KEGG Automatic Annotation Server Research Articles

Metagenomics and proteomics profiling of extracellular polymeric substances from municipal waste sludge and their application for soil and water bioremediation

This study assessed the components of anaerobically digested sludge, activated sludge, and microbial and extracellular polymeric substance (EPS) enzymes to identify the mechanisms underlying nitrogen removal and soil regeneration. 16S rRNA gene amplicon-based sequencing was used to determine the microbial community composition and the related National Center for Biotechnology Information (NCBI) protein database was used to construct a conventional library from the observed community. EPS components were identified using gel-free proteomic (Liquid Chromatography with tandem mass spectrometry-LC/MS/MS) methods. Alginate-like EPS from aerobically activated sludge have strong potential for soil aggregation and water-holding capacity, whereas total EPS from anaerobic sludge have significant potential for ammonia removal under salt stress. Fourier transform infrared spectroscopy (FTIR) revealed that both EPS may contain proteins, carbohydrates, humic compounds, uronic acid, and DNA and determined the presence of O–H, N–H, C–N, CO, and C–H functional groups. These results demonstrate that the overall enzyme activity may be inactivated at 30 g L-1 of salinity. An annotation found in Kyoto Encyclopedia of Genes and Genomes (KEGG)- KEGG Automatic Annotation Server (KAAS) revealed that the top two metabolic activities in the EPS generated from the anaerobic sludge were methane and nitrogen metabolism. Therefore, we focused on the nitrogen metabolism reference map 00910. EPS from the anaerobically digested sludge exhibited nitrate reductase, nitrite reductase, and dehydrogenase activities. Assimilatory nitrate reduction, denitrification, nitrification, and anammox removed ammonia biochemically. The influence of microbial extracellular metabolites on water-holding capacity and soil aggregation was also investigated. The KAAS-KEGG annotation server was used to identify the main enzymes in the activated sludge-derived alginate-like extracellular EPS (ALE-EPS) samples. These include hydrolases, oxidoreductases, lyases, ligases, and transporters, which contribute to soil fertility and stability. This study improves our understanding of the overall microbial community structure and the associated biochemical processes, which are related to distinct functional genes or enzymes involved in nitrogen removal and soil aggregation. In contrast to conventional methods, microbial association with proteomics can be used to investigate ecological relationships, establishments, key player species, and microbial responses to environmental changes. Linking the metagenome to off-gel proteomics and bioinformatics solves the problem of analyzing metabolic pathways in complex environmental samples in a cost-effective manner.

Niche adaptation of Frankia do not drastically influence their metabolic profiling

ABSTRACT Metabolic-profiling (metabolomics/metabonomics) is a measurement strategy among biological systems for low-molecular-weight metabolites and their corresponding intermediates acting as dynamic physiological, pathophysiological and developmental stimuli. Information regarding the metabolomics of Frankia is elusive. In this study, we analyzed 48 Frankia whole-genome sequences. Kyoto Encyclopedia of Genes and Genomes (KEGG) database and KEGG Automatic Annotation Server (KAAS) were used for the prediction and functional annotation of genes associated with diverse metabolic pathways. Comprehensive codon and amino-acid usage analyses along with tRNA adaptation index and dN/dS-based evolutionary inquest were performed. We have used the Database-of-Essential-Genes (DEG) server to predict the essentiality of the metabolic pathway-related-genes. We found a strong effect of compositional constraints on the metabolic pathway genes of Frankia. The evolutionary analysis revealed the conserved nature of the studied genes. Since the metabolic pathway-related genes were found to be housekeeping genes, they are conserved and less evolved. This was further validated with a dN/dS value of less than 1. Moreover, the number of synonymous changes was more than non-synonymous changes so that the amino acid sequence remains identical. The cluster of orthologs (COG) analysis revealed “metabolism” as the main functional category of the studied gene set. The core metabolic pathway-related genes among considered Frankia strains accumulated 115 genes. This study for the first time reveals the genomic characteristics of the metabolic pathway-related genes among Frankia. From all the analysis, we could hypothesize that in spite of their host specificity and differential taxonomical distribution, Frankia maintain a core metabolic proficiency among them.

