Coding limits on the number of transcription factors

Transcription factor proteins bind specific DNA sequences to control the expression of genes. They contain DNA binding domains which belong to several super-families, each with a specific mechanism of DNA binding. The total number of transcription factors encoded in a genome increases with the number of genes in the genome. Here, we examined the number of transcription factors from each super-family in diverse organisms. We find that the number of transcription factors from most super-families appears to be bounded. For example, the number of winged helix factors does not generally exceed 300, even in very large genomes. The magnitude of the maximal number of transcription factors from each super-family seems to correlate with the number of DNA bases effectively recognized by the binding mechanism of that super-family. Coding theory predicts that such upper bounds on the number of transcription factors should exist, in order to minimize cross-binding errors between transcription factors. This theory further predicts that factors with similar binding sequences should tend to have similar biological effect, so that errors based on mis-recognition are minimal. We present evidence that transcription factors with similar binding sequences tend to regulate genes with similar biological functions, supporting this prediction. The present study suggests limits on the transcription factor repertoire of cells, and suggests coding constraints that might apply more generally to the mapping between binding sites and biological function.Comment: http://www.weizmann.ac.il/complex/tlusty/papers/BMCGenomics2006.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1590034/ http://www.biomedcentral.com/1471-2164/7/23

Large-scale mapping of sequence-function relations in small regulatory RNAs reveals plasticity and modularity

Two decades into the genomics era the question of mapping sequence to function has evolved from identifying functional elements to characterizing their quantitative properties including, in particular, their specificity and efficiency. Here, we use a large-scale approach to establish a quantitative map between the sequence of a bacterial regulatory RNA and its efficiency in modulating the expression of its targets. Our approach generalizes the sort-seq method, introduced recently to analyze promoter sequences, in order to accurately quantify the efficiency of a large library of sequence variants. We focus on two small RNAs (sRNAs) in E. coli, DsrA and RyhB, and their regulation of both repressed and activated targets. In addition to precisely identifying functional elements in the sRNAs, our data establish quantitative relationships between structural and energetic features of the sRNAs and their regulatory activity, and characterize a large set of direct and indirect interactions between nucleotides. A core of these interactions supports a model where specificity can be enhanced by a rigid molecular structure. Both sRNAs exhibit a modular design with limited cross-interactions, dividing the requirements for structural stability and target binding among modules

Quantitative effect of target translation on small RNA efficacy reveals a novel mode of interaction

Small regulatory RNAs (sRNAs) in bacteria regulate many important cellular activities under normal conditions and in response to stress. Many sRNAs bind to the mRNA targets at or near the 5′ untranslated region (UTR) resulting in translation inhibition and accelerated degradation. Often the sRNA-binding site is adjacent to or overlapping with the ribosomal binding site (RBS), suggesting a possible interplay between sRNA and ribosome binding. Here we combine quantitative experiments with mathematical modeling to reveal novel features of the interaction between small RNAs and the translation machinery at the 5′UTR of a target mRNA. By measuring the response of a library of reporter targets with varied RBSs, we find that increasing translation rate can lead to increased repression. Quantitative analysis of these data suggests a recruitment model, where bound ribosomes facilitate binding of the sRNA. We experimentally verified predictions of this model for the cell-to-cell variability of target expression. Our findings offer a framework for understanding sRNA silencing in the context of bacterial physiology

A Universal Mechanism Ties Genotype to Phenotype in Trinucleotide Diseases

Trinucleotide hereditary diseases such as Huntington disease and Friedreich ataxia are cureless diseases associated with inheriting an abnormally large number of DNA trinucleotide repeats in a gene. The genes associated with different diseases are unrelated and harbor a trinucleotide repeat in different functional regions; therefore, it is striking that many of these diseases have similar correlations between their genotype, namely the number of inherited repeats and age of onset and progression phenotype. These correlations remain unexplained despite more than a decade of research. Although mechanisms have been proposed for several trinucleotide diseases, none of the proposals, being disease-specific, can account for the commonalities among these diseases. Here, we propose a universal mechanism in which length-dependent somatic repeat expansion occurs during the patient's lifetime toward a pathological threshold. Our mechanism uniformly explains for the first time to our knowledge the genotype–phenotype correlations common to trinucleotide disease and is well-supported by both experimental and clinical data. In addition, mathematical analysis of the mechanism provides simple explanations to a wide range of phenomena such as the exponential decrease of the age-of-onset curve, similar onset but faster progression in patients with Huntington disease with homozygous versus heterozygous mutation, and correlation of age of onset with length of the short allele but not with the long allele in Friedreich ataxia. If our proposed universal mechanism proves to be the core component of the actual mechanisms of specific trinucleotide diseases, it would open the search for a uniform treatment for all these diseases, possibly by delaying the somatic expansion process

Amplification of multiple genomic loci from single cells isolated by laser micro-dissection of tissues

<p>Abstract</p> <p>Background</p> <p>Whole genome amplification (WGA) and laser assisted micro-dissection represent two recently developed technologies that can greatly advance biological and medical research. WGA allows the analysis of multiple genomic loci from a single genome and has been performed on single cells from cell suspensions and from enzymatically-digested tissues. Laser micro-dissection makes it possible to isolate specific single cells from heterogeneous tissues.</p> <p>Results</p> <p>Here we applied for the first time WGA on laser micro-dissected single cells from stained tissue sections, and developed a protocol for sequentially performing the two procedures. The combined procedure allows correlating the cell's genome with its natural morphology and precise anatomical position. From each cell we amplified 122 genomic and mitochondrial loci. In cells obtained from fresh tissue sections, 64.5% of alleles successfully amplified to ~700000 copies each, and mitochondrial DNA was amplified successfully in all cells. Multiplex PCR amplification and analysis of cells from pre-stored sections yielded significantly poorer results. Sequencing and capillary electrophoresis of WGA products allowed detection of slippage mutations in microsatellites (MS), and point mutations in P53.</p> <p>Conclusion</p> <p>Comprehensive genomic analysis of single cells from stained tissue sections opens new research opportunities for cell lineage and depth analyses, genome-wide mutation surveys, and other single cell assays.</p

Coarse-Graining and Self-Dissimilarity of Complex Networks

Can complex engineered and biological networks be coarse-grained into smaller and more understandable versions in which each node represents an entire pattern in the original network? To address this, we define coarse-graining units (CGU) as connectivity patterns which can serve as the nodes of a coarse-grained network, and present algorithms to detect them. We use this approach to systematically reverse-engineer electronic circuits, forming understandable high-level maps from incomprehensible transistor wiring: first, a coarse-grained version in which each node is a gate made of several transistors is established. Then, the coarse-grained network is itself coarse-grained, resulting in a high-level blueprint in which each node is a circuit-module made of multiple gates. We apply our approach also to a mammalian protein-signaling network, to find a simplified coarse-grained network with three main signaling channels that correspond to cross-interacting MAP-kinase cascades. We find that both biological and electronic networks are 'self-dissimilar', with different network motifs found at each level. The present approach can be used to simplify a wide variety of directed and nondirected, natural and designed networks.Comment: 11 pages, 11 figure

Statistical significance of rich-club phenomena in complex networks

We propose that the rich-club phenomena in complex networks should be defined in the spirit of bootstrapping, in which a null model is adopted to assess the statistical significance of the rich-club detected. Our method can be served as a definition of rich-club phenomenon and is applied to analyzing three real networks and three model networks. The results improve significantly compared with previously reported results. We report a dilemma with an exceptional example, showing that there does not exist an omnipotent definition for the rich-club phenomenon.Comment: 3 Revtex pages + 5 figure