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Despite growing knowledge of the functions of individual human transcriptional effector domains, much less is understood about how multiple effector domains within the same protein combine to regulate gene expression. Here, we measure transcriptional activity for 8,400 effector domain combinations by recruiting them to reporter genes in human cells. In our assay, weak and moderate activation domains synergize to drive strong gene expression, while combining strong activators often results in weaker activation. In contrast, repressors combine linearly and produce full gene silencing, and repressor domains often overpower activation domains. We use this information to build a synthetic transcription factor whose function can be tuned between repression and activation independent of recruitment to target genes by using a small molecule drug. Altogether, we outline the basic principles of how effector domains combine to regulate gene expression and demonstrate their value in building precise and flexible synthetic biology tools., Data and code for publication: "High-throughput functional characterization of combinations of transcriptional activators and repressors"

Additional file 1: Supplementary Figure 1. In vitro dCas9 and Cas9 CasKAS profiles around the mouse Nanog locus using the “Nanog-sg2” and “Nanog-sg3” sgRNAs. Supplementary Figure 2. CasKAS signal in vitro is specific to the activity of the dCas9/Cas9 protein combined with its sgRNA. Supplementary Figure 3. CasKAS signal in vitro around the VEGFA gene with the VEGFA sgRNA. Supplementary Figure 4. CasKAS signal in vivo around the VEGFA gene with the VEGFA sgRNA. Supplementary Figure 5. Time course of in vivo CasKAS signal around the EMX1 gene with the EMX1 sgRNA using dCas9. Supplementary Figure 6. Time course of in vivo CasKAS signal around the VEGFA gene with the VEGFA sgRNA using dCas9. Supplementary Figure 7. Time course of in vivo CasKAS signal around the EMX1 gene with the EMX1 sgRNA using active Cas9. Supplementary Figure 8. Time course of in vivo CasKAS signal around the VEGFA gene with the VEGFA sgRNA using active Cas9. Supplementary Figure 9. CasKAS signal in vitro around the CD2 gene with two different sgRNA targeting the gene. Supplementary Figure 10. CasKAS signal in vivo (HEK293 cells, harvested at 48 hours) around the CD2 gene with two different sgRNA targeting the gene. Supplementary Figure 11. CasKAS signal in vitro around the CD90/THY1 gene with two different sgRNA targeting the gene. Supplementary Figure 12. CasKAS signal in vivo (HEK293 cells, harvested at 48 hours) around the CD90/THY1 gene with two different sgRNA targeting the gene. Supplementary Figure 13. CasKAS signal in vitro around the CD45/PTPRC gene with two different sgRNA targeting the gene. Supplementary Figure 14. CasKAS signal in vivo (HEK293 cells, harvested at 48 hours) around the CD45/PTPRC gene with two different sgRNA targeting the gene. Supplementary Figure 15. CasKAS signal in vitro around the CD298/ATP1B3 gene with two different sgRNA targeting the gene. Supplementary Figure 16. CasKAS signal in vivo (HEK293 cells, harvested at 48 hours) around the CD298/ATP1B3 gene with two different sgRNA targeting the gene. Supplementary Figure 17. CasKAS identifies proper off-target sites that are missed by sgRNA prediction algorithms. Supplementary Figure 18. In vitro dCas9 and Cas9 CasKAS profiles for the “Nanog-sg2” sgRNA. Supplementary Figure 19. In vitro dCas9 and Cas9 CasKAS profiles for the “Nanog-sg3” sgRNA. Supplementary Figure 20. In vitro dCas9 and Cas9 CasKAS profiles for the “EMX1 Tsai” sgRNA. Supplementary Figure 21. In vitro dCas9 and Cas9 CasKAS profiles for the “VEGFA-site1” sgRNA. Supplementary Figure 22. In vitro dCas9 and Cas9 CasKAS profiles for the “CD2-1” sgRNA. Supplementary Figure 22. In vitro dCas9 and Cas9 CasKAS profiles for the “CD2-1” sgRNA. Supplementary Figure 23. In vitro dCas9 and Cas9 CasKAS profiles for the “CD2-2” sgRNA. Supplementary Figure 24. In vitro dCas9 and Cas9 CasKAS profiles for the “CD45-1” sgRNA. Supplementary Figure 25. In vitro dCas9 and Cas9 CasKAS profiles for the “CD45-2” sgRNA. Supplementary Figure 26. In vitro