Draft genome analysis, poly-phasic study and lipid biosynthesis pathway of Scenedesmus sp. SVMIICT1

A comprehensive polyphasic evaluation of a microalgal isolate Scenedesmus sp. SVMIICT1 through morphological, biochemical, photosynthetic characterization, next-generation sequencing and lipid pathway analysis was reported. The strain was cultivated photo-autotrophically, where the maximum photosynthetic yield (FV/FM) of 0.75 was observed on the 4th day with optimal PSII photochemical efficiency. Enhanced electron transport rate (ETR(I)) with inhibitor 3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU) resulted in cyclic electron flow. A fair share of carbohydrate content (36 µg/mg) was ascribed to the presence of pyrenoid towards higher CO2 sequestration pursuant to carbon concentrating mechanism (CCM). Denovo sequencing of the genome was assembled, annotated for the prediction of gene and protein. KEGG automatic annotation server (KAAS) analysis depicted the presence of genes accompanying the biosynthesis of the glycerophospholipid pathway. Fatty acid profile represented a higher fraction of palmitic acid (C16:0; 41.6%) followed by alpha-linolenic acid (C18:3; 44.5%).

Subtractive proteomics approach to Unravel the druggable proteins of the emerging pathogen Waddlia chondrophila and drug repositioning on its MurB protein

Waddlia chondrophila is an emerging pathogen that has been implicated in numerous unpropitious pregnancy events in humans and ruminants. Taking into account its association with abortigenic events, possible modes of transmission, and future risk, immediate clinical measures are required to prevent widespread damage caused by this organism and hence this study. Here, a subtractive proteomics approach was employed to identify druggable proteins of W. chondrophila. Considering the essential genes, antibiotic resistance proteins, and virulence factors, 676 unique important proteins were initially identified for this bacterium. Afterward, NCBI BLASTp performed against human proteome identified 223 proteins that were further pushed into KEGG Automatic Annotation Server (KAAS) for automatic annotation. Using the information from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database 14 Waddlia specific metabolic pathways were identified with respect to humans. Analyzing the data from KAAS and KEGG databases, forty-eight metabolic pathway-dependent, and seventy metabolic pathway independent proteins were identified. Standalone BLAST search against DrugBank FDA approved drug targets revealed eight proteins that are finally considered druggable proteins. Prediction of three-dimensional structures was done for the eight proteins through homology modeling and the Ramachandran plot model showed six models as a valid prediction. Finally, virtual screening against MurB protein was performed using FDA approved drugs to employ the drug repositioning strategy. Three drugs showed promising docking results that can be used for therapeutic purposes against W. chondrophila following the clinical validation of the study.

Dataset of de novo assembly and functional annotation of the transcriptomes of three native oleaginous microalgae from the Peruvian Amazon.

Microalgae are photosynthetic organisms with cosmopolitan distribution (i.e., marine, freshwater and terrestrial habitats) and possess a great diversity of species [1] and consequently an immense variation in biochemical compositions [2]. To date genomic information is available mainly from the model green microalga Chlamydomonas reinhardtii[3]. Here we provide the dataset of a de novo assembly and functional annotation of the transcriptomes of three native oleaginous microalgae from the Peruvian Amazon. Native oleaginous microalgae species Ankistrodesmus sp., Chlorella sp., and Scenedesmus sp. were cultured in triplicate using Chu-10 medium with or without a source of nitrate (NaNO3). Total RNA was purified, the cDNA libraries were constructed and sequenced as paired-end reads on an Illumina HiSeq™2500 platform. Transcriptomes were de novo assembled using Trinity v2.9.1. A total of 48,554 transcripts (range from 250 to 7966 bp; N50 = 1047) for Ankistrodesmus sp., 108,126 transcripts (range from 250 to 8160 bp; N50 = 1090) for Chlorella sp., and 77,689 transcripts (range from 250 to 8481 bp; N50 = 1281) for Scenedesmus sp. were de novo assembled. Completeness of the assembled transcriptomes were evaluated with the Benchmarking Universal Single-Copy Orthologs (BUSCO) software v2/v3. Functional annotation of the assembled transcriptomes was conducted with TransDecoder v3.0.1 and the web-based platforms Kyoto Encyclopedia of Genes and Genomes (KEGG) Automatic Annotation Server (KAAS) and FunctionAnnotator. The raw reads were deposited into NCBI and are accessible via BioProject accession number PRJNA628966 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA628966) and Sequence Read Archive (SRA) with accession numbers: SRX8295665 (https://www.ncbi.nlm.nih.gov/sra/SRX8295665), SRX8295666 (https://www.ncbi.nlm.nih.gov/sra/SRX8295666), SRX8295667 (https://www.ncbi.nlm.nih.gov/sra/SRX8295667), SRX8295668 (https://www.ncbi.nlm.nih.gov/sra/SRX8295668), SRX8295669 (https://www.ncbi.nlm.nih.gov/sra/SRX8295669), and SRX8295670 (https://www.ncbi.nlm.nih.gov/sra/SRX8295670). Additionally, transcriptome shotgun assembly sequences and functional annotations are available via Discover Mendeley Data (https://data.mendeley.com/datasets/47wdjmw9xr/1).

