Biochemical Properties and Substrate Specificities of a Recombinantly Produced Azotobacter vinelandii Alginate Lyase

Alginate is a polysaccharide composed of β-d-mannuronic acid (M) and α-l-guluronic acid (G). An Azotobacter vinelandii alginate lyase gene, algL, was cloned, sequenced, and expressed in Escherichia coli. The deduced molecular mass of the corresponding protein is 41.4 kDa, but a signal peptide is cleaved off, leaving a mature protein of 39 kDa. Sixty-three percent of the amino acids in this mature protein are identical to those in AlgL from Pseudomonas aeruginosa. AlgL was partially purified, and the activity was found to be optimal at a pH of 8.1 to 8.4 and at 0.35 M NaCl. Divalent cations are not necessary for activity. The pI of the enzyme is 5.1. When an alginate rich in mannuronic acid was used as the substrate, the K_m was found to be 4.6 × 10⁻⁴ M (sugar residues). AlgL was found to cleave M-M and M-G bonds but not G-M or G-G bonds. Bonds involving acetylated residues were also cleaved, but this activity may be sensitive to the extent of acetylation.

Alginate is a family of 1-4-linked copolymers of β-d-mannuronic acid (M) and α-l-guluronic acid (G). It is produced by brown algae and by some bacteria belonging to the genera Azotobacter and Pseudomonas (8, 17, 18, 31). The polymer is widely used in industry and biotechnology (36, 44), and the genetics of its biosynthesis in Pseudomonas aeruginosa has been extensively studied due to its role in the disease cystic fibrosis (33). In bacterial alginates, some of the M residues may be O-2- and/or O-3-acetylated (42). The polymer is initially synthesized as mannuronan, and the G residues are introduced at the polymer level by mannuronan C-5-epimerases (13, 22, 23). The epimerized alginates contain a mixture of blocks of consecutive G residues (G blocks), consecutive M residues (M blocks), and alternating M and G residues (MG blocks). Alginates from Pseudomonas sp. do not contain G blocks (42).Alginate lyases catalyze the depolymerization of alginates by β-elimination, generating a molecule containing 4-deoxy-l-erythro-hex-4-enepyranosyluronate at the nonreducing end. Such lyases have been found in organisms using alginate as a carbon source, in bacteriophages specific for alginate-producing organisms, and in alginate-producing bacteria (45). An alginate molecule may contain four different glycosidic bonds, M-M, G-M, M-G, or G-G, and the relative rates at which each of these bonds are cleaved vary among different lyases (36a). The lyases also differ in the extent to which they are affected by acetylation (35, 43, 46).Davidson et al. (10) described an Azotobacter vinelandii lyase which preferred M blocks as a substrate. Kennedy et al. (28) later reported the purification of periplasmic alginate lyases from A. vinelandii and from Azotobacter chroococcum which also seemed to prefer deacetylated, M-rich alginate. The activities of these enzymes were found to be optimal at pH 6.8 and 7.2, respectively, while the enzyme reported by Davidson et al. (10) was found to display optimal activity at pH 7.8.A gene, algL, encoding an alginate lyase has been cloned from P. aeruginosa (2, 41). The gene was found to be located in a cluster containing most of the genes necessary for the biosynthesis of alginate. A homologous gene cluster has recently been identified in A. vinelandii (38) and shown to encode an alginate lyase (32). In our previous report, we showed that plasmid pHE102, which contains a part of this gene cluster, contains a DNA sequence sharing homology with algL from P. aeruginosa (38). We have now subcloned, sequenced, and expressed this gene in Escherichia coli. The lyase was shown to preferentially cleave deacetylated M-M and M-G bonds, but acetylated substrates were also cleaved.     

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]     

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]     

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]     

Sip1, a Novel RS Domain-Containing Protein Essential for Pre-mRNA Splicing

Previous studies have shown that protein-protein interactions among splicing factors may play an important role in pre-mRNA splicing. We report here identification and functional characterization of a new splicing factor, Sip1 (SC35-interacting protein 1). Sip1 was initially identified by virtue of its interaction with SC35, a splicing factor of the SR family. Sip1 interacts with not only several SR proteins but also with U1-70K and U2AF65, proteins associated with 5′ and 3′ splice sites, respectively. The predicted Sip1 sequence contains an arginine-serine-rich (RS) domain but does not have any known RNA-binding motifs, indicating that it is not a member of the SR family. Sip1 also contains a region with weak sequence similarity to the Drosophila splicing regulator suppressor of white apricot (SWAP). An essential role for Sip1 in pre-mRNA splicing was suggested by the observation that anti-Sip1 antibodies depleted splicing activity from HeLa nuclear extract. Purified recombinant Sip1 protein, but not other RS domain-containing proteins such as SC35, ASF/SF2, and U2AF65, restored the splicing activity of the Sip1-immunodepleted extract. Addition of U2AF65 protein further enhanced the splicing reconstitution by the Sip1 protein. Deficiency in the formation of both A and B splicing complexes in the Sip1-depleted nuclear extract indicates an important role of Sip1 in spliceosome assembly. Together, these results demonstrate that Sip1 is a novel RS domain-containing protein required for pre-mRNA splicing and that the functional role of Sip1 in splicing is distinct from those of known RS domain-containing splicing factors.Pre-mRNA splicing takes place in spliceosomes, the large RNA-protein complexes containing pre-mRNA, U1, U2, U4/6, and U5 small nuclear ribonucleoprotein particles (snRNPs), and a large number of accessory protein factors (for reviews, see references 21, 22, 37, 44, and 48). It is increasingly clear that the protein factors are important for pre-mRNA splicing and that studies of these factors are essential for further understanding of molecular mechanisms of pre-mRNA splicing.Most mammalian splicing factors have been identified by biochemical fractionation and purification (3, 15, 19, 31–36, 45, 69–71, 73), by using antibodies recognizing splicing factors (8, 9, 16, 17, 61, 66, 67, 74), and by sequence homology (25, 52, 74).Splicing factors containing arginine-serine-rich (RS) domains have emerged as important players in pre-mRNA splicing. These include members of the SR family, both subunits of U2 auxiliary factor (U2AF), and the U1 snRNP protein U1-70K (for reviews, see references 18, 41, and 59). Drosophila alternative splicing regulators transformer (Tra), transformer 2 (Tra2), and suppressor of white apricot (SWAP) also contain RS domains (20, 40, 42). RS domains in these proteins play important roles in pre-mRNA splicing (7, 71, 75), in nuclear localization of these splicing proteins (23, 40), and in protein-RNA interactions (56, 60, 64). Previous studies by us and others have demonstrated that one mechanism whereby SR proteins function in splicing is to mediate specific protein-protein interactions among spliceosomal components and between general splicing factors and alternative splicing regulators (1, 1a, 6, 10, 27, 63, 74, 77). Such protein-protein interactions may play critical roles in splice site recognition and association (for reviews, see references 4, 18, 37, 41, 47 and 59). Specific interactions among the splicing factors also suggest that it is possible to identify new splicing factors by their interactions with known splicing factors.Here we report identification of a new splicing factor, Sip1, by its interaction with the essential splicing factor SC35. The predicted Sip1 protein sequence contains an RS domain and a region with sequence similarity to the Drosophila splicing regulator, SWAP. We have expressed and purified recombinant Sip1 protein and raised polyclonal antibodies against the recombinant Sip1 protein. The anti-Sip1 antibodies specifically recognize a protein migrating at a molecular mass of approximately 210 kDa in HeLa nuclear extract. The anti-Sip1 antibodies sufficiently deplete Sip1 protein from the nuclear extract, and the Sip1-depleted extract is inactive in pre-mRNA splicing. Addition of recombinant Sip1 protein can partially restore splicing activity to the Sip1-depleted nuclear extract, indicating an essential role of Sip1 in pre-mRNA splicing. Other RS domain-containing proteins, including SC35, ASF/SF2, and U2AF65, cannot substitute for Sip1 in reconstituting splicing activity of the Sip1-depleted nuclear extract. However, addition of U2AF65 further increases splicing activity of Sip1-reconstituted nuclear extract, suggesting that there may be a functional interaction between Sip1 and U2AF65 in nuclear extract.     

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]