In organ transplantation, infection and rejection are major causes of graft loss linked by the net state of immunosuppression. To diagnose and treat these conditions earlier, and to improve long-term patient outcomes, refined strategies for the monitoring of patients after graft transplantation are needed. Here, we show that a fast and inexpensive assay based on CRISPR-Cas13 accurately detects BK polyomavirus DNA and cytomegalovirus DNA from patient-derived blood and urine samples, as well as CXCL9 mRNA (a marker of graft rejection) at elevated levels in urine samples from patients experiencing acute renal-transplant rejection. The assay, which we adapted for lateral-flow readout, enables via simple visualization the post-transplantation monitoring of common opportunistic viral infections and of graft rejection, and should facilitate point-of-care posttransplantation monitoring.

While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules. Toehold switches, which are programmable nucleic acid sensors, face an analogous design bottleneck; our limited understanding of how sequence impacts functionality often necessitates expensive, time-consuming screens to identify effective switches. Here, we introduce Sequence-based Toehold Optimization and Redesign Model (STORM) and Nucleic-Acid Speech (NuSpeak), two orthogonal and synergistic deep learning architectures to characterize and optimize toeholds. Applying techniques from computer vision and natural language processing, we ‘un-box’ our models using convolutional filters, attention maps, and in silico mutagenesis. Through transfer-learning, we redesign sub-optimal toehold sensors, even with sparse training data, experimentally validating their improved performance. This work provides sequence-to-function deep learning frameworks for toehold selection and design, augmenting our ability to construct potent biological circuit components and precision diagnostics.

Mammalian genomes are folded into tens of thousands of long-range looping interactions. The cause and effect relationship between looping and genome function is poorly understood, and the extent to which loops are dynamic on short time scales remains an unanswered question. Here we engineer a new class of synthetic architectural proteins for directed rearrangement of the 3-D genome using blue light. We target our light-activated-dynamic-looping (LADL) system to two genomic anchors with CRISPR guide RNAs and induce their spatial co-localization via light-induced heterodimerization of cryptochrome 2 and a dCas9-CIBN fusion protein. We apply LADL to redirect a stretch enhancer (SE) away from its endogenous Klf4 target gene and to the Zfp462 promoter. Using single molecule RNA FISH, we demonstrate that de novo formation of the Zfp462-SE loop correlates with a modest but significant increase in Zfp462 expression. LADL facilitates co-localization of genomic loci without exogenous chemical cofactors and will enable future efforts to engineer reversible and oscillatory loops on short time scales.

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Mammalian genomes are folded into tens of thousands of long-range looping interactions1,2. The cause and effect relationship between looping and genome function is poorly understood, and the extent to which chromatin loops are dynamic on short time scales remains a fundamental unanswered question. Currently available strategies for loop engineering involve synthetic transcription factors tethered to dCas93,4 or zinc fingers5,6, which are constitutively expressed5,6 or induced on long time scales by the presence of a small molecule3. Here we report a new class of 3-D optoepigenetic tools for the directed rearrangement of 3-D chromatin looping on short time scales using blue light. We create synthetic architectural proteins by fusing the CIBN protein subunit from Arabidopsis thaliana with enzymatically dead Cas9 (dCas9). We target our light-activated dynamic looping system (LADL) to two genomic anchors with CRISPR guide RNAs and engineer their spatial co-localization via light-induced heterodimerization of the cryptochrome 2 (CRY2) protein with dCas9-CIBN. We apply LADL to redirect a stretch enhancer (SE) away from its endogenous Klf4 target gene and to the Zfp462 promoter. Looping changes occur as early as four hours after light induction. Using single molecule RNA FISH, we observe a LADL-induced increase in the total nascent Zfp462 transcripts and the number of Zfp462 alleles expressing simultaneously per cell. Moreover, LADL also increased synchronous Sox2 expression after reinforcement of a known Sox2-SE looping interaction. LADL facilitates loop synchronization across a large population of cells without exogenous chemical cofactors and can enable future efforts to engineer reversible and oscillatory looping on short time scales.

While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of novel circuit components beyond nature-inspired templates can prove itself challenging without well-established design rules. Toehold switches -programmable nucleic acid sensors -face an analogous prediction and design bottleneck: our limited understanding of how sequence impacts functionality can require expensive, time-consuming screens for effective switches. Here, we introduce the Sequence-based Toehold Optimization and Redesign Model (STORM), a deep learning architecture that applies gradient ascent to re-engineer poorlyperforming toeholds. Based on a dataset of 91,534 toehold switches, we examined convolutional filters and saliency maps of sequences to interpret our sequence-to-function model, identifying hot spots where mutations change toehold effectiveness and features unique to high-performing switches. Our modeling platform provides frameworks for future toehold selection, augmenting our ability to construct potent synthetic circuit components and precision diagnostics, and enabling straightforward translation of this in silico workflow to other circuitries. 3Main Text:

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