Gene knockdown by structure defined single-stem loop small non-coding RNAs with programmable regulatory activities

Abstract

Gene regulation by trans-acting small RNAs (sRNAs) has considerable advantages over other gene regulation strategies. However, synthetic sRNAs mainly take natural sRNAs (MicC or SgrS) as backbones and comprise three functional elements folding into two or more stem-loop structures: an mRNA base pairing region, an Hfq-binding structure, and a rho-independent terminator. Due to limited numbers of natural sRNAs and complicated backbone structures, synthetic sRNAs suffer from low activity programmability and poor structural modularity. Moreover, natural sRNA backbone sequences may increase the possibility of unwanted recombination. Here, we present a bottom-up approach for creating structure defined single-stem loop small non-coding RNAs (ssl-sRNAs), which contain a standardized scaffold of a 7 bp-stem-4 nt-loop-polyU-tail and a 24 nt basing pairing region covering the first eight codons. Particularly, ssl-sRNA requires no independent Hfq-binding structure, as the polyU tail fulfills the roles of binding Hfq. A thermodynamic-based scoring model and a web server sslRNAD (http://www.kangzlab.cn/) were developed for automated design of ssl-sRNAs with well-defined structures and programmable activities. ssl-sRNAs displayed weak polar effects when regulating polycistronic mRNAs. The ssl-sRNA designed by sslRNAD showed regulatory activities in both Escherichia coli and Bacillus subtilis. A streamlined workflow was developed for the construction of customized ssl-sRNA and ssl-sRNA libraries. As examples, the E. coli cell morphology was easily modified and new target genes of ergothioneine biosynthesis were quickly identified with ssl-sRNAs. ssl-sRNA and its designer sslRNAD enable researchers to rapidly design sRNAs for knocking down target genes.

Keywords: De novo design; Ergothioneine; Metabolic engineering; Regulatory RNA; Synthetic biology.

Fig. 1

Design single stem-loop sRNA (ssl-sRNA) to repress gene expression. (A) The predicted secondary structures of Spot 42, MicA and synthetic sRNAs. ‘AU’ box or polyU tail binding Hfq [8] is shown in orange or blue, respectively. Base pairing probabilities are indicated by the color gradient. (B) GFP repression strengths of the three anti-gfp sRNAs constructed from Long scaffold, Middle scaffold or Short scaffold. A 24 nt sequence targeting to nowhere fused to the scaffolds was used as the control. (C) Alignment of the Short scaffold and its variants. The reverse complement of first 24 nt of the gfp coding region are shaded blue. (D) GFP repression strengths of the seven anti-gfp ssl-sRNAs (M1-M7) shown in c. A 24 nt-long sequence targeting nowhere was fused to the scaffold of M7 to create the control ssl-sRNA. (E) Predicted secondary structure of the mini (M7) scaffold. (F) Fluorescence microscopy of E. coli BL21 (DE3) expressing gfp and M7_anti-gfp sRNA or M7_control sRNA. Flu, fluorescence microscopy; Ph, phase contrast microscopy; Scale bar, 10 μm. (G) Northern blot analysis of the expression of the synthetic anti-gfp sRNAs. 5S rRNA was used as the internal standard. Samples were cultivated for 6 h and collected to extract total RNA. Biotin-labeled probes binding to 5S rRNA or the 24 nt base pairing region of gfp were used to detect 5S rRNA or anti-gfp sRNAs. In (B) and (D), the data are expressed as the mean ± S.D. from three (n = 3) biologically independent replicates.

Fig. 2

Repress chromosomal gene and polycistronic operon with ssl-sRNAs. (A) Repression of chromosomal β-galactosidase gene lacZ with M7_anti-lacZ sRNA. The expression of lacZ gene and M7_anti-lacZ sRNA was induced by IPTG. (B) and (C) Analysis the polar effect of ssl-sRNAs in regulating polycistronic operon. Genes gfp and BhepIII were assembled into an operon under the control of T7 promoter and terminator. Expression of gfp and BhepIII and the three ssl-sRNAs were induced with IPTG. Cells were collected after 12 h cultivation at 37 °C to measure the fluorescence intensity and enzymatic activities. SD, Shine–Dalgarno sequence; Fluorescence intensity/OD₆₀₀, Flu/OD₆₀₀. The data are expressed as the mean ± S.D. from three (n = 3, in C) or six (n = 6 in B) biologically independent replicates. Statistical evaluation (p value) was performed by two-sample t-test. **p < 0.01, *p < 0.05.