Cas9 CasKAS profiles for the “CD90-1” sgRNA. Supplementary Figure 27. In vitro dCas9 and Cas9 CasKAS profiles for the “CD90-2” sgRNA. Supplementary Figure 28. In vitro dCas9 and Cas9 CasKAS profiles for the “CD298-1” sgRNA. Supplementary Figure 29. In vitro dCas9 and Cas9 CasKAS profiles for the “CD298-2” sgRNA. Supplementary Figure 30. Multiple sequence alignment of offtarget sites identified by in vitro dCas9 and Cas9 CasKAS for the “Nanog-sg2” sgRNA. Supplementary Figure 31. Multiple sequence alignment of offtarget sites identified by in vitro dCas9 and Cas9 CasKAS for the “Nanog-sg3” sgRNA. Supplementary Figure 32. Multiple sequence alignment of offtarget sites identified by in vitro dCas9 and Cas9 CasKAS for the “EMX1 Tsai” sgRNA. Supplementary Figure 33. Multiple sequence alignment of offtarget sites identified by in vitro dCas9 and Cas9 CasKAS for the “VEGFA-site1” sgRNA. Supplementary Figure 34. Multiple sequence alignment of offtarget sites identified by in vitro dCas9 and Cas9 CasKAS for the “CD2-1” sgRNA. Supplementary Figure 35. Multiple sequence alignment of offtarget sites identified by in vitro dCas9 and Cas9 CasKAS for the “CD-2” sgRNA. Supplementary Figure 36. Multiple sequence alignment of offtarget sites identified by in vitro dCas9 and Cas9 CasKAS for the “CD45-1” sgRNA. Supplementary Figure 37. Multiple sequence alignment of off-target sites identified by in vitro dCas9 and Cas9 CasKAS for the “CD45-2” sgRNA. Supplementary Figure 38. Multiple sequence alignment of off-target sites identified by in vitro Cas9 CasKAS for the “CD90-1” sgRNA. Supplementary Figure 39. Multiple sequence alignment of offtarget sites identified by in vitro dCas9 and Cas9 CasKAS for the “CD90-2” sgRNA.. Supplementary Figure 40. Multiple sequence alignment of off-target sites identified by in vitro dCas9 and Cas9 CasKAS for the “CD298-1” sgRNA. Supplementary Figure 41. Multiple sequence alignment of off-target sites identified by in vitro dCas9 and Cas9 CasKAS for the “CD298-2” sgRNA. Supplementary Figure 42. Multiple sequence alignment of off-target sites identified by in vitro dCas9 and Cas9 CasKAS for the “sgRNA #1” sgRNA outside the list of predicted off-targets by Cass-OFFinder. Supplementary Figure 43. Cutting profiles around on- and off-target sites for the VEGFA sgRNA. Supplementary Figure 44. Cutting profiles around the top 100 on- and off-target sites for the “CD2-1” sgRNA. Supplementary Figure 45. Cutting profiles around the top 100 on- and off-target sites for the “CD2-2” sgRNA. Supplementary Figure 46. Cutting profiles around the top 100 on- and off-target sites for the “CD45-1” sgRNA. Supplementary Figure 47. Cutting profiles around the top 100 on- and off-target sites for the “CD45-2” sgRNA. Supplementary Figure 48. Cutting profiles around the top 100 on- and off-target sites for the “CD90-1” sgRNA. Supplementary Figure 49. Cutting profiles around the top 100 on- and off-target sites for the “CD90-2” sgRNA. Supplementary Figure 50. Cutting profiles around the top 100 on- and off-target sites for the “CD298-1” sgRNA. Supplementary Figure 51. Cutting profiles around the top 100 on- and off-target sites for the “CD298-2” sgRNA. Supplementary Figure 52. Amplicon sequencing of DNA edits with the EMX1 sgRNA. Supplementary Figure 53. Amplicon sequencing of DNA edits with the VEGFA sgRNA. Supplementary Figure 54. Comparing in vitro dCas9 results to using ChIP-seq and CHANGE-seq for off-target profiling. Supplementary Figure 55. Comparing in vitro dCas9 results to using DISCOVER-seq for off-target profiling. Supplementary Figure 56. Comparing in vitro dCas9 results to using DISCOVER-seq for off-target profiling. Supplementary Figure 57. Comparing in vitro dCas9 results to using GUIDE-seq for off-target profiling. Supplementary Figure 58. Comparing in vitro dCas9 results to using Digenome-seq for off-target profiling. Supplementary Figure 59. Most sgRNAs in the human genome contain multiple G nucleotides and are thus subject to labeling by N3-kethoxal. Supplementary Figure 60. Absence of strong correlation between the number of G nucleotides in a sgRNA off-target site and CasKAS signal. Supplementary Figure 61. CasKAS can be performed on pre-sheared DNA.

