Narrow Results

The occurrence of many congenital syndromes has long been an enigma. Clinically, the phenotype of any given genetic defect usually varies to some extent, whilst, pathogenetically, features within each syndrome are probably interconnected, albeit by largely unknown mechanisms. Through its unique theories such as the Jing-Mai (variously translated as the Channels, Vessels or Meridians), Zang-Fu (the Yin and Yang internal organs) and Wu-Xing (translated as the Five-Phase Correspondence or Five-Element theory), traditional Chinese medicine (TCM) seems to have comprehensively summarized the makeup of the human phenotypes. By combining the above TCM theories with modern medical knowledge, the intrinsic mechanisms between various aspects of the phenotypic makeup of the human individual, i.e. the Human Phenome, may be deduced. Analysis of congenital syndromes in light of the Human Phenome seems to suggest that various genetic defects may cause diseases in a similar fashion; i.e. primarily with structural abnormalities distributed along the four Jing-Mai connected with the Kidneys (midline defects) as well as "Marrow" aberrations (anomalies of hematology/immunology, endocrine, central nervous system and the bones). The derived Human Phenome may thereby enable a better understanding of such conditions and provide a model for the study of multigenic traits. On the other hand, blind spots of clinical observation and unknown aspects of human nature, e.g. circuits formed by the Jing-Mai, symmetries of the Jing-Mai and Zang-Fu, and correspondences between body physiques, spiritual factors and the external world may also be deduced. The TCM-based Human Phenome may thereby offer a fresh view for genotype-phenotype correlations, insights into gene-development mechanisms, as well as potential directions for the development of new treatments.

This stage of our journey through the universe of one-dimensional binary Cellular Automata is devoted to period-1 rules, constituting the first of the six groups in which we systematized the 88 globally-independent CA rules.

The first part of this article is mainly dedicated to reviewing the terminology and the empirical results found in the previous papers of our quest. We also introduce the concept of the ω-limit orbit with the purpose of linking our work to the classical theory of nonlinear dynamical systems. Moreover, we present the basin tree diagrams of all period-1 rules — except for rule , which is trivial — along with their Boolean cubes and time-1 characteristic functions.

In the second part, we prove a theorem demonstrating that all rules belonging to group 1 have robust period-1 rules for any finite, and infinite, bit-string length L. This is the first time we give analytical results on the behavior of CA local rules for large values of L and, consequently, for bi-infinite bit strings.

The theoretical treatment is complemented by two remarkable practical results: an explicit formula for generating isomorphic basin trees, and an algorithm for creating new periodic orbits by concatenation. We also provide several examples of both of them, showing how they help to avoid tedious simulations.

The 11th part of our tour through one-dimensional binary Cellular Automata concerns period-2 rules, which form the second group in our classification of the 88 globally-independent CA rules according to the properties of their periodic orbits. In this article, we display the basin tree diagrams of all period-2 rules along with their time-2 characteristic functions, and then we prove that all rules belonging to group 2 have robust period-2 ω-limit orbits for any finite, and infinite, bit string length. This rigorous result, which pairs with the one about period-1 rules given in the tenth installment of our chronicle, confirms what we stated about period-2 rules on the basis of empirical evidence. In the second part of this tutorial, we introduce the notion of quasi global-equivalence and prove that there are only 82 quasi globally-independent CA rules. For the first time, we show that the space-time patterns of globally-independent local rules can depend on each other, and we present an example of quasi-global transformation. We also define the super string 𝄞, and its unique decimal representation x_𝄞, dubbed the super decimal, which provides a completely transparent yet rigorous proof that rule is chaotic when L → ∞. Moreover, we present the basin tree generation formulas, which uncover the analytical relationships between basin trees of globally-equivalent rules. Last but not least, for pedagogical and epistemological reasons, we conclude this paper with the selection of rule, instead of rule , as the prototypic universal Turing machine for our future discourse.

Cell morphology is an often-utilized feature in biology and medical science because changes in the shape of a cell indicate that some abnormal alterations may have occurred within the cell. However, there is little knowledge about the relationship between phenotype and genes. In this study, for determining the genetic effect on phenotypes, we designed a new algorithm called phenotype analysis with layered conjecture (PALACE); this algorithm which infers a gene network that represents the dependency of morphological features using a comprehensive yeast phenotype dataset resulting from the deletion of one gene. PALACE first creates gene groups in which the abnormal phenotypes of its members are mutually close; then, it generates a network with groups based on inclusion relations between abnormal phenotypes. A network inferred from 172 transcriptional genes comprises 63 gene groups and 183 edges. The inferred network has biologically reasonable features; however, it is independent of known metabolic networks. Therefore, the network is expected to include information leading to new insights into cell biology.

