Genome Annotation Information Research Articles

Genome Analysis of the Yeast M14, an Industrial Brewing Yeast Strain Widely Used in China

The lager brewing yeast M14 is the most widely used yeast strain in the high gravity brewing process in China. To investigate the characteristics of this strain, the genome of the yeast M14 was sequenced and the genome annotation information is presented in this study. The current assembly contained 133 scaffolds and its total size was around 23 Mb with a GC content of 38.98%. The brewing yeast M14 is a hybrid Saccharomyces cerevisiae × Saccharomyces uvarum at the genomic level and its genome is comprised of one circular mitochondrial genome originating from S. uvarum. Furthermore, the functions of the 9,796 protein coding genes were annotated and their functions were analyzed using the Swiss-Prot database. Among them, the key genes responsible for typical lager brewing yeast characteristics, such as maltotriose uptake and sulfite production, were annotated and analyzed. Interestingly, nine specific genes present in the brewing yeast M14 were not found in the genome of either S. uvarum CBS 7001 or S. cerevisiae S288C, which are very close to strain M14 in the phylogenetic relationship. These nine genes encoding proteins were melibiase, DNA replication protein, fructose symporter, hypothetical protein, hypothetical protein M773_09155, LIF1, minor spike protein H, ribosomal protein S27, and mitochondrial chaperones, respectively. The genome sequence of the yeast strain M14 provides a new tool to better understand brewing yeast behavior in industrial beer production.

Sequential analysis of genome of Thelazia callipaeda

To investigate the molecular characteristics of genome sequence of Thelazia callipaeda (T. cp). The obtained T. cp genome assembling data were annotated by using a combination of ab initio gene by softwares, GeneMark and GeneID, and the homology of the experimentally confirmed genes was predicted by software GeMoMa. The results were integrated by software EVM to predict all genes of genome. The obtained genes were annotated in the common public database and three dedicated databases (CAZyme, TCDB and PHI), respectively. The Scaffolds and Contigs gene structure of T. cp genome (79.34 Mb) was analyzed, and a total of 6 333 genes were obtained. The sequence search was conducted in the public databases using BLASTx, of which 97.85% of the genes could be annotated. The genes annotated in the NR database were the most (98.69%), and those enriched in the KEGG pathway were the least (50.50%). The functional genes were blasted by KOG database and totally 4 517 genes were found. The three special databases (CAZyme, TCDB and PHI) were used to annotate all the genes, and 136, 139 and 1 498 genes were assigned respectively, and the number of genes in the PHI database was the largest. In the cytochrome proprietary database, 238 cytochrome P450 genes were predicted. We have preliminarily revealed the T. cp genome structure characteristics and annotation information, and totally 6 333 genes are obtained.

Incorporating Functional Genomic Information to Enhance Polygenic Signal and Identify Variants Involved in Gene-by-Environment Interaction for Young Adult Alcohol Problems.

Characterizing aggregate genetic risk for alcohol misuse and identifying variants involved in gene-by-environment (G×E) interaction effects has so far been a major challenge. We hypothesized that functional genomic information could be used to enhance detection of polygenic signal underlying alcohol misuse and to prioritize identification of single nucleotide polymorphisms (SNPs) most likely to exhibit G×E effects. We examined these questions in the young adult FinnTwin12 sample (n=1,170). We used genomewide association estimates from an independent sample to derive 2 types of polygenic scores for alcohol problems in FinnTwin12. Genomewide polygenic scores included all SNPs surpassing a designated p-value threshold. DNase polygenic scores were a subset of the genomewide polygenic scores including only variants in DNase I hypersensitive sites (DHSs), which are open chromatin marks likely to index regions with a regulatory function. We conducted parallel analyses using height as a nonpsychiatric model phenotype to evaluate the consistency of effects. For the G×E analyses, we examined whether SNPs in DHSs were overrepresented among SNPs demonstrating significant G×E effects in an interaction between romantic relationship status and intoxication frequency. Contrary to our expectations, we found that DNase polygenic scores were not more strongly predictive of alcohol problems than conventional polygenic scores. However, variants in DNase polygenic scores had per-SNP effects that were up to 1.4 times larger than variants in conventional polygenic scores. This same pattern of effects was also observed in supplementary analyses with height. In G×E models, SNPs in DHSs were modestly overrepresented among SNPs with significant interaction effects for intoxication frequency. These findings highlight the potential utility of integrating functional genomic annotation information to increase the signal-to-noise ratio in polygenic scores and identify genetic variants that may be most susceptible to environmental modification.

