Efficient Gap Repair Catalyzed In Vitro by an Intrinsic DNA Polymerase Activity of Human Immunodeficiency Virus Type 1 Integrase

Cleavage and DNA joining reactions, carried out by human immunodeficiency virus type 1 (HIV-1) integrase, are necessary to effect the covalent insertion of HIV-1 DNA into the host genome. For the integration of HIV-1 DNA into the cellular genome to be completed, short gaps flanking the integrated proviral DNA must be repaired. It has been widely assumed that host cell DNA repair enzymes are involved. Here we report that HIV-1 integrase multimers possess an intrinsic DNA-dependent DNA polymerase activity. The activity was characterized by its dependence on Mg²⁺, resistance to N-ethylmaleimide, and inhibition by 3′-azido-2′,3′-dideoxythymidine-5′-triphosphate, coumermycin A₁, and pyridoxal 5′-phosphate. The enzyme efficiently utilized poly(dA)-oligo(dT) or self-annealing oligonucleotides as a template primer but displayed relatively low activity with gapped calf thymus DNA and no activity with poly(dA) or poly(rA)-oligo(dT). A monoclonal antibody binding specifically to an epitope comprised of amino acids 264 to 273 near the C terminus of HIV-1 integrase severely inhibited the DNA polymerase activity. A deletion of 50 amino acids at the C terminus of integrase drastically altered the gel filtration properties of the DNA polymerase, although the level of activity was unaffected by this mutation. The DNA polymerase efficiently extended a hairpin DNA primer up to 19 nucleotides on a T₂₀ DNA template, although addition of the last nucleotide occurred infrequently or not at all. The ability of integrase to repair gaps in DNA was also investigated. We designed a series of gapped molecules containing a single-stranded region flanked by a duplex U5 viral arm on one side and by a duplex nonviral arm on the other side. Molecules varied structurally depending on the size of the gap (one, two, five, or seven nucleotides), their content of T’s or C’s in the single-stranded region, whether the CA dinucleotide in the viral arm had been replaced with a nonviral sequence, or whether they contained 5′ AC dinucleotides as unpaired tails. The results indicated that the integrase DNA polymerase is specifically designed to repair gaps efficiently and completely, regardless of gap size, base composition, or structural features such as the internal CA dinucleotide or unpaired 5′-terminal AC dinucleotides. When the U5 arm of the gapped DNA substrate was removed, leaving a nongapped DNA template-primer, the integrase DNA polymerase failed to repair the last nucleotide in the DNA template effectively. A post-gap repair reaction did depend on the CA dinucleotide. This secondary reaction was highly regulated. Only two nucleotides beyond the gap were synthesized, and these were complementary to and dependent for their synthesis on the CA dinucleotide. We were also able to identify a specific requirement for the C terminus of integrase in the post-gap repair reaction. The results are consistent with a direct role for a heretofore unsuspected DNA polymerase function of HIV-1 integrase in the repair of short gaps flanking proviral DNA integration intermediates that arise during virus infection.Integration of human immunodeficiency virus type 1 (HIV-1) DNA is an essential step in the replicative cycle of the virus (6, 13, 16, 29, 41). The initial steps whereby HIV-1 DNA becomes covalently associated with the host DNA are mediated by the viral integrase protein. Two distinct chemical reactions are involved. In a processing step, integrase cleaves viral DNA endonucleolytically, resulting in the removal of a GT dinucleotide from the 3′ ends of the DNA (15, 48, 51). Once in the nucleus, concerted cleavage and DNA strand transfer reactions, involving viral and host DNA, enable the processed 3′ termini to become covalently joined to a host DNA target site. The intermediate produced in this manner contains unpaired 5′ ends adjacent to five-base gaps. Completion of integration requires the repair of these gaps and the joining of the 5′ ends of viral DNA to the host DNA (2). The relatively rapid kinetics of 5′-end joining in vivo has been used as a basis on which to argue in favor of a role for integrase in this step of integration (40). Although integrase can catalyze the latter reaction in vitro, albeit inefficiently (28), it has been generally assumed that host cell enzymes perform gap repair and 5′-end joining.Structural, functional, and mutational studies have defined integrase as a 32-kDa protein that can be divided into three distinct functional domains (50). The catalytic core, including amino acids 50 to 212, contains a triad of acidic amino acids (Asp 64, Asp 116, and Glu 152) that form a highly conserved D,D-35-E motif. In the three-dimensional crystal structure, these amino acids are in close proximity (10). Mutation of any one of these acidic residues severely hampers the ability of integrase to catalyze endonucleolytic cleavage and DNA strand transfer (5, 9, 12, 13, 27, 31, 32). The C terminus binds DNA nonspecifically and is required for cleavage and integration activity (47, 49, 52, 53). The amino terminus contains a zinc finger or HHCC motif, which coordinates a molar equivalent of zinc (4). This domain influences DNA binding (21, 25, 47), although it does not bind DNA on its own (26, 38).In the functional integration complex, integrase is believed to act as a multimer (11, 24, 46). Transcomplementation, in which DNA strand transfer and cleavage activities are restored by mixing nonfunctional mutants, implies that the active form of integrase is minimally a dimer (46). Integrase can exist in equilibrium between dimeric and tetrameric forms, and multimerization determinants can be identified within the integrase protein (1). Association of one molar equivalent of zinc with a soluble mutant of integrase favored the formation of the tetrameric form of the protein (54).The present study was undertaken to further characterize HIV-1 integrase by searching for novel enzymatic activities that may be associated with this viral protein. We chose specifically to look for an associated DNA polymerase activity in an attempt to elucidate the final steps in integration, namely, gap repair and 5′-end joining.     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]