Influence of metabolic network structure and function on enzyme evolution

BACKGROUND: Most studies of molecular evolution are focused on individual genes and proteins. However, understanding the design principles and evolutionary properties of molecular networks requires a system-wide perspective. In the present work we connect molecular evolution on the gene level with system properties of a cellular metabolic network. In contrast to protein interaction networks, where several previous studies investigated the molecular evolution of proteins, metabolic networks have a relatively well-defined global function. The ability to consider fluxes in a metabolic network allows us to relate the functional role of each enzyme in a network to its rate of evolution. RESULTS: Our results, based on the yeast metabolic network, demonstrate that important evolutionary processes, such as the fixation of single nucleotide mutations, gene duplications, and gene deletions, are influenced by the structure and function of the network. Specifically, central and highly connected enzymes evolve more slowly than less connected enzymes. Also, enzymes carrying high metabolic fluxes under natural biological conditions experience higher evolutionary constraints. Genes encoding enzymes with high connectivity and high metabolic flux have higher chances to retain duplicates in evolution. In contrast to protein interaction networks, highly connected enzymes are no more likely to be essential compared to less connected enzymes. CONCLUSION: The presented analysis of evolutionary constraints, gene duplication, and essentiality demonstrates that the structure and function of a metabolic network shapes the evolution of its enzymes. Our results underscore the need for systems-based approaches in studies of molecular evolution

Estimating enrichment of repetitive elements from high-throughput sequence data

We describe computational methods for analysis of repetitive elements from short-read sequencing data, and apply them to study histone modifications associated with the repetitive elements in human and mouse cells. Our results demonstrate that while accurate enrichment estimates can be obtained for individual repeat types and small sets of repeat instances, there are distinct combinatorial patterns of chromatin marks associated with major annotated repeat families, including H3K27me3/H3K9me3 differences among the endogenous retroviral element classes

Identifying metabolic enzymes with multiple types of association evidence

BACKGROUND: Existing large-scale metabolic models of sequenced organisms commonly include enzymatic functions which can not be attributed to any gene in that organism. Existing computational strategies for identifying such missing genes rely primarily on sequence homology to known enzyme-encoding genes. RESULTS: We present a novel method for identifying genes encoding for a specific metabolic function based on a local structure of metabolic network and multiple types of functional association evidence, including clustering of genes on the chromosome, similarity of phylogenetic profiles, gene expression, protein fusion events and others. Using E. coli and S. cerevisiae metabolic networks, we illustrate predictive ability of each individual type of association evidence and show that significantly better predictions can be obtained based on the combination of all data. In this way our method is able to predict 60% of enzyme-encoding genes of E. coli metabolism within the top 10 (out of 3551) candidates for their enzymatic function, and as a top candidate within 43% of the cases. CONCLUSION: We illustrate that a combination of genome context and other functional association evidence is effective in predicting genes encoding metabolic enzymes. Our approach does not rely on direct sequence homology to known enzyme-encoding genes, and can be used in conjunction with traditional homology-based metabolic reconstruction methods. The method can also be used to target orphan metabolic activities

Post-Summit Results, Delegates’ Summit: Best Practice and Definitions – Algorithm and Algorithm Signification

Post-Summit Results, Delegates' Summit, September 11, 2023, The (12+1)th Symposium on Advanced Computation and Information in Natural and Applied Sciences (SACINAS), The 21th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM), September 11-17, 2023, Heraklion, Crete, Greec

Expression dynamics of a cellular metabolic network

Toward the goal of understanding system properties of biological networks, we investigate the global and local regulation of gene expression in the Saccharomyces cerevisiae metabolic network. Our results demonstrate predominance of local gene regulation in metabolism. Metabolic genes display significant coexpression on distances smaller than the average network distance, a behavior supported by the distribution of transcription factor binding sites in the metabolic network and genome context associations. Positive gene coexpression decreases monotonically with distance in the network, while negative coexpression is strongest at intermediate network distances. We show that basic topological motifs of the metabolic network exhibit statistically significant differences in coexpression behavior

Circadian Kinetics of Cell Cycle Progression in Adult Neurogenic Niches of a Diurnal Vertebrate

The circadian system may regulate adult neurogenesis via intracellular molecular clock mechanisms or by modifying the environment of neurogenic niches, with daily variation in growth factors or nutrients depending on the animal's diurnal or nocturnal lifestyle. In a diurnal vertebrate, zebrafish, we studied circadian distribution of immunohistochemical markers of the cell division cycle (CDC) in 5 of the 16 neurogenic niches of adult brain, the dorsal telencephalon, habenula, preoptic area, hypothalamus, and cerebellum. We find that common to all niches is the morning initiation of G1/S transition and daytime S-phase progression, overnight increase in G2/M, and cycle completion by late night. This is supported by the timing of gene expression for critical cell cycle regulators cyclins D, A2, and B2 and cyclin-dependent kinase inhibitor p20 in brain tissue. The early-night peak in p20, limiting G1/S transition, and its phase angle with the expression of core clock genes, Clock1 and Per1, are preserved in constant darkness, suggesting intrinsic circadian patterns of cell cycle progression. The statistical modeling of CDC kinetics reveals the significant circadian variation in cell proliferation rates across all of the examined niches, but interniche differences in the magnitude of circadian variation in CDC, S-phase length, phase angle of entrainment to light or clock, and its dispersion. We conclude that, in neurogenic niches of an adult diurnal vertebrate, the circadian modulation of cell cycle progression involves both systemic and niche-specific factors. SIGNIFICANCE STATEMENT This study establishes that in neurogenic niches of an adult diurnal vertebrate, the cell cycle progression displays a robust circadian pattern. Common to neurogenic niches located in diverse brain regions is daytime progression of DNA replication and nighttime mitosis, suggesting systemic regulation. Differences between neurogenic niches in the phase and degree of S-phase entrainment to the clock suggest additional roles for niche-specific regulatory mechanisms. Understanding the circadian regulation of adult neurogenesis can help optimize the timing of therapeutic approaches in patients with brain traumas or neurodegenerative disorders and preserve neural stem cells during cytostatic cancer therapies

Bayesian approach to single-cell differential expression analysis

Single-cell data provide a means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression-magnitude distortions typical of single-cell RNA-sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise.Molecular and Cellular BiologyStem Cell and Regenerative Biolog

Cell type ontologies of the Human Cell Atlas

Massive single-cell profiling efforts have accelerated our discovery of the cellular composition of the human body while at the same time raising the need to formalize this new knowledge. Here, we discuss current efforts to harmonize and integrate different sources of annotations of cell types and states into a reference cell ontology. We illustrate with examples how a unified ontology can consolidate and advance our understanding of cell types across scientific communities and biological domains