Tag: Synthetic biology

  • Fate induction in CD8 CAR T cells through asymmetric cell division

    [ad_1]

    Lentiviral constructs and production, in vitro transcription, cell lines and cell culture

    All cells in this study were cultured in RPMI 1640 supplemented with 10% fetal bovine serum (FBS), 10 mM HEPES and 1% penicillin/streptomycin at 37 °C in fully humidified environment with 5% CO2 unless otherwise indicated. Cell lines were evaluated for mycoplasma contamination.

    We used single chain variable fragment (scFv)-based CARs against human CD19 (clone FMC63) and the human TCR δ chain (clone 5A6.E9) with an appended N-terminal pentaglycine tag in a third generation lentiviral backbone (Extended Data Fig. 1). Both CAR constructs used scFvs in the light-chain-heavy-chain configuration followed by a CD8α hinge and transmembrane domain and CD137 and CD3ζ cytoplasmic domains. Furthermore, CARs were cloned with an IgG4 hinge instead of CD8α hinge domains (Extended Data Fig. 6c) with standard cloning methods.

    To create a human CD19-sortase construct, we cloned human CD19 (C-terminal truncation) into a third generation lentiviral backbone that provided an IgH signal peptide followed by a flag-tag, low-affinity sortase mutant15 and a linker peptide. Similarly, we cloned a γδ TCR (clone PEER59) into this vector (both chains as a single transcript separated by a P2A site). Constructs were obtained as double-stranded DNA (dsDNA) fragments from IDT, digested and ligated into the lentiviral backbones using standard cloning techniques. We cloned extra TCR chains into lentiviral backbones (CD3γ-P2A-CD3δ and CD3ζ-P2A-CD3ε plasmids) to facilitate expression of the γδ TCR in non-T cell lines. Nucleotide and amino acid sequences of all constructs used in this study are listed in Supplementary Table 1. Lentivirus was produced as previously described using Lenti-X 293T cells60,61.

    For in vitro messenger RNA (mRNA) transcription, CAR constructs were cloned into a pDrive.150 poly(A) backbone62 using standard cloning techniques and linear mRNA was transcribed using the T7 mScript Standard mRNA Production System (Cellscript). Linear dsDNA templates were generated by digesting with either ClaI or SpeI, and mRNA was synthesized following the manufacturer’s recommendations for a Cap 1-mRNA and roughly 150 base-long poly(A)-tail. mRNA was purified with an RNeasy Mini Kit (Qiagen), eluted in RNase-free water at 1 μg μl−1 and aliquoted and stored at −80 °C.

    To disrupt the endogenous CD19 locus in Nalm6 cells (provided by M. Milone, originally obtained from DSMZ) and to create a cell line only expressing CD19-sortase or γδ TCR-sortase, two guide RNAs targeting the human CD19 locus were obtained (IDT, sequences in Supplementary Table 2) and 5 × 106 Nalm6 cells were electroporated with a total of 50 pM ribonucleoprotein (consisting of Cas9 (IDT) and single-guide RNA (sgRNA)) in a total volume of 20 μl of Lonza P3 buffer (P3 primary cell 4D-Nucleofector X kit S) with a Lonza 4D-Nucleofector Core Unit (pulse protocol EO115) according to the manufacturer’s protocol. After CD19 disruption, Nalm6 cells were cultured at 0.2–1 × 106 cells per ml in standard medium for 14 days before sorting CD19 negative cells by FACS (BD Biosciences AriaII). Initial disruption efficiency was greater than 90%, which increased to more than 99% after sorting. CD19 negative Nalm6 cells were transduced with target proteins (CD3γ, CD3δ, CD3ζ, CD3ε, γδ TCR-sortase or CD19-sortase) and positive cells were enriched by FACS.

    All target cells in this study express green fluorescent protein (GFP)-click beetle green luciferase. Target cells were sorted on a regular basis to ensure persistence of the luciferase and surface antigen expression over several passages.

    Bulk and CD8 CART production

    Bulk or CD8 only primary human T cells from healthy donors were obtained from the University of Pennsylvania Human Immunology Core, stimulated with anti-CD3/anti-CD28 beads (Dynabeads, Thermo Fisher) for 24 h before transduction with lentiviral CAR constructs. Anti-CD3/anti-CD28 magnetic beads were removed on day 4 after transduction and the IL-2 concentration was gradually lowered from 100 to 25 IU ml−1 by day 8 after activation and 0 IU ml−1 by day 10 after activation. Cell medium replacement and quantification of cell size and number (Coulter Multisizer 4e) occurred every 2–3 days. CAR transduction efficiency was determined by flow using a polyclonal antimurine Fab antibody conjugated to biotin (Jackson ImmunoResearch) and streptavidin-PE. Non-transduced T cells from the same donor were stained under identical conditions and used as negative control.

    To generate naive or effector CARTs, naive and effector T cells were isolated from bulk primary human T cells and were electroporated with mRNA encoding CARs. Naive T cells were isolated either with the Naive Pan T Cell Isolation Kit (Miltenyi Biotec, catalogue no. 130-097-095) or with a positive selection of CD62L and subsequent negative selection for CD45RA+ cells. For the latter approach, cells were stained with anti-CD62L-PE (BioLegend, DREG-56, catalogue no. 304840) and enriched with the anti-PE MultiSort kit (Miltenyi Biotec, catalogue no. 130-090-757) and LS column (Miltenyi Biotec, catalogue no. 130-042-401), with the flowthrough reserved for the isolation of effector T cells described below. CD62L+ cells were flushed out and separated from MultiSort MicroBeads using the MultiSort Release Reagent and centrifugation. CD45RA+CD62L+ cells were subsequently isolated by negative isolation using CD45RO MicroBeads (Miltenyi Biotec, catalogue no. 130-046-001) and two columns (Miltenyi LS). To isolate effector T cells of the same donor as the naive T cells, flowthrough from the first CD62L selection was added to the column (Miltenyi LD) for negative selection of CD62L cells. More than 95% population purity (determined by flow cytometry) was used in the presented studies. Following isolation, naive and effector cells were electroporated with 10 μg mRNA/1 × 107 T cells encoding the CARs using Lonza 4D-Nucleofector Core Unit (pulse code EH115) according to the manufacturer’s protocol.

