Salivary alpha-amylase and cortisol responsiveness following electrical stimulation stress in patients with the generalized type of social anxiety disorder.

INTRODUCTION: Social anxiety disorder is believed to be a stress-induced disease. Although it can be inferred from the symptoms during attacks that there exists some abnormality of autonomic nervous system in any of the stress systems in social anxiety disorder, little evidence has been reported. This study focused on comparing the reactivity of 2 stress systems, the autonomic nervous system (ANS) and the hypothalamic-pituitary-adrenal (HPA) axis in patients with social anxiety disorder. METHODS: 32 patients with the generalized type of social anxiety disorder were compared with 80 age- and gender-matched controls. We collected saliva samples from patients and controls before and after electrical stimulation to measure the concentrations of salivary alpha-amylase (sAA) and salivary cortisol. Profile of Mood State (POMS) and State-Trait Anxiety Inventory (STAI) scores and Heart Rate Variability (HRV) were also determined following stimulation. RESULTS: SAA in patients displayed a significantly higher level at baseline and a significantly larger response to electrical stimulation as compared to controls, whereas no group differences were seen in any HRV. Neither within-subject nor group differences were seen in salivary cortisol levels. CONCLUSIONS: These results suggest that SAD patients displayed enhanced ANS (but not HPA axis) activity vs. healthy controls.

Structure of the human interleukin-2 receptor gene.

The gene encoding the human interleukin-2 (IL-2) receptor consists of 8 exons spanning more than 25 kilobases on chromosome 10. Exons 2 and 4 were derived from a gene duplication event and unexpectedly also are homologous to the recognition domain of human complement factor B. Alternative messenger RNA (mRNA) splicing may delete exon 4 sequences, resulting in a mRNA that does not encode a functional IL-2 receptor. Leukemic T cells infected with HTLV-I and normal activated T cells express IL-2 receptors with identical deduced protein sequences. Receptor gene transcription is initiated at two principal sites in normal activated T cells. Adult T cell leukemia cells infected with HTLV-I show activity at both of these sites, but also at a third transcription initiation site.

A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters.

The availability of computerized knowledge on biochemical pathways in the KEGG database opens new opportunities for developing computational methods to characterize and understand higher level functions of complete genomes. Our approach is based on the concept of graphs; for example, the genome is a graph with genes as nodes and the pathway is another graph with gene products as nodes. We have developed a simple method for graph comparison to identify local similarities, termed correlated clusters, between two graphs, which allows gaps and mismatches of nodes and edges and is especially suitable for detecting biological features. The method was applied to a comparison of the complete genomes of 10 microorganisms and the KEGG metabolic pathways, which revealed, not surprisingly, a tendency for formation of correlated clusters called FRECs (functionally related enzyme clusters). However, this tendency varied considerably depending on the organism. The relative number of enzymes in FRECs was close to 50% for Bacillus subtilis and Escherichia coli, but was <10% for SYNECHOCYSTIS: and Saccharomyces cerevisiae. The FRECs collection is reorganized into a collection of ortholog group tables in KEGG, which represents conserved pathway motifs with the information about gene clusters in all the completely sequenced genomes.

Automatic detection of conserved gene clusters in multiple genomes by graph comparison and P-quasi grouping.

We previously reported two graph algorithms for analysis of genomic information: a graph comparison algorithm to detect locally similar regions called correlated clusters and an algorithm to find a graph feature called P-quasi complete linkage. Based on these algorithms we have developed an automatic procedure to detect conserved gene clusters and align orthologous gene orders in multiple genomes. In the first step, the graph comparison is applied to pairwise genome comparisons, where the genome is considered as a one-dimensionally connected graph with genes as its nodes, and correlated clusters of genes that share sequence similarities are identified. In the next step, the P-quasi complete linkage analysis is applied to grouping of related clusters and conserved gene clusters in multiple genomes are identified. In the last step, orthologous relations of genes are established among each conserved cluster. We analyzed 17 completely sequenced microbial genomes and obtained 2313 clusters when the completeness parameter P: was 40%. About one quarter contained at least two genes that appeared in the metabolic and regulatory pathways in the KEGG database. This collection of conserved gene clusters is used to refine and augment ortholog group tables in KEGG and also to define ortholog identifiers as an extension of EC numbers.

The role of log-normal dynamics in the evolution of biochemical pathways.

