PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 12456892

An mRNA structure that controls gene expression by binding FMN.

Wade C Winkler·Smadar Cohen-Chalamish·Ronald R Breaker
View original source ↗Export metadata ↓

Abstract

The RFN element is a highly conserved domain that is found frequently in the 5'-untranslated regions of prokaryotic mRNAs that encode for flavin mononucleotide (FMN) biosynthesis and transport proteins. We report that this domain serves as the receptor for a metabolite-dependent riboswitch that directly binds FMN in the absence of proteins. Our results also indicate that in Bacillus subtilis, the riboswitch most likely controls gene expression by causing premature transcription termination of the ribDEAHT operon and precluding access to the ribosome-binding site of ypaA mRNA. Sequence and structural analyses indicate that the RFN element is a natural FMN-binding aptamer, the allosteric character of which is harnessed to control gene expression.

Explore related subjects

5' Untranslated RegionsAllosteric RegulationBacillus subtilisBase SequenceCarrier ProteinsEscherichia coli ProteinsFlavin MononucleotideFlavin-Adenine DinucleotideGene Expression Regulation, BacterialMembrane Transport ProteinsMolecular Sequence DataNucleic Acid ConformationNucleotide DeaminasesRNA, BacterialRNA, MessengerRegulatory Sequences, Nucleic AcidRiboflavinStructure-Activity RelationshipSugar Alcohol DehydrogenasesTranscription, GeneticJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wade C Winkler, Smadar Cohen-Chalamish, Ronald R Breaker. 2002-11-27. An mRNA structure that controls gene expression by binding FMN.. https://doi.org/10.1073/pnas.212628899

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

A trainable language model with potential to modulate translation rates in non-model organisms by generating upstream untranslated region sequence libraries.

Tuning protein expression in non-model organisms is often constrained by the lack of validated genetic parts and predictive design tools. Translational tuning through the modulation of upstream untranslated regions (5'-UTRs) offers a potentially organism-agnostic route, but existing methods typically rely on mechanistic assumptions, prior knowledge that may not be available in non-model contexts, or the screening of sequence libraries. Here, we present a simple generative approach for creating synthetic 5'-UTR libraries based solely on the genomic sequence statistics of any desired organism. The method uses a sliding-window n-gram language model applied to native 5'-UTR sequences to produce novel sequences that preserve organism-specific base distributions and motifs without hard-coding specific motifs or mechanistic rules into inflexible statistical templates. We have applied this approach to the model bacterium Escherichia coli and the non-model probiotic Limosilactobacillus reuteri. Libraries of approximately 1,000 sequences were generated for each organism, from which about 100 unique sequences were experimentally tested for translation of a fluorescent reporter protein. In both organisms, the synthetic libraries yielded a broad range of translation levels from this relatively small number of tested variants. Sequences derived from an organism's own genomic statistics provided a more uniformly distributed range of translation rates in that organism than sequences derived from the other species. Correlations of individual sequence performance across the two species were weak, and thermodynamic predictions of ribosome binding strength showed very little predictive power, especially in the non-model L. reuteri. The results demonstrate that simple statistical language model approaches applied to genomic data can generate functional translational regulatory sequence libraries without detailed mechanistic knowledge or explicit reference to consensus motifs. The approach requires minimal computational resources, avoids reproducing native sequences, and can be readily applied to any organism with a sequenced genome. This strategy may lower technical barriers to expression tuning in non-model organisms.

5' Untranslated Regions↗
PubMed2026

An expanded realm of anti-CRISPR-associated proteins and regulatory mechanisms.

Many bacteriophages encode anti-CRISPR (Acr) proteins that inhibit bacterial CRISPR-Cas immune systems. Rapid acr gene expression upon phage entry enables CRISPR-Cas neutralization but can impact phage fitness if unregulated. Therefore, Acr production is often controlled by distinct families of co-encoded anti-CRISPR-associated (Aca) proteins, which are usually helix-turn-helix (HTH) regulators that bind DNA within acr-aca operon promoters. Previously, we demonstrated that the Aca2 family additionally represses Acr production translationally by binding structured RNA motifs within the 5' untranslated region (UTR) of the acr-aca mRNA. Here, through systematic bioinformatic analyses, we provide evidence of structured RNA motifs in the 5' UTRs of operons encoding members of other Aca families and show that Aca1 also specifically binds its cognate RNA motif. Additionally, many Aca proteins are predicted to regulate not only their own but also adjacent operons with potential anti-defence genes. Indeed, we show that Aca14, newly identified in this study, represses two predicted anti-defence operons. Aca14 is a ribbon-helix-helix domain protein, revealing regulatory diversity beyond the canonical HTH Aca family members. Collectively, our findings expand our understanding of acr regulation in mobile genetic elements and reveal novel mechanisms by which phages fine-tune anti-defence gene expression.

5' Untranslated Regions↗
PubMed2026

Functional analysis of stem-loop structures within the SARS-CoV-2 5' untranslated region using a plasmid-based reporter system.

The 5' untranslated region (5'UTR) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contains highly conserved stem-loop structures that regulate viral gene expression. This study investigated the functional contributions of selected 5'UTR stem-loop elements to reporter gene expression using a plasmid-based mammalian expression system. Five constructs were tested using a non-integrating plasmid: the wild-type (WT) 5'UTR fused to GFP under the CMV promoter, and four deletion variants (&#x394;B, &#x394;C, &#x394;D, and &#x394;E) corresponding to deletions of stem-loop 4 (SL4), SL4.5, SL5, and SL5a, respectively. Following transfection into HEK293 cells, GFP fluorescence was quantified using a fluorescence microplate reader, and relative GFP transcript abundance was assessed by RT-qPCR. Deletion of SL4 (&#x394;B) resulted in marked reduction in both fluorescence and relative transcript abundance compared to WT construct, indicating substantially reduced reporter gene expression. In contrast, deletion of SL4.5, SL5, or SL5a did not produce the pronounced reduction observed for &#x394;B, although descriptive RT-qPCR analysis indicated differences in relative transcript abundance among these variants. Statistical analysis of fluorescence data demonstrated significant differences among constructs (one-way ANOVA, p&#x2009;<&#x2009;0.05). Because the reporter assay was based on plasmid expression, the observed differences likely reflect combined contributions from transcription, transcript abundance, RNA stability, and translation rather than translation alone. These findings demonstrate that the SL4 region contributes substantially to reporter gene expression in this experimental system, whereas the remaining stem-loop regions examined exert comparatively modest effects. This study provides additional insight into the functional organization of the SARS-CoV-2 5'UTR and establishes a framework for future investigations aimed at distinguished the transcriptional, post-transcriptional, and translational contributions of individual RNA structural elements.

5' Untranslated Regions↗