    To disrupt the endogenous TCR, T cells were cotransduced with lentiviral CAR constructs and pCAT003, a lentivirus transfer plasmid encoding sgRNA targeting TRAC and gift from J. Doudna (Addgene plasmid no. 171628)63. Immediately following debeading, up to 4 × 106 T cells were electroporated with 50 pM of Cas9 as described above with the modification of pulse code EO115. TCR cells were negatively selected using CD3 MicroBeads (Miltenyi Biotec, catalogue no. 130-097-043) and LD column according to the manufacturer’s protocol.

    To genetically disrupt IKZF1, 1 × 106 T cells were electroporated immediately following debeading with 50 pM Cas9 and 100 nM guide RNA (IDT, Supplementary Table 2) as described above, with the modification of pulse code EH115. Genomic DNA was isolated using DNeasy Blood & Tissue Kit (Qiagen, catalogue no. 69504) according to the manufacturer’s protocol. The targeted IKZF1 locus was amplified using indicated primers (Supplementary Table 3). Quantification of genetic editing efficiencies were estimated using tracking of indels by decomposition64. Western blots were performed and stained using rabbit antiIkaros (IKZF1) monoclonal antibody (Cell Signaling, 9034S; dilution 1:1,000), digital antirabbit-HRP (Kindle Biosciences, LLC, R1006; dilution 1:1,000), mouse anti-β-actin monoclonal antibody (Cell Signaling, 3700S; dilution 1:3,000) and digital antimouse-HRP (Kindle Biosciences, LLC, R1005; dilution 1:3,000). Uncropped images of blots are provided in Supplementary Fig. 1. Pharmacologic depletion of IKZF1 was performed using 0.1 μM lenalidomide (MedChemExpress, catalogue no. HY-A0003).

    LIPSTIC assay

    Biotinylated LPETG peptide (biotin-aminohexanoic acidLPETGS, C-terminal amide, 95% purity)15 was purchased from LifeTein (custom synthesis), reconstituted in PBS at 10 mM and stored at −80 °C.

    To label target cells, Nalm6 cells (expressing sortase-tethered target molecules) were incubated with biotinylated LPETG peptide (100 μM, LifeTein) for 30 min at 37 °C in RPMI/10%FBS, followed by washing three times to remove excess soluble peptide. Sortase-bound LPETG was then labelled with fluorescent streptavidin (PE, AF647 or APC; 10 μg ml−1; BioLegend) for 30 min at 37 °C. Cells were then washed three times and resuspended at 1 × 106 cells per ml.

    All LIPSTIC assays were performed using fully rested T cells that had not demonstrated cell number increases in roughly 2 days. For the CARTs in the presented studies, the transduction efficiencies were between 20 and 85%, and the cell sizes of rested T cells were between 200 and 260 fl, which was achieved 12–15 days after activation. T cells were stained with CellTrace Violet following the manufacturer’s recommendations with the following modifications: T cells were stained at a concentration of 1 × 107 cells per ml for 10 min at 37 °C. Target cells and CARTs were mixed in a six-well plate well in a total volume of 5.5–7 ml, and CART to target ratios ranged from 0.3:1 to 4.25:1. Cells were incubated for 72 h before cell sorting (BD Biosciences AriaII) and subsequent analysis of first-division daughter cells.

    A second target encounter LIPSTIC assay was performed using sorted first-division LPETG+ or LPETG cells. Target cells labelled with a second colour fluorescent streptavidin and CARTs were mixed in a 96-well plate in a total volume of 200 μl and a 1:1 CART:target ratio. Second-division daughter cells were sorted 24 h after coincubation.

    Sorting gates were established for live single cells that were negative for GFP (excluding target cells), positive for Cell Trace Violet (CTV) and positive or negative for LPETG. LPETG positivity was determined relative to untransduced T cells, CARTs incubated without target cells or irrelevant CARTs incubated with target cells (threshold for LPETG positivity was generally the same for all controls).

    Multiparametric flow cytometry analysis of T cells

    Unless otherwise specified, antibodies were purchased from BioLegend.

    In vitro and in vivo LIPSTIC assay populations were sorted and subsequently phenotyped by staining with 1:200 CD8-APCH7 (SK1, BD Biosciences, catalogue no. 561423), 5:400 CD4-BUV805 (SK3, BD Biosciences, catalogue no. 612887), 1:160 CD45RA-BUV395 (HI100, BD Biosciences, catalogue no. 740298), 5:400 CD45RO (UCHL1, catalogue no. 304234), 1:40 CD25-BV711 (M-A251, catalogue no. 356138) and/or 1:250 CD62L-PE (DREG-56, catalogue no. 304840).