The study of the scale-free topology in non-biological and biological networks and the dynamics that can explain this fascinating property of complex systems have captured the attention of the scientific community in the last years. Here, we analyze the biochemical pathways of three organisms (Methanococcus jannaschii, Escherichia coli, Saccharomyces cerevisiae) which are representatives of the main kingdoms Archaea, Bacteria and Eukaryotes during the course of the biological evolution. We can consider two complementary representations of the biochemical pathways: the enzymes network and the chemical compounds network. In this article, we propose a stochastic model that explains that the scale-free topology with exponent in the vicinity of gamma approximately 3/2 found across these three organisms is governed by the log-normal dynamics in the evolution of the enzymes network. Precisely, the fluctuations of the connectivity degree of enzymes in the biochemical pathways between evolutionary distant organisms follow the same conserved dynamical principle, which in the end is the origin of the stationary scale-free distribution observed among species, from Archaea to Eukaryotes. In particular, the log-normal dynamics guarantees the conservation of the scale-free distribution in evolving networks. Furthermore, the log-normal dynamics also gives a possible explanation for the restricted range of observed exponents gamma in the scale-free networks (i.e., gamma > or = 3/2). Finally, our model is also applied to the chemical compounds network of biochemical pathways and the Internet network.

The detection and classification of membrane-spanning proteins.

Discriminant analysis can be used to precisely classify membrane proteins as integral or peripheral and to estimate the odds that the classification is correct. Specifically, using 102 membrane proteins from the National Biomedical Research Foundation (NBRF) database we find that discrimination between integral and peripheral membrane proteins can be achieved with 99% reliability. Hydrophobic segments of integral membrane proteins can also be distinguished from interior segments of globular soluble proteins with better than 95% reliability. We also propose a procedure for determining boundaries of membrane-spanning segments and apply it to several integral membrane proteins. For the limited data available (such as on transplantation antigens), the residues at the boundaries of a membrane-spanning segment are predictable to within the error inherent in the concept of boundary. As a specific indication of resolution, seven membrane-spanning segments of bacteriorhodopsin are resolved with no information other than sequence, and the predicted boundary residues agree with the experimental data on proteolytic cleavage sites. Several definitive but yet to be tested predictions are also made, and the relation to other predictive methods is briefly discussed. A computer program in FORTRAN for prediction of membrane-spanning segments is available from the authors.

Prediction of protein function from sequence properties. Discriminant analysis of a data base.

The protein superfamilies in the National Biomedical Research Foundation sequence data base cluster into six groups that can be distinguished on the basis of four variables characterizing amino acid composition and local sequence properties. The variables are average hydrophobicity, net charge, sequence length and periodic variation in hydrophobic residues along the chain. The clusters they distinguish are: globins; chromosomal proteins; contractile system proteins and respiratory proteins other than cytochromes; enzyme inhibitors and toxins; enzymes except hydrolases; and all other proteins. The overall probability of correctly allocating a given protein to one of these functional groups is 0.76, with the allocation reliability being highest for globins (0.97) and for chromosomal proteins (0.93).

Protein network inference from multiple genomic data: a supervised approach.

MOTIVATION: An increasing number of observations support the hypothesis that most biological functions involve the interactions between many proteins, and that the complexity of living systems arises as a result of such interactions. In this context, the problem of inferring a global protein network for a given organism, using all available genomic data about the organism, is quickly becoming one of the main challenges in current computational biology. RESULTS: This paper presents a new method to infer protein networks from multiple types of genomic data. Based on a variant of kernel canonical correlation analysis, its originality is in the formalization of the protein network inference problem as a supervised learning problem, and in the integration of heterogeneous genomic data within this framework. We present promising results on the prediction of the protein network for the yeast Saccharomyces cerevisiae from four types of widely available data: gene expressions, protein interactions measured by yeast two-hybrid systems, protein localizations in the cell and protein phylogenetic profiles. The method is shown to outperform other unsupervised protein network inference methods. We finally conduct a comprehensive prediction of the protein network for all proteins of the yeast, which enables us to propose protein candidates for missing enzymes in a biosynthesis pathway. AVAILABILITY: Softwares are available upon request.

Flexible construction of hierarchical scale-free networks with general exponent.

Extensive studies have been done to understand the principles behind architectures of real networks. Recently, evidence for hierarchical organization in many real networks has also been reported. Here, we present a hierarchical model that reproduces the main experimental properties observed in real networks: scale-free of degree distribution P (k) [frequency of the nodes that are connected to k other nodes decays as a power law P (k) approximately k(-gamma) ] and power-law scaling of the clustering coefficient C (k) approximately k(-1) . The major points of our model can be summarized as follows. (a) The model generates networks with scale-free distribution for the degree of nodes with general exponent gamma>2 , and arbitrarily close to any specified value, being able to reproduce most of the observed hierarchical scale-free topologies. In contrast, previous models cannot obtain values of gamma>2.58 . (b) Our model has structural flexibility because (i) it can incorporate various types of basic building blocks (e.g., triangles, tetrahedrons, and, in general, fully connected clusters of n nodes) and (ii) it allows a large variety of configurations (i.e., the model can use more than n-1 copies of basic blocks of n nodes). The structural features of our proposed model might lead to a better understanding of architectures of biological and nonbiological networks.

