Predictable Engineering of Signal-Dependent Cis-Regulatory Elements

Cis-regulatory elements (CREs) control how genes respond to external signals, but the principles governing their structure and function remain poorly understood. While differential transcription factor binding is known to regulate gene expression, how CREs integrate the amount and combination of inputs to secure precise spatiotemporal profiles of gene expression remains unclear. Here, we developed a high-throughput combinatorial screening strategy, that we term NeMECiS , to investigate signal-dependent synthetic CREs (synCREs) in differentiating mammalian stem cells. By concatenating fragments of functional CREs from genes that respond to Sonic Hedgehog in the developing vertebrate neural tube, we found that CRE activity follows hierarchical design rules. While individual 200-base-pair fragments showed minimal activity, their combinations generated thousands of functional signal-responsive synCREs, many exceeding the activity of natural sequences. Statistical modelling revealed CRE function can be decomposed into specific quantitative contributions in which sequence fragments combine through a multiplicative rule, tuned by their relative positioning and spacing. These findings provide a predictive framework for CRE redesign, which we used to engineer synthetic CREs that alter the pattern of motor neuron differentiation in neural tissue. These findings establish quantitative principles for engineering synthetic regulatory elements with programmable signal responses to rewire genetic circuits and control stem cell differentiation, providing a basis for understanding developmental gene regulation and designing therapeutic gene expression systems. Overall design: The NeMECiS plasmid library (PLASMID) was constructed by sequential, nested Golden Gate, assembling exhaustive three-way concatemers of 25x 200bp CRE fragments (synCREs), generating a theoretical library of 15,625. CRE fragment composition and order are defined by a set of deterministic barcodes, which are flanked by Nextera partial adaptors. The sequencing libraries for both the raw plasmid library and the flow-sorted neural progenitor library were prepared in an equivalent manner. DNA was extracted, and barcode concatemers were amplified through Nextera-primed PCR. Reads have a generic form: [NexteraRd1]AGCA[8bpBarcode]GATT[8bpBarcode]AGCA[8bpBarcode]GATT[Terminator][NexteraRd2] Where: [NexteraRd1] = TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG [Terminator] = GATTAATAAAGGAAATTTATTTCATTGCAATAGTGTGTTGGAATTTGTGTCTCTCA [NexteraRd2] = CTGTCTCTTATACACATCTCCGAGCCCACGAGAC For neural progenitor experiments: To measure SAG-dependent activity of synCREs, the following was performed. In duplicate, 10^7 mESCs were infected with the NeMECiS lentivirus library in ESGRO at MOI<1, maintained in CellBIND T175 flasks, selected with Puromycin Dihydrochloride. Cells were then expanded for one passage. mESCs were then differentiated to neural progenitors under 0nM or 500nM SAG, plating per replicate 6x10^7 cells onto 1% vitronectin coated plates. Briefly, cells were maintained in bFGF for 48 hours, bFGF + CHIR99021 for 20 hours, then each replicate was replated onto 1% GelTrex coated plates at a 1:1 ratio in 100nM RA, supplementing one of the halves with 500nM SAG. At day 6 of the protocol, 30% of each sample (two replicates x two SAG conditions) was pelleted and frozen. The remaining 70% were flow sorted into four fluorescence bins corresponding to four evenly spaced 15% percentile intervals (13.3% spacing between bins). Unsorted and sorted samples were run on a flow analyser to measure fluorescence of each pool. Sorted samples were then pelleted. Both the sorted and unsorted pellets were processed with the Zymo Research Quick-DNA Miniprep Plus Kit to extract genomic DNA, then barcode concatemers were amplified with Nextera-primed PCRs. All sequencing libraries were checked for quality before sequencing using a Bioanalyzer and KAPA Library Quanitification Complete Kit Universal. Sequencing files for sorted and unsorted neural progenitors have the following naming structure: SAMPLEINFO_ID_LANE_READ_001.fastq.gz SAMPLEINFO contains information about the replicate, the SAG concentration, and the sort-status. For example: “102” represents replicate 1, 0nM SAG and sort bin 2; “10A” represents replicate 1, 0nM SAG and unsorted ('all'); “251” represents replicate 2, 500nM SAG and sort bin 1. Data was processed using a custom pipeline (see below for details). By-experiment processed data files (Rep1/2; SAG=0nM/500nM) are provided.