Under nitrogen deprivation, filaments of the cyanobacterium Anabaena undergo a process of development, resulting in a one-dimensional pattern of nitrogen-fixing heterocysts separated by about ten photosynthetic vegetative cells. Many aspects of gene expression before nitrogen deprivation and during the developmental process remain to be elucidated. Furthermore, the coupling of gene expression fluctuations between cells along a multicellular filament is unknown. We studied the statistics of fluctuations of gene expression of HetR, a transcription factor essential for heterocyst differentiation, both under steady-state growth in nitrogen-rich conditions and at different times following nitrogen deprivation, using a chromosomally-encoded translational hetR-gfp fusion. Statistical analysis of fluorescence at the individual cell level in wild-type and mutant filaments demonstrates that expression fluctuations of hetR in nearby cells are coupled, with a characteristic spatial range of circa two to three cells, setting the scale for cellular interactions along a filament. Correlations between cells predominantly arise from intercellular molecular transfer and less from cell division. Fluctuations after nitrogen step-down can build up on those under nitrogen-replete conditions. We found that under nitrogen-rich conditions, basal, steady-state expression of the HetR inhibitor PatS, cell-cell communication influenced by the septal protein SepJ and positive HetR auto-regulation are essential determinants of fluctuations in hetR expression and its distribution along filaments. A comparison between the expression of hetR-gfp under nitrogen-rich and nitrogen-poor conditions highlights the differences between the two HetR inhibitors PatS and HetN, as well as the differences in specificity between the septal proteins SepJ and FraC/FraD. Activation, inhibition and cell-cell communication lie at the heart of developmental processes. Our results show that proteins involved in these basic ingredients combine together in the presence of inevitable stochasticity in gene expression, to control the coupled fluctuations of gene expression that give rise to a one-dimensional developmental pattern in this organism.
The search for specific sequences on long genomes is a key process in many biological contexts. How can specific target sequences be located with high efficiency, within physiologically relevant times? We addressed this question for viral integration, a fundamental mechanism of horizontal gene transfer driving prokaryotic evolution, using the infection of Escherichia coli bacteria with bacteriophage λ and following the establishment of a lysogenic state. Following the targeting process in individual live E. coli cells in real time revealed that λ DNA remains confined near the entry point of a cell following infection. The encounter between the 15-bp-long target sequence on the chromosome and the recombination site on the viral genome is facilitated by the directed motion of bacterial DNA generated during chromosome replication, in conjunction with constrained diffusion of phage DNA. Moving the native bacterial integration site to different locations on the genome and measuring the integration frequency in these strains reveals that the frequencies of the native site and a site symmetric to it relative to the origin are similar, whereas both are significantly higher than when the integration site is moved near the terminus, consistent with the replication-driven mechanism we propose. This novel search mechanism is yet another example of the exquisite coevolution of λ with its host.target location | establishment of lysogeny | viral transduction T he search for specific sequences along genomic DNA plays a key role in the location of specific sites by transcription factors (1), the repair of DNA lesions (2), and horizontal gene transfer (3). Common to these processes is a search through a very large number of possible sequences because of the long genomes involved. A fundamental question is how specific target sequences can be located with high efficiency, within physiologically relevant times. This question is crucial to understand viral transduction, one of the fundamental mechanisms of horizontal gene transfer driving the evolution of prokaryotes (3, 4). In transduction, a viral genome integrates at a unique site on a bacterial genome following infection, conferring new traits such as pathogenicity (5). A classic example of transduction is furnished by the infection of Escherichia coli cells by bacteriophage λ.Infection of an E. coli host by the temperate bacteriophage λ begins with the binding of the phage to the E. coli maltose pore LamB (6, 7). The phage then injects its DNA into the cell, a process that lasts about 5 min (8). Infection can lead to two possible outcomes, lysis or lysogeny, which reflect alternative pathways of gene expression (9-11). In the lytic pathway, execution of a viral gene expression cascade leads to the replication of the viral DNA, resulting in cell death and lysis to release about 100 phage progeny (12). Alternatively, by establishing lysogeny, the phage shuts off the lytic cycle and locates with high efficiency (13) a unique sequence along the cellular genome where it integrates i...
Under nitrogen deprivation, the one-dimensional cyanobacterial organism Anabaena sp. PCC 7120 develops patterns of single, nitrogen-fixing cells separated by nearly regular intervals of photosynthetic vegetative cells. We study a minimal, stochastic model of developmental patterns in Anabaena that includes a nondiffusing activator, two diffusing inhibitor morphogens, demographic fluctuations in the number of morphogen molecules, and filament growth. By tracking developing filaments, we provide experimental evidence for different spatiotemporal roles of the two inhibitors during pattern maintenance and for small molecular copy numbers, justifying a stochastic approach. In the deterministic limit, the model yields Turing patterns within a region of parameter space that shrinks markedly as the inhibitor diffusivities become equal. Transient, noise-driven, stochastic Turing patterns are produced outside this region, which can then be fixed by downstream genetic commitment pathways, dramatically enhancing the robustness of pattern formation, also in the biologically relevant situation in which the inhibitors' diffusivities may be comparable.
Galectin-8, a mammalian -galactoside binding lectin, functions as an extracellular matrix protein that forms high affinity interactions with integrins. Here we demonstrated that soluble galectin-8 inhibits cell cycle progression and induces growth arrest. These effects cannot be attributed to interference with cell adhesion but can be attributed to a 4 -5-fold increase in the cellular content of the cyclin-dependent kinase inhibitor p21, which was already evident following a 4-h incubation of H1299 cells with galectin-8. The increase in p21 levels was preceded by a 3-5-fold increase in JNK and protein kinase B (PKB) activities. Accordingly, SP600125, the inhibitor of JNK, and wortmannin, the inhibitor of phosphatidylinositol 3-kinase, which is the upstream activator of PKB, inhibited the increase in the cellular content of p21. Furthermore, overexpression of a dominant inhibitory form of SEK1, the upstream kinase regulator of JNK, inhibited both JNK activation and p21 accumulation. When p21 expression was inhibited by cycloheximide, galectin-8 directed the cells toward apoptosis, which involves induction of poly(ADP-ribose) polymerase cleavage. Indeed, galectin-8-induced apoptosis was 2-fold higher in HTC (p21-null) cells when compared with parental HTC cells. Because overexpression of galectin-8 attenuates the rate of DNA synthesis, stable colonies that overexpress and secrete galectin-8 can be generated only in cells overexpressing a growth factor receptor, such as the insulin receptor. These results implicate galectin-8 as a modulator of cellular growth through up-regulation of p21. This process involves activation of JNK, which enhances the synthesis of p21, combined with the activation of PKB, which inhibits p21 degradation. These effects of the lectin depended upon protein-sugar interactions and were induced when galectin-8 was present as a soluble ligand or when it was overexpressed in cells.
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