    For in vivo studies, samples were stained with CD8-APC/Cy5.5 (RFT8, SouthernBiotech, catalogue no. 9536-18), CD4-PE/Cy5.5 (RFT4, SouthernBiotech, catalogue no. 9522-16), CD3-BV605 (OKT3, catalogue no. 317322), CD19-APC (HIB19, catalogue no. 302212), CD45RA-BUV395 (HI100, catalogue no. 740298), CD45RO-BV785 (UCHL1, catalogue no. 304234), CD62L-PE (DREG-56, catalogue no. 304840), TCR-alpha/beta-BV421 (IP26, catalogue no. 306722) and CD45-PECy7 (QA17A19, catalogue no. 393408) at 1:100 dilution. Whole blood was stained in Trucount tubes (BD Biosciences) and fixed with FacsLyse solution (BD Biosciences) according to the manufacturer’s recommendations. Single-cell suspensions from spleen samples were produced by homogenization of the tissue through a 70 μm mesh followed by treatment with Red Blood Cell Lysis Buffer (BioLegend) according to the manufacturer’s recommendations and stained in PBS, 1% FBS and cell numbers were quantified with CountBright Plus Absolute Counting Beads (Thermo Fisher).

    Samples were analysed on an LSRII, LSR Fortessa or FACSymphony A3 Cell Analyzer (BD Biosciences). The population of interest was gated based on forward- versus side-scatter characteristics followed by singlet gating. Data were analysed with FlowJo v.10 (Tree Star). Graphs and statistical analyses were generated using GraphPad Prism v.9.4.0.

    Live-cell microscopy

    For live-cell imaging to capture CARTs undergoing the first cell division, LPETG-positive CARTs before the first cell division were isolated using fluorescence activated cell sorting after 48 h of coincubation with target cells and then imaged in a humidified incubation chamber at 37 °C in 5% CO2 on a Zeiss Observer 7 equipped with a Zeiss Axiocam 702 monochrome CMOS camera, a Zeiss Axiocam 503 colour CCD camera and a Colibri 7 LED light source in Definite Focus mode in a 35 mm glass bottom dish (Ibidi) every 3 min.

    To image the transfer of LPETG peptide from target to CART cells, CTV-labelled T cells were incubated with LPETG peptide-loaded target cells at an effector to target cell ratio of 1:5. The excess of target cells in this context increases the frequency of observing the interaction between CARTs and targets. Cells were placed in a 35 mm glass bottom dish (Ibidi) and analysed in a humidified incubation chamber at 37 °C in 5% CO2. Photographs were acquired in the GFP, CTV and AF647 channels in Definite Focus mode on a Zeiss Observer 7 every 45 s for 50 min. Images were acquired with ×40 objective using Zen (Blue edition) software (v.2.5, Zeiss). Videos were created with Fiji-ImageJ.

    Metabolic analysis

    T cell metabolic profiles were assessed using the Seahorse mitochondrial stress test (Agilent Technologies). Individual wells of an XF96 cell-culture microplate were coated with CellTak as per the manufacturer’s instructions. The matrix was adsorbed overnight at 37 °C, aspirated, air-dried and stored at 4 °C until use. Mitochondrial function was assessed on day 0 or day 1 after sorting proximal or distal or undivided cells. T cells were resuspended in non-buffered RPMI 1640 medium containing 5.5 mM glucose, 2 mM l-glutamine and 1 mM sodium pyruvate and seeded at 1.5 × 105 cells per well. The microplate was centrifuged at 1,000g for 5 min and incubated in standard culture conditions for 60 min. During instrument calibration (30 min), the cells were switched to a CO2-free 37 °C incubator. XF96 assay cartridges were calibrated according to the manufacturer’s instructions. Cellular OCRs and ECARs were measured under basal conditions and following treatment with 1.5 μM oligomycin, 1.5 μM FCCP and 40 nM rotenone, with 1 μM antimycin A (XF Cell Mito Stress kit, Agilent). OCR/ECAR ratios are calculated using the mean OCR and ECAR of 3–5 replicates for each population.

    In vivo studies

    Immunodeficient NSG (NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ) mice were bred in house under an approved Institutional Animal Care and Use Committee (IACUC) protocol and maintained under pathogen-free conditions. To facilitate engraftment of T cells, bulk (CD4+ and CD8+) T cells were used in in vivo studies19. Sample sizes were not predetermined based on statistical methods but were chosen based on preliminary data and previously published results. For all in vivo experiments, treatment groups were randomly selected by the cage number. In vivo injections were performed in a blinded fashion by a member of the Ellebrecht or Payne laboratories or a staff member of the Human Stem Cell and Xenograft Core of the University of Pennsylvania.

    For in vivo LIPSTIC, LPETG-labelled target cells were injected intraperitoneally immediately followed by CTV-labelled CARTs. A 1:1 target:CART ratio was maintained, with a total of 1 × 107 total T cells injected. Mice were euthanized 2 days following injection. Cells were collected using peritoneal wash three times using 5 ml of ice-cold 2% FBS and PBS. First-division daughter cells were sorted and assessed by flow cytometry as described above.

    For functional longevity studies, 2.5 × 105 anti-CD19 CAR proximal- or distal-daughter cells, non-activated resting anti-CD19 CARTs or non-transduced T cells were intravenously injected into NSG mice on day 0. After 35 days, mice were challenged with 1 × 106 Nalm6 cells. Leukaemia burden was determined by bioluminescence imaging. Bioluminescence was quantified with an IVIS Lumina III (PerkinElmer) 2–3 times per week after Nalm6 injection as previously described3.