Construction and analysis of a profile library characterizing groups of structurally known proteins.

A new sequence motif library StrProf was constructed characterizing the groups of related proteins in the PDB three-dimensional structure database. For a representative member of each protein family, which was identified by cross-referencing the PDB with the PIR superfamily classification, a group of related sequences was collected by the BLAST search against the nonredundant protein sequence database. For every group, the motifs were identified automatically according to the criteria of conservation and uniqueness of pentapeptide patterns and with a dual dynamic programming algorithm. In the StrProf library, motifs are represented by profile matrices rather than consensus patterns to allow more flexible search capabilities. Another dynamic programming algorithm was then developed to search this motif library. When the computationally derived StrProf was compared with PROSITE, which is a manually derived motif library in the best consensus pattern representation, the numbers of identified patterns were comparable. StrProf missed about one third of the PROSITE motifs, but there were also new motifs lacking in PROSITE. The new library was incorporated in SMART (Sequence Motif Analysis and Retrieval Tool), a computer tool designed to help search and annotate biologically important sites in an unknown protein sequence. The client program is available free of charge through the Internet.

Prediction of gene expression specificity by promoter sequence patterns.

We present here a heuristic method toward predicting the expression specificity in the transcriptional process, which is known to be regulated in large part by promoter sequences, by observing the appearance of conserved sequence patterns in a group of known promoters, such as for housekeeping or tissue-specific genes. Statistically conserved patterns were automatically extracted from a set of unaligned sequences up to 200 bp upstream of the transcription initiation site, by a standard procedure using the Markov chain and binomial distribution models. Furthermore, to obtain signal sequences of optimal lengths we devised a method that combines the multiple alignment and the analysis of the information content (or relative entropy). Groups of related promoters were compiled from the EPD eukaryotic promoter database and the EMBL nucleic acid sequence database. Each promoter was examined for its specificity by linear discriminant analysis to test the validity of the extracted patterns. Our method could correctly discriminate 77.6% of the housekeeping gene promoters and 62.9% of the liver promoters.

Characterization of genes encoding multi-domain proteins in the genome of the filamentous nitrogen-fixing Cyanobacterium anabaena sp. strain PCC 7120.

Computational analysis of gene structures in the genome of Anabaena sp. PCC 7120 revealed the presence of a large number of genes encoding proteins with multiple functional domains. This was most evident in the genes for signal transduction pathway and the related systems. Comparison of the putative amino acid sequences of the gene products with those in the Pfam database indicated that and PAS domains which may be involved in signal recognition were extremely abundant in Anabaena: 87 GAF domains in 62 ORFs and 140 PAS domains in 59 ORFs. As for the two-component signal transduction system, 73, 53, and 77 genes for simple sensory His kinases, hybrid His kinases and simple response regulators, respectively, many of which contained additional domains of diverse functions, were presumptively assigned. A total of 52 ORFs encoding putative Hanks-type Ser/Thr protein kinases with various domains such as WD-repeat, GAF and His kinase domains, as well as genes for presumptive protein phosphatases, were also identified. In addition, genes for putative transcription factors and for proteins in the cAMP signal transduction system harbored complex gene structures with multiple domains.

Distribution profiles of GC content around the translation initiation site in different species.

We have analyzed the distribution of guanine-cytosine (GC) content around the translation initiation site in genomic DNA sequences of different species. A set of sequences belonging to one species is aligned at the translation initiation site, and the average GC content is calculated for 100 base windows over a range of 500 bases each for upstream and downstream region. Consistent with previous observations that coding regions are more GC-rich than non-coding regions, we observe a jump in the GC content at the translation initiation site, except for vertebrate sequences. It was also found that the overall shape of the GC content profile is similar within each organism group even though the average GC contents can be very different. Furthermore, by examining different profiles for different species, we have found a negative correlation between the average GC content and the jump size at the translation initiation site. Apparently, more AT-rich genomes, which tend to lack macroscopic mosaic structures, exhibit more marked differences in the GC content at the microscopic level.

The size differences among mammalian introns are due to the accumulation of small deletions.

In order to investigate the molecular mechanisms that alter intron size, we conducted an extensive interspecies comparison of homologous introns among three mammalian groups: human, artiodactyls, and rodents. The size differences of introns were statistically significant among all three groups (longest intron was for human and shortest for rodents), and appear to be due to the accumulation of small deletions, according to the separate count of insertion and deletion frequencies. The distribution of intron size differences also has a shape similar to that for the distribution of insertion/deletion sizes found in pseudogenes. It is suggested that introns are selectively neutral to small-scale changes of the genome size, which inherently contain the bias of favoring short deletions against short insertions.