Fig. 3

Resolving the core scaffold for ssl-sRNAs design. (A) Creation of synthetic ssl-sRNAs from the trimmed GadY terminator, the trimmed conserved prokaryotic intrinsic terminator and the trimmed T7 terminator. The secondary structures of the terminators and the activities of GadY_anti-gfp sRNA, CT_anti-gfp sRNA and GadY_anti-gfp sRNA on repressing GFP expression. (B) Northern blot analysis of the expression of the anti-gfp ssl-sRNAs created in (A). 5S rRNA was used as the internal standard. Samples were cultivated for 6 h and collected to extract total RNA. Biotin-labeled probes binding to 5S rRNA or the 24 nt base pairing region of gfp were used to detect 5S rRNA or synthetic sRNAs. (C) and (D) Mutagenesis of the GadY terminator. The GadY terminator was progressively mutagenized by nucleotide substitutions, insertions or deletions in the stem–loop region to construct the designated mutants of the GadY terminator. (E) and (F) The activities of anti-gfp ssl-sRNAs constructed from GadY mutants constructed in (C) and (D) on repressing GFP expression. All cultivations were performed at 37 °C in LB medium supplemented with 0.1 mM IPTG and necessary antibiotics. The expressions of plasmid-encoded gfp and ssl-sRNAs were driven by T7 RNA polymerase encoded in the genome of E. coli BL1 (DE3). Fluorescence intensity/OD₆₀₀, Flu/OD₆₀₀. Base pairing probabilities are indicated by the color gradient. All the data are expressed as the mean ± S.D. from three (n = 3) biologically independent replicates. (G) The core scaffold of ssl-sRNAs.

Fig. 4

Development of ssl-sRNAs scoring function and web-based de novo designer. (A) Correlation of ssl-sRNA activities and overall minimal free energy of the core scaffolds constructed in Fig. S4A. (B) A scoring function correlates the thermodynamic details of the decomposed secondary structures of the core scaffolds and the conferred anti-gfp ssl-sRNA activities analyzed by multiple linear regression (see Fig. S4B for calculation process). (C) Workflow of web tool for de novo design of ssl-sRNAs. A local Python program generates random sequences that would form core scaffolds upon the input of target sequences. NUPACK (4.0.0.23) is recruited to examine the overall structure of the generated ssl-sRNAs. Candidates with satisfactory secondary structures are transferred to RNAevel to analyze thermodynamic details. Afterward, the scores of the ssl-sRNAs are calculated to classify the sRNAs into groups with ‘strong repression’, ‘moderate repression’ and ‘weak repression’. (D) Snapshots of the interfaces of the input and output interfaces of the web designer. (E) Workflow of ssl-sRNA expression vector construction. Customer-defined promoters and ssl-sRNA sequences are all automatically included in sslRNAD design primers (two pairs) that can be used for PCR experiments with desired plasmids. The PCR fragments become self-cyclized in E. coli cells via the homologous ends conferred by the primers to form the ssl-sRNA expression vectors.

Fig. 5

Constitutive expression of de novo designed anti-ftsZ ssl-sRNA to regulate the morphology of E. coli. Expression of anti-ftsZ ssl-sRNA or M7_Control sRNA (control sRNA) with Anderson J23105 promote or Anderson J23100 promoter. Cells were cultivated at 37 °C for 1.5–2.5 days on LB agar supplemented with necessary antibiotic, collected for Nile red staining and subsequently subjected to phase contrast microscopy (Ph) and fluorescence microscopy (Flu) to visualize cell membrane structures and cell morphologies. Insets are magnified (3.6×) part of the micrographs. White arrows indicate highly refractive particles strongly stained with Nile red. All scale bars, 10 μm.

Fig. 6

Large-scale screening of target genes to improve ergothioneine biosynthesis with a ssl-sRNA library. (A) The metabolic pathways and the genes involved in the synthesis of ergothioneine from glucose. Underlined genes are essential for the synthesis of ergothioneine with heterogenous genes (egtB, egtC, egtD, egtE) underlined. The genes to be repressed are in red (strong repression) or blue (moderate repression) to improve the availabilities of precursors, SAM (S-adenosyl methionine)/methionine, histidine, glutamate and cysteine. (B) Streamlined workflow of the ssl-sRNA library construction and target gene screening. ssl-sRNAs and primers for constructing the expression vectors of ssl-sRNAs were designed by sslRNAD (Fig. 4E). After mixing all the primers in equimolar proportion, one-pot PCR was subsequently performed with the pTarget-F plasmid as template. PCR products carrying the ssl-sRNA library were transformed to ergothioneine parental strain as shown in Fig. 4E. Transformants were picked randomly and cultivated in 24 well plate. HPLC was then performed to quantify the titer of ergothioneine in each well. Representative strains were selected for shake flask cultivation and DNA sequencing. (C) Overlayed HPLC spectra of the 24 well plate cultures of 213 transformants and the control strain. The peak height of the ergothioneine produced by the control strain was indicated. (D) Concentrations of ergothioneine in shake flask cultures. Thirteen indicated representative strains (blue columns) and the control strain (grey column) were analyzed. All the data are expressed as the mean ± S.D. from three (n = 3) biologically independent replicates. Statistical evaluation (p value) comparing to the control strain expressing M7_Control sRNA was performed by two-sample t-test. **p < 0.01, *p < 0.05.