Dataset of de novo assembly and functional annotation of the transcriptome during germination and initial growth of seedlings of Myrciaria Dubia "camu-camu".

Myrciaria dubia “camu-camu” is a native shrub of the Amazon that is commonly found in areas that are flooded for three to four months during the annual hydrological cycle. This plant species is exceptional for its capacity to biosynthesize and accumulate important quantities of a variety of health-promoting phytochemicals, especially vitamin C [1], yet few genomic resources are available [2]. Here we provide the dataset of a de novo assembly and functional annotation of the transcriptome from a pool of samples obtained from seeds during the germination process and seedlings during the initial growth (until one month after germination). Total RNA/mRNA was purified from different types of plant materials (i.e., imbibited seeds, germinated seeds, and seedlings of one, two, three, and four weeks old), pooled in equimolar ratio to generate the cDNA library and RNA paired-end sequencing was conducted on an Illumina HiSeq™2500 platform. The transcriptome was de novo assembled using Trinity v2.9.1 and SuperTranscripts v2.9.1. A total of 21,161 transcripts were assembled ranging in size from 500 to 10,001 bp with a N50 value of 1,485 bp. Completeness of the assembly dataset was assessed using the Benchmarking Universal Single-Copy Orthologs (BUSCO) software v2/v3. Finally, the assembled transcripts were functionally annotated using TransDecoder v3.0.1 and the web-based platforms Kyoto Encyclopedia of Genes and Genomes (KEGG) Automatic Annotation Server (KAAS), and FunctionAnnotator. The raw reads were deposited into NCBI and are accessible via BioProject accession number PRJNA615000 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA615000) and Sequence Read Archive (SRA) with accession number SRX7990430 (https://www.ncbi.nlm.nih.gov/sra/SRX7990430). Additionally, transcriptome shotgun assembly sequences and functional annotations are available via Discover Mendeley Data (https://data.mendeley.com/datasets/2csj3h29fr/1).

Genome-Wide Analysis Reveals Genetic Potential for Aromatic Compounds Biodegradation of Sphingopyxis.

Members of genus Sphingopyxis are frequently found in diverse eco-environments worldwide and have been traditionally considered to play vital roles in the degradation of aromatic compounds. Over recent decades, many aromatic-degrading Sphingopyxis strains have been isolated and recorded, but little is known about their genetic nature related to aromatic compounds biodegradation. In this study, bacterial genomes of 19 Sphingopyxis strains were used for comparative analyses. Phylogeny showed an ambiguous relatedness between bacterial strains and their habitat specificity, while clustering based on Cluster of Orthologous Groups suggested the potential link of functional profile with substrate-specific traits. Pan-genome analysis revealed that 19 individuals were predicted to share 1,066 orthologous genes, indicating a high genetic homogeneity among Sphingopyxis strains. Notably, KEGG Automatic Annotation Server results suggested that most genes pertaining aromatic compounds biodegradation were predicted to be involved in benzoate, phenylalanine, and aminobenzoate metabolism. Among them, β-ketoadipate biodegradation might be the main pathway in Sphingopyxis strains. Further inspection showed that a number of mobile genetic elements varied in Sphingopyxis genomes, and plasmid-mediated gene transfer coupled with prophage- and transposon-mediated rearrangements might play prominent roles in the evolution of bacterial genomes. Collectively, our findings presented that Sphingopyxis isolates might be the promising candidates for biodegradation of aromatic compounds in pollution sites.