The biofilm wrinkle evolution is the growth mechanism by which bacteria regulate their physiological state in response to the environmental change. We use the parameter of surface complexity to describe different wrinkle patterns. The surface complexity is defined that the biofilm surface area contact with the air is divided by the projected area of the biofilm. We find that the biofilm surface complexity variation is positively proportional to the number of spores. Although each wrinkle pattern has various wrinkle thickness and width, surface complexities of some patterns are almost same, which guarantees cells have enough living space. Through the observation of the growth of the damaged biofilm, we further find that the biofilm expansion along the circumferential direction is faster than that along radial direction, which means that the internal stress along the circumferential direction contributes the wrinkle formation. Our work provides a new perspective to study biofilm morphologies, and relates the morphology evolution with phenotypes in the Bacillus subtilis biofilm.

Despite spectacular progress in biophysics, molecular biology and biochemistry our ability to predict the dynamic behavior of multicellular systems under different conditions is very limited. An important reason for this is that still not enough is known about how cells change their physical and biological properties by genetic or metabolic regulation, and which of these changes affect the cell behavior. For this reason, it is difficult to predict the system behavior of multicellular systems in case the cell behavior changes, for example, as a consequence of regulation or differentiation. The rules that underlie the regulation processes have been determined on the time scale of evolution, by selection on the phenotypic level of cells or cell populations. We illustrate by detailed computer simulations in a multi-scale approach how cell behavior controlled by regulatory networks may emerge as a consequence of an evolutionary process, if either the cells, or populations of cells are subject to selection on particular features. We consider two examples, migration strategies of single cells searching a signal source, or aggregation of two or more cells within minimal multiscale models of biological evolution. Both can be found for example in the life cycle of the slime mold Dictyostelium discoideum. However, phenotypic changes that can lead to completely different modes of migration have also been observed in cells of multi-cellular organisms, for example, as a consequence of a specialization in stem cells or the de-differentiation in tumor cells. The regulatory networks are represented by Boolean networks and encoded by binary strings. The latter may be considered as encoding the genetic information (the genotype) and are subject to mutations and crossovers. The cell behavior reflects the phenotype. We find that cells adopt naturally observed migration strategies, controlled by networks that show robustness and redundancy. The model simplicity allow us to unambiguously analyze the regulatory networks and the resulting phenotypes by different measures and by knockouts of regulatory elements. We illustrate that in order to maintain a cells' phenotype in case of a knockout, the cell may have to be able to deal with contradictory information. In summary, both the cell phenotype as well as the emerged regulatory network behave as their biological counterparts observed in nature.

Recently, a number of collaborative large-scale mouse mutagenesis programs have been launched. These programs aim for a better understanding of the roles of all individual coding genes and the biological systems in which these genes participate. In international efforts to share phenotypic data among facilities/institutes, it is desirable to integrate information obtained from different phenotypic platforms reliably. Since the definitions of specific phenotypes often depend on a tacit understanding of concepts that tends to vary among different facilities, it is necessary to define phenotypes based on the explicit evidence of assay results. We have developed a website termed PhenoSITE (Phenome Semantics Information with Terminology of Experiments: ), in which we are trying to integrate phenotype-related information using an experimental-evidence–based approach. The site's features include (1) a baseline database for our phenotyping platform; (2) an ontology associating international phenotypic definitions with experimental terminologies used in our phenotyping platform; (3) a database for standardized operation procedures of the phenotyping platform; and (4) a database for mouse mutants using data produced from the large-scale mutagenesis program at RIKEN GSC. We have developed two types of integrated viewers to enhance the accessibility to mutant resource information. One viewer depicts a matrix view of the ontology-based classification and chromosomal location of each gene; the other depicts ontology-mediated integration of experimental protocols, baseline data, and mutant information. These approaches rely entirely upon experiment-based evidence, ensuring the reliability of the integrated data from different phenotyping platforms.

