Coronary artery disease (CAD) is a complex, multifactorial disease and a leading cause of mortality world wide. Over the past decades, great efforts have been made to elucidate the underlying genetic basis of CAD and massive data have been accumulated. To integrate these data together and to provide a useful resource for researchers, we developed the CADgene, a comprehensive database for CAD genes. We manually extracted CAD-related evidence for more than 300 candidate genes for CAD from over 1300 publications of genetic studies. We classified these candidate genes into 12 functional categories based on their roles in CAD. For each gene, we extracted detailed information from related studies (e.g. the size of case–control, population, SNP, odds ratio, P-value, etc.) and made useful annotations, which include general gene information, Gene Ontology annotations, KEGG pathways, protein–protein interactions and others. Besides the statistical number of studies for each gene, CADgene also provides tools to search and show the most frequently studied candidate genes. In addition, CADgene provides cumulative data from 11 publications of CAD-related genome-wide association studies. CADgene has a user-friendly web interface with multiple browse and search functions. It is freely available at http://www.bioguo.org/CADgene/.

Human Bex2 (brain expressed X-linked, hBex2) is highly expressed in the embryonic brain, but its function remains unknown. We have identified that LMO2, a LIM-domain containing transcriptional factor, specifically interacts with hBex2 but not with mouse Bex1 and Bex2. The interaction was confirmed both by pull-down with GST-hBex2 and by coimmunoprecipitation assays in vivo. Using electrophoretic mobility shift assay, we have demonstrated the physical interaction of hBex2 and LMO2 as part of a DNA-binding protein complex. We have also shown that hBex2 can enhance the transcriptional activity of LMO2 in vivo. Furthermore, using mammalian two-hybrid analysis, we have identified a neuronal bHLH protein, NSCL2, as a novel binding partner for LMO2. We then showed that LMO2 could up-regulate NSCL2-dependent transcriptional activity, and hBex2 augmented this effect. Thus, hBex2 may act as a specific regulator during embryonic development by modulating the transcriptional activity of a novel E-box sequence-binding complex that contains hBex2, LMO2, NSCL2 and LDB1.

The identification of regulatory motifs is important for the study of gene expression. Here we present a suite of programs that we have developed to search for regulatory sequence motifs: (i) BioProspector, a Gibbs-sampling-based program for predicting regulatory motifs from co-regulated genes in prokaryotes or lower eukaryotes; (ii) CompareProspector, an extension to BioProspector which incorporates comparative genomics features to be used for higher eukaryotes; (iii) MDscan, a program for finding protein–DNA interaction sites from ChIP-on-chip targets. All three programs examine a group of sequences that may share common regulatory motifs and output a list of putative motifs as position-specific probability matrices, the individual sites used to construct the motifs and the location of each site on the input sequences. The web servers and executables can be accessed at http://seqmotifs.stanford.edu.

The Human Metabolome Database (HMDB) (www.hmdb.ca) is a resource dedicated to providing scientists with the most current and comprehensive coverage of the human metabolome. Since its first release in 2007, the HMDB has been used to facilitate research for nearly 1000 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 3.0) has been significantly expanded and enhanced over the 2009 release (version 2.0). In particular, the number of annotated metabolite entries has grown from 6500 to more than 40 000 (a 600% increase). This enormous expansion is a result of the inclusion of both ‘detected’ metabolites (those with measured concentrations or experimental confirmation of their existence) and ‘expected’ metabolites (those for which biochemical pathways are known or human intake/exposure is frequent but the compound has yet to be detected in the body). The latest release also has greatly increased the number of metabolites with biofluid or tissue concentration data, the number of compounds with reference spectra and the number of data fields per entry. In addition to this expansion in data quantity, new database visualization tools and new data content have been added or enhanced. These include better spectral viewing tools, more powerful chemical substructure searches, an improved chemical taxonomy and better, more interactive pathway maps. This article describes these enhancements to the HMDB, which was previously featured in the 2009 NAR Database Issue. (Note to referees, HMDB 3.0 will go live on 18 September 2012.).

