A novel N-terminal domain may dictate the glucose response of Mondo proteins.

Glucose is a fundamental energy source for both prokaryotes and eukaryotes. The balance between glucose utilization and storage is integral for proper energy homeostasis, and defects are associated with several diseases, e.g. type II diabetes. In vertebrates, the transcription factor ChREBP is a major component in glucose metabolism, while its ortholog MondoA is involved in glucose uptake. Both MondoA and ChREBP contain five Mondo conserved regions (MCRI-V) that affect their cellular localization and transactivation ability. While phosphorylation has been shown to affect ChREBP function, the mechanisms controlling glucose response of both ChREBP and MondoA remain elusive. By incorporating sequence analysis techniques, structure predictions, and functional annotations, we synthesized data surrounding Mondo family proteins into a cohesive, accurate, and general model involving the MCRs and two additional domains that determine ChREBP and MondoA glucose response. Paramount, we identified a conserved motif within the transactivation region of Mondo family proteins and propose that this motif interacts with the phosphorylated form of glucose. In addition, we discovered a putative nuclear receptor box in non-vertebrate Mondo and vertebrate ChREBP sequences that reveals a potentially novel interaction with nuclear receptors. These interactions are likely involved in altering ChREBP and MondoA conformation to form an active complex and induce transcription of genes involved in glucose metabolism and lipogenesis.

Brain cancer prognosis: independent validation of a clinical bioinformatics approach.

Translational and evidence based medicine can take advantage of biotechnology advances that offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. The clinical information hidden in these data can be clarified with clinical bioinformatics approaches. We have recently proposed a method to analyze different layers of high-throughput (omic) data to preserve the emergent properties that appear in the cellular system when all molecular levels are interacting. We show here that this method applied to brain cancer data can uncover properties (i.e. molecules related to protective versus risky features in different types of brain cancers) that have been independently validated as survival markers, with potential important application in clinical practice.

Phylogenetic analysis and classification of the fungal bHLH domain.

The basic Helix-Loop-Helix (bHLH) domain is an essential highly conserved DNA-binding domain found in many transcription factors in all eukaryotic organisms. The bHLH domain has been well studied in the Animal and Plant Kingdoms but has yet to be characterized within Fungi. Herein, we obtained and evaluated the phylogenetic relationship of 490 fungal-specific bHLH containing proteins from 55 whole genome projects composed of 49 Ascomycota and 6 Basidiomycota organisms. We identified 12 major groupings within Fungi (F1-F12); identifying conserved motifs and functions specific to each group. Several classification models were built to distinguish the 12 groups and elucidate the most discerning sites in the domain. Performance testing on these models, for correct group classification, resulted in a maximum sensitivity and specificity of 98.5% and 99.8%, respectively. We identified 12 highly discerning sites and incorporated those into a set of rules (simplified model) to classify sequences into the correct group. Conservation of amino acid sites and phylogenetic analyses established that like plant bHLH proteins, fungal bHLH-containing proteins are most closely related to animal Group B. The models used in these analyses were incorporated into a software package, the source code for which is available at www.fungalgenomics.ncsu.edu.

Evolution of the Max and Mlx networks in animals.

Transcription factors (TFs) are essential for the regulation of gene expression and often form emergent complexes to perform vital roles in cellular processes. In this paper, we focus on the parallel Max and Mlx networks of TFs because of their critical involvement in cell cycle regulation, proliferation, growth, metabolism, and apoptosis. A basic-helix-loop-helix-zipper (bHLHZ) domain mediates the competitive protein dimerization and DNA binding among Max and Mlx network members to form a complex system of cell regulation. To understand the importance of these network interactions, we identified the bHLHZ domain of Max and Mlx network proteins across the animal kingdom and carried out several multivariate statistical analyses. The presence and conservation of Max and Mlx network proteins in animal lineages stemming from the divergence of Metazoa indicate that these networks have ancient and essential functions. Phylogenetic analysis of the bHLHZ domain identified clear relationships among protein families with distinct points of radiation and divergence. Multivariate discriminant analysis further isolated specific amino acid changes within the bHLHZ domain that classify proteins, families, and network configurations. These analyses on Max and Mlx network members provide a model for characterizing the evolution of TFs involved in essential networks.