Characteristics of Color Development in Seeds of Brown- and Yellow-Seeded Heading Chinese Cabbage and Molecular Analysis of Brsc, the Candidate Gene Controlling Seed Coat Color

The proanthocyanidin (PA) is the main flavonoids which affect the seed coat color in Brassica species. In this paper, characteristics of color development and accumulation of flavonoids were analyzed in the seeds of brown-seeded (B147) and yellow-seeded (B80) heading Chinese cabbage (Brassica rapa L. ssp. Pekinensis). It is found that the content of phenolic compounds in B147 were significantly more than that of B80 by using dimethylaminocinnamaldehyde (DMACA) staining and toluidine blue O (TBO) staining. In previous studies, the locus associated with seed coat color has been mapped. The results of whole genome re-sequencing showed that there are large fragment deletions variation in the mapping region between the brown-seeded parent ‘92S105’ and the yellow-seeded parent ‘91-125.’ Based on the B. rapa genome annotation information, the TRANSPARENT TESTA GLABRA 1 (TTG1), is likely to be the candidate gene controlling seed coat color. A 94-base deletion was found in the 96th base downstream of the initiation codon in the TTG1 of yellow seed, thus, the termination codon TGA was occurred in the 297th base which makes the full length of TTG1 of yellow seed is 300 bp. Based on the differential sequences of TTG1 of brown and yellow seed, a functional marker, Brsc-yettg1, was developed to detect the variation of TTG1. Quantitative real-time PCR analysis of BrTTG1 in different tissues showed that expression levels of BrTTG1 was not tissue-specific. During the whole seed development period, the expression of BrTTG1 in B147 was higher than that of B80. The expression levels of four structural genes, BrDFR, BrANS, BrANR1, and BrANR2 in B147 were also higher than those in B80. The co-segregation molecular markers obtained in this report and TTG1 related information provide a basis for further understanding of the molecular mechanism of seed coat color in heading Chinese cabbage.

GUIDEseq: a bioconductor package to analyze GUIDE-Seq datasets for CRISPR-Cas nucleases

BackgroundGenome editing technologies developed around the CRISPR-Cas9 nuclease system have facilitated the investigation of a broad range of biological questions. These nucleases also hold tremendous promise for treating a variety of genetic disorders. In the context of their therapeutic application, it is important to identify the spectrum of genomic sequences that are cleaved by a candidate nuclease when programmed with a particular guide RNA, as well as the cleavage efficiency of these sites. Powerful new experimental approaches, such as GUIDE-seq, facilitate the sensitive, unbiased genome-wide detection of nuclease cleavage sites within the genome. Flexible bioinformatics analysis tools for processing GUIDE-seq data are needed.ResultsHere, we describe an open source, open development software suite, GUIDEseq, for GUIDE-seq data analysis and annotation as a Bioconductor package in R. The GUIDEseq package provides a flexible platform with more than 60 adjustable parameters for the analysis of datasets associated with custom nuclease applications. These parameters allow data analysis to be tailored to different nuclease platforms with different length and complexity in their guide and PAM recognition sequences or their DNA cleavage position. They also enable users to customize sequence aggregation criteria, and vary peak calling thresholds that can influence the number of potential off-target sites recovered. GUIDEseq also annotates potential off-target sites that overlap with genes based on genome annotation information, as these may be the most important off-target sites for further characterization. In addition, GUIDEseq enables the comparison and visualization of off-target site overlap between different datasets for a rapid comparison of different nuclease configurations or experimental conditions. For each identified off-target, the GUIDEseq package outputs mapped GUIDE-Seq read count as well as cleavage score from a user specified off-target cleavage score prediction algorithm permitting the identification of genomic sequences with unexpected cleavage activity.ConclusionThe GUIDEseq package enables analysis of GUIDE-data from various nuclease platforms for any species with a defined genomic sequence. This software package has been used successfully to analyze several GUIDE-seq datasets. The software, source code and documentation are freely available at http://www.bioconductor.org/packages/release/bioc/html/GUIDEseq.html.