The RNA transcriptome varies in response to cellular differentiation as well as environmental factors, and can be characterized by the diversity and abundance of transcript isoforms. Differential transcription analysis, the detection of differences between the transcriptomes of different cells, may improve understanding of cell differentiation and development and enable the identification of biomarkers that classify disease types. The availability of high-throughput short-read RNA sequencing technologies provides in-depth sampling of the transcriptome, making it possible to accurately detect the differences between transcriptomes. In this article, we present a new method for the detection and visualization of differential transcription. Our approach does not depend on transcript or gene annotations. It also circumvents the need for full transcript inference and quantification, which is a challenging problem because of short read lengths, as well as various sampling biases. Instead, our method takes a divide-and-conquer approach to localize the difference between transcriptomes in the form of alternative splicing modules (ASMs), where transcript isoforms diverge. Our approach starts with the identification of ASMs from the splice graph, constructed directly from the exons and introns predicted from RNA-seq read alignments. The abundance of alternative splicing isoforms residing in each ASM is estimated for each sample and is compared across sample groups. A non-parametric statistical test is applied to each ASM to detect significant differential transcription with a controlled false discovery rate. The sensitivity and specificity of the method have been assessed using simulated data sets and compared with other state-of-the-art approaches. Experimental validation using qRT-PCR confirmed a selected set of genes that are differentially expressed in a lung differentiation study and a breast cancer data set, demonstrating the utility of the approach applied on experimental biological data sets. The software of DiffSplice is available at http://www.netlab.uky.edu/p/bioinfo/DiffSplice.

Long non-coding RNAs (lncRNAs) that have no protein-coding capacity make up a large proportion of the transcriptome of various species. Many lncRNAs are expressed within the animal central nervous system in spatial- and temporal-specific patterns, indicating that lncRNAs play important roles in cellular processes, neural development, and even in cognitive and behavioral processes. However, relatively little is known about their in vivo functions and underlying molecular mechanisms in the nervous system. Here, we report a neural-specific Drosophila lncRNA, CASK regulatory gene (CRG), which participates in locomotor activity and climbing ability by positively regulating its neighboring gene CASK (Ca2+/calmodulin-dependent protein kinase). CRG deficiency led to reduced locomotor activity and a defective climbing ability—phenotypes that are often seen in CASK mutant. CRG mutant also showed reduced CASK expression level while CASK over-expression could rescue the CRG mutant phenotypes in reciprocal. At the molecular level, CRG was required for the recruitment of RNA polymerase II to the CASK promoter regions, which in turn enhanced CASK expression. Our work has revealed new functional roles of lncRNAs and has provided insights to explore the pathogenesis of neurological diseases associated with movement disorders.

RecR is an important recombination mediator protein in the RecFOR pathway. RecR together with RecO and RecF facilitates RecA nucleoprotein filament formation and homologous pairing. Structural and biochemical studies of Thermoanaerobacter tengcongensis RecR (TTERecR) and its series mutants revealed that TTERecR uses the N-N dimer as a basic functional unit to interact with TTERecO monomer. Two TTERecR N-N dimers form a ring-shaped tetramer via an interaction between their C-terminal regions. The tetramer is a result of crystallization only. Hydrophobic interactions between the entire helix-hairpin-helix domains within the N-terminal regions of two TTERecR monomers are necessary for formation of a RecR functional N-N dimer. The TTERecR N-N dimer conformation also affects formation of a hydrophobic patch, which creates a binding site for TTERecO in the TTERecR Toprim domain. In addition, we demonstrate that TTERecR does not bind single-stranded DNA (ssDNA) and binds double-stranded DNA very weakly, whereas TTERecOR complex can stably bind DNA, with a higher affinity for ssDNA than double-stranded DNA. Based on these results, we propose an interaction model for the RecOR:ssDNA complex.