Joint analysis of transcriptional and post- transcriptional brain tumor data: searching for emergent properties of cellular systems.

BACKGROUND: Advances in biotechnology offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. However, to date, most computational and algorithmic efforts have been directed at mining data from each of these molecular levels (genomic, transcriptional, etc.) separately. In view of the rapid advances in technology (new generation sequencing, high-throughput proteomics) it is important to address the problem of analyzing these data as a whole, i.e. preserving the emergent properties that appear in the cellular system when all molecular levels are interacting. We analyzed one of the (currently) few datasets that provide both transcriptional and post-transcriptional data of the same samples to investigate the possibility to extract more information, using a joint analysis approach. RESULTS: We use Factor Analysis coupled with pre-established knowledge as a theoretical base to achieve this goal. Our intention is to identify structures that contain information from both mRNAs and miRNAs, and that can explain the complexity of the data. Despite the small sample available, we can show that this approach permits identification of meaningful structures, in particular two polycistronic miRNA genes related to transcriptional activity and likely to be relevant in the discrimination between gliosarcomas and other brain tumors. CONCLUSIONS: This suggests the need to develop methodologies to simultaneously mine information from different levels of biological organization, rather than linking separate analyses performed in parallel.

Site-specific evolutionary rates in proteins are better modeled as non-independent and strictly relative.

MOTIVATION: In a nucleotide or amino acid sequence, not all sites evolve at the same rate, due to differing selective constraints at each site. Currently in computational molecular evolution, models incorporating rate heterogeneity always share two assumptions. First, the rate of evolution at each site is assumed to be independent of every other site. Second, the values of these rates are assumed to be drawn from a known prior distribution. Although often assumed to be small, the actual effect of these assumptions has not been previously quantified in the literature. RESULTS: Herein we describe an algorithm to simultaneously infer the set of n-1 relative rates that parameterize the likelihood of an n-site alignment. Unlike previous work (a) these relative rates are completely identifiable and distinct from the branch-length parameters, and (b) a far more general class of rate priors can be used, and their effects quantified. Although described in a Bayesian framework, we discuss a future maximum likelihood extension. CONCLUSIONS: Using both synthetic data and alignments from the Myc, Max and p53 protein families, we find that inferring relative rather than absolute rates has several advantages. First, both empirical likelihoods and Bayes factors show strong preference for the relative-rate model, with a mean Delta ln P=-0.458 per alignment site. Second, the computed likelihoods and Bayes factors were essentially independent of the relative-rate prior, indicating that good estimates of the posterior rate distribution are not required a priori. Third, a novel finding is that rates can be accurately inferred even when up to approximately 4 substitutions per site have occurred. Thus biologically relevant putative hypervariable sites can be identified as easily as conserved sites. Lastly, our model treats rates and tree branch-lengths as completely identifiable, allowing for the first time coherent simultaneous inference of branch-lengths and site-specific evolutionary rates. AVAILABILITY: Source code for the utility described is available under a BSD-style license at http://www.fernandes.org/txp/article/9/site-specific-relative-evolutionary-rates.

Biochemical and functional evidence of p53 homology is inconsistent with molecular phylogenetics for distant sequences.

The tumor suppressor p53 is mutated in approximately 50% of all human cancer cases worldwide. It is commonly assumed that the phylogenetic history of this important tumor suppressor has been thoroughly studied; however, few detailed studies of the entire extended p53 protein family have been reported, and none comprehensively and simultaneously consider functional, molecular, and phylogenetic data. Herein we examine a diverse collection of reported p53-like protein sequences, including representatives from the arthropods, nematodes, and protists, with the goal of answering several important questions. First, what evidence supports these highly divergent proteins being true homologues to the p53 family? Second, is the inferred overall family phylogeny concordant with known structures and functions? Third, does the extended p53 family possess recognizable conserved sites outside of the within-chordate, highly-conserved DNA-binding domain? Our study shows that the biochemical and functional evidence of p53 homology for nematodes, arthropods, and protists is inconsistent with their implied phylogenetic relationship within the overall family. Although these divergent sequences are always reported as functionally similar to human p53, our results confirm and extend the hypothesis that p63 is a far more appropriate protein for comparison. Within these divergent sequences, we find minimal conservation within the DNA-binding domain, and no conservation elsewhere. Taken together, our findings suggest that these sequences are not bona fide homologues of the extended p53 family and provide baseline criteria for the future identification and characterization of distant p53-family homologues.

Hepatic endopolyploidy as a cellular consequence of age-specific selection for rate of development in mice.

Endopolyploidy is the generation of polyploid cells by DNA replication without subsequent cell division and is correlated with hypertrophic growth or growth via cell size. Thus, selection that alters growth may also change onset and frequency of endopolyploidy as a correlated response. We search for endopolyploidy in the liver in response to age-specific restricted index selection for the rate of development. Polyploidy changes over ontogeny are described in five mouse lines: two selected for divergence in early growth (0-10 days of age), two selected for divergence in late growth (28-56 days of age), and one randombred control. Polyploid cell frequency within each line increased as ontogeny continued, as expected from previous research. However, selection for altered growth clearly plays a role in the frequency and onset of polyploid cells. Lines selected for divergence in early growth have polyploidy differences after weaning that are not seen in adult mice. However, lines selected for divergence in late growth are divergent in frequency of polyploid cells, starting near sexual maturity and continuing into adulthood.

Application of complex demodulation on bZIP and bHLH-PAS protein domains.

Proteins are built with molecular modular building blocks such as an alpha-helix, beta-sheet, loop region and other structures. This is an economical way of constructing complex molecules. Periodicity analysis of protein sequences has allowed us to obtain meaningful information concerning their structure, function and evolution. In this work, complex demodulation (CDM) is introduced to detect functional regions in protein sequences data. More specifically, we analyzed bZIP and bHLH-PAS protein domains. Complex demodulation provided insightful information about changing amplitudes of periodic components in protein sequences. Furthermore, it was found that the local amplitude minimum or local amplitude maximum of the 3.6-aa periodic component is associated with protein structural or functional information due to the observation that the extrema are mainly located in the boundary area of two structural or functional regions.

Molecular architecture of the DNA-binding region and its relationship to classification of basic helix-loop-helix proteins.

Multivariate statistical analyses are used to explore the molecular architecture of the DNA-binding and dimerization regions of basic helix-loop-helix (bHLH) proteins. Alphabetic amino acid data are transformed to biologically meaningful quantitative values using a set of 5 multivariate "indices." These multivariate indices summarize variation in a large suite of amino acid physiochemical attributes and reflect variability in polarity-accessibility-hydrophobicity, propensity for secondary structure, molecular size, codon composition, and electrostatic charge. Using these index score data, discriminant analyses describe the multidimensional aspects of physiochemical variation and clarify the structural basis of the prevailing evolutionary classification of bHLH proteins. A small number of amino acids from both the binding dimerization domains, when considered simultaneously, accurately distinguish the 5 known DNA-binding groups. The relevant sites often have well-documented structural and functional characteristics.

Spectral analysis of sequence variability in basic-helix-loop-helix (bHLH) protein domains.

The basic helix-loop-helix (bHLH) family of transcription factors is used as a paradigm to explore structural implications of periodicity patterns in amino acid sequence variability. A Boltzmann-Shannon entropy profile represents site-by-site amino acid variation in the bHLH domain. Spectral analysis of almost 200 bHLH sequences documents the periodic nature of the bHLH sequence variation. Spectral analyses provide strong evidence that the patterns of amino acid variation in large numbers of sequences conform to the classical alpha-helix three-dimensional structure periodicity of 3.6 amino acids per turn. Multivariate indices of amino acid physiochemical attributes derived from almost 500 amino acid attributes are used to provide information regarding the underlying causal components of the bHLH sequence variability. Five multivariate attribute indices are used that reflect patterns in i) polarity - hydrophobicity - accessibility, ii) propensity for secondary structures, iii) molecular volume, iv) codon composition and v) electrostatic charge. Multiple regression analyses of the entropy values as dependent variables and the factor score means and variances as independent variables are used to partition variation in entropy values into their underlying causal structural components.