Most clustering techniques do not incorporate phenotypic data. Limited biological interpretation is garnered from the informal process of clustering biological samples and then labeling groups with the phenotypes of the samples. A more formal approach of clustering samples is presented. The method utilizes simulated annealing of the Modk-prototypes objective function. Separate weighting terms are used for microarray, clinical chemistry, and histopathology measurements to control the influence of each data domain on the clustering of the samples. The weights are adapted during the clustering process. A cluster's prototype is representative of the phenotype of the cluster members. Genes are extracted from phenotypic prototypes obtained from the livers of rats exposed to acetaminophen (an analgesic and antipyretic agent) that differed in the extent of centrilobular necrosis. Map kinase signaling and linoleic acid metabolism were significant biological processes influenced by the exposures of acetaminophen that manifested centrilobular necrosis.

We re-examine the evolutionary dynamics of RNA secondary structures under directional selection towards an optimum RNA structure. We find that the punctuated equilibria lead to a very slow approach to the optimum, following on average an inverse power of the evolutionary time. In addition, our study of the trajectories shows that the out-of-equilibrium effects due to the evolutionary process are very weak. In particular, the distribution of genotypes is close to that arising during equilibrium stabilizing selection. As a consequence, the evolutionary dynamics leave almost no measurable out-of-equilibrium trace, only the transition genotypes (close to the border between different periods of stasis) have atypical mutational properties.

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common hereditary enzymatic disorder of red blood cells in humans due to mutations in the G6PD gene. The G6PD enzyme catalyzes the first step in the pentose phosphate pathway to protect cells against oxidative stress. Mutations in the G6PD gene will cause functional variants with various biochemical and clinical phenotypes. So far, about 160 mutations along with more than 400 biochemical variants have been described. G6PD-MutDB is a disease-specific resource of G6PD deficiency, collecting and integrating G6PD mutations with biochemical and clinical phenotypes. Data of G6PD deficiency is manually extracted from published papers, focusing primarily on variants with identified mutation and well-described quantitative phenotypes. G6PD-MutDB implements an approach, CNSHA predictor, to help identify a potential chronic non-spherocytic hemolytic anemia (CNSHA) phenotype of an unknown mutation. G6PD-MutDB is believed to facilitate analysis of relationship between molecular mutation and functional phenotype of G6PD deficiency owing to convenient data resource and useful tools. This database is available from .

Systematic studies have revealed that single gene deletions often display little phenotypic effects under laboratory conditions and that in many cases gene dispensability depends on the experimental conditions. To elucidate the environmental dependency of genes, we analyzed the effects of gene deletions by Phenotype MicroArray™ (PM), a system for quantitative screening of thousands of phenotypes in a high-throughput manner. Here, we proposed a new statistical approach to minimize error inherent in measurements of low respiration rates and find which mutants showed significant phenotypic changes in comparison to the wild-type. We show analyzing results from comprehensive PM assays of 298 single-gene knockout mutants in the Keio collection and two additional mutants under 1,920 different conditions. We focused on isozymes of these genes as simple duplications and analyzed correlations between phenotype changes and protein expression levels. Our results revealed divergence of the environmental dependency of the gene among the knockout genes and have also given some insights into possibilities of alternative pathways and availabilities of information on protein synthesis patterns to classify or predict functions of target genes from systematic phenotype screening.

Researchers hope that establishing a notion of proximity using topology will help to clarify the biological processes underlying the evolution of living organisms. The simple model presented here, using RNA shapes, can carry over to more general and complex genotype–phenotype systems. Proximity is an important component of continuity, in both real-world and topological terms. Consequently, phenotype spaces provide an appropriate setting for modeling and investigating continuous and discontinuous evolutionary change.

Nonsteroidal anti-inflammatory drugs (NSAIDs) are widely used for their analgesic, antipyretic, and anti-inflammatory properties. However, they are also associated with a broad spectrum of hypersensitivity reactions, ranging from mild cutaneous manifestations to severe systemic responses. The complex nature of these reactions and their underlying mechanisms pose challenges in diagnosis and management.

We review the clinical evidence to six distinct clinical phenotypes of NSAID hypersensitivity, including: NSAID-induced urticaria/angioedema or anaphylaxis (SNIUAA), NSAID-exacerbated respiratory disease (NERD), NSAID-exacerbated cutaneous disease (NECD), NSAID-induced urticaria/angioedema (NIUA), food-dependent NSAID-induced hypersensitivity reactions (FDNIH), and selective NSAID-induced delayed reactions (SNIDR). This review aims to provide a comprehensive review of NSAID hypersensitivity reactions based on clinical phenotyping, which can aid in understanding and managing these adverse events.