MAPLE 2.3.0: an improved system for evaluating the functionomes of genomes and metagenomes

MAPLE is an automated system for inferring the potential comprehensive functions harbored by genomes and metagenomes. To reduce runtime in MAPLE analyzing the massive amino acid datasets of over 1 million sequences, we improved it by adapting the KEGG automatic annotation server to use GHOSTX and verified no substantial difference in the MAPLE results between the original and new implementations.

Identification of putative drug targets and annotation of unknown proteins in Tropheryma whipplei

Tropheryma whipplei (T. whipplei) is the causative agent of Whipple’s disease and blood culture-negative endocarditis. Due to the variability of symptoms, the disease is often poorly diagnosed.Treatment for this bacterial infection is often lengthy, and improper uptake of antibiotics has resulted in relapses in many patients. In the present study, using available bioinformatic tools and databases such as the Cluster Database at High Identity with Tolerance (CD-HIT), the Basic Local Alignment Search Tool for proteins (BLASTp), the Database of Essential Genes (DEG), and the DrugBank database, 13 putative drug targets were identified in T. whipplei by subtractive genome analysis that could be targeted with currently available drugs (experimental or approved). Further, a 3D model was generated for one of these putative drug targets, the T. whipplei DNA ligase, and in silico docking was performed with pyridochromanone and adenosine-derived inhibitors using the AutoDock Vina. Additionally, many of the T. whipplei protein sequences in the National Center for Biotechnology Information (NCBI) protein database were unknown/uncurated. Using available web servers e.g. the KEGG Automatic Annotation Server (KAAS), the BLASTp, the Conserved Domain Architecture Retrieval Tool (CDAT) and the Protein families (Pfam), the function/remote/domain homology for nearly 80% of these uncurated protein sequences were annotated. The data obtained in the present study will aid physicians and researchers alike in curbing this bacterial infection.

Data Mining Approaches to Identify Biomineralization Related Sequences

Proteomics is an efficient high throughput technique developed to identify proteins from a crude extract using sequence homology. Advances in Next Generation Sequencing (NGS) have led to increase knowledge of several non-model species. In the field of calcium carbonate biomineralization, the paucity of available sequences (such as the ones of mollusc shells) is still a bottleneck in most proteomic studies. Indeed, this technique needs proteins databases to find homology. The aim of this study was to perform different data mining approaches in order to identify novel shell proteins. To this end, we disposed of several publicly non-model molluscs databases. Previously identified molluscan shell matrix sequences were used as keyword to query annotated databases. BLAST tools and KASS program (KEGG Automatic Annotation Server) were developed to analyse other non-annotated databases. Our results suggest that the efficiency of these methods depends on the quality of the shared data. Finally, an in-house shell matrix protein database has been generated.

Transcriptome analysis of the mammary gland from GH transgenic goats during involution

Mammary glands are organs for milk production in female mammals. Growth hormone (GH) is known to affect the growth and development of the mammary gland, as well as to increase milk production in dairy goats. This study performed a comprehensive expression profiling of genes expressed in the mammary gland of early involution GH transgenic (n=4) and non-transgenic goats (n=4) by RNA sequencing. RNA was extracted from mammary gland tissues collected at day 3 of involution. Gene expression analysis was conducted by Illumina RNA sequencing and sequence reads were assembled and analyzed using TopHat. FPKM (fragments per kilobase of exon per million) values were analyzed for differentially expressed genes using the Cufflinks package. Gene ontology analysis of differentially expressed genes was categorized using agriGO, while KEGG pathway analysis was performed with the online KEGG automatic annotation server. Our results revealed that 75% of NCBI goat annotated genes were expressed during early involution. A total of 18,323 genes were expressed during early involution in GH transgenic goats, compared with 18,196 expressed genes during early involution of non-transgenic goats. In these expressed genes, the majority (17,589) were ubiquitously expressed in GH transgenic and non-transgenic goats. However, there were 745 differentially expressed genes, 421 of which were upregulated and 324 were downregulated in GH transgenic goats. GO and KEGG pathway analysis showed that these genes were involved in mammary gland physiology, including cell adhesion molecules, ECM-receptor interaction, Jak-STAT signaling pathway, and fat metabolism. Our results demonstrated that the GH receptor was strongly affected in GH transgenic goats, which may activate the IGF-1/Stat3 signaling pathway. Overall, our study provided a global view of the transcriptome during involution of GH transgenic and non-transgenic goats, which increases our understanding of the biology of involution in the goat.