YdiV is a negative regulator of cell motility. It interacts with FlhD4C2 complex, a product of flagellar master operon, which works as the transcription activator of all other flagellar operons. Here, we report the crystal structures of YdiV and YdiV2–FlhD2 complex at 1.9 Å and 2.9 Å resolutions, respectively. Interestingly, YdiV formed multiple types of complexes with FlhD4C2. YdiV1–FlhD4C2 and YdiV2–FlhD4C2 still bound to DNA, while YdiV3–FlhD4C2 and YdiV4–FlhD4C2 did not. DNA bound FlhD4C2 through wrapping around the FlhC subunit rather than the FlhD subunit. Structural analysis showed that only two peripheral FlhD subunits were accessible for YdiV binding, forming the YdiV2–FlhD4C2 complex without affecting the integrity of ring-like structure. YdiV2–FlhD2 structure and the negative staining electron microscopy reconstruction of YdiV4–FlhD4C2 suggested that the third and fourth YdiV molecule bound to the FlhD4C2 complex through squeezing into the ring-like structure of FlhD4C2 between the two internal D subunits. Consequently, the ring-like structure opened up, and the complex lost DNA-binding ability. Thus, YdiV inhibits FlhD4C2 only at relatively high concentrations.

High-throughput bisulfite sequencing is widely used to measure cytosine methylation at single-base resolution in eukaryotes. It permits systems-level analysis of genomic methylation patterns associated with gene expression and chromatin structure. However, methods for large-scale identification of methylation patterns from bisulfite sequencing are lacking. We developed a comprehensive tool, CpG_MPs, for identification and analysis of the methylation patterns of genomic regions from bisulfite sequencing data. CpG_MPs first normalizes bisulfite sequencing reads into methylation level of CpGs. Then it identifies unmethylated and methylated regions using the methylation status of neighboring CpGs by hotspot extension algorithm without knowledge of pre-defined regions. Furthermore, the conservatively and differentially methylated regions across paired or multiple samples (cells or tissues) are identified by combining a combinatorial algorithm with Shannon entropy. CpG_MPs identified large amounts of genomic regions with different methylation patterns across five human bisulfite sequencing data during cellular differentiation. Different sequence features and significantly cell-specific methylation patterns were observed. These potentially functional regions form candidate regions for functional analysis of DNA methylation during cellular differentiation. CpG_MPs is the first user-friendly tool for identifying methylation patterns of genomic regions from bisulfite sequencing data, permitting further investigation of the biological functions of genome-scale methylation patterns.

Facilitated by the rapid progress of high-throughput sequencing technology, a large number of long noncoding RNAs (lncRNAs) have been identified in mammalian transcriptomes over the past few years. LncRNAs have been shown to play key roles in various biological processes such as imprinting control, circuitry controlling pluripotency and differentiation, immune responses and chromosome dynamics. Notably, a growing number of lncRNAs have been implicated in disease etiology. With the increasing number of published lncRNA studies, the experimental data on lncRNAs (e.g. expression profiles, molecular features and biological functions) have accumulated rapidly. In order to enable a systematic compilation and integration of this information, we have updated the NONCODE database (http://www.noncode.org) to version 3.0 to include the first integrated collection of expression and functional lncRNA data obtained from re-annotated microarray studies in a single database. NONCODE has a user-friendly interface with a variety of search or browse options, a local Genome Browser for visualization and a BLAST server for sequence-alignment search. In addition, NONCODE provides a platform for the ongoing collation of ncRNAs reported in the literature. All data in NONCODE are open to users, and can be downloaded through the website or obtained through the SOAP API and DAS services.