Gaussian quadrature formulae for arbitrary positive measures.

We present computational methods and subroutines to compute Gaussian quadrature integration formulas for arbitrary positive measures. For expensive integrands that can be factored into well-known forms, Gaussian quadrature schemes allow for efficient evaluation of high-accuracy and -precision numerical integrals, especially compared to general ad hoc schemes. In addition, for certain well-known density measures (the normal, gamma, log-normal, Student's t, inverse-gamma, beta, and Fisher's F) we present exact formulae for computing the respective quadrature scheme.

Networks of coevolving sites in structural and functional domains of serpin proteins.

Amino acids do not occur randomly in proteins; rather, their occurrence at any given site is strongly influenced by the amino acid composition at other sites, the structural and functional aspects of the region of the protein in which they occur, and the evolutionary history of the protein. The goal of our research study is to identify networks of coevolving sites within the serpin proteins (serine protease inhibitors) and classify them as being caused by structural-functional constraints or by evolutionary history. To address this, a matrix of pairwise normalized mutual information (NMI) values was computed among amino acid sites for the serpin proteins. The NMI matrix was partitioned into orthogonal patterns of amino acid variability by factor analysis. Each common factor pattern was interpreted as having phylogenetic and/or structural-functional explanations. In addition, we used a bootstrap factor analysis technique to limit the effects of phylogenetic history on our factor patterns. Our results show an extensive network of correlations among amino acid sites in key functional regions (reactive center loop, shutter, and breach). Additionally, we have discovered long-range coevolution for packed amino acids within the serpin protein core. Lastly, we have discovered a group of serpin sites which coevolve in the hydrophobic core region (s5B and s4B) and appear to represent sites important for formation of the "native" instead of the "latent" serpin structure. This research provides a better understanding on how protein structure evolves; in particular, it elucidates the selective forces creating coevolution among protein sites.

Solving the protein sequence metric problem.

Biological sequences are composed of long strings of alphabetic letters rather than arrays of numerical values. Lack of a natural underlying metric for comparing such alphabetic data significantly inhibits sophisticated statistical analyses of sequences, modeling structural and functional aspects of proteins, and related problems. Herein, we use multivariate statistical analyses on almost 500 amino acid attributes to produce a small set of highly interpretable numeric patterns of amino acid variability. These high-dimensional attribute data are summarized by five multidimensional patterns of attribute covariation that reflect polarity, secondary structure, molecular volume, codon diversity, and electrostatic charge. Numerical scores for each amino acid then transform amino acid sequences for statistical analyses. Relationships between transformed data and amino acid substitution matrices show significant associations for polarity and codon diversity scores. Transformed alphabetic data are used in analysis of variance and discriminant analysis to study DNA binding in the basic helix-loop-helix proteins. The transformed scores offer a general solution for analyzing a wide variety of sequence analysis problems.

Sequence signatures and the probabilistic identification of proteins in the Myc-Max-Mad network.

Accurate identification of specific groups of proteins by their amino acid sequence is an important goal in genome research. Here we combine information theory with fuzzy logic search procedures to identify sequence signatures or predictive motifs for members of the Myc-Max-Mad transcription factor network. Myc is a well known oncoprotein, and this family is involved in cell proliferation, apoptosis, and differentiation. We describe a small set of amino acid sites from the N-terminal portion of the basic helix-loop-helix (bHLH) domain that provide very accurate sequence signatures for the Myc-Max-Mad transcription factor network and three of its member proteins. A predictive motif involving 28 contiguous bHLH sequence elements found 337 network proteins in the GenBank NR database with no mismatches or misidentifications. This motif also identifies at least one previously unknown fungal protein with strong affinity to the Myc-Max-Mad network. Another motif found 96% of known Myc protein sequences with only a single mismatch, including sequences from genomes previously not thought to contain Myc proteins. The predictive motif for Myc is very similar to the ancestral sequence for the Myc group estimated from phylogenetic analyses. Based on available crystal structure studies, this motif is discussed in terms of its functional consequences. Our results provide insight into evolutionary diversification of DNA binding and dimerization in a well characterized family of regulatory proteins and provide a method of identifying signature motifs in protein families.