Further enhancement of FR901469 productivity by co-overexpression of cpcA, a cross-pathway control gene, and frbF in fungal sp. No. 11243

FR901469 is a secondary metabolite with antifungal activity, produced by fungal sp. No. 11243. In our previous study, we constructed the frbF overexpression mutant (TFH2-2) from the wild-type strain. FR901469 productivity of TFH2-2 was 3.4 times higher than that of the wild-type strain. To further enhance FR901469 productivity in TFH2-2, we attempted to find genes from the genome that limited the productivity as bottlenecks in this study. Based on both correlation analysis of gene expression level against FR901469 productivity and genome annotation information, the cross-pathway control gene A (cpcA) was most predicted as the bottleneck. The cpcA and frbF co-overexpression mutant named TFCH3 was then constructed from TFH2-2. As a result, FR901469 productivity of TFCH3 was enhanced at 1.8 times higher than that of TFH2-2. Transcriptome analysis revealed that many genes involved in amino acid biosynthesis and encoding tRNA ligases were significantly upregulated in TFCH3, which implied increase of amino acids as the substrates of FR901469 would be a reason of further productivity enhancement.

ProBAMsuite, a Bioinformatics Framework for Genome-Based Representation and Analysis of Proteomics Data

To facilitate genome-based representation and analysis of proteomics data, we developed a new bioinformatics framework, proBAMsuite, in which a central component is the protein BAM (proBAM) file format for organizing peptide spectrum matches (PSMs)1 within the context of the genome. proBAMsuite also includes two R packages, proBAMr and proBAMtools, for generating and analyzing proBAM files, respectively. Applying proBAMsuite to three recently published proteomics datasets, we demonstrated its utility in facilitating efficient genome-based sharing, interpretation, and integration of proteomics data. First, the interpretation of proteomics data is significantly enhanced with the rich genomic annotation information. Second, PSMs can be easily reannotated using user-specified gene annotation schemes and assembled into both protein and gene identifications. Third, using the genome as a common reference, proBAMsuite facilitates seamless proteomics and proteogenomics data integration. Finally, proBAM files can be readily visualized in genome browsers and thus bring proteomics data analysis to a general audience beyond the proteomics community. Results from this study establish proBAMsuite as a useful bioinformatics framework for proteomics and proteogenomics research.

RSIADB, a collective resource for genome and transcriptome analyses in Rhizoctonia solani AG1 IA.

Rice [Oryza sativa (L.)] feeds more than half of the world’s population. Rhizoctonia solani is a major fungal pathogen of rice causing extreme crop losses in all rice-growing regions of the world. R. solani AG1 IA is a major cause of sheath blight in rice. In this study, we constructed a comprehensive and user-friendly web-based database, RSIADB, to analyse its draft genome and transcriptome. The database was built using the genome sequence (10 489 genes) and annotation information for R. solani AG1 IA. A total of six RNAseq samples of R. solani AG1 IA were also analysed, corresponding to 10, 18, 24, 32, 48 and 72 h after infection of rice leaves.The RSIADB database enables users to search, browse, and download gene sequences for R. solani AG1 IA, and mine the data using BLAST, Sequence Extractor, Browse and Construction Diagram tools that were integrated into the database. RSIADB is an important genomic resource for scientists working with R. solani AG1 IA and will assist researchers in analysing the annotated genome and transcriptome of this pathogen. This resource will facilitate studies on gene function, pathogenesis factors and secreted proteins, as well as provide an avenue for comparative analyses of genes expressed during different stages of infection.Database URL: http://genedenovoweb.ticp.net:81/rsia/index.php

Draft Genome Sequence of <i>Bacillus thuringiensis</i> Strain S3076-1

Abstract Bacillus thuringiensis S3076-1 can produce some parasporal crystal with a variety of forms, which may have insecticidal activity to Lepidoptera insects. In this study, the de novo whole genome sequence of Bt S3076-1 was carried out by employing Illumina HiSeq 2000 and generated 1.42 Gb raw data. Through the original reads data quality control, filtering and SOAPdenovo short sequence assembly to get a total of 231 scaffolds, the genome size was 6.42 Mb, N50 length was 97325 bp, GC content of 34.81%. The genomic information of 14 Bt strains in NCBI database were selected as the reference, and the genomic sequences of Scaffolds were assembled to obtain the 3 replicators. Among them, the size of the genome was 5.72 Mb, the content of GC was about 35.00%, and the length of the genomic sequences of the four plasmids were 181 kb and 518 kb, and the GC contents were 33.10% and 33.13%, respectively. Based on the genome sequence and annotation information, the genome of the visual sketch was created by GCviewer server. The whole genome sequencing and construction of Bt S3076-1 provide the basis for the functional study of the strain, and contribute to the identification of Bt S3076-1 insecticidal toxin protein.