DNA methylation is an important epigenetic modification for genomic regulation in higher organisms that plays a crucial role in the initiation and progression of diseases. The integration and mining of DNA methylation data by methylation-specific PCR and genome-wide profiling technology could greatly assist the discovery of novel candidate disease biomarkers. However, this is difficult without a comprehensive DNA methylation repository of human diseases. Therefore, we have developed DiseaseMeth, a human disease methylation database (http://bioinfo.hrbmu.edu.cn/diseasemeth). Its focus is the efficient storage and statistical analysis of DNA methylation data sets from various diseases. Experimental information from over 14 000 entries and 175 high-throughput data sets from a wide number of sources have been collected and incorporated into DiseaseMeth. The latest release incorporates the gene-centric methylation data of 72 human diseases from a variety of technologies and platforms. To facilitate data extraction, DiseaseMeth supports multiple search options such as gene ID and disease name. DiseaseMeth provides integrated gene methylation data based on cross-data set analysis for disease and normal samples. These can be used for in-depth identification of differentially methylated genes and the investigation of gene–disease relationship.

Transcription factors (TFs) are proteins that bind to specific DNA sequences, thereby playing crucial roles in gene-expression regulation through controlling the transcription of genetic information from DNA to RNA. Transcription cofactors and chromatin remodeling factors are also essential in the gene transcriptional regulation. Identifying and annotating all the TFs are primary and crucial steps for illustrating their functions and understanding the transcriptional regulation. In this study, based on manual literature reviews, we collected and curated 72 TF families for animals, which is currently the most complete list of TF families in animals. Then, we systematically characterized all the TFs in 50 animal species and constructed a comprehensive animal TF database, AnimalTFDB. To better serve the community, we provided detailed annotations for each TF, including basic information, gene structure, functional domain, 3D structure hit, Gene Ontology, pathway, protein–protein interaction, paralogs, orthologs, potential TF-binding sites and targets. In addition, we collected and annotated transcription cofactors and chromatin remodeling factors. AnimalTFDB has a user-friendly web interface with multiple browse and search functions, as well as data downloading. It is freely available at http://www.bioguo.org/AnimalTFDB/.

Knowledge and investigation of therapeutic targets (responsible for drug efficacy) and the targeted drugs facilitate target and drug discovery and validation. Therapeutic Target Database (TTD, http://bidd.nus.edu.sg/group/ttd/ttd.asp) has been developed to provide comprehensive information about efficacy targets and the corresponding approved, clinical trial and investigative drugs. Since its last update, major improvements and updates have been made to TTD. In addition to the significant increase of data content (from 1894 targets and 5028 drugs to 2025 targets and 17 816 drugs), we added target validation information (drug potency against target, effect against disease models and effect of target knockout, knockdown or genetic variations) for 932 targets, and 841 quantitative structure activity relationship models for active compounds of 228 chemical types against 121 targets. Moreover, we added the data from our previous drug studies including 3681 multi-target agents against 108 target pairs, 116 drug combinations with their synergistic, additive, antagonistic, potentiative or reductive mechanisms, 1427 natural product-derived approved, clinical trial and pre-clinical drugs and cross-links to the clinical trial information page in the ClinicalTrials.gov database for 770 clinical trial drugs. These updates are useful for facilitating target discovery and validation, drug lead discovery and optimization, and the development of multi-target drugs and drug combinations.

DNA methylation plays critical roles in transcriptional regulation and chromatin remodeling. Differentially methylated regions (DMRs) have important implications for development, aging and diseases. Therefore, genome-wide mapping of DMRs across various temporal and spatial methylomes is important in revealing the impact of epigenetic modifications on heritable phenotypic variation. We present a quantitative approach, quantitative differentially methylated regions (QDMRs), to quantify methylation difference and identify DMRs from genome-wide methylation profiles by adapting Shannon entropy. QDMR was applied to synthetic methylation patterns and methylation profiles detected by methylated DNA immunoprecipitation microarray (MeDIP-chip) in human tissues/cells. This approach can give a reasonable quantitative measure of methylation difference across multiple samples. Then DMR threshold was determined from methylation probability model. Using this threshold, QDMR identified 10 651 tissue DMRs which are related to the genes enriched for cell differentiation, including 4740 DMRs not identified by the method developed by Rakyan et al. QDMR can also measure the sample specificity of each DMR. Finally, the application to methylation profiles detected by reduced representation bisulphite sequencing (RRBS) in mouse showed the platform-free and species-free nature of QDMR. This approach provides an effective tool for the high-throughput identification of potential functional regions involved in epigenetic regulation.