    In the stress-test model, NSG mice were injected with 1 × 106 Nalm6 cells on day 0. Engraftment of Nalm6 was confirmed on day 3 by bioluminescence imaging. On day 4, mice were treated with 2.5 × 105 proximal or distal-daughter anti-CD19 CARTs; 2.5 × 106 non-activated resting anti-CD19 CARTs or non-transduced T cells; or 1.3 × 106 bulk restimulated anti-CD19 CARTs (unsorted) by intravenous injection. Leukaemia burden was determined with bioluminescence imaging as above. Mice were euthanized when they had reached a total bioluminescence flux of at least 1 × 1011 photons per second, demonstrating loss of leukaemia control.

    Peripheral blood was obtained by retro-orbital bleeding. Mice were euthanized for organ harvest according to local IACUC guidelines, and spleen and blood samples were assessed by flow cytometry as described above. In accordance with the approved IACUC protocol for these studies, humane endpoints for euthanizing mice were established and not exceeded in this study. Mice were monitored at least twice weekly for symptoms. If severe lethargy or weakness, hunching, emaciated body condition (body condition score of 1 out of 5) or loss of 20% or more body weight, were observed, mice were euthanized. If a body condition score of 2 out of 5 was observed and accompanied by lethargy, mice were euthanized. Furthermore, mice were euthanized when their total bioluminescence flux exceeded 1 × 1011. All studies involving animals were performed under a protocol approved by the University of Pennsylvania IACUC.

    Luciferase-based in vitro cytotoxicity assay

    Cytotoxicity assays were performed either on day 1 or 4 after first cell division as previously described3. Click beetle green luciferase-expressing target Nalm6 cells were cocultured with proximal, distal or resting CARTs or donor-matched non-transduced T cells at indicated E:T ratios. To test TCR-mediated cytotoxicity of mRNA-electroporated naive and effector CARTs, K562 cells positive for CD64 (FcγRI) were incubated with 100 μg ml−1 anti-human CD3 (OKT3, Invitrogen, catalogue no. 16-0037-85) for 30 min on ice and washed twice were used as target cells.

    Single-cell multiomic analysis

    First-division proximal, first-division distal, activated-undivided and resting CD8 CARTs (1.5 × 105 cells each), sorted as described above from the LIPSTIC assay, were separately incubated in flow cytometry staining buffer (BioLegend) with a custom TotalSeq-C antibody cocktail (Supplementary Table 4, BioLegend 900000114, lot B311489) in 100 μl for 30 min at 4 °C before washing three times. Cell concentration was adjusted to 1.5 × 106 live cells per ml. 10,000 live CD8 T cells from each LIPSTIC population were loaded onto NextGem K chips (10X Genomics) and processed in a 10X Chromium device according to the manufacturer’s recommendations. A biological replicate was performed with CART from a separate donor. Library preparation was performed according to the 10 × 5′ V2 protocol for antibody-derived tags (ADT), gene expression (GEX) and paired alpha and beta TCR chains. Complementary DNA and subsequent library intermediates were checked for correct size, appropriate quantity and quality with a DNA high-sensitivity kit on a Bioanalyzer 2100 (Agilent). Libraries were sequenced in paired-end dual-index mode for 150 × 2 cycles on a NovaSeq 6000 sequencer (Illumina, one lane of a S4 cartridge). All cells in each experiment were sorted and stained on the same day and libraries were processed in parallel and sequenced in the same lane to minimize batch effects. Counts for demultiplexed GEX, ADT and TCR libraries were obtained with the STAR method of the Cell Ranger multi pipeline (10X Genomics, Cell Ranger v.6.1.2) using the human GENCODE v.32/Ensembl 98 GRCh38 reference (detailed version by 10X Genomics: Human (GRCh38) 2020-A, Human (GRCh38) v.5.0.0), which then were aggregated with the Cell Ranger aggr pipeline with read depth normalization to further reduce batch effects across libraries. Downstream analysis was performed with the Seurat V4 R package22. To remove doublets and low cells, cells with more than 25% mitochondrial gene transcripts, less than 7.5% ribosomal gene transcripts, transcript counts less than 500 or greater than 40,000, or a minimum number of detected genes of less than 500 were excluded. Counts were single-cell transformed using the sctransform V2 and glmGamPoi packages65. Dimensionality reduction was performed based on ADT counts with subsequent analysis of genes and surface proteins of interest and differentially expressed genes or surface proteins for TN-, TCM-, TEM– and TRM-like subsets. RNA velocity analysis was performed by counting spliced and unspliced transcripts in Cell Ranger binary alignment map output files with the velocyto package31 using the same transcriptome reference gene transfer format file (refdata-gex-GRCh38-2020-A) that was used for the initial Cell Ranger run. Output loom files were then used in scvelo after export of TN, TCM, TEM and TRM expression matrices containing proximal and distal first-division daughter cells from Seurat and conversion to SCANPY/ANNDATA objects66. Global velocity vectors and velocities of genes of interest were computed and visualized in stochastic or dynamic mode with scvelo30. Regulon analysis (gene modules co-expressed with transcription factors and with correct cis-regulatory upstream motif for a respective transcription factor) was performed using a list of 1,390 human transcription factors (https://github.com/aertslab/pySCENIC/blob/master/resources/hs_hgnc_curated_tfs.txt) with the Python version of SCENIC (that is, pySCENIC v.0.11.2)32,67 after importing expression matrices from Seurat to SCANPY (v.1.7.2). Gene-set enrichment analysis68,69 was performed on each T cell subsets with the GSEA function of clusterProfiler70 in R. T cell clonal analysis was performed with scRepertoire71 in R using the ‘strict’ setting, which requires identical V, D (if applicable), J and C genes in addition to identical CDR3 nucleotide sequence of both TCR chains to identify T cells belonging to the same clonotype.