RNA‑seq analysis of synovial fibroblasts in human rheumatoid arthritis.

The aim of the present study was to identify differentially expressed genes (DEGs) between individuals with rheumatoid arthritis(RA) and healthy controls, in order to provide a theoretical foundation for RA diagnosis and targeted gene therapy. Illumina mRNA sequence data (RNA‑Seq) corresponding to RA and control samples were downloaded from the Sequence Read Archive (SRA) database. Gene Ontology(GO) enrichment analysis was performed with the GOstat tool in order to identify over‑represented biological functions among DEGs, and the related Kyoto Encyclopedia of Genes and Genomes(KEGG) pathways were identified using the KEGG Automatic Annotation Server(KAAS). A total of 293DEGs were identified, among which 16DEGs have been previously shown to associate with RA, such as those encoding matrix metalloproteinase‑1 (MMP‑1), interleukin‑1 receptor type1(IL1R1), and chemokine (C-X3-C motif) ligand 1 (CX3CL1). GO functional annotation and enrichment analysis showed that the DEGs are enriched for 309GO terms, mainly protein‑protein interactions, membrane formation and stability. KEGG pathway analysis demonstrated that these DEGs are involved in 131pathways, including Wnt and calcium signaling, and cell adhesion molecule(CAM)-related pathways. In conclusion, the results provide both expansive and detailed insights into the molecular pathogenesis of RA, particularly with regards to the development of therapeutic targets, and may inspire further experimentation aiming to identify new strategies for RA treatment.

In Silico Identification of Novel Candidate Drug Targets in Haemophilus Influenzae Rd KW20

Background: Globally, respiratory diseases cause an estimated 1.9 million deaths per year. One of the most important aetiological organisms of both adult and childhood respiratory disease is Non-Typeable Haemophilus influenzae (NTHi). NTHi is frequently isolated from the respiratory tract during episodes of sinusitis, Otitis Media and pneumonia and is the most common cause of Chronic Obstructive Pulmonary Disease (COPD) and bronchiectasis exacerbations. Methods: The work has been effectively complemented with the compilation of the Database of Essential Genes (DEG) of H. influenzae Rd KW20. Each protein is subjected to BLASTP in NCBI server http://www.ncbi.nlm.nih.gov/blastp. The candidate drug targets are determined by KAAS (KEGG Automatic Annotation Server), KEGG ORTHOLOGY and KEGG GENES. Results: The given gram negative bacteria H. influenzae Rd KW20 has six distinguished domains i.e., cytoplasmic, cytoplasmic membrane, periplasmic, outer membrane, extracellular and unknown domains. Out of 642 essential genes, the predicted non-human Homologous are 412 in the different domain of given bacteria. With the help of KAAS (KEGG Automatic Annotation Server), KEGG ORTHOLOGY and KEGG GENES, we successfully identified 35 novel drug targets which have common metabolic pathways both in host and pathogen & pathogen specific metabolic pathways. Conclusion: The novel drug targets suggest those genes which are active in both the host and pathogen metabolic pathway and should be a pathogen specific metabolic pathway. The important drug target regions are vacJ, lepB, emrB, MurG & dgkA. vacJ is present in outer membrane while lepB, emrB, MurG&dgkA are present in cytoplasmic membrane. All these genes are fully sequenced and specifically annotated in KEGG PATHWAY.

Genome-wide analysis of regulatory proteases sequences identified through bioinformatics data mining in Taenia solium.