Carotenoid identification and molecular analysis of carotenoid isomerase-encoding BrCRTISO, the candidate gene for inner leaf orange coloration in Chinese cabbage

In Chinese cabbage (Brassica rapa L. ssp. pekinensis), the locus associated with orange inner leaves has been mapped. The mechanism responsible for carotenoid pigment synthesis in orange inner leaves of Chinese cabbage is still unclear, however, and limited molecular research has been conducted on the candidate gene. In this study, high-performance liquid chromatographic analysis indicated that orange leaf coloration in Chinese cabbage is due to accumulation of prolycopene and other carotenoid pigments. Mapping of simple sequence repeat and insertion/deletion markers from 269 individuals of an F2 population pinpointed the Bror locus within a 1.0-cM interval, with the physical distance of 65.555 kb, on B. rapa chromosome A09. Using B. rapa genome annotation information, the candidate gene BrCRTISO (Bra031539) was predicted to reside within the mapped Bror locus. Coding and promoter regions were sequenced in white- and orange-type homozygous lines. A 6-bp deletion and 53 single nucleotide polymorphisms (SNPs), corresponding to two glutamic acid deletions and 12 amino acid mutations, were identified in the coding sequence of the orange-type cultivar. The orange-type promoter was approximately 141 bp shorter than that of the white type, and many SNPs were located in the promoter region. Two functional markers were designed and found to cosegregate with the Bror locus in F2 individuals. Expression analysis demonstrated that BrCRTISO transcription levels were higher in white-type Chinese cabbage than in the orange-type cultivar. These results offer insights into the molecular mechanism of carotenoid biosynthesis and lay the foundation for molecular breeding of orange-type Chinese cabbage.

Kernel-based whole-genome prediction of complex traits: a review.

Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

VCGDB: a dynamic genome database of the Chinese population

BackgroundThe data released by the 1000 Genomes Project contain an increasing number of genome sequences from different nations and populations with a large number of genetic variations. As a result, the focus of human genome studies is changing from single and static to complex and dynamic. The currently available human reference genome (GRCh37) is based on sequencing data from 13 anonymous Caucasian volunteers, which might limit the scope of genomics, transcriptomics, epigenetics, and genome wide association studies.DescriptionWe used the massive amount of sequencing data published by the 1000 Genomes Project Consortium to construct the Virtual Chinese Genome Database (VCGDB), a dynamic genome database of the Chinese population based on the whole genome sequencing data of 194 individuals. VCGDB provides dynamic genomic information, which contains 35 million single nucleotide variations (SNVs), 0.5 million insertions/deletions (indels), and 29 million rare variations, together with genomic annotation information. VCGDB also provides a highly interactive user-friendly virtual Chinese genome browser (VCGBrowser) with functions like seamless zooming and real-time searching. In addition, we have established three population-specific consensus Chinese reference genomes that are compatible with mainstream alignment software.ConclusionsVCGDB offers a feasible strategy for processing big data to keep pace with the biological data explosion by providing a robust resource for genomics studies; in particular, studies aimed at finding regions of the genome associated with diseases.

Integrated analysis of whole-genome paired-end and mate-pair sequencing data for identifying genomic structural variations in multiple myeloma.

We present a pipeline to perform integrative analysis of mate-pair (MP) and paired-end (PE) genomic DNA sequencing data. Our pipeline detects structural variations (SVs) by taking aligned sequencing read pairs as input and classifying these reads into properly paired and discordantly paired categories based on their orientation and inferred insert sizes. Recurrent SV was identified from the discordant read pairs. Our pipeline takes into account genomic annotation and genome repetitive element information to increase detection specificity. Application of our pipeline to whole-genome MP and PE sequencing data from three multiple myeloma cell lines (KMS11, MM.1S, and RPMI8226) recovered known SVs, such as heterozygous TRAF3 deletion, as well as a novel experimentally validated SPI1 – ZNF287 inter-chromosomal rearrangement in the RPMI8226 cell line.