    Seurat analysis was performed in R v.4.3.1, velocity analysis was performed in Python v.3.10.4 and regulon analysis was performed in Python v.3.7.12 in accordance with the respective pipeline requirements.

    Statistical analysis

    Statistical significance was determined with two-sided tests unless otherwise indicated. Where appropriate and as indicated, P values were adjusted for multiple testing (Benjamini–Hochberg). Q values were calculated with clusterprofiler in R. Whenever individual data points are presented, a horizontal line represents the mean value of the group. Survival in in vivo experiments was defined as time until the predetermined bioluminescence threshold was reached. Kaplan–Meier statistical analysis was used to compare survival between groups. Unless otherwise indicated, asterisks depict the following significance values: *P < 0.05, **P < 0.01, ***P < 0.001. P values less than 0.05 were considered statistically significant. The mean fluorescence intensity in flow cytometry plots is labelled with a cross in each gate. Statistical analysis was performed with the respective pipelines as mentioned above or with GraphPad Prism v.9.4.0.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

    [ad_2]

    Source link

  • Selective haematological cancer eradication with preserved haematopoiesis

    [ad_1]

    Structural dataset and computational analysis

    The experimentally determined 3D structure of the CD45 extracellular domain was retrieved from the PDB (5FMV)22. The per-residue relative solvent accessibility area was computed using a previously published algorithm45 implemented in FreeSASA46 using default parameters. Prediction of B-cell epitopes was based on BepiPred-2.0 (ref. 47) using the default threshold (0.5) for epitope residues nomination. The EV mutation sequence analysis framework48 was used to search for the CD45 sequence with the non-redundant UniProtKB database49. A multiple-sequence alignment was built using five iterations of the jackhammer HMM search algorithm50 with default significance score for the inclusion of homologous sequences.

    Plasmid cloning

    For sgRNA cloning into the px458 host vector (a gift from F. Zhang) (Supplementary Table 1), forward and reverse primers containing complementary CRISPR RNA (crRNA) sequences flanked by BbsI restriction sites were used (Supplementary Table 4). The px458 plasmid was double digested with AgeI-HF (NEB, R3552S) and EcoRI-HF (NEB, R3101S) to eliminate the regions coding for GFP and Cas9. The px458 vector was then digested using BbsI (ThermoFisher Scientific, ER1012), gel purified and ligated with the phosphorylated and annealed crRNA oligonucleotides (called sgRNA plasmid once cloned).

    To transiently overexpress CD45 mutants, we introduced each variant of interest in a plasmid expressing WT CD45RO. Briefly, we digested the pCD45RABC plasmid (Sino Biological) (Supplementary Table 1) with HindIII-HF (NEB, R3104S) and XcmI (NEB, R0533L) to remove the alternative spliced exons A, B and C. The point mutations of the human CD45 variants were then introduced into the plasmid expressing CD45RO using PCR (Supplementary Table 4).

    Plasmids

    All ligations were transformed in JM109-competent bacteria (Promega, P9751). BE plasmids were from Addgene (SPACE-NG, ABE8e-NG, ABEmax-SpRY, ABEmax-SpG, CBE4max-NG, CBE4max-SpG and xCas9(3.7)-BE4) (Supplementary Table 1).

    BE mRNA and sgRNAs

    ABE8e-NG mRNA (capped (cap 1) using CleanCap AG; fully substituted with 5-methoxy-U; 120A polyA tail) and ABE8e(TadA-8e V106W)-SpRY mRNA (capped (cap 1) using CleanCap AG 3′-O-methylation; fully substituted with N1-methyl-pseudo-U; 80A polyA tail) were from Trilink Biotechnologies and Tebu-bio. We used 100-base lyophilized chemically modified sgRNAs from Synthego using their CRISPRevolution sgRNA EZ Kit service and resuspended at 100 µM (3.2 µg µl−1) in 1× TE buffer from Synthego (10 nM Tris, 1 mM EDTA, pH 8.0; chemical modifications include 2′-O-methylation of the three first and last bases and 3′ phosphorothioate bonds between the first three and last two bases of each sgRNA).

    Genomic DNA extraction, PCR and Sanger sequencing

    Cells from the BE plasmid screening were lysed in tail lysis buffer (100 mM Tris pH 8.5, 5 mM Na-EDTA, 0.2% SDS, 200 mM NaCl) containing proteinase K (Sigma-Aldrich) at 56 °C (1,000 rpm) for 1 h. The DNA was precipitated with isopropanol (1:1 volume ratio) and washed in 70% ethanol. The DNA was then resuspended in H2O and the genomic DNA concentration was measured with a NanoDrop device (Thermo Fisher).

    For samples containing few cells, genomic DNA was extracted using QuickExtract (Lucigen, QE09050). Cell pellets were resuspended in 30 µl QuickExtract, incubated at 60 °C for 6 min, vortexed for 1 min and subsequently re-incubated at 98 °C for 10 min.

    PCR was performed using GoTaq G2 Green Master Mix (Promega, M782B). The gDNA of samples analysed by NGS was extracted using QuickExtract (Lucigen, QE09050) or the Quick-DNA 96 Plus kit (Zymo, D4070) and the genomic DNA concentration was measured with a Qubit device (Thermo Fisher).

    For Sanger sequencing, different PCR primers were used depending on the CD45 exon targeted by the sgRNA and the sequencing technology (Supplementary Table 4). Sequencing of PCR amplicons was done at Microsynth and sequencing chromatograms were analysed using the EditR R package51 to quantify BE efficiencies.

