Biomedical subjects

Liran Carmel

Publications and source records attributed to Liran Carmel.

4 recordsLinked to original sources

PubMed2006

Genome-wide analysis of substrate specificities of the Escherichia coli haloacid dehalogenase-like phosphatase family.

Haloacid dehalogenase (HAD)-like hydrolases are a vast superfamily of largely uncharacterized enzymes, with a few members shown to possess phosphatase, beta-phosphoglucomutase, phosphonatase, and dehalogenase activities. Using a representative set of 80 phosphorylated substrates, we characterized the substrate specificities of 23 soluble HADs encoded in the Escherichia coli genome. We identified small molecule phosphatase activity in 21 HADs and beta-phosphoglucomutase activity in one protein. The E. coli HAD phosphatases show high catalytic efficiency and affinity to a wide range of phosphorylated metabolites that are intermediates of various metabolic reactions. Rather than following the classical "one enzyme-one substrate" model, most of the E. coli HADs show remarkably broad and overlapping substrate spectra. At least 12 reactions catalyzed by HADs currently have no EC numbers assigned in Enzyme Nomenclature. Surprisingly, most HADs hydrolyzed small phosphodonors (acetyl phosphate, carbamoyl phosphate, and phosphoramidate), which also serve as substrates for autophosphorylation of the receiver domains of the two-component signal transduction systems. The physiological relevance of the phosphatase activity with the preferred substrate was validated in vivo for one of the HADs, YniC. Many of the secondary activities of HADs might have no immediate physiological function but could comprise a reservoir for evolution of novel phosphatases.

Catalysis↗

PubMed2006

Unifying measures of gene function and evolution.

Recent genome analyses revealed intriguing correlations between variables characterizing the functioning of a gene, such as expression level (EL), connectivity of genetic and protein-protein interaction networks, and knockout effect, and variables describing gene evolution, such as sequence evolution rate (ER) and propensity for gene loss. Typically, variables within each of these classes are positively correlated, e.g. products of highly expressed genes also have a propensity to be involved in many protein-protein interactions, whereas variables between classes are negatively correlated, e.g. highly expressed genes, on average, evolve slower than weakly expressed genes. Here, we describe principal component (PC) analysis of seven genome-related variables and propose biological interpretations for the first three PCs. The first PC reflects a gene's 'importance', or the 'status' of a gene in the genomic community, with positive contributions from knockout lethality, EL, number of protein-protein interaction partners and the number of paralogues, and negative contributions from sequence ER and gene loss propensity. The next two PCs define a plane that seems to reflect the functional and evolutionary plasticity of a gene. Specifically, PC2 can be interpreted as a gene's 'adaptability' whereby genes with high adaptability readily duplicate, have many genetic interaction partners and tend to be non-essential. PC3 also might reflect the role of a gene in organismal adaptation albeit with a negative rather than a positive contribution of genetic interactions; we provisionally designate this PC 'reactivity'. The interpretation of PC2 and PC3 as measures of a gene's plasticity is compatible with the observation that genes with high values of these PCs tend to be expressed in a condition- or tissue-specific manner. Functional classes of genes substantially vary in status, adaptability and reactivity, with the highest status characteristic of the translation system and cytoskeletal proteins, highest adaptability seen in cellular processes and signalling genes, and top reactivity characteristic of metabolic enzymes.

Animals↗

PubMed2006

Gene expression profile of empirically delineated classes of unexplained chronic fatigue.

OBJECTIVES: To identify the underlying gene expression profiles of unexplained chronic fatigue subjects classified into five or six class solutions by principal component (PCA) and latent class analyses (LCA). METHODS: Microarray expression data were available for 15,315 genes and 111 female subjects enrolled from a population-based study on chronic fatigue syndrome. Algorithms were developed to assign gene scores and threshold values that signified the contribution of each gene to discriminate the multiclasses in each LCA solution. Unsupervised dimensionality reduction was first used to remove noise or otherwise uninformative gene combinations, followed by supervised dimensionality reduction to isolate gene combinations that best separate the classes. RESULTS: The authors' gene score and threshold algorithms identified 32 and 26 genes capable of discriminating the five and six multiclass solutions, respectively. Pair-wise comparisons suggested that some genes (zinc finger protein 350 [ZNF350], solute carrier family 1, member 6 [SLC1A6], F-box protein 7 [FBX07] and vacuole 14 protein homolog [VAC14]) distinguished most classes of fatigued subjects from healthy subjects, whereas others (patched homolog 2 [PTCH2] and T-cell leukemia/lymphoma [TCL1A]) differentiated specific fatigue classes. CONCLUSION: A computational approach was developed for general use to identify discriminatory genes in any multiclass problem. Using this approach, differences in gene expression were found to discriminate some classes of unexplained chronic fatigue, particularly one termed interoception.

Algorithms↗

PubMedDate not supplied

Combining hierarchy and energy for drawing directed graphs.

We present an algorithm for drawing directed graphs which is based on rapidly solving a unique one-dimensional optimization problem for each of the axes. The algorithm results in a clear description of the hierarchy structure of the graph. Nodes are not restricted to lie on fixed horizontal layers, resulting in layouts that convey the symmetries of the graph very naturally. The algorithm can be applied without change to cyclic or acyclic digraphs and even to graphs containing both directed and undirected edges. We also derive a hierarchy index from the input digraph, which quantitatively measures its amount of hierarchy.

Algorithms↗