IDBA-tran: a more robust de novo de Bruijn graph assembler for transcriptomes with uneven expression levels

Motivation: RNA sequencing based on next-generation sequencing technology is effective for analyzing transcriptomes. Like de novo genome assembly, de novo transcriptome assembly does not rely on any reference genome or additional annotation information, but is more difficult. In particular, isoforms can have very uneven expression levels (e.g. 1:100), which make it very difficult to identify low-expressed isoforms. One challenge is to remove erroneous vertices/edges with high multiplicity (produced by high-expressed isoforms) in the de Bruijn graph without removing correct ones with not-so-high multiplicity from low-expressed isoforms. Failing to do so will result in the loss of low-expressed isoforms or having complicated subgraphs with transcripts of different genes mixed together due to erroneous vertices/edges.Contributions: Unlike existing tools, which remove erroneous vertices/edges with multiplicities lower than a global threshold, we use a probabilistic progressive approach to iteratively remove them with local thresholds. This enables us to decompose the graph into disconnected components, each containing a few genes, if not a single gene, while retaining many correct vertices/edges of low-expressed isoforms. Combined with existing techniques, IDBA-Tran is able to assemble both high-expressed and low-expressed transcripts and outperform existing assemblers in terms of sensitivity and specificity for both simulated and real data.Availability: http://www.cs.hku.hk/∼alse/idba_tran.Contact: chin@cs.hku.hkSupplementary information: Supplementary data are available at Bioinformatics online.

Robustness analysis of a constraint-based metabolic model links cell growth and proteomics of Thermoanaerobacter tengcongensis under temperature perturbation

The integration of omic data with metabolic networks has been demonstrated to be an effective approach to elucidate the underlying metabolic mechanisms in life. Because the metabolic pathways of Thermoanaerobacter tengcongensis (T. tengcongensis) are incomplete, we used a 1-(13)C-glucose culture to monitor intracellular isotope-labeled metabolites by GC/MS and identified the gap gene in glucose catabolism, Re-citrate synthase. Based on genome annotation and biochemical information, we reconstructed the metabolic network of glucose metabolism and amino acid synthesis in T. tengcongensis, including 253 reactions, 227 metabolites, and 236 genes. Furthermore, we performed constraint based modeling (CBM)-derived robustness analysis on the model to study the dynamic changes of the metabolic network. By perturbing the culture temperature from 75 to 55 °C, we collected the bacterial growth rates and differential proteomes. Assuming that protein abundance changes represent metabolic flux variations, we proposed that the robustness analysis of the CBM model could decipher the effect of proteome change on the bacterial growth under perturbation. For approximately 73% of the reactions, the predicted cell growth changes due to such reaction flux variations matched the observed cell growth data. Our study, therefore, indicates that differential proteome data can be integrated with metabolic network modeling and that robustness analysis is a strong method for representing the dynamic change in cell phenotypes under perturbation.

Tracking and coordinating an international curation effort for the CCDS Project

The Consensus Coding Sequence (CCDS) collaboration involves curators at multiple centers with a goal of producing a conservative set of high quality, protein-coding region annotations for the human and mouse reference genome assemblies. The CCDS data set reflects a ‘gold standard’ definition of best supported protein annotations, and corresponding genes, which pass a standard series of quality assurance checks and are supported by manual curation. This data set supports use of genome annotation information by human and mouse researchers for effective experimental design, analysis and interpretation. The CCDS project consists of analysis of automated whole-genome annotation builds to identify identical CDS annotations, quality assurance testing and manual curation support. Identical CDS annotations are tracked with a CCDS identifier (ID) and any future change to the annotated CDS structure must be agreed upon by the collaborating members. CCDS curation guidelines were developed to address some aspects of curation in order to improve initial annotation consistency and to reduce time spent in discussing proposed annotation updates. Here, we present the current status of the CCDS database and details on our procedures to track and coordinate our efforts. We also present the relevant background and reasoning behind the curation standards that we have developed for CCDS database treatment of transcripts that are nonsense-mediated decay (NMD) candidates, for transcripts containing upstream open reading frames, for identifying the most likely translation start codons and for the annotation of readthrough transcripts. Examples are provided to illustrate the application of these guidelines.Database URL: http://www.ncbi.nlm.nih.gov/CCDS/CcdsBrowse.cgi

Incorporating Genomic Annotation into a Hidden Markov Model for DNA Methylation Tiling Array Data