    Next-generation amplicon sequencing

    For NGS, targeted amplicon libraries were generated using a three-step PCR protocol. In brief, nested PCRs were done on genomic DNA samples using KAPA HiFi HotStart polymerase (Roche) (Supplementary Table 4). After Illumina barcoding (Nextera indices, Illumina) using KAPA HiFi HotStart polymerase (Roche), PCR samples were pooled, purified using AMPure XP beads (Beckman Coulter) and quantified using Qubit dsDNA HS assay kit (Thermo Fisher). Libraries were paired-end sequenced on an Illumina Miniseq instrument using the Illumina Miniseq Mid output kit (300 cycles) with 50% PhiX spike-in (Illumina). After demultiplexing, each sample was assessed for quality using FastQC52 and processed using the CRISPResso2 tool53. For each of the samples, we provided the reference amplicon sequence (hg38) and the guide RNA sequence (reverse complement) and defined the quantification window centre to −10, the quantification window size to 15 and the plot window size to 30. We applied minimum paired end reads overlap between 10 and 200 and provided the following Trimmomatic sentence: ILLUMINACLIP:NexteraPE-PE:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36. Finally, we used a custom R script (https://gitlab.com/JekerLab/cd45_shielding) to count and translate into amino acid each allele from the CRISPResso2 output file Alleles_frequency_table.txt in the quantification window. Alleles with less than 0.8% frequency were considered as ‘other’.

    Genetic determination of human chimerism

    Genetic discrimination between PDX- and HSPC-derived cells was performed using the Devyser Chimerism NGS kit (Devyser) according to the manufacturer’s recommendations. In brief, sequencing libraries were prepared from genomic DNA targeting 24 polymorphic insertion–deletion markers distributed on 16 different chromosomes. The libraries were sequenced on a MiniSeq (Illumina) instrument using the high-throughput flow cell, generating 74-bp pair-end reads. An informative marker set was defined for the PDX donor/HSPC donor pair. It consisted of 10 markers that reliably discriminated between DNA from PDX and HSPC donor cells. The average proportion of leukaemia donor-specific reads to total reads was calculated to determine the proportion of PDX cells in each sample. It was confirmed that the genomic DNA of the mouse host did not interfere with the analysis.

    CHANGE-seq-BE

    Genomic DNA was extracted from human peripheral blood mononuclear cells (PBMCs) using the Puregene tissue kit (Qiagen, 158063) according to the manufacturer’s instructions including the proteinase-K and RNase steps (Qiagen, 158143 and 158153). CHANGE-seq-BE was adapted and modified from the original CHANGE-seq method54 to validate genome-wide activity for ABEs28. Similar to CHANGE-seq, purified genomic DNA tagmented with a custom Tn5-transposome to generate an average length of 650 bp and followed by gap repair with Kapa HiFi HotStart Uracil + DNA Polymerase (KAPA Biosystems, KK2802) and Taq DNA ligase (NEB, M0208L). Gap-repaired DNA was treated with USER enzyme (NEB, M5505L) and T4 polynucleotide kinase (NEB, M0201L). Intramolecular circularization of the DNA was performed with T4 DNA ligase (NEB, M0202L) and residual linear DNA was degraded by a cocktail of exonucleases containing plasmid-safe ATP-dependent DNase (Lucigen, E3110K), lambda exonuclease (NEB, M0262L), exonuclease I (NEB, M0293L) and exonuclease III (NEB, M0206L). The circularized DNA was then treated with Quick CIP (NEB, M025L) to dephosphorylate 5′ and 3′ ends of any residual linear DNA. Circularized genomic DNA (125 ng) was treated with ABE8e–SpRY:sgRNA-49.3 complexes in vitro in a 50 μl reaction for 24 h at 37 °C. ABE RNP complexes nicked the targeted DNA strand and deaminated adenine bases to inosine in the non-targeted stranded DNA of both on- and off-target sites. Further enzymatic steps were included with ABE treatment in the CHANGE-seq-BE method to generate double-strand breaks. Nicked DNA circles were treated with endonuclease V in 10× NEB buffer 4 (NEB, M0305S). Endo V cleaved DNA adjacent to inosines to generate linear DNA with 5′ overhangs. Gaps were filled with klenow fragments (3′ > 5′ exo) and deoxyribonucleotide triphosphates (dNTPs) (NEB, M0212L) in NEB buffer 2. End-repaired DNA products were A-tailed and further ligated with a hairpin adapter using an HTP library preparation kit (Kapa, KK8235), USER treated and amplified by PCR-barcoded universal primers with NEBNext multiplex oligonucleotides for illumina (NEB, E7600S), using Kapa HiFi HotStart uracil master mix. PCR libraries were quantified by quantitative PCR (KAPA Biosystems, KK4824) and sequenced with 151-8-8151 cycles on an Illumina NextSeq 2000 instrument. CHANGE-seq-BE data analyses were performed using open-source software: https://github.com/tsailabSJ/changeseq/tree/dev.

    rhAmpSeq

    Validation of off-target sites was performed using the rhAmpSeq system from IDT. rhAmpSeq primer panels for targeted amplification were generated using the rhAmpSeq design tool defining the insert size between 150 and 250 bp. Applied primer sequences are listed in Supplementary Table 4. A rhAmpSeq CRISPR library was prepared according to the manufacturer’s instructions and sequenced on an Illumina MiniSeq instrument (MiniSeq high output kit, 300 cycles). Custom python code and open-source bioinformatic tools were used to analyse rhAmpSeq data. First, we generated FASTQ format files by demultiplexing high-throughput-sequencing BCL data files. Next, the reads were processed using CRISPRessoPooled (v.2.0.41) with quantification_window_size 10, quantification_window_center −10, base_editor_output, conversion_nuc_from A, conversion_nuc_to G. The allele frequency table from the output files was used to calculate the A•T-to-G•C editing frequency. Specifically, the editing frequency for each on- or off-target site was defined as the ratio between the number of reads containing the edited base (that is, G) in a window from positions 4 to 10 of each protospacer (where the GAA PAM is positions 21–23) and the total number of reads. To calculate the statistical significance of off-target editing, we applied a method previously described55. In brief, a 2-by-2 contingency table was constructed using the number of edited reads and the number of unedited reads in the treated sample and its corresponding control sample. Next, a χ2 test was done. The FDR was calculated using the Benjamini–Hochberg method. Significant off-targets were defined on the basis of: first, FDR ≤ 0.05 and second, the difference in editing frequency between treated and control (≥1%).