Maintenance of differentiated functional phenotype within in vitro chondrocyte culture requires seeding at high densities with large numbers of cells. However, optimal cell seeding numbers and densities remain elusive due to multiple varying parameters and different methodologies utilized in previous studies. In the current study, we tried to investigate the relationship between cell seeding number and differentiated functional phenotype of in vitro cultured chondrocytes. Varying numbers of primary porcine chondrocytes (0.25, 2.5, 25 and 250 K) were seeded in 96 well-plates and cultured for 4 weeks. Cell proliferation, glycosaminoglycan (GAG) production and gene expression levels of Sox9, aggrecan, COL II and COL I were evaluated. The results showed that GAG content was high in the 0.25 and 25 K groups, gene expression of Sox9 was high in the 2.5, 25 and 250 K groups and expression of COL II was high in the 25 K group, whereas expression of COL I was low in the 0.25, 25 and 250 K groups. It is concluded that the seeding number and density of the 25 K (78 K cells/cm²) group achieved the optimal balance between functional phenotype of individual cells and the total ECM production for in vitro cultured chondrocytes.

Phenotype MicroArray (PM) technology is high-throughput phenotyping system [1] and is directly applicable to assay the effects of genetic changes in cells. In this study, we performed comprehensive PM analysis using single gene deletion mutants of central metabolic pathway and related genes. To elucidate the structure of central metabolic networks in Escherichia coli K-12, we focused 288 different PM conditions of carbon and nitrogen sources and performed bioinformatic analysis. For data processing, we employed noise reduction procedures. The distance between each of the mutants was defined by Manhattan distance and agglomerative Ward's hierarchical method was applied for clustering analysis. As a result, five clusters were revealed which represented to activate or repress cellular respiratory activities. Furthermore, the results might suggest that Glyceraldehyde-3P plays a key role as a molecular switch of central metabolic network.

Phenome-wide association studies (PheWAS) allow agnostic investigation of common genetic variants in relation to a variety of phenotypes but preserving the power of PheWAS requires careful phenotypic quality control (QC) procedures. While QC of genetic data is well-defined, no established QC practices exist for multi-phenotypic data. Manually imposing sample size restrictions, identifying variable types/distributions, and locating problems such as missing data or outliers is arduous in large, multivariate datasets. In this paper, we perform two PheWAS on epidemiological data and, utilizing the novel software CLARITE (CLeaning to Analysis: Reproducibility-based Interface for Traits and Exposures), showcase a transparent and replicable phenome QC pipeline which we believe is a necessity for the field. Using data from the Ludwigshafen Risk and Cardiovascular (LURIC) Health Study we ran two PheWAS, one on cardiac-related diseases and the other on polyunsaturated fatty acids levels. These phenotypes underwent a stringent quality control screen and were regressed on a genome-wide sample of single nucleotide polymorphisms (SNPs). Seven SNPs were significant in association with dihomo-γ-linolenic acid, of which five were within fatty acid desaturases FADS1 and FADS2. PheWAS is a useful tool to elucidate the genetic architecture of complex disease phenotypes within a single experimental framework. However, to reduce computational and multiple-comparisons burden, careful assessment of phenotype quality and removal of low-quality data is prudent. Herein we perform two PheWAS while applying a detailed phenotype QC process, for which we provide a replicable pipeline that is modifiable for application to other large datasets with heterogenous phenotypes. As investigation of complex traits continues beyond traditional genome wide association studies (GWAS), such QC considerations and tools such as CLARITE are crucial to the in the analysis of non-genetic big data such as clinical measurements, lifestyle habits, and polygenic traits.

This stage of our journey through the universe of one-dimensional binary Cellular Automata is devoted to period-1 rules, constituting the first of the six groups in which we systematized the 88 globally-independent CA rules.

The first part of this article is mainly dedicated to reviewing the terminology and the empirical results found in the previous papers of our quest. We also introduce the concept of the ω-limit orbit with the purpose of linking our work to the classical theory of nonlinear dynamical systems. Moreover, we present the basin tree diagrams of all period-1 rules — except for rule , which is trivial — along with their Boolean cubes and time-1 characteristic functions.

