Transcription regulation networks control the expression of genes. The transcription networks of well-studied microorganisms appear to be made up of a small set of recurring regulation patterns, called network motifs. The same network motifs have recently been found in diverse organisms from bacteria to humans, suggesting that they serve as basic building blocks of transcription networks. Here I review network motifs and their functions, with an emphasis on experimental studies. Network motifs in other biological networks are also mentioned, including signalling and neuronal networks.

/pdf/network-motifs-theory-and-experimental-approaches-40rp9yj6zc.pdf

Network motifs: theory and experimental approaches

http://archive.gersteinlab.org/papers/e-print/trans-reg/reprint.pdf

Structure and evolution of transcriptional regulatory networks.

Bacteria induce stress responses that protect the cell from lethal factors such as DNA-damaging agents. Bacterial populations also form persisters, dormant cells that are highly tolerant to antibiotics and play an important role in recalcitrance of biofilm infections. Stress response and dormancy appear to represent alternative strategies of cell survival. The mechanism of persister formation is unknown, but isolated persisters show increased levels of toxin/antitoxin (TA) transcripts. We have found previously that one or more components of the SOS response induce persister formation after exposure to a DNA-damaging antibiotic. The SOS response induces several TA genes in Escherichia coli. Here, we show that a knockout of a particular SOS-TA locus, tisAB/istR, had a sharply decreased level of persisters tolerant to ciprofloxacin, an antibiotic that causes DNA damage. Step-wise administration of ciprofloxacin induced persister formation in a tisAB-dependent manner, and cells producing TisB toxin were tolerant to multiple antibiotics. TisB is a membrane peptide that was shown to decrease proton motive force and ATP levels, consistent with its role in forming dormant cells. These results suggest that a DNA damage–induced toxin controls production of multidrug tolerant cells and thus provide a model of persister formation.

https://repository.library.northeastern.edu/files/neu:329515/fulltext.pdf

Ciprofloxacin Causes Persister Formation by Inducing the TisB toxin in Escherichia coli

Most microbial communities consist of a genetically diverse assembly of different organisms, and the level of genetic diversity plays an important part in community properties and functions. However, biological diversity also arises at a lower level of biological organization, between genetically identical cells that reside in the same microenvironment. In this Review, I outline the molecular mechanisms responsible for phenotypic heterogeneity and discuss how phenotypic heterogeneity allows genotypes to persist in fluctuating environments. I also describe how it promotes interactions between phenotypic subpopulations in clonal groups, providing microbial groups with new functionality.

A functional perspective on phenotypic heterogeneity in microorganisms

Biological systems that perform multiple tasks face a fundamental trade-off: A given phenotype cannot be optimal at all tasks. Here we ask how trade-offs affect the range of phenotypes found in nature. Using the Pareto front concept from economics and engineering, we find that best-trade-off phenotypes are weighted averages of archetypes--phenotypes specialized for single tasks. For two tasks, phenotypes fall on the line connecting the two archetypes, which could explain linear trait correlations, allometric relationships, as well as bacterial gene-expression patterns. For three tasks, phenotypes fall within a triangle in phenotype space, whose vertices are the archetypes, as evident in morphological studies, including on Darwin's finches. Tasks can be inferred from measured phenotypes based on the behavior of organisms nearest the archetypes.

Evolutionary Trade-Offs, Pareto Optimality, and the Geometry of Phenotype Space

E. coli is widely used for systems biology research; there exists a need, however, for tools that can be used to accurately and comprehensively measure expression dynamics in individual living cells. To address this we present a library of transcriptional fusions of gfp to each of about 2,000 different promoters in E. coli K12, covering the great majority of the promoters in the organism. Each promoter fusion is expressed from a low-copy plasmid. We demonstrate that this library can be used to obtain highly accurate dynamic measurements of promoter activity on a genomic scale, in a glucose-lactose diauxic shift experiment. The library allowed detection of about 80 previously uncharacterized transcription units in E. coli, including putative internal promoters within previously known operons, such as the lac operon. This library can serve as a tool for accurate, high-resolution analysis of transcription networks in living E. coli cells.

A comprehensive library of fluorescent transcriptional reporters for Escherichia coli

Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as “phenotypic noise.” In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alone.

/pdf/a-genome-wide-analysis-of-promoter-mediated-phenotypic-noise-b7irluyoj9.pdf

A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli

Figure S6 is incorrectly a duplicate of Figure S7. The correct Figure S6 can be viewed here:


Click here for additional data file.(244K, pdf)


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Ilya Kikoin

Papers

A comprehensive library of fluorescent transcriptional reporters for Escherichia coli

A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli

Correction: A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli