Biomedical subjects

Ite A Laird-Offringa

Publications and source records attributed to Ite A Laird-Offringa.

13 recordsLinked to original sources

PubMed2006

The role of RNA structure in the interaction of U1A protein with U1 hairpin II RNA.

The N-terminal RNA Recognition Motif (RRM1) of the spliceosomal protein U1A interacting with its target U1 hairpin II (U1hpII) has been used as a paradigm for RRM-containing proteins interacting with their RNA targets. U1A binds to U1hpII via direct interactions with a 7-nucleotide (nt) consensus binding sequence at the 5' end of a 10-nt loop, and via hydrogen bonds with the closing C-G base pair at the top of the RNA stem. Using surface plasmon resonance (Biacore), we have examined the role of structural features of U1hpII in binding to U1A RRM1. Mutational analysis of the closing base pair suggests it plays a minor role in binding and mainly prevents "breathing" of the loop. Lengthening the stem and nontarget part of the loop suggests that the increased negative charge of the RNA might slightly aid association. However, this is offset by an increase in dissociation, which may be caused by attraction of the RRM to nontarget parts of the RNA. Studies of a single stranded target and RNAs with untethered loops indicate that structure is not very relevant for association but is important for complex stability. In particular, breaking the link between the stem and the 5' side of the loop greatly increases complex dissociation, presumably by hindering simultaneous contacts between the RRM and stem and loop nucleotides. While binding of U1A to a single stranded target is much weaker than to U1hpII, it occurs with nanomolar affinity, supporting recent evidence that binding of unstructured RNA by U1A has physiological significance.

Base Pairing↗

PubMed2006

The role of positively charged amino acids and electrostatic interactions in the complex of U1A protein and U1 hairpin II RNA.

Previous kinetic investigations of the N-terminal RNA recognition motif (RRM) domain of spliceosomal protein U1A, interacting with its RNA target U1 hairpin II, provided experimental evidence for a 'lure and lock' model of binding in which electrostatic interactions first guide the RNA to the protein, and close range interactions then lock the two molecules together. To further investigate the 'lure' step, here we examined the electrostatic roles of two sets of positively charged amino acids in U1A that do not make hydrogen bonds to the RNA: Lys20, Lys22 and Lys23 close to the RNA-binding site, and Arg7, Lys60 and Arg70, located on 'top' of the RRM domain, away from the RNA. Surface plasmon resonance-based kinetic studies, supplemented with salt dependence experiments and molecular dynamics simulation, indicate that Lys20 predominantly plays a role in association, while nearby residues Lys22 and Lys23 appear to be at least as important for complex stability. In contrast, kinetic analyses of residues away from the RNA indicate that they have a minimal effect on association and stability. Thus, well-positioned positively charged residues can be important for both initial complex formation and complex maintenance, illustrating the multiple roles of electrostatic interactions in protein-RNA complexes.

Amino Acid Sequence↗

PubMed2005

Kinetic analysis of the role of the tyrosine 13, phenylalanine 56 and glutamine 54 network in the U1A/U1 hairpin II interaction.

The A protein of the U1 small nuclear ribonucleoprotein particle, interacting with its stem-loop RNA target (U1hpII), is frequently used as a paradigm for RNA binding by recognition motif domains (RRMs). U1A/U1hpII complex formation has been proposed to consist of at least two steps: electrostatically mediated alignment of both molecules followed by locking into place, based on the establishment of close-range interactions. The sequence of events between alignment and locking remains obscure. Here we examine the roles of three critical residues, Tyr13, Phe56 and Gln54, in complex formation and stability using Biacore. Our mutational and kinetic data suggest that Tyr13 plays a more important role than Phe56 in complex formation. Mutational analysis of Gln54, combined with molecular dynamics studies, points to Arg52 as another key residue in association. Based on our data and previous structural and modeling studies, we propose that electrostatic alignment of the molecules is followed by hydrogen bond formation between the RNA and Arg52, and the sequential establishment of interactions with loop bases (including Tyr13). A quadruple stack, sandwiching two bases between Phe56 and Asp92, would occur last and coincide with the rearrangement of a C-terminal helix that partially occludes the RRM surface in the free protein.

Amino Acid Sequence↗

PubMed2005

Distinct DNA methylation profiles in malignant mesothelioma, lung adenocarcinoma, and non-tumor lung.

DNA methylation markers provide a powerful tool to make diagnoses based on genetic material obtained directly from tumors or from "remote" locations such as sputum, pleural fluid, or serum. In particular when limited cell numbers are available, amplifyable DNA markers can provide a very sensitive tool for cancer detection and classification. Malignant mesothelioma (MM), an aggressive cancer strongly associated with asbestos exposure, can be difficult to distinguish from adenocarcinoma of the lung when limited material is available. In an attempt to identify molecular markers for MM and adenocarcinoma, we examined the DNA methylation status of 14 loci. Analysis of methylation levels in 10 MM and 8 adenocarcinoma cell lines showed that methylation of APC was significantly elevated in adenocarcinoma compared to MM cell lines (P=0.0003), while methylation of CDH1 was higher in MM (P<0.02). Subsequent examination of the methylation status of the 14 loci in 6 MM and 7 adenocarcinoma primary tumors, which yielded similar methylation profiles, supported these observations. Comparison of methylation in MM cell lines and tumors versus non-tumor lung tissue indicated that APC exhibits less methylation in MM (P=0.003) while RASSF1, PGR1, ESR1, and CDH1 show more methylation in MM, the latter two showing the most significant difference between the two tissue types (P< or = 0.0001). Comparison of methylation in adenocarcinoma cell lines and tumors versus non-tumor lung tissue showed methylation of ESR1, PGR1 and RASSF1 to be significantly elevated in adenocarcinoma, with RASSF1 being most significant (P=0.0002). Thus, with the examination of 14 loci, we have identified 5 candidates that show potential for distinguishing between MM, adenocarcinoma and/or non-cancer lung. Our observations support the strong potential of methylation markers as tools for accurate diagnosis of neoplasms in and around the lung.

Adenocarcinoma↗

PubMed2004

Solution structure of the complex formed by the two N-terminal RNA-binding domains of nucleolin and a pre-rRNA target.

Nucleolin is a 70 kDa multidomain protein involved in several steps of eukaryotic ribosome biogenesis. In vitro selection in combination with mutagenesis and structural analysis identified binding sites in pre-rRNA with the consensus (U/G)CCCG(A/G) in the context of a hairpin structure, the nucleolin recognition element (NRE). The central region of the protein contains four tandem RNA-binding domains (RBDs), of which the first two are responsible for the RNA-binding specificity and affinity for NREs. Here, we present the solution structure of the 28 kDa complex formed by the two N-terminal RNA-binding domains of nucleolin (RBD12) and a natural pre-rRNA target, b2NRE. The structure demonstrates that the sequence-specific recognition of the pre-rRNA NRE is achieved by intermolecular hydrogen bonds and stacking interactions involving mainly the beta-sheet surfaces of the two RBDs and the linker residues. A comparison with our previously determined NMR structure of RBD12 in complex with an in vitro selected RNA target, sNRE, shows that although the sequence-specific recognition of the loop consensus nucleotides is the same in the two complexes, they differ in several aspects. While the protein makes numerous specific contacts to the non-consensus nucleotides in the loop E motif (S-turn) in the upper part of the sNRE stem, nucleolin RBD12 contacts only consensus nucleotides in b2NRE. The absence of these upper stem contacts from the RBD12/b2NRE complex results in a much less stable complex, as demonstrated by kinetic analyses. The role of the loop E motif in high-affinity binding is supported by gel-shift analyses with a series of sNRE mutants. The less stable interaction of RBD12 with the natural RNA target is consistent with the proposed role of nucleolin as a chaperone that interacts transiently with pre-rRNA to prevent misfolding.

Animals↗

PubMed2004

A comparison of cluster analysis methods using DNA methylation data.

MOTIVATION: Aberrant DNA methylation is common in cancer. DNA methylation profiles differ between tumor types and subtypes and provide a powerful diagnostic tool for identifying clusters of samples and/or genes. DNA methylation data obtained with the quantitative, highly sensitive MethyLight technology is not normally distributed; it frequently contains an excess of zeros. Established tools to analyze this type of data do not exist. Here, we evaluate a variety of methods for cluster analysis to determine which is most reliable. RESULTS: We introduce a Bernoulli-lognormal mixture model for clustering DNA methylation data obtained using MethyLight. We model the outcomes using a two-part distribution having discrete and continuous components. It is compared with standard cluster analysis approaches for continuous data and for discrete data. In a simulation study, we find that the two-part model has the lowest classification error rate for mixture outcome data compared with other approaches. The methods are illustrated using DNA methylation data from a study of lung cancer cell lines. Compared with competing hierarchical clustering methods, the mixture model approaches have the lowest cross-validation error for detecting lung cancer subtype (non-small versus small cell). The Bernoulli-lognormal mixture assigns observations to subgroups with the lowest uncertainty. AVAILABILITY: Software is available upon request from the authors. SUPPLEMENTARY INFORMATION: http://www-rcf.usc.edu/~kims/SupplementaryInfo.html

Algorithms↗

PubMed2004

Classification of individual lung cancer cell lines based on DNA methylation markers: use of linear discriminant analysis and artificial neural networks.

The classification of small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) can pose diagnostic problems due to inter-observer variability and other limitations of histopathology. There is an interest in developing classificatory models of lung neoplasms based on the analysis of multivariate molecular data with statistical methods and/or neural networks. DNA methylation levels at 20 loci were measured in 41 SCLC and 46 NSCLC cell lines with the quantitative real-time PCR method MethyLight. The data were analyzed with artificial neural networks (ANN) and linear discriminant analysis (LDA) to classify the cell lines into SCLC or into NSCLC. Models used either data from all 20 loci, or from five significant DNA methylation loci that were selected by a step-wise back-propagation procedure (PTGS2, CALCA, MTHFR, ESR1, and CDKN2A). The data were sorted randomly by cell line into 10 different data sets, each with training and testing subsets composed of 71 and 16 of the cases, respectively. Ten ANN models were trained using the 10 data sets: five using 20 variables, and five using the five variables selected by step-wise back-propagation. The ANN models with 20 input variables correctly classified 100% of the cell lines, while the models with only five variables correctly classified 87 to 100% of cases. For comparison, 10 different LDA models were trained and tested using the same data sets with either the original data or with logarithmically transformed data. Again, half of the models used all 20 variables while the others used only the five significant variables. LDA models provided correct classifications in 62.5% to 87.5% of cases. The classifications provided by all of the different models were compared with kappa statistics, yielding kappa values ranging from 0.25 to 1.0. We conclude that ANN models based on DNA methylation profiles can objectively classify SCLC and NSCLC cells lines with substantial to perfect concordance, while LDA models based on DNA methylation profiles provide poor to substantial concordance. Our work supports the promise of ANN analysis of DNA methylation data as a powerful approach for the development of automated methods for lung cancer classification.

Carcinoma, Non-Small-Cell Lung↗

PubMed2003

Characterization of the interaction between neuronal RNA-binding protein HuD and AU-rich RNA.

Hu proteins have been shown to bind to AU-rich elements (AREs) in the 3'-untranslated region of unstable mRNAs. They can thereby inhibit the decay of labile transcripts by antagonizing destabilizing proteins that target these AU-rich sequences. Here we examine the sequence preferences of HuD to elucidate its possible role in counteracting mRNA decay. Using repeats of the prototype destabilizing sequence UU(AUUU)nAUU, we show that all three HuD RNA-binding domains participate in binding to AU-tracts that can be as short as 13 residues, depending on the position of the remaining As. Removal of the A residues, resulting in a poly(U)-tract, increased the affinity of HuD for RNA, suggesting that the presence of As in destabilizing elements might favor the recruitment of other proteins and/or prevent HuD from binding too tightly to AREs. In vitro selection experiments with randomized RNAs confirmed the preference of HuD for poly(U). RNA binding analysis of the related protein HuB showed a similar preference for poly(U). In contrast, tristetraprolin, an mRNA destabilizing protein, strongly prefers AU-rich RNA. Many labile mRNAs contain U-tracts in or near their AREs. Individual AREs may thus differentially affect mRNA half-life by recruiting a unique complement of stabilizing and destabilizing factors.