Transcription co-activators CBP and p300 are recruited by sequence-specific transcription factors to specific genomic loci to control gene expression. A highly conserved domain in CBP/p300, the TAZ2 domain, mediates direct interaction with a variety of transcription factors including the myocyte enhancer factor 2 (MEF2). Here we report the crystal structure of a ternary complex of the p300 TAZ2 domain bound to MEF2 on DNA at 2.2Å resolution. The structure reveals three MEF2:DNA complexes binding to different sites of the TAZ2 domain. Using structure-guided mutations and a mammalian two-hybrid assay, we show that all three interfaces contribute to the binding of MEF2 to p300, suggesting that p300 may use one of the three interfaces to interact with MEF2 in different cellular contexts and that one p300 can bind three MEF2:DNA complexes simultaneously. These studies, together with previously characterized TAZ2 complexes bound to different transcription factors, demonstrate the potency and versatility of TAZ2 in protein–protein interactions. Our results also support a model wherein p300 promotes the assembly of a higher-order enhanceosome by simultaneous interactions with multiple DNA-bound transcription factors.

MicroRNAs (miRNAs) are involved in the fine control of cell proliferation and differentiation during the development of the nervous system. MiR-124, a neural specific miRNA, is expressed from the beginning of eye development in Xenopus, and has been shown to repress cell proliferation in the optic cup, however, its role at earlier developmental stages is unclear. Here, we show that this miRNA exerts a different role in cell proliferation at the optic vesicle stage, the stage which precedes optic cup formation. We show that miR-124 is both necessary and sufficient to promote cell proliferation and repress neurogenesis at the optic vesicle stage, playing an anti-neural role. Loss of miR-124 upregulates expression of neural markers NCAM, N-tubulin while gain of miR-124 downregulates these genes. Furthermore, miR-124 interacts with a conserved miR-124 binding site in the 3′-UTR of NeuroD1 and negatively regulates expression of the proneural marker NeuroD1, a bHLH transcription factor for neuronal differentiation. The miR-124-induced effect on cell proliferation can be antagonized by NeuroD1. These results reveal a novel regulatory role of miR-124 in neural development and uncover a previously unknown interaction between NeuroD1 and miR-124.

The information of protein targets and small molecule has been highly valued by biomedical and pharmaceutical research. Several protein target databases are available online for FDA-approved drugs as well as the promising precursors that have largely facilitated the mechanistic study and subsequent research for drug discovery. However, those related resources regarding to herbal active ingredients, although being unusually valued as a precious resource for new drug development, is rarely found. In this article, a comprehensive and fully curated database for Herb Ingredients’ Targets (HIT, http://lifecenter.sgst.cn/hit/) has been constructed to complement above resources. Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for user's reference. Derived from more than 3250 literatures, it currently contains 5208 entries about 1301 known protein targets (221 of them are described as direct targets) affected by 586 herbal compounds from more than 1300 reputable Chinese herbs, overlapping with 280 therapeutic targets from Therapeutic Targets Database (TTD), and 445 protein targets from DrugBank corresponding to 1488 drug agents. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases.