In the second part, we prove a theorem demonstrating that all rules belonging to group 1 have robust period-1 rules for any finite, and infinite, bit-string length L. This is the first time we give analytical results on the behavior of CA local rules for large values of L and, consequently, for bi-infinite bit strings.

The theoretical treatment is complemented by two remarkable practical results: an explicit formula for generating isomorphic basin trees, and an algorithm for creating new periodic orbits by concatenation. We also provide several examples of both of them, showing how they help to avoid tedious simulations.

The investigation of phenotypes in model organisms has the potential to reveal the molecular mechanisms underlying disease. The large-scale comparative analysis of phenotypes across species can reveal novel associations between genotypes and diseases. We use the PhenomeNET network of phenotypic similarity to suggest genotype–disease association, combine them with drug–gene associations available from the PharmGKB database, and infer novel associations between drugs and diseases. We evaluate and quantify our results based on our method's capability to reproduce known drug–disease associations. We find and discuss evidence that levonorgestrel, tretinoin and estradiol are associated with cystic fibrosis (p < 2:65 · 10⁻⁶, p < 0:002 and p < 0:031, Wilcoxon signedrank test, Bonferroni correction) and that ibuprofen may be active in chronic lymphocytic leukemia (p < 2:63 p < 0:03110⁻²³ Wilcoxon signed-rank test, Bonferroni correction). To enable access to our results, we implement a web server and make our raw data freely available. Our results are the first steps in implementing an integrated system for the analysis and prediction of drug–disease associations for rare and orphan diseases for which the molecular basis is not known.

The 11th part of our tour through one-dimensional binary Cellular Automata concerns period-2 rules, which form the second group in our classification of the 88 globally-independent CA rules according to the properties of their periodic orbits. In this article, we display the basin tree diagrams of all period-2 rules along with their time-2 characteristic functions, and then we prove that all rules belonging to group 2 have robust period-2 ω-limit orbits for any finite, and infinite, bit string length. This rigorous result, which pairs with the one about period-1 rules given in the tenth installment of our chronicle, confirms what we stated about period-2 rules on the basis of empirical evidence. In the second part of this tutorial, we introduce the notion of quasi global-equivalence and prove that there are only 82 quasi globally-independent CA rules. For the first time, we show that the space-time patterns of globally-independent local rules can depend on each other, and we present an example of quasi-global transformation. We also define the super string, and its unique decimal representation , dubbed the super decimal, which provides a completely transparent yet rigorous proof that rule 170 is chaotic when L → ∞. Moreover, we present the basin tree generation formulas, which uncover the analytical relationships between basin trees of globally-equivalent rules. Last but not least, for pedagogical and epistemological reasons, we conclude this paper with the selection of rule137, instead of rule 110, as the prototypic universal Turing machine for our future discourse.

There is growing use of ontologies for the measurement of cross-species phenotype similarity. Such similarity measurements contribute to diverse applications, such as identifying genetic models for human diseases, transferring knowledge among model organisms, and studying the genetic basis of evolutionary innovations. Two organismal features, whether genes, anatomical parts, or any other inherited feature, are considered to be homologous when they are evolutionarily derived from a single feature in a common ancestor. A classic example is the homology between the paired fins of fishes and vertebrate limbs. Anatomical ontologies that model the structural relations among parts may fail to include some known anatomical homologies unless they are deliberately added as separate axioms. The consequences of neglecting known homologies for applications that rely on such ontologies has not been well studied. Here, we examine how semantic similarity is affected when external homology knowledge is included. We measure phenotypic similarity between orthologous and non-orthologous gene pairs between humans and either mouse or zebrafish, and compare the inclusion of real with faux homology axioms. Semantic similarity was preferentially increased for orthologs when using real homology axioms, but only in the more divergent of the two species comparisons (human to zebrafish, not human to mouse), and the relative increase was less than 1% to non-orthologs. By contrast, inclusion of both real and faux random homology axioms preferentially increased similarities between genes that were initially more dissimilar in the other comparisons. Biologically meaningful increases in semantic similarity were seen for a select subset of gene pairs. Overall, the effect of including homology axioms on cross-species semantic similarity was modest at the levels of divergence examined here, but our results hint that it may be greater for more distant species comparisons.