    Cell line culture conditions

    All cancer cell lines (listed in Supplementary Table 5) were cultured in RPMI-1640 (Sigma-Aldrich, R8758) supplemented with 10% heat-inactivated FCS (Gibco Life Technologies) and 2 mM GlutaMAX (ThermoFisher Scientific, 35050061) at 37 °C.

    All cell lines were retrovirally transduced with MI-Luciferase-IRES-mCherry (gift from X. Sun; Supplementary Table 1). Cells were then FACS-sorted on the basis of mCherry expression. After expansion, MOLM-14 and OCI–AML2 were profiled for short tandem repeats and tested negative for mycoplasma before being frozen until further use. Jurkat and NALM-6 were purchased from ATCC and were therefore not profiled for short tandem repeats.

    DF-1 cells were cultured in DMEM high-glucose medium (Sigma-Aldrich, D5796) supplemented with 10% non-heat-inactivated FCS and 2 mM GlutaMAX at 39 °C (Supplementary Table 5).

    Human primary T cell culture and activation conditions

    Leukocyte buffy coats from anonymous healthy human donors were purchased from the blood-donation centre at Basel (Blutspendezentrum SRK beider Basel, BSZ). PBMCs were isolated by density centrifugation using SepMate tubes (StemCell Technologies, 85450) and the density gradient medium Ficoll-Paque (GE Healthcare) according to the manufacturer’s protocol. Frozen PBMCs were thawed and human primary T cells were then isolated using an EasySep human T cell isolation kit (Stemcell Technologies, 17951) following the manufacturer’s protocol. T cells were cultured overnight without stimulation at a density of 1.5 × 106 cells per ml in RPMI-1640 medium supplemented with 10% heat-inactivated human serum (AB+, male; purchased from BSZ), 10 mM HEPES (Sigma-Aldrich), 2 mM GlutaMAX, 1 mM sodium pyruvate, 0.05 mM 2-mercaptoethanol and 1% MEM non-essential amino acids (all from Gibco Life Technologies). The next day, the human primary T cells were activated with interleukin-2 (IL-2) (150 U ml−1, proleukin, University Hospital Basel), IL-7 (5 ng ml−1, R&D Systems), IL-15 (5 ng m−1, R&D Systems) and Dynabead Human T-Activator CD3/CD28 (1:1 beads:cells ratio) (Gibco, 11132D). The activated cells were de-beaded before electroporation.

    hCD34+ HSPC isolation and culture conditions until electroporation

    Leukopaks were purchased from CytoCare and hCD34+ HSPCs were isolated by the LP-34 process using CliniMACS Prodigy (Miltenyi). Isolated hCD34+ HSPCs were thawed and grown in HSPC medium for two days until electroporation (StemSpan SFEM II (StemCell, 09655) supplemented with 100 ng ml−1 human stem cell factor (hSCF) (Miltenyi, 130-096-695), 100 ng ml−1 human FMS-like tyrosine kinase ligand (hFlt3)-ligand (Miltenyi, 130-096-479), 100 ng ml−1 human thrombopoietin (hTPO) (Miltenyi, 130-095-752) and 60 ng ml−1 hIL-3 (Miltenyi, 130-095-069).

    Electroporation conditions

    K562 cells (2 × 106) were resuspended in buffer T and mixed with 5 µg BE plasmid (Supplementary Table 1) and 1.5 µg sgRNA plasmid for co-electroporation using a Neon transfection system (ThermoFisher, MPK10096; 1,450 V, 10 ms, 3 pulses). To monitor the electroporation efficiency, GFP expression was evaluated 24 h after electroporation using an optical microscope. In Extended Data Fig. 1f, BE results are displayed using a custom BE score: log((sum of editing frequencies per condition/number of edited positions per condition)+1).

    De-beaded human activated T cells (1 × 106) were resuspended in 100 µl Lonza supplemented P3 electroporation buffer with 7.5 µg BE mRNA (1 µg µl−1) and 7.5 µg sgRNA (3.2 µg µl−1) (Supplementary Table 2) and electroporated using the 4D-Nucleofector system (Lonza) with program EH-115. Immediately after electroporation, 900 µl pre-warmed human T-cells medium was added directly in the cuvettes and incubated for 20 min at 37 °C for the T cells to recover. Cells were then transferred in 48-well flat-bottomed plates (Corning, 3548) and the medium was supplemented with 500 U ml−1 IL-2. The medium was renewed every two days.

    hCD34+ HSPCs (1 × 106) were electroporated 48 h after thawing with 7.5 µg BE mRNA (1 µg µl−1) and 13.6 µg sgRNA (3.2 µg µl−1) (Supplementary Table 2) with a 1:100 BE:sgRNA molar ratio, following the same protocol as for human T cells but with program CA-137. Electroporated hCD34+ HSPCs were kept in culture at 0.5 × 106 cells per ml in a six-well flat-bottomed plate (Corning, 3516) in HSPC medium supplemented with 100 ng ml−1 hSCF, 100 ng ml−1 hFlt3–ligand and 100 ng ml−1 hTPO for in vivo applications and with the addition of 60 ng ml–1 hIL-3 for in vitro assays. The medium was renewed every five days. Edited hCD34+ HSPCs prepared for in vivo injection were frozen two days after electroporation in cryo-preservation CryoStor CS10 medium (Stem Cell Technologies, 07930) at a density of 10 × 106 cells ml−1.