In silico drug target identification, which includes many distinct algorithms for finding disease genes and proteins, is the first step in the drug discovery pipeline. When the 3D structures of the targets are available, the problem of target identification is usually converted to finding the best interaction mode between the potential target candidates and small molecule probes. Pharmacophore, which is the spatial arrangement of features essential for a molecule to interact with a specific target receptor, is an alternative method for achieving this goal apart from molecular docking method. PharmMapper server is a freely accessed web server designed to identify potential target candidates for the given small molecules (drugs, natural products or other newly discovered compounds with unidentified binding targets) using pharmacophore mapping approach. PharmMapper hosts a large, in-house repertoire of pharmacophore database (namely PharmTargetDB) annotated from all the targets information in TargetBank, BindingDB, DrugBank and potential drug target database, including over 7000 receptor-based pharmacophore models (covering over 1500 drug targets information). PharmMapper automatically finds the best mapping poses of the query molecule against all the pharmacophore models in PharmTargetDB and lists the top N best-fitted hits with appropriate target annotations, as well as respective molecule’s aligned poses are presented. Benefited from the highly efficient and robust triangle hashing mapping method, PharmMapper bears high throughput ability and only costs 1 h averagely to screen the whole PharmTargetDB. The protocol was successful in finding the proper targets among the top 300 pharmacophore candidates in the retrospective benchmarking test of tamoxifen. PharmMapper is available at http://59.78.96.61/pharmmapper.

EndoTT encoded by tte0829 of Thermoanaerobacter tengcongensis binds and cleaves single-stranded (ss) and damaged double-stranded (ds) DNA in vitro as well as binding dsDNA. In the presence of a low concentration of NaCl, EndoTT cleaved ss regions of damaged dsDNA efficiently but did not cleave DNA that was entirely ss or ds. At high concentrations of NaCl or MgCl2 or ATP, there was also specific cleavage of ssDNA. This suggested a preference for ss/ds junctions to stimulate cleavage of the DNA substrates. EndoTT has six specific sites (a–f) in the oriC region (1–70 nt) of T. tengcongensis. Substitutions of nucleotides around site c prevented cleavage by EndoTT of both sites c and d, implying that the cleavage specificity may depend on both the nucleotide sequence and the secondary structure of the ssDNA. A C-terminal sub-fragment of EndoTT (residues 107–216) had both endonucleolytic and DNA-binding activity, whereas an N-terminal sub-fragment (residues 1–110) displayed only ssDNA-binding activity. Site-directed mutations showed that G170, R172 and G177 are required for the endonuclease activity of EndoTT, but not for DNA-binding, whereas D171, R178 and G189 are partially required for the DNA-binding activity.

Most small nucleolar RNAs (snoRNAs) guide rRNA nucleotide modifications, some participate in pre-rRNA cleavages, and a few have both functions. These activities involve direct base-pairing of the snoRNA with pre-rRNA using different domains. It is not known if the modification and processing functions occur independently or in a coordinated manner. We address this question by mutational analysis of a yeast box H/ACA snoRNA that mediates both processing and modification. This snoRNA (snR10) contains canonical 5′- and 3′-hairpin structures with a guide domain for pseudouridylation in the 3′ hairpin. Our functional mapping results show that: (i) processing requires the 5′ hairpin exclusively, in particular a 7-nt element; (ii) loss of the 3′ hairpin or pseudouridine does not affect rRNA processing; (iii) a single nucleotide insertion in the guide domain shifts modification to an adjacent uridine in rRNA, and severely impairs both processing and cell growth; and (iv) the deleterious effects of the insertion mutation depend on the presence of the processing element in the 5′ hairpin, but not modification of the novel site. Together, the results suggest that the snoRNA hairpins function in a coordinated manner and that their interactions with pre-rRNA could be coupled.

Carbonic anhydrase IX (CAIX) plays an important role in the growth and survival of tumor cells. MORC2 is a member of the MORC protein family. The MORC proteins contain a CW-type zinc finger domain and are predicted to have the function of regulating transcription, but no MORC2 target genes have been identified. Here we performed a DNA microarray hybridization and found CAIX mRNA to be down-regulated 8-fold when MORC2 was overexpressed. This result was further confirmed by northern and western blot analysis. Our results also showed that the protected region 4 (PR4) was important for the repression function of MORC2. Moreover, MORC2 decreased the acetylation level of histone H3 at the CAIX promoter. Meanwhile, trichostatin A (TSA) had an increasing effect on CAIX promoter activity. Among the six HDACs tested, histone deacetylase 4 (HDAC4) had a much more prominent effect on CAIX repression. ChIP and ChIP Re-IP assays showed that MORC2 and HDAC4 were assembled on the same region of the CAIX promoter. Importantly, we further confirmed that both proteins are simultaneously present in the PR4-binding complex. These results may contribute to understanding the molecular mechanisms of CAIX regulation.

