Biology subjects

Lucks, J. B.

Publications and source records attributed to Lucks, J. B..

3 recordsLinked to original sources

bioRxiv2018

Deconstructing cell-free extract preparation for in vitro activation of transcriptional genetic circuitry

Recent advances in cell-free gene expression (CFE) systems have enabled their use for a host of synthetic biology applications, particularly for rapid prototyping of genetic circuits designed as biosensors. Despite the proliferation of cell-free protein synthesis platforms, the large number of currently existing protocols for making CFE extracts muddles the collective understanding of how the method by which an extract is prepared affects its functionality. Specifically, a key goal toward developing cell-free biosensors based on native genetic regulators is activating the transcriptional machinery present in bacterial extracts for protein synthesis. However, protein yields from genes transcribed in vitro by the native Escherichia coli RNA polymerase are quite low in conventional crude extracts originally optimized for expression by the bacteriophage transcriptional machinery. Here, we show that cell-free expression of genes under bacterial {sigma}70 promoters is constrained by the rate of transcription in crude extracts and that processing the extract with a ribosomal run-off reaction and subsequent dialysis can alleviate this constraint. Surprisingly, these processing steps only enhance protein synthesis in genes under native regulation, indicating that the translation rate is unaffected. We further investigate the role of other common process variants on extract performance and demonstrate that bacterial transcription is inhibited by including glucose in the growth culture, but is unaffected by flash-freezing the cell pellet prior to lysis. Our final streamlined protocol for preparing extract by sonication generates extract that facilitates expression from a diverse set of sensing modalities including protein and RNA regulators. We anticipate that this work will clarify the methodology for generating CFE extracts that are active for biosensing and will encourage the further proliferation of cell-free gene expression technology for new applications.

synthetic biology↗

bioRxiv2018

Computationally Reconstructing Cotranscriptional RNA Folding Pathways from Experimental Data Reveals Rearrangement of Non-Native Folding Intermediates

The series of RNA folding events that occur during transcription, or a cotranscriptional folding pathway, can critically influence the functional roles of RNA in the cell. Here we present a method, Reconstructing RNA Dynamics from Data (R2D2), to uncover details of cotranscriptional folding pathways by predicting RNA secondary and tertiary structures from cotranscriptional SHAPE-Seq data. We applied R2D2 to the folding of the Escherichia coli Signal Recognition Particle (SRP) RNA sequence and show that this sequence undergoes folding through non-native intermediate structures that require significant structural rearrangement before reaching the functional native structure. Secondary structure folding pathway predictions and all-atom molecular dynamics simulations of folding intermediates suggest that this rearrangement can proceed through a toehold mediated strand displacement mechanism, which can be disrupted and rescued with point mutations. These results demonstrate that even RNAs with simple functional folds can undergo complex folding processes during synthesis, and that small variations in their sequence can drastically affect their cotranscriptional folding pathways.\n\nHighlights- Computational methods predict RNA structures from cotranscriptional SHAPE-Seq data\n- The E. coli SRP RNA folds into non-native structural intermediates cotranscriptionally\n- These structures rearrange dynamically to form an extended functional fold\n- Point mutations can disrupt and rescue cotranscriptional RNA folding pathways

biochemistry↗

bioRxiv2018

Estimating RNA structure chemical probing reactivities from reverse transcriptase stops and mutations.

Chemical probing experiments interrogate RNA structures by creating covalent adducts on RNA molecules in structure-dependent patterns. Adduct positions are then detected through conversion of the modified RNAs into complementary DNA (cDNA) by reverse transcription (RT) as either stops (RT-stops) or mutations (RT-mutations). Statistical analysis of the frequencies of RT-stops and RT-mutations can then be used to estimate a measure of chemical probing reactivity at each nucleotide of an RNA, which reveals properties of the underlying RNA structure. Inspired by recent work that showed that different reverse transcriptase enzymes show distinct biases for detecting adducts as either RT-stops or RT-mutations, here we use a statistical modeling framework to derive an equation for chemical probing reactivity using experimental signatures from both RT-stops and RT-mutations within a single experiment. The resulting formula intuitively matches the expected result from considering reactivity to be defined as the fraction of adduct observed at each position in an RNA at the end of a chemical probing experiment. We discuss assumptions and implementation of the model, as well as ways in which the model may be experimentally validated.

biochemistry↗