Robust stochastic Turing patterns in the development of a one-dimensional cyanobacterial organism

Patti, Francesca Di; Lavacchi, Laura; Arbel-Goren, Rinat; Schein-Lubomirsky, Leora; Fanelli, Duccio; Stavans, Joel

doi:10.1371/journal.pbio.2004877

Cited by 34 publications

(44 citation statements)

References 81 publications

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“…The core regulatory circuit of heterocyst differentiation in Anabaena 7120 is summarized in Fig 7 . This coordination scenario involving multiple activating/inhibiting factors may help to refine the current models [ 51 , 52 ] for heterocyst differentiation and patterning.…”

Section: Discussionmentioning

confidence: 99%

Expression from DIF1-motif promoters of hetR and patS is dependent on HetZ and modulated by PatU3 during heterocyst differentiation

Zhang

Wang

et al. 2020

PLoS ONE

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HetR and PatS/PatX-derived peptides are the activator and diffusible inhibitor for cell differentiation and patterning in heterocyst-forming cyanobacteria. HetR regulates target genes via HetR-recognition sites. However, some genes (such as patS/patX) upregulated at the early stage of heterocyst differentiation possess DIF1 (or DIF +) motif (TCCGGA) promoters rather than HetR-recognition sites; hetR possesses both predicted regulatory elements. How HetR controls heterocyst-specific expression from DIF1 motif promoters remains to be answered. This study presents evidence that the expression from DIF1 motif promoters of hetR, patS and patX is more directly dependent on hetZ, a gene regulated by HetR via a HetR-recognition site. The HetR-binding site upstream of hetR is not required for the autoregulation of hetR. PatU3 (3 0 portion of PatU) that interacts with HetZ may modulate the expression of hetR, hetZ and patS. These findings contribute to understanding of the mutual regulation of hetR, hetZ-patU and patS/patX in a large group of multicellular cyanobacteria.

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Section: Discussionmentioning

confidence: 99%

Expression from DIF1-motif promoters of hetR and patS is dependent on HetZ and modulated by PatU3 during heterocyst differentiation

Zhang

Wang

et al. 2020

PLoS ONE

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“…The patS and hetN genes appeared in the E‐DIF and L‐DIF clusters, respectively. This is consistent with recent observations concerning the relative timing of their expression (Di Patti et al ., ), and the roles in early establishment (PatS) or maintenance (HetN) of the pattern (Yoon and Golden, ; Callahan and Buikema, ). Recently, it has been demonstrated that patS and hetN are not functionally equivalent when their promoter regions are switched, therefore suggesting their respective timing of expression is critical for proper function (Rivers et al ., ).…”

Section: Discussionmentioning

confidence: 99%

Elements of the heterocyst‐specific transcriptome unravelled by co‐expression analysis in Nostoc sp. PCC 7120

Brenes‐Álvarez

Mitschke

Olmedo‐Verd

et al. 2019

Environmental Microbiology

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Summary Nitrogen is frequently limiting microbial growth in the environment. As a response, many filamentous cyanobacteria differentiate heterocysts, cells devoted to N2 fixation. Heterocyst differentiation is under the control of the master regulator HetR. Through the characterization of the HetR‐dependent transcriptome in Nostoc sp. PCC 7120, we identified the new candidate genes likely involved in heterocyst differentiation. According to their maximum induction, we defined E‐DIF (early in differentiation) and L‐DIF (late in differentiation) genes. Most of the genes known to be involved in the critical aspects of heterocyst differentiation or function were also classified into these groups, showing the validity of the approach. Using fusions to gfp, we verified the heterocyst‐specific transcription of several of the found genes, antisense transcripts and potentially trans‐acting sRNAs. Through comparative sequence analysis of promoter regions, we noticed the prevalence of the previously described DIF1 motif and identified a second motif, called DIF2, in other promoters of the E‐DIF cluster. Both motifs are widely conserved in heterocystous cyanobacteria. We assigned alr2522 as a third member, besides nifB and nifP, to the CnfR regulon. The elements identified here are of interest for understanding cell differentiation, engineering of biological nitrogen fixation or production of O2‐sensitive molecules in cyanobacteria.

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“…After the completion and acceptance of this work, an independent observation of stochastic Turing patterns in the cyanobacteria colonies of Anabaena sp. was reported (31).…”

Section: Methodsmentioning

confidence: 96%

Stochastic Turing patterns in a synthetic bacterial population

Karig

Martini

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

168

155

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The origin of biological morphology and form is one of the deepest problems in science, underlying our understanding of development and the functioning of living systems. In 1952, Alan Turing showed that chemical morphogenesis could arise from a linear instability of a spatially uniform state, giving rise to periodic pattern formation in reaction-diffusion systems but only those with a rapidly diffusing inhibitor and a slowly diffusing activator. These conditions are disappointingly hard to achieve in nature, and the role of Turing instabilities in biological pattern formation has been called into question. Recently, the theory was extended to include noisy activator-inhibitor birth and death processes. Surprisingly, this stochastic Turing theory predicts the existence of patterns over a wide range of parameters, in particular with no severe requirement on the ratio of activator-inhibitor diffusion coefficients. To explore whether this mechanism is viable in practice, we have genetically engineered a synthetic bacterial population in which the signaling molecules form a stochastic activator-inhibitor system. The synthetic pattern-forming gene circuit destabilizes an initially homogenous lawn of genetically engineered bacteria, producing disordered patterns with tunable features on a spatial scale much larger than that of a single cell. Spatial correlations of the experimental patterns agree quantitatively with the signature predicted by theory. These results show that Turing-type pattern-forming mechanisms, if driven by stochasticity, can potentially underlie a broad range of biological patterns. These findings provide the groundwork for a unified picture of biological morphogenesis, arising from a combination of stochastic gene expression and dynamical instabilities.

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Robust stochastic Turing patterns in the development of a one-dimensional cyanobacterial organism

Cited by 34 publications

References 81 publications

Expression from DIF1-motif promoters of hetR and patS is dependent on HetZ and modulated by PatU3 during heterocyst differentiation

Expression from DIF1-motif promoters of hetR and patS is dependent on HetZ and modulated by PatU3 during heterocyst differentiation

Elements of the heterocyst‐specific transcriptome unravelled by co‐expression analysis in Nostoc sp. PCC 7120

Stochastic Turing patterns in a synthetic bacterial population

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