Animals↗

PubMed2002

Lipopolysaccharide-induced methylation of HuR, an mRNA-stabilizing protein, by CARM1. Coactivator-associated arginine methyltransferase.

The RNA-binding protein HuR stabilizes labile mRNAs carrying AU-rich instability elements. This mRNA stabilization can be induced by hypoxia, lipopolysaccharide, and UV light. The mechanism by which these stimuli activate HuR is unclear and might be related to post-translational modification of this protein. Here we show that HuR can be methylated on arginine. However, HuR is not a substrate for PRMT1, the most prominent protein-arginine methyltransferase in mammalian cells, which methylates a number of heterogeneous nuclear ribonucleoproteins. Instead, HuR is specifically methylated by coactivator-associated arginine methyltransferase 1 (CARM1), a protein-arginine methyltransferase previously shown to serve as a transcriptional coactivator. By analyzing methylation of specific HuR arginine-to-lysine mutants and by sequencing radioactively methylated HuR peptides, Arg(217) was identified as the major HuR methylation site. Arg(217) is located in the hinge region between the second and third of the three HuR RNA recognition motif domains. Antibodies against a methylated HuR peptide were used to demonstrate in vivo methylation of HuR. HuR methylation increased in cells that overexpressed CARM1. Importantly, lipopolysaccharide stimulation of macrophages, which leads to HuR-mediated stabilization of tumor necrosis factor alpha mRNA in these cells, caused increased methylation of endogenous HuR. Thus, CARM1, which plays a role in transcriptional activation through histone H3 methylation, may also play a role in post-transcriptional gene regulation by methylating HuR.

Animals↗

PubMed2002

DNA methylation analysis: a powerful new tool for lung cancer diagnosis.

Carcinoma of the lung is the most common cause of cancer death worldwide. The estimated 5-year survival ranges from 6-16%, depending on the cell type. The best opportunity for improving survival of lung cancer patients is through early detection, when curative surgical resection is possible. Although the subjects at increased risk for developing carcinoma of the lung (long-term smokers) can be identified, only 10-20% of this group will ultimately develop the disease. Screening tests of long-term smokers employed to date (radiography and sputum cytology) have not been successful in reducing lung cancer mortality. The application of molecular markers specific for lung cancer offers new possibilities for early detection. Hypermethylation of CpG islands in the promoter regions of genes is a common phenomenon in lung cancer, as demonstrated by the analysis of the methylation status of over 40 genes from lung cancer tumors, cell lines, patient sputum and/or serum. Determination of the methylation patterns of multiple genes to obtain complex DNA methylation signatures promises to provide a highly sensitive and specific tool for lung cancer diagnosis. When combined with the development of non-invasive methods to detect such signatures, this may provide a viable method to screen subjects at risk for lung cancer.

Biomarkers, Tumor↗

PubMed2002

Complex role of the beta 2-beta 3 loop in the interaction of U1A with U1 hairpin II RNA.

RNA recognition motifs (RRMs) are characterized by highly conserved regions located centrally on a beta-sheet, which forms the RNA binding surface. Variable flanking regions, such as the loop connecting beta-strands 2 and 3, are thought to be important in determining the RNA-binding specificities of individual RRMs. The N-terminal RRM of the spliceosomal U1A protein mediates binding to an RNA hairpin (U1hpII) in the U1 small nuclear RNA. In this complex, the beta(2)-beta(3) loop protrudes through the 10-nucleotide RNA loop. Shortening of the RNA loop strongly perturbs binding, suggesting that an optimal "fit" of the beta(2)-beta(3) loop into the RNA loop is an important factor in complexation. To understand this interaction further, we mutated or deleted loop residues Lys(50) and Met(51), which protrude centrally into the RNA loop but do not make any direct contacts to the bases. Using BIACORE, we analyzed the ability of these U1A mutants to bind to wild type RNAs, or RNAs with shortened loops. Alanine replacement mutations only modestly affected binding to wild type U1hpII. Interestingly, simultaneous replacement of Lys(50) and Met(51) with alanine appeared to alleviate the loss of binding caused by shortening of the RNA loop. Deletion of Lys(50) or Met(51) caused a dramatic loss in stability of the U1A.U1hpII complex. However, deletion of both residues simultaneously was much less deleterious. Simulated annealing molecular dynamics analyses suggest this is due to the ability of this mutant to rearrange flanking amino acids to substitute for the two deleted residues. The double deletion mutant also exhibited substantially reduced negative effects of RNA loop shortening, suggesting the rearranged loop is better able to accommodate a short RNA loop. Our results indicate that one of the roles of the beta(2)-beta(3) loop is to provide a steric fit into the RNA loop, thereby stabilizing the RNA.protein complex.

Alanine↗

PubMed2002

Kinetic studies of RNA-protein interactions using surface plasmon resonance.

Although structural, biochemical, and genetic studies have provided much insight into the determinants of specificity and affinity of proteins for RNA, little is currently known about the kinetics that underlie RNA-protein interactions. Protein-RNA complexes are dynamic, and the kinetics of binding and release could influence many processes, such as the ability of RNA-binding proteins to compete for binding sites, the sequential assembly of ribonucleoprotein complexes, and the ability of bound RNA to move between cellular compartments. Therefore, to attain a complete and biologically relevant understanding of RNA-protein interactions, complex formation must be studied not only in equilibrated reactions, but also as a dynamic process. BIACORE, a surface plasmon resonance-based biosensor technology, allows intermolecular interactions to be measured in real time, and can provide both equilibrium and kinetic information about complex formation. This technology is a powerful tool with which to study the dynamics of RNA-protein interactions. We have used BIACORE extensively to obtain detailed insight into the interaction between RNA and proteins carrying RNA recognition motif domains. Here we discuss the physical principles on which BIACORE is based, and the required instrumentation. We describe how to design well-controlled RNA-protein interaction experiments aimed at yielding high-quality data, and outline the steps required for data analysis. In addition, we present examples to illustrate how kinetic studies have provided us with unique insights into the interaction of the spliceosomal U1A protein and the neuronal HuD protein with their respective RNA targets.

Biosensing Techniques↗

PubMed2002

Hierarchical clustering of lung cancer cell lines using DNA methylation markers.

Recent analyses of global and gene-specific methylation patterns in cancer cells have suggested that cancers from different organs demonstrate distinct patterns of CpG island hypermethylation. Although certain CpG islands are frequently methylated in many different kinds of cancer, others are methylated only in specific tumor types. Because distinct patterns of CpG island hypermethylation can be seen in tumors from different organs, it seems likely that histological subtypes of cancer within a given organ may exhibit distinct methylation patterns as well. The goal of our study was to determine whether the patterns of CpG island hypermethylation could be used to distinguish between different histological subtypes of lung cancer. We analyzed the methylation status of 23 loci in 91 lung cancer cell lines using the quantitative real-time PCR method MethyLight. Genes PTGS2 (COX2), CALCA, MTHFR, ESR1, MGMT, MYOD1, and APC showed statistically significant differences in the level of CpG island methylation between small cell lung cancer (SCLC) and non-small cell lung cancer cell lines (NSCLC). Hierarchical clustering using a panel consisting of these seven loci yielded two major groups, one of which contained 78% of the SCLC lines. Within this group, a large cluster consisted almost exclusively of SCLC cell lines. Our results show that DNA methylation patterns differ between NSCLC and SCLC cell lines and suggest that these patterns could be developed into a powerful molecular marker to achieve accurate diagnosis of lung cancer.

Biomarkers, Tumor↗