Additional file 2. Review history.

To elucidate principles operating in native biological systems and to develop novel biotechnologies, synthetic biology aims to build and integrate synthetic gene circuits within native transcriptional networks. The utility of synthetic gene circuits for cell engineering relies on the ability to control the expression of all constituent transgene components. Transgene silencing, defined as the loss of expression over time, persists as an obstacle for engineering primary cells and stem cells with transgenic cargos. In this review, we highlight the challenge that transgene silencing poses to the robust engineering of mammalian cells, outline potential molecular mechanisms of silencing, and present approaches for preventing transgene silencing. We conclude with a perspective identifying future research directions for improving the performance of synthetic gene circuits., Cell Systems, 13 (12), ISSN:2405-4720

Pooled CRISPR-Cas9 screens have recently emerged as a powerful method for functionally characterizing regulatory elements in the non-coding genome, but off-target effects in these experiments have not been systematically evaluated. Here, we conducted a genome-scale screen for essential CTCF loop anchors in the K562 leukemia cell line. Surprisingly, the primary drivers of signal in this screen were single guide RNAs (sgRNAs) with low specificity scores. After removing these guides, we found that there were no CTCF loop anchors critical for cell growth. We also observed this effect in an independent screen fine-mapping the core motifs in enhancers of the GATA1 gene. We then conducted screens in parallel with CRISPRi and CRISPRa, which do not induce DNA damage, and found that an unexpected and distinct set of off-targets also caused strong confounding growth effects with these epigenome-editing platforms. Promisingly, strict filtering of CRISPRi libraries using GuideScan specificity scores removed these confounded sgRNAs and allowed for the identification of essential enhancers, which we validated extensively. Together, our results show off-target activity can severely limit identification of essential functional motifs by active Cas9, while strictly filtered CRISPRi screens can be reliably used for assaying larger regulatory elements.

Schistosomiasis is one of the most important and widespread neglected tropical diseases (NTD), with over 200 million people infected in more than 70 countries; the disease has nearly 800 million people at risk in endemic areas. Although mass drug administration is a cost-effective approach to reduce occurrence, extent, and severity of the disease, it does not provide protection to subsequent reinfection. Interventions that target the parasites’ intermediate snail hosts are a crucial part of the integrated strategy required to move toward disease elimination. The recent revolution in gene drive technology naturally leads to questions about whether gene drives could be used to efficiently spread schistosome resistance traits in a population of snails and whether gene drives have the potential to contribute to reduced disease transmission in the long run. Responsible implementation of gene drives will require solutions to complex challenges spanning multiple disciplines, from biology to policy. This Review Article presents collected perspectives from practitioners of global health, genome engineering, epidemiology, and snail/schistosome biology and outlines strategies for responsible gene drive technology development, impact measurements of gene drives for schistosomiasis control, and gene drive governance. Success in this arena is a function of many factors, including gene-editing specificity and efficiency, the level of resistance conferred by the gene drive, how fast gene drives may spread in a metapopulation over a complex landscape, ecological sustainability, social equity, and, ultimately, the reduction of infection prevalence in humans. With combined efforts from across the broad global health community, gene drives for schistosomiasis control could fortify our defenses against this devastating disease in the future.

Schistosomiasis is one of the most important and widespread neglected tropical diseases (NTD), with over 200 million people infected in more than 70 countries; the disease has nearly 800 million people at risk in endemic areas. Although mass drug administration is a cost-effective approach to reduce occurrence, extent, and severity of the disease, it does not provide protection to subsequent reinfection. Interventions that target the parasites' intermediate snail hosts are a crucial part of the integrated strategy required to move toward disease elimination. The recent revolution in gene drive technology naturally leads to questions about whether gene drives could be used to efficiently spread schistosome resistance traits in a population of snails and whether gene drives have the potential to contribute to reduced disease transmission in the long run. Responsible implementation of gene drives will require solutions to complex challenges spanning multiple disciplines, from biology to policy. This Review Article presents collected perspectives from practitioners of global health, genome engineering, epidemiology, and snail/schistosome biology and outlines strategies for responsible gene drive technology development, impact measurements of gene drives for schistosomiasis control, and gene drive governance. Success in this arena is a function of many factors, including gene-editing specificity and efficiency, the level of resistance conferred by the gene drive, how fast gene drives may spread in a metapopulation over a complex landscape, ecological sustainability, social equity, and, ultimately, the reduction of infection prevalence in humans. With combined efforts from across the broad global health community, gene drives for schistosomiasis control could fortify our defenses against this devastating disease in the future.