BackgroundCysticercosis remains a major neglected tropical disease of humanity in many regions, especially in sub-Saharan Africa, Central America and elsewhere. Owing to the emerging drug resistance and the inability of current drugs to prevent re-infection, identification of novel vaccines and chemotherapeutic agents against Taenia solium and related helminth pathogens is a public health priority. The T. solium genome and the predicted proteome were reported recently, providing a wealth of information from which new interventional targets might be identified. In order to characterize and classify the entire repertoire of protease-encoding genes of T. solium, which act fundamental biological roles in all life processes, we analyzed the predicted proteins of this cestode through a combination of bioinformatics tools. Functional annotation was performed to yield insights into the signaling processes relevant to the complex developmental cycle of this tapeworm and to highlight a suite of the proteases as potential intervention targets.ResultsWithin the genome of this helminth parasite, we identified 200 open reading frames encoding proteases from five clans, which correspond to 1.68% of the 11,902 protein-encoding genes predicted to be present in its genome. These proteases include calpains, cytosolic, mitochondrial signal peptidases, ubiquitylation related proteins, and others. Many not only show significant similarity to proteases in the Conserved Domain Database but have conserved active sites and catalytic domains. KEGG Automatic Annotation Server (KAAS) analysis indicated that ~60% of these proteases share strong sequence identities with proteins of the KEGG database, which are involved in human disease, metabolic pathways, genetic information processes, cellular processes, environmental information processes and organismal systems. Also, we identified signal peptides and transmembrane helices through comparative analysis with classes of important regulatory proteases. Phylogenetic analysis using Bayes approach provided support for inferring functional divergence among regulatory cysteine and serine proteases.ConclusionNumerous putative proteases were identified for the first time in T. solium, and important regulatory proteases have been predicted. This comprehensive analysis not only complements the growing knowledge base of proteolytic enzymes, but also provides a platform from which to expand knowledge of cestode proteases and to explore their biochemistry and potential as intervention targets.Electronic supplementary materialThe online version of this article (doi: 10.1186/1471-2164-15-428) contains supplementary material, which is available to authorized users.

De novo transcriptome analysis using 454 pyrosequencing of the Himalayan Mayapple, Podophyllum hexandrum

BackgroundThe Himalayan or Indian Mayapple (Podophyllum hexandrum Royle) produces podophyllotoxin, which is used in the production of semisynthetic anticancer drugs. High throughput transcriptome sequences or genomic sequence data from the Indian Mayapple are essential for further understanding of the podophyllotoxin biosynthetic pathway.Results454 pyrosequencing of a P. hexandrum cell culture normalized cDNA library generated 2,667,207 raw reads and 1,503,232 high quality reads, with an average read length of 138 bp. The denovo assembly was performed by Newbler using default and optimized parameters. The optimized parameter generated 40, 380 assembled sequences, comprising 12,940 contigs and 27,440 singlets which resulted in better assembly as compared to default parameters. BLASTX analysis resulted in the annotation of 40,380 contigs/singlet using a cut-off value of ≤1E-03. High similarity to Medicago truncatula using optimized parameters and to Populus trichocarpa using default parameters was noted. The Kyoto encyclopedia of genes and genomes (KEGG) analysis using KEGG Automatic Annotation Server (KAAS) combined with domain analysis of the assembled transcripts revealed putative members of secondary metabolism pathways that may be involved in podophyllotoxin biosynthesis. A proposed schematic pathway for phenylpropanoids and podophyllotoxin biosynthesis was generated. Expression profiling was carried out based on fragments per kilobase of exon per million fragments (FPKM). 1036 simple sequence repeats were predicted in the P. hexandrum sequences. Sixty-nine transcripts were mapped to 99 mature and precursor microRNAs from the plant microRNA database. Around 961 transcripts containing transcription factor domains were noted. High performance liquid chromatography analysis showed the peak accumulation of podophyllotoxin in 12-day cell suspension cultures. A comparative qRT-PCR analysis of phenylpropanoid pathway genes identified in the present data was performed to analyze their expression patterns in 12-day cell culture, callus and rhizome.ConclusionsThe present data will help the identification of the potential genes and transcription factors involved in podophyllotoxin biosynthesis in P. hexandrum. The assembled transcripts could serve as potential candidates for marker discovery and conservation, which should form the foundations for future endeavors.

Transcriptome analyses of a Chinese hazelnut species Corylus mandshurica.