Prediction of higher order functional networks from genomic data.

Post-genomics may be defined in different ways depending on how one views the challenges after the discovery of the genome. A traditional view is to follow the concept of the central dogma in molecular biology, namely from genome to transcriptome to proteome. Projects are ongoing to analyse gene expression profiles both at the mRNA and protein levels, and to catalogue protein 3D structure families, which will no doubt help the understanding of the information in the genome. However, once complete, such experimentally determined catalogues of genes, RNAs and proteins only tell us about the building blocks of life. They do not tell us much about how life operates as a system, such as higher order functional behaviours of the cell or the organism. Thus, an alternative view of post-genomics is to go up from the molecular level to the cellular level and eventually to still higher levels, i.e., the biological systems. Bioinformatics provides basic concepts as well as practical methods to integrate this view with the traditional view and to analyse complex interactions among building blocks and with dynamic environments.

Prediction of in-vivo modification sites of proteins from their primary structures.

In order to make better use of the information contained in rapidly expanding amino acid sequence data, a new method to predict various modification sites of proteins from their primary structures is presented. It is also applicable to the prediction of other functional sites in proteins. Here we show the examples of N-glycosylation and serine/threonine phosphorylation sites. The method is essentially an elaboration of consensus sequence pattern matching based on stepwise discriminant analysis. The occurring amino acids near a potential modification site are represented by six numerical values which reflect various properties of amino acids. Longer-range effects around these sites are also considered. The stepwise procedure enabled us to automatically select effective features for discrimination. A computer program with our method first identifies potential modification sites by a sequence pattern, NX(S/T) for N-glycosylation or (S/T) for phosphorylation, and then decides by discriminant analysis whether a potential site is likely to be a true modification site. The prediction accuracy in the second step of discrimination was about 60% for glycosylation sites and about 80% for phosphorylation sites.

Structural changes and fluctuations of proteins. II. Analysis of the denaturation of globular proteins.

The statistical thermodynamic model of protein structure proposed in paper I is developed with special attention to the hydrophobic interaction. Calorimetric measurements of the thermal denaturation of five globular proteins, ribonuclease A, lysozyme, alpha-chymotrypsin, cytochrome c, and myoglobin, are quantitatively analyzed using the model. The thermodynamic parameters obtained by the least squares method reflect the global, average properties of proteins and are in good agreement with the expected values estimated from experimental and theoretical studies for model peptides. The average bond energy epsilon is well related to the tertiary structure of each protein. However, the difference in the parameters between different proteins is not observed for the cooperative energy ZJ and the chain entropy alpha. The individuality of a protein as far as its structural stability is concerned, is mainly reflected by the parameter gamma specifying the hydrophobic nature of a protein. The model is further applied in the analysis of several aspects of the structural stability of globular proteins. Denaturation induced by denaturants, salts, and pH are also explained by the model in a unified manner.

Extraction of correlated gene clusters by multiple graph comparison.

This paper presents a new method to extract a set of correlated genes with respect to multiple biological features. Relationships among genes on a specific feature are encoded as a graph structure whose nodes correspond to genes. For example, the genome is a graph representing positional correlations of genes on the chromosome, the pathway is a graph representing functional correlations of gene products, and the expression profile is a graph representing gene expression similarities. When a set of genes are localized in a single graph, such as a gene cluster on the chromosome, an enzyme cluster in the metabolic pathway, or a set of coexpressed genes in the microarray gene expression profile, this may suggest a functional link among those genes. The functional link would become stronger when the clusters are correlated; namely, when a set of corresponding genes form clusters in multiple graphs. The newly introduced heuristic algorithm extracts such correlated gene clusters as isomorphic subgraphs in multiple graphs by using inter-graph links that are defined based on biological relevance. Using the method, we found E.coli correlated gene clusters in which genes are related with respect to the positions in the genome and the metabolic pathway, as well as the 3D structural similarity. We also analyzed protein-protein interaction data by two-hybrid experiments and gene coexpression data by microarrays in S.cerevisiae, and estimated the possibility of utilizing our method for screening the datasets that are likely to contain many false positive relations.

Computation with the KEGG pathway database.

We introduce and discuss a new computational approach towards prediction and inference of biological functions from genomic sequences by making use of the pathway data in KEGG. Due to its piecewise nature, the current approach of predicting each gene function based on sequence similarity searches often fails to reconstruct cellular functions with all necessary components. The pathway diagram in KEGG, which may be considered a wiring diagram of molecules in biological systems, can be utilised as a reference for functional reconstruction. KEGG also contains binary relations that represent molecular interactions and relations and that can be utilised for computing and comparing pathways.