With the rapid progress of biological research, great demands are proposed for integrative knowledge-sharing systems to efficiently support collaboration of biological researchers from various fields. To fulfill such requirements, we have developed a data-centric knowledge-sharing platform WebLab for biologists to fetch, analyze, manipulate and share data under an intuitive web interface. Dedicated space is provided for users to store their input data and analysis results. Users can upload local data or fetch public data from remote databases, and then perform analysis using more than 260 integrated bioinformatic tools. These tools can be further organized as customized analysis workflows to accomplish complex tasks automatically. In addition to conventional biological data, WebLab also provides rich supports for scientific literatures, such as searching against full text of uploaded literatures and exporting citations into various well-known citation managers such as EndNote and BibTex. To facilitate team work among colleagues, WebLab provides a powerful and flexible sharing mechanism, which allows users to share input data, analysis results, scientific literatures and customized workflows to specified users or groups with sophisticated privilege settings. WebLab is publicly available at http://weblab.cbi.pku.edu.cn, with all source code released as Free Software.

The tumor suppressor, p53, is a transcription factor which can modulate the transcription of a number of target genes that are involved in cell-cycle arrest and apoptosis. However, the apoptotic pathway mediated by p53 is not fully understood. Here, we showed that N-Myc downstream-regulated gene 2 (NDRG2) is a new target gene that is regulated by p53. NDRG2 mRNA and protein levels can be upregulated in a p53-dependent manner. The first intron of the NDRG2 gene contains a site that binds p53 directly and mediates wild-type p53-dependent transactivation. In addition, silencing of NDRG2 attenuates p53-mediated apoptosis, whereas overexpression of NDRG2 suppresses tumor cell growth, regardless of the presence or absence of p53. Our results indicate that NDRG2 is a novel p53-inducible target that is involved in the p53-mediated apoptosis pathway.

Expression of the gene cluster icaADBC is necessary for biofilm production in Staphylococcus epidermidis. The ica operon is negatively controlled by the repressor IcaR. Here, the crystal structure of IcaR was determined and the refined structure revealed a homodimer comprising entirely α-helices, typical of the tetracycline repressor protein family for gene regulations. The N-terminal domain contains a conserved helix-turn-helix DNA-binding motif with some conformational variations, indicating flexibility in this region. The C-terminal domain shows a complementary surface charge distribution about the dyad axis, ideal for efficient and specific dimer formation. The results of the electrophoretic mobility shift assay and isothermal titration calorimetry suggested that a 28 bp core segment of the ica operator is implicated in the cooperative binding of two IcaR dimers on opposite sides of the duplex DNA. Computer modeling based on the known DNA-complex structure of QacR and site-specific mutagenesis experiments showed that direct protein–DNA interactions are mostly conserved, but with slight variations for recognizing the different sequences. By interfering with the binding of IcaR to DNA, aminoglycoside gentamicin and other antibiotics may activate the icaADBC genes and elicit biofilm production in S. epidermidis, and likely S. aureus, as a defense mechanism.

The NONCODE database is an integrated knowledge database designed for the analysis of non-coding RNAs (ncRNAs). Since NONCODE was first released 3 years ago, the number of known ncRNAs has grown rapidly, and there is growing recognition that ncRNAs play important regulatory roles in most organisms. In the updated version of NONCODE (NONCODE v2.0), the number of collected ncRNAs has reached 206 226, including a wide range of microRNAs, Piwi-interacting RNAs and mRNA-like ncRNAs. The improvements brought to the database include not only new and updated ncRNA data sets, but also an incorporation of BLAST alignment search service and access through our custom UCSC Genome Browser. NONCODE can be found under http://www.noncode.org or http://noncode.bioinfo.org.cn.