Phylogenetic analysis of plant basic helix-loop-helix proteins.

The basic helix-loop-helix (bHLH) family of proteins is a group of functionally diverse transcription factors found in both plants and animals. These proteins evolved early in eukaryotic cells before the split of animals and plants, but appear to function in 'plant-specific' or 'animal-specific' processes. In animals bHLH proteins are involved in regulation of a wide variety of essential developmental processes. On the contrary, bHLH proteins have not been extensively studied in plants. Those that have been characterized function in anthocyanin biosynthesis, phytochrome signaling, globulin expression, fruit dehiscence, carpel and epidermal development. We have identified 118 different bHLH genes in the completely sequenced Arabidopsis thaliana genome and 131 bHLH genes in the rice genome. Here we report a phylogenetic analysis of these genes, including 46 genes from other plant species and a classification of these proteins into 15 distinct plant clades. Results imply a polyphyletic origin for the plant bHLH proteins related only by their bHLH DNA binding motif. We suggest that plant bHLH proteins are under weaker selective constraints than their animal counterparts and that lineage specific expansions and subfunctionalization have fashioned regulatory proteins for plant specific functions.

QUANTITATIVE GENETIC MODELS FOR DEVELOPMENT, EPIGENETIC SELECTION, AND PHENOTYPIC EVOLUTION.

Herein we describe a general multivariate quantitative genetic model that incorporates two potentially important developmental phenomena, maternal effects and epigenetic effects. Maternal and epigenetic effects are defined as partial regression coefficients and phenotypic variances are derived in terms of age-specific genetic and environmental variances. As a starting point, the traditional quantitative genetic model of additive gene effects and random environmental effects is cast in a developmental time framework. From this framework, we first extend a maternal effects model to include multiple developmental ages for the occurrence of maternal effects. An example of maternal effects occurring at multiple developmental ages is prenatal and postnatal maternal effects in mammals. Subsequently, a model of intrinsic and epigenetic effects in the absence of maternal effects is described. It is shown that genetic correlations can arise through epigenetic effects, and in the absence of other developmental effects, epigenetic effects are in general confounded with age-specific intrinsic genetic effects. Finally, the two effects are incorporated into the basic quantitative genetic model. For this more biologically realistic model combining maternal and epigenetic effects, it is shown that the phenotypic regressions of offspring on mother and offspring on father can be used in some cases to estimate simultaneously maternal effects and epigenetic effects.

UTERINE EFFECTS, EPIGENETICS, AND POSTNATAL SKELETAL DEVELOPMENT IN THE MOUSE.

Reciprocal embryo transfer experiments show that skeletal dimensions in adult mice are significantly influenced by the genotype of the female providing the uterine environment in which they were raised. Embryo transfers among C3HeB/FeJ, SWR/J, and the C3SWF, hybrid strain (C3H females x SWR males) permit separation of uterine maternal genotype effects from effects arising from the progeny's own genotype. Many different aspects of adult skeletal form are significantly influenced by uterine genotype and, in some instances, the pattern of these effects correlates with events during skeletal embryology. Analyses involving the highly heterozygous C3SWF1 strain demonstrate the existence of significant dominance in maternal genes affecting skeletal development in the progeny. Further, there is a large skeletal effect due to progeny heterosis. Uterine Utter size can be manipulated as a nonheritable component of variability in embryo transfer experiments, and it has a large and systemic effect on skeletal growth and morphogenesis that persists in adult mice. Heritable uterine maternal effects are epigenetic interactions during development that can be incorporated into models of evolutionary change to provide a more complete picture of the causal agents producing morphological change.