    CFU assays

    The CFU assay was started 72 h after gene editing. For each condition, 1.1 ml semi-solid methylcellulose medium (StemCell Technologies) containing 200 cells was plated in a well of a SmartDish (StemCell Technologies, 27370) in duplicates. The cells were incubated at 37 °C, for 14 days. The resulting progenitor colonies were counted and scored using STEMVision Analysis (StemCell Technologies) according to the manufacturer’s instructions. The mean of the total number of colonies in the NTC samples for each experiment was set as 1.

    DF-1 cell-transfection conditions

    Plasmid (6.5 µg) encoding WT hCD45RO or its variants was mixed with 200 µl serum-free DMEM medium and 19.5 µl polyethylenimine (1 mg ml−1; Chemie Brunschwig, POL23966-100). The transfection mix was added dropwise to 1 × 106 DF-1 cells plated the day before in a six-well plate. Cells were analysed 48 h later by flow cytometry.

    In vitro ADC-mediated killing assays

    For in vitro ADC killing assays, 5,000 base-edited human activated T cells or 25,000 base-edited hCD34+ HSPCs were plated five days after electroporation in 96-well plates (flat-bottomed for T cells and round-bottomed for HSPCs; Corning 3596 and 3799, respectively) in 100 µl of corresponding medium (supplemented with only 50 U ml−1 of IL-2 for human T cells). For ADC killing assays involving saporin, a 100 nM stock was prepared by incubating the biotinylated antibody (BC8 or MIRG451 mAbs) and saporin–streptavidin (ATS-Bio, IT-27-1000) at a 1:1 molar ratio for 30 min at room temperature.

    For in vitro ADC killing of co-cultures, 12,500 Jurkat cells were stained for 20 min with CTV (Invitrogen, C34557A) at 37 °C and then seeded at a 1:1 cell ratio with 12,500 base-edited hCD34+ HSPCs five days post-electroporation in 96-well round-bottom plates in 100 µl HSPC medium with corresponding concentrations of CIM053–SG3376 (ADC Therapeutics). Cells were incubated for 72 h at 37 °C, stained for flow cytometry or cell sorting and analysed using a BD LSRFortessa. Genomic DNA was extracted for sequencing.

    For in vitro ADC killing of mCherry–luciferase-marked tumour cell lines (Jurkat, NALM-6, OCI-AML-2 and MOLM-14), 2,000 cells were plated in 384-well plates in medium with or without 30 min pre-incubation at 37 °C with 50 µg ml−1 (333.33 nM) naked CIM053 antibody (40 µl final total volume per well). Following a 72 h incubation period, 5 μl firefly d-luciferin (0.75 mg ml−1 resuspended in medium (Biosynth, L-8220)) was added to each well and incubated for 5 min at room temperature. Luminescence readouts were recorded using a BioTek Synergy H1 plate reader.

    Expression and purification of soluble CD45wt and CD45 variants

    For precise antibody–protein affinity measurements and biophysical characterization, CD45wt and variants containing only D1 and D2 of the ECD were produced. The protein sequence (residues 225–394) was histidine tagged at the carboxy terminus and contains few N- and C-terminal added amino acids (full WT sequence: ETGIEGRKPTCDEKYANITVDYLYNKETKLFTAKLNVNENVECGNNTCTNNEVHNLTECKNASVSISHNSCTAPDKTLILDVPPGVEKFQLHDCTQVEKADTTICLKWKNIETFTCDTQNITYRFQCGNMIFDNKEIKLENLEPEHEYKCDSEILYNNHKFTNASKIIKTDFGSPGEGTKHHHHHH, SEQ ID 57, Uniprot ID P08575). Expi293F GnTI cells (Thermo Fisher, A39240) that lack N-acetylglucosaminyltransferase I (GnTI) activity and therefore lack complex N-glycans were used for protein expression. After collection, the protein was purified using Ni-NTA chromatography followed by digestion of high-mannose glycans with endoglycosidase H (EndoHf; New England BioLabs, P0703S) at 37 °C overnight. EndoHf was removed from the protein solution with amylose resin and the CD45 protein was further purified by size-exclusion chromatography in buffer comprising 20 mM HEPES, pH 7.4, 150 mM NaCl. Peak monomer and dimer fractions (where needed) were concentrated using a 10 kDa cut-off Amicon centrifugal filter (UFC8010) and protein aliquots were flash-frozen in liquid nitrogen before storage at −150 °C. Variant CD45 proteins were produced using the same experimental procedure. The monomer content percentage for each protein was taken from the size-exclusion chromatogram.

    Binding analysis of soluble CD45 proteins by BLI

    Analysis of MIRG451 and BC8 binding to the selected variants was performed on an Octet system RED96e (Sartorius) or R8 (Sartorius) at 25 °C with shaking at 1,000 rpm using 1× kinetic buffer (Sartorius, 18-1105). The selected variants were screened for their ability to bind to MIRG451 and BC8 using different concentrations of CD45 (WT or variant). MIRG451 was captured by an anti-human Fc-capture biosensor (