Search

Chemokines drive cell migration through their interactions with seven-transmembrane (7TM) chemokine receptors on cell surfaces. The atypical chemokine receptor 3 (ACKR3) binds chemokines CXCL11 and CXCL12 and signals exclusively through β-arrestin-mediated pathways, without activating canonical G-protein signalling. This receptor is upregulated in numerous cancers making it a potential drug target. Here we collected over 100 distinct structural probes from radiolytic footprinting, disulfide trapping, and mutagenesis to map the structures of ACKR3:CXCL12 and ACKR3:small-molecule complexes, including dynamic regions that proved unresolvable by X-ray crystallography in homologous receptors. The data are integrated with molecular modelling to produce complete and cohesive experimentally driven models that confirm and expand on the existing knowledge of the architecture of receptor:chemokine and receptor:small-molecule complexes. Additionally, we detected and characterized ligand-induced conformational changes in the transmembrane and intracellular regions of ACKR3 that elucidate fundamental structural elements of agonism in this atypical receptor., The atypical chemokine receptor 3 (ACKR3) is important for cell migration in development and cancer. Here the authors combine radiolytic footprinting, disulfide trapping, mutagenesis and molecular modelling to characterize the ligand interactions and ligand-induced conformational changes in ACKR3.

Background Progesterone promotes uterine relaxation and is essential for the maintenance of pregnancy. Withdrawal of progesterone activity and increased inflammation within the uterine tissues are key triggers for parturition. Progesterone actions in myometrial cells are mediated by two progesterone receptor (PR) isoforms, PR-A and PR-B, that function as ligand-activated transcription factors. PR-B mediates relaxatory actions of progesterone, in part, by decreasing myometrial cell responsiveness to pro-inflammatory stimuli. These same pro-inflammatory stimuli promote the expression of PR-A which inhibits the anti-inflammatory activity of PR-B. Competitive interaction between the progesterone receptors then augments myometrial responsiveness to pro-inflammatory stimuli. The interaction between PR-B transcriptional activity and inflammation in the pregnancy myometrium is examined using a dynamical systems model in which quiescence and labor are represented as phase-space equilibrium points. Our model shows that PR-B transcriptional activity and the inflammatory load determine the stability of the quiescent and laboring phenotypes. The model is tested using published transcriptome datasets describing the mRNA abundances in the myometrium before and after the onset of labor at term. Surrogate transcripts were selected to reflect PR-B transcriptional activity and inflammation status. Results The model coupling PR-B activity and inflammation predicts contractile status (i.e., laboring or quiescent) with high precision and recall and outperforms uncoupled single and two-gene classifiers. Linear stability analysis shows that phase space bifurcations exist in our model that may reflect the phenotypic states of the pregnancy uterus. The model describes a possible tipping point for the transition of the quiescent to the contractile laboring phenotype. Conclusions Our model describes the functional interaction between the PR-A:PR-B hypothesis and tissue level inflammation in the pregnancy uterus and is a first step in more sophisticated dynamical systems modeling of human partition. The model explains observed biochemical dynamics and as such will be useful for the development of a range of systems-based models using emerging data to predict preterm birth and identify strategies for its prevention.

Plant HXXXD acyltransferase-catalyzed malonylation is an important modification reaction in elaborating the structural diversity of flavonoids and anthocyanins, and a universal adaptive mechanism to detoxify xenobiotics. Nicotiana tabacum malonyltransferase 1 (NtMaT1) is a member of anthocyanin acyltransferase subfamily that uses malonyl-CoA (MLC) as donor catalyzing transacylation in a range of flavonoid and naphthol glucosides. To gain insights into the molecular basis underlying its catalytic mechanism and versatile substrate specificity, we resolved the X-ray crystal structure of NtMaT1 to 3.1 Å resolution. The structure comprises two α/β mixed subdomains, as typically found in the HXXXD acyltransferases. The partial electron density map of malonyl-CoA allowed us to reliably dock the entire molecule into the solvent channel and subsequently define the binding sites for both donor and acceptor substrates. MLC bound to the NtMaT1 occupies one end of the long solvent channel between two subdomains. On superimposing and comparing the structure of NtMaT1 with that of an enzyme from anthocyanin acyltransferase subfamily from red chrysanthemum (Dm3Mat3) revealed large architectural variation in the binding sites, both for the acyl donor and for the acceptor, although their overall protein folds are structurally conserved. Consequently, the shape and the interactions of malonyl-CoA with the binding sites' amino acid residues differ substantially. These major local architectural disparities point to the independent, divergent evolution of plant HXXXD acyltransferases in different species. The structural flexibility of the enzyme and the amendable binding pattern of the substrates provide a basis for the evolution of the distinct, versatile substrate specificity of plant HXXXD acyltransferases.

