ABSTRACT
RNA degradation is an important process that influences the ultimate concentration of individual proteins inside cells. While the main enzymes that facilitate this process have been identified, global maps of RNA turnover are available for only a few species. Even in these cases, there are few sequence elements that are known to enhance or destabilize a native transcript; even fewer confer the same effect when added to a heterologous transcript. To address this knowledge gap, we assayed genome-wide RNA degradation in the cyanobacterium Synechococcus sp. strain PCC 7002 by collecting total RNA samples after stopping nascent transcription with rifampin. We quantified the abundance of each position in the transcriptome as a function of time using RNA-sequencing data and later analyzed the global mRNA decay map using machine learning principles. Half-lives, calculated on a per-ORF (open reading frame) basis, were extremely short, with a median half-life of only 0.97 min. Despite extremely rapid turnover of most mRNA, transcripts encoding proteins involved in photosynthesis were both highly expressed and highly stable. Upon inspection of these stable transcripts, we identified an enriched motif in the 3' untranslated region (UTR) that had similarity to Rho-independent terminators. We built statistical models for half-life prediction and used them to systematically identify sequence motifs in both 5' and 3' UTRs that correlate with stabilized transcripts. We found that transcripts linked to a terminator containing a poly(U) tract had a longer half-life than both those without a poly(U) tract and those without a terminator.IMPORTANCE RNA degradation is an important process that affects the final concentration of individual mRNAs, affecting protein expression and cellular physiology. Studies of how RNA is degraded increase our knowledge of this fundamental process as well as enable the creation of genetic tools to manipulate RNA stability. By studying global transcript turnover, we searched for sequence elements that correlated with transcript (in)stability and used these sequences to guide tool design. This study probes global RNA turnover in a cyanobacterium, Synechococcus sp. strain PCC 7002, that both has a unique array of RNases that facilitate RNA degradation and is an industrially relevant strain that could be used to convert CO2 and sunlight into useful products.
ABSTRACT
Ligand-responsive allosteric transcription factors (aTF) play a vital role in genetic circuits and high-throughput screening because they transduce biochemical signals into gene expression changes. Programmable control of gene expression from aTF-regulated promoter is important because different downstream effector genes function optimally at different expression levels. However, tuning gene expression of native promoters is difficult due to complex layers of homeostatic regulation encoded within them. We engineered synthetic promoters de novo by embedding operator sites with varying affinities and radically reshaped binding preferences within a minimal, constitutive Escherichia coli promoter. Multiplexed cell-based screening of promoters for three TetR-like aTFs generated with this approach gave rich diversity of gene expression levels, dynamic ranges and ligand sensitivities and were 50- to 100-fold more active over their respective native promoters. Machine learning on our dataset revealed that relative position of the core motif and bases flanking the core motif play an important role in modulating induction response. Our generalized approach yields customizable and programmable aTF-regulated promoters for engineering cellular pathways and enables the discovery of new small molecule biosensors.