Abstract DNA methylation is an epigenetic modification that plays an important role in many biological processes. To better understand this epigenetic mechanism, genome-wide investigations of DNA methylation can be conducted via tiling array technology. In such DNA methylation profiling studies, statistical methods are employed to estimate the DNA methylation status of each probe represented on the tiling array. Although many of the current methods account for the inherent dependency between neighboring probes, they do not effectively utilize the wealth of information available in genomic annotation databases. Since previous studies indicate that DNA methylation patterns of some organisms differ by genomic element (e.g., gene, intergenic region), such genomic annotation information may be useful in predicting DNA methylation status. In this work, a novel statistical model is proposed within the hidden Markov model framework to incorporate genomic annotation information in the prediction of DNA methylation status. The advantages of incorporating genomic annotation via the proposed method are investigated through a simulation study and real data applications.

Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052

BackgroundSolventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol--an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i) naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii) can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis). Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications.ResultsWe present the first genome-scale metabolic model (iCM925) for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases--highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate iCM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles--suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations--constrained to reflect experimental rates--originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fisher's exact test). Inhibition of the hydrogenase reaction was found to have a strong effect on butanol formation--as experimentally observed.ConclusionsMicrobial production of butanol by C. beijerinckii offers a promising, sustainable, method for generation of this important chemical and potential biofuel. iCM925 is a predictive model that can accurately reproduce physiological behavior and provide insight into the underlying mechanisms of microbial butanol production. As such, the model will be instrumental in efforts to better understand, and metabolically engineer, this microorganism for improved butanol production.

Identification of key mechanisms controlling gene expression in Leishmania infected macrophages using genome-wide promoter analysis

The present study describes the in silico prediction of the regulatory network of Leishmania infected human macrophages. The construction of the gene regulatory network requires the identification of Transcription Factor Binding Sites (TFBSs) in the regulatory regions (promoters, enhancers) of genes that are regulated upon Leishmania infection. The promoters of human, mouse, rat, dog and chimpanzee genes were first identified in the whole genomes using available experimental data on full length cDNA sequences or deep CAGE tag data (DBTSS, FANTOM3, FANTOM4), mRNA models (ENSEMBL), or using hand annotated data (EPD, TRANSFAC). A phylogenetic footprinting analysis and a MATCH analysis of the promoter sequences were then performed to predict TFBS. Then, an SQL database that integrates all results of promoter analysis as well as other genome annotation information obtained from ENSEMBL, TRANSFAC, TRED (Transcription Regulatory Element Database), ORegAnno and the ENCODE project, was established. Finally publicly available expression data from human Leishmania infected macrophages were analyzed using the genome-wide information on predicted TFBS with the computer system ExPlain™. The gene regulatory network was constructed and activated signal transduction pathways were revealed. The Irak1 pathway was identified as a key pathway regulating gene expression changes in Leishmania infected macrophages.

Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement

BackgroundPichia pastoris has been recognized as an effective host for recombinant protein production. A number of studies have been reported for improving this expression system. However, its physiology and cellular metabolism still remained largely uncharacterized. Thus, it is highly desirable to establish a systems biotechnological framework, in which a comprehensive in silico model of P. pastoris can be employed together with high throughput experimental data analysis, for better understanding of the methylotrophic yeast's metabolism.ResultsA fully compartmentalized metabolic model of P. pastoris (iPP668), composed of 1,361 reactions and 1,177 metabolites, was reconstructed based on its genome annotation and biochemical information. The constraints-based flux analysis was then used to predict achievable growth rate which is consistent with the cellular phenotype of P. pastoris observed during chemostat experiments. Subsequent in silico analysis further explored the effect of various carbon sources on cell growth, revealing sorbitol as a promising candidate for culturing recombinant P. pastoris strains producing heterologous proteins. Interestingly, methanol consumption yields a high regeneration rate of reducing equivalents which is substantial for the synthesis of valuable pharmaceutical precursors. Hence, as a case study, we examined the applicability of P. pastoris system to whole-cell biotransformation and also identified relevant metabolic engineering targets that have been experimentally verified.ConclusionThe genome-scale metabolic model characterizes the cellular physiology of P. pastoris, thus allowing us to gain valuable insights into the metabolism of methylotrophic yeast and devise possible strategies for strain improvement through in silico simulations. This computational approach, combined with synthetic biology techniques, potentially forms a basis for rational analysis and design of P. pastoris metabolic network to enhance humanized glycoprotein production.