BackgroundCorylus was renowned for its production of hazelnut and taxol. To understand the local adaptation of Chinese species and speed up breeding efforts in China, we analyzed the leaf transcriptome of Corylus mandshurica, which had a high tolerance to fungal infections and cold.ResultsA total of 12,255,030 clean pair-end reads were generated and then assembled into 37,846 Expressed Sequence Tag (EST) sequences. During functional annotation, 26,565 ESTs were annotated with Gene Ontology (GO) terms using Blast2go and 11,056 ESTs were grouped into the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using KEGG Automatic Annotation Server (KAAS). We identified 45 ESTs that were homologous to enzymes and transcription factors responsible for taxol synthesis. The most differentiated orthologs between C. mandshurica and a European congener, C. avellana, were enriched in stress tolerance to fungal resistance and cold.ConclusionsIn this study, we detected a set of genes related to taxol synthesis in a taxol-producing angiosperm species for the first time and found a close relationship between most differentiated genes and different adaptations to fungal infection and cold in C. mandshurica and C. avellana. These findings provided tools to improve our understanding of local adaptation, genetic breeding and taxol production in hazelnut.

Generation and analysis of expressed sequence tags from the medicinal plant Salvia miltiorrhiza

Salvia miltiorrhiza Bge. is a well-known traditional Chinese herb. Its roots have been formulated and used clinically for the treatment of various diseases. However, little genetic information has so far been available and this fact has become a major obstacle for molecular studies. To address this lack of genetic information, an Expressed Sequence Tag (EST) library from whole plantlets of S. miltiorrhiza was generated. From the 12959 cDNA clones that were randomly selected and subjected to single-pass sequencing from their 5' ends, 10288 ESTs (with sizes > or = 100 bp) were selected and assembled into 1288 contigs, leaving 2937 singletons, for a total of 4225 unigenes. These were analyzed using BLASTX (against protein databases), RPS-BLAST (against a conserved domain database) as well as the web-based KEGG Automatic Annotation Server for metabolic enzyme assignment. Based on the metabolic enzyme assignment, expression patterns of 14 secondary metabolic enzyme genes in different organs and under different treatments were verified using real-time PCR analysis. Additionally, a total of 122 microsatellites were identified from the ESTs, with 89 having sufficient flanking sequences for primer design. This set of ESTs represents a significant proportion of the S. miltiorrhiza transcriptome, and gives preliminary insights into the gene complement of S. miltiorrhiza. They will prove useful for uncovering secondary metabolic pathways, analyzing cDNA-array based gene expression, genetic manipulation to improve yield of desirable secondary products, and molecular marker identification.

KAAS: an automatic genome annotation and pathway reconstruction server

The number of complete and draft genomes is rapidly growing in recent years, and it has become increasingly important to automate the identification of functional properties and biological roles of genes in these genomes. In the KEGG database, genes in complete genomes are annotated with the KEGG orthology (KO) identifiers, or the K numbers, based on the best hit information using Smith–Waterman scores as well as by the manual curation. Each K number represents an ortholog group of genes, and it is directly linked to an object in the KEGG pathway map or the BRITE functional hierarchy. Here, we have developed a web-based server called KAAS (KEGG Automatic Annotation Server: http://www.genome.jp/kegg/kaas/) i.e. an implementation of a rapid method to automatically assign K numbers to genes in the genome, enabling reconstruction of KEGG pathways and BRITE hierarchies. The method is based on sequence similarities, bi-directional best hit information and some heuristics, and has achieved a high degree of accuracy when compared with the manually curated KEGG GENES database.

Overview of KEGG applications to omics-related research

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a bioinformatics resource for analyzing cells and organisms from not only the genomic perspective but also a high-level perspective, integrating together genomic, chemical and network information. Accessible from http://www.genome.jp/, it basically consists of four databases: PATHWAY, GENES, LIGAND and BRITE. The KEGG PATHWAY database provides pathway diagrams, represented as networks of interactions that occur in the cell. These can be viewed according to organism or as generic “reference” maps. KEGG GENES is the collection of genes that are found in the complete genomes that are registered in KEGG. It serves as the repository of genes linked from the pathway diagrams. KEGG LIGAND is a database of compounds, glycans, reactions and enzymes. Finally, KEGG BRITE contains the KEGG Orthology, or KO, which is a manually curated identification system of gene orthologs. It also contains classifications of chemical compounds and enzymatic reactions. KO has become an indispensable tool for the functional annotation of new genomes, and it plays a key part in the KAAS (KEGG Automatic Annotation Server) tool.