Metallomics and metalloproteomics are emerging fields addressing the role, uptake, transport and storage of trace metals essential for protein functions. The methodologies utilized in metallomics and metalloproteomics to provide information on the identity, quantity and function of metalloproteins are discussed. The most widely used approach is through inductively coupled plasma mass spectrometry to identify the metal bound to a protein, and electrospray ionization mass spectrometry to elucidate the structure, dynamics and function of a metal-protein complex. Other approaches include X-ray absorption and X-ray fluorescence spectroscopies, and bioinformatics sequence analysis. X-ray absorption spectroscopy utilizing a synchrotron radiation source is a powerful tool to provide a direct analysis of metal bound to proteins and proteomic metal distribution in biological matrices. With the advent of genome sequencing, a large database of protein primary structures has been established, and specific tools to identify metalloproteins in the genome sequences have been developed.

The New York SGX Research Center for Structural Genomics (NYSGXRC) of the NIGMS Protein Structure Initiative (PSI) has applied its high-throughput X-ray crystallographic structure determination platform to systematic studies of all human protein phosphatases and protein phosphatases from biomedically-relevant pathogens. To date, the NYSGXRC has determined structures of 21 distinct protein phosphatases: 14 from human, 2 from mouse, 2 from the pathogen Toxoplasma gondii, 1 from Trypanosoma brucei, the parasite responsible for African sleeping sickness, and 2 from the principal mosquito vector of malaria in Africa, Anopheles gambiae. These structures provide insights into both normal and pathophysiologic processes, including transcriptional regulation, regulation of major signaling pathways, neural development, and type 1 diabetes. In conjunction with the contributions of other international structural genomics consortia, these efforts promise to provide an unprecedented database and materials repository for structure-guided experimental and computational discovery of inhibitors for all classes of protein phosphatases.

Network-based analyses are commonly used as powerful tools to interpret the findings of genome-wide association studies (GWAS) in a functional context. In particular, identification of disease-associated functional modules, i.e., highly connected protein-protein interaction (PPI) subnetworks with high aggregate disease association, are shown to be promising in uncovering the functional relationships among genes and proteins associated with diseases. An important issue in this regard is the scoring of subnetworks by integrating two quantities: disease association of individual gene products and network connectivity among proteins. Current scoring schemes either disregard the level of connectivity and focus on the aggregate disease association of connected proteins or use a linear combination of these two quantities. However, such scoring schemes may produce arbitrarily large subnetworks which are often not statistically significant or require tuning of parameters that are used to weigh the contributions of network connectivity and disease association. Here, we propose a parameter-free scoring scheme that aims to score subnetworks by assessing the disease association of interactions between pairs of gene products. We also incorporate the statistical significance of network connectivity and disease association into the scoring function. We test the proposed scoring scheme on a GWAS dataset for two complex diseases type II diabetes (T2D) and psoriasis (PS). Our results suggest that subnetworks identified by commonly used methods may fail tests of statistical significance after correction for multiple hypothesis testing. In contrast, the proposed scoring scheme yields highly significant subnetworks, which contain biologically relevant proteins that cannot be identified by analysis of genome-wide association data alone. We also show that the proposed scoring scheme identifies subnetworks that are reproducible across different cohorts, and it can robustly recover relevant subnetworks at lower sampling rates.

