequences covering 187 species from 3,478 online NTM genomes, and then performed a comprehensive comparative genomic analysis to identify differential genomic characteristics and featured genes/protein domains between RGM and SGM.ResultsOur findings reveal that RGM has a larger genome size, more genes, lower GC content, and more featured genes/protein domains in metabolism of some main substances (e.g. carbohydrates, amino acids, nucleotides, ions, and coenzymes), energy metabolism, signal transduction, replication, transcription, and translation processes, which are essential for its rapid growth requirements. On the other hand, SGM has a smaller genome size, fewer genes, higher GC content, and more featured genes/protein domains in lipid and secondary metabolite metabolisms and cellular defense mechanisms, which help enhance its genome stability and environmental adaptability. Additionally, orthogroup analysis revealed the important roles of bacterial division and bacteriophage associated genes in RGM and secretion system related genes for better environmental adaptation in SGM. Notably, PCoA analysis of the top 20 genes/protein domains showed precision classification between RGM and SGM, indicating the credibility of our screening/classification strategies.DiscussionOverall, our findings shed light on differential underlying molecular mechanisms in survival state, adaptation and pathogenicity between RGM and SGM, show the potential for our comparative genomic pipeline to investigate differential genes/protein domains at whole genomic level across different bacterial species on a large scale, and provide an important reference and improved understanding of NTM.</p

Image_1_Comparative genomic analysis reveals differential genomic characteristics and featured genes between rapid- and slow-growing non-tuberculous mycobacteria.TIF

IntroductionNon-tuberculous mycobacteria (NTM) is a major category of environmental bacteria in nature that can be divided into rapidly growing mycobacteria (RGM) and slowly growing mycobacteria (SGM) based on their distinct growth rates. To explore differential molecular mechanisms between RGM and SGM is crucial to understand their survival state, environmental/host adaptation and pathogenicity. Comparative genomic analysis provides a powerful tool for deeply investigating differential molecular mechanisms between them. However, large-scale comparative genomic analysis between RGM and SGM is still uncovered.MethodsIn this study, we screened 335 high-quality, non-redundant NTM genome sequences covering 187 species from 3,478 online NTM genomes, and then performed a comprehensive comparative genomic analysis to identify differential genomic characteristics and featured genes/protein domains between RGM and SGM.ResultsOur findings reveal that RGM has a larger genome size, more genes, lower GC content, and more featured genes/protein domains in metabolism of some main substances (e.g. carbohydrates, amino acids, nucleotides, ions, and coenzymes), energy metabolism, signal transduction, replication, transcription, and translation processes, which are essential for its rapid growth requirements. On the other hand, SGM has a smaller genome size, fewer genes, higher GC content, and more featured genes/protein domains in lipid and secondary metabolite metabolisms and cellular defense mechanisms, which help enhance its genome stability and environmental adaptability. Additionally, orthogroup analysis revealed the important roles of bacterial division and bacteriophage associated genes in RGM and secretion system related genes for better environmental adaptation in SGM. Notably, PCoA analysis of the top 20 genes/protein domains showed precision classification between RGM and SGM, indicating the credibility of our screening/classification strategies.DiscussionOverall, our findings shed light on differential underlying molecular mechanisms in survival state, adaptation and pathogenicity between RGM and SGM, show the potential for our comparative genomic pipeline to investigate differential genes/protein domains at whole genomic level across different bacterial species on a large scale, and provide an important reference and improved understanding of NTM.</p

Image_2_Comparative genomic analysis reveals differential genomic characteristics and featured genes between rapid- and slow-growing non-tuberculous mycobacteria.TIF

IntroductionNon-tuberculous mycobacteria (NTM) is a major category of environmental bacteria in nature that can be divided into rapidly growing mycobacteria (RGM) and slowly growing mycobacteria (SGM) based on their distinct growth rates. To explore differential molecular mechanisms between RGM and SGM is crucial to understand their survival state, environmental/host adaptation and pathogenicity. Comparative genomic analysis provides a powerful tool for deeply investigating differential molecular mechanisms between them. However, large-scale comparative genomic analysis between RGM and SGM is still uncovered.MethodsIn this study, we screened 335 high-quality, non-redundant NTM genome sequences covering 187 species from 3,478 online NTM genomes, and then performed a comprehensive comparative genomic analysis to identify differential genomic characteristics and featured genes/protein domains between RGM and SGM.ResultsOur findings reveal that RGM has a larger genome size, more genes, lower GC content, and more featured genes/protein domains in metabolism of some main substances (e.g. carbohydrates, amino acids, nucleotides, ions, and coenzymes), energy metabolism, signal transduction, replication, transcription, and translation processes, which are essential for its rapid growth requirements. On the other hand, SGM has a smaller genome size, fewer genes, higher GC content, and more featured genes/protein domains in lipid and secondary metabolite metabolisms and cellular defense mechanisms, which help enhance its genome stability and environmental adaptability. Additionally, orthogroup analysis revealed the important roles of bacterial division and bacteriophage associated genes in RGM and secretion system related genes for better environmental adaptation in SGM. Notably, PCoA analysis of the top 20 genes/protein domains showed precision classification between RGM and SGM, indicating the credibility of our screening/classification strategies.DiscussionOverall, our findings shed light on differential underlying molecular mechanisms in survival state, adaptation and pathogenicity between RGM and SGM, show the potential for our comparative genomic pipeline to investigate differential genes/protein domains at whole genomic level across different bacterial species on a large scale, and provide an important reference and improved understanding of NTM.</p