Structural GenomiX, Inc. (SGX), four New York area institutions, and two University of California schools have formed the New York Structural GenomiX Research Consortium (NYSGXRC), an industrial/academic Research Consortium that exploits individual core competencies to support all aspects of the NIH-NIGMS funded Protein Structure Initiative (PSI), including protein family classification and target selection, generation of protein for biophysical analyses, sample preparation for structural studies, structure determination and analyses, and dissemination of results. At the end of the PSI Pilot Study Phase (PSI-1), the NYSGXRC will be capable of producing 100-200 experimentally determined protein structures annually. All Consortium activities can be scaled to increase production capacity significantly during the Production Phase of the PSI (PSI-2). The Consortium utilizes both centralized and de-centralized production teams with clearly defined deliverables and hand-off procedures that are supported by a web-based target/sample tracking system (SGX Laboratory Information Data Management System, LIMS, and NYSGXRC Internal Consortium Experimental Database, ICE-DB). Consortium management is provided by an Executive Committee, which is composed of the PI and all Co-PIs. Progress to date is tracked on a publicly available Consortium web site (http://www.nysgxrc.org) and all DNA/protein reagents and experimental protocols are distributed freely from the New York City Area institutions. In addition to meeting the requirements of the Pilot Study Phase and preparing for the Production Phase of the PSI, the NYSGXRC aims to develop modular technologies that are transferable to structural biology laboratories in both academe and industry. The NYSGXRC PI and Co-PIs intend the PSI to have a transforming effect on the disciplines of X-ray crystallography and NMR spectroscopy of biological macromolecules. Working with other PSI-funded Centers, the NYSGXRC seeks to create the structural biology laboratory of the future. Herein, we present an overview of the organization of the NYSGXRC and describe progress toward development of a high-throughput Gene--Structure platform. An analysis of current and projected consortium metrics reflects progress to date and delineates opportunities for further technology development.

To verify the genome annotation and to create a resource to functionally characterize the proteome, we attempted to Gateway-clone all predicted protein-encoding open reading frames (ORFs), or the 'ORFeome,' of Caenorhabditis elegans. We successfully cloned approximately 12,000 ORFs (ORFeome 1.1), of which roughly 4,000 correspond to genes that are untouched by any cDNA or expressed-sequence tag (EST). More than 50% of predicted genes needed corrections in their intron-exon structures. Notably, approximately 11,000 C. elegans proteins can now be expressed under many conditions and characterized using various high-throughput strategies, including large-scale interactome mapping. We suggest that similar ORFeome projects will be valuable for other organisms, including humans.

The formation of individual tertiary contacts of the Tetrahymena L-21 Sca I ribozyme has been monitored by hydroxyl radical footprinting and its global conformation by analytical ultracentrifugation as a function of monovalent ion concentration in the absence of divalent ions. Advanced methods of data analysis, which allow the hydroxyl radical reactivity of every nucleotide to be quantified, permit monitoring of each and every structural element of the RNA. Monovalent ion-mediated global compaction of the ribozyme is accompanied by the formation of native tertiary contacts; most native tertiary contacts are evident except several that are located near where divalent ions are observed in crystallographic structures. Non-native tertiary contacts are also observed at low but not high concentrations of monovalent ions. In light of recent studies that have shown that the presence of monovalent ions greatly accelerates the Mg2+-dependent folding of the Tetrahymena ribozyme, the present studies suggest that Na+ concentration changes not only the starting position of the RNA on its folding funnel but also pushes it deep into the well by forming native tertiary contacts and, thus, favoring fast and correct folding pathways.

For proteins, understanding tertiary interactions involved in local versus global unfolding has become increasingly important for understanding the nature of the native state ensemble, the mechanisms of unfolding, and the stability of both the native and intermediate states in folding. In this work we have addressed related questions with respect to RNA structure by combining chemical denaturation and hydroxyl radical footprinting methods. We have determined unfolding isotherms for each of 26 discrete sites of protection located throughout the Tetrahymena thermophila group I ribozyme. The cooperativity of folding, m-value, and the free energy, DeltaG degrees N-U, associated with formation of each tertiary contact was determined by analysis of the isotherms. The DeltaG degrees N-U values measured in this study vary from 1.7 +/- 0.2 to 7. 6 +/- 1.2 kcal mol-1. Thus, the stability of these discrete tertiary contacts vary by almost 104. In addition, an intradomain contact and three interdomain contacts show high cooperativity (m-values of 1.1 +/- 0.2 to 1.7 +/- 0.3 kcal mol-1 M-1) indicating that these contacts exhibit global cooperatively in their folding behavior. This new approach to examining RNA stability provides an exciting comparison to our understanding of protein structure and folding mechanisms.

Background Interactions among genomic loci (also known as epistasis) have been suggested as one of the potential sources of missing heritability in single locus analysis of genome-wide association studies (GWAS). The computational burden of searching for interactions is compounded by the extremely low threshold for identifying significant p-values due to multiple hypothesis testing corrections. Utilizing prior biological knowledge to restrict the set of candidate SNP pairs to be tested can alleviate this problem, but systematic studies that investigate the relative merits of integrating different biological frameworks and GWAS data have not been conducted. Results We developed four biologically based frameworks to identify pairwise interactions among candidate SNP pairs as follows: (1) for each human protein-coding gene, a set of SNPs associated with that gene was constructed providing a gene-based interaction model, (2) for each known biological pathway, a set of SNPs associated with the genes in the pathway was constructed providing a pathway-based interaction model, (3) a set of SNPs associated with genes in a disease-related subnetwork provides a network-based interaction model, and (4) a framework is based on the function of SNPs. The last approach uses expression SNPs (eSNPs or eQTLs), which are SNPs or loci that have defined effects on the abundance of transcripts of other genes. We constructed pairs of eSNPs and SNPs located in the target genes whose expression is regulated by eSNPs. For all four frameworks the SNP sets were exhaustively tested for pairwise interactions within the sets using a traditional logistic regression model after excluding genes that were previously identified to associate with the trait. Using previously published GWAS data for type 2 diabetes (T2D) and the biologically based pair-wise interaction modeling, we identify twelve genes not seen in the previous single locus analysis. Conclusion We present four approaches to detect interactions associated with complex diseases. The results show our approaches outperform the traditional single locus approaches in detecting genes that previously did not reach significance; the results also provide novel drug targets and biomarkers relevant to the underlying mechanisms of disease.

Background Sequences up to several megabases in length have been found to be present in individual genomes but absent in the human reference genome. These sequences may be common in populations, and their absence in the reference genome may indicate rare variants in the genomes of individuals who served as donors for the human genome project. As the reference genome is used in probe design for microarray technology and mapping short reads in next generation sequencing (NGS), this missing sequence could be a source of bias in functional genomic studies and variant analysis. One End Anchor (OEA) and/or orphan reads from paired-end sequencing have been used to identify novel sequences that are absent in reference genome. However, there is no study to investigate the distribution, evolution and functionality of those sequences in human populations. Results To systematically identify and study the missing common sequences (micSeqs), we extended the previous method by pooling OEA reads from large number of individuals and applying strict filtering methods to remove false sequences. The pipeline was applied to data from phase 1 of the 1000 Genomes Project. We identified 309 micSeqs that are present in at least 1% of the human population, but absent in the reference genome. We confirmed 76% of these 309 micSeqs by comparison to other primate genomes, individual human genomes, and gene expression data. Furthermore, we randomly selected fifteen micSeqs and confirmed their presence using PCR validation in 38 additional individuals. Functional analysis using published RNA-seq and ChIP-seq data showed that eleven micSeqs are highly expressed in human brain and three micSeqs contain transcription factor (TF) binding regions, suggesting they are functional elements. In addition, the identified micSeqs are absent in non-primates and show dynamic acquisition during primate evolution culminating with most micSeqs being present in Africans, suggesting some micSeqs may be important sources of human diversity. Conclusions 76% of micSeqs were confirmed by a comparative genomics approach. Fourteen micSeqs are expressed in human brain or contain TF binding regions. Some micSeqs are primate-specific, conserved and may play a role in the evolution of primates. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-685) contains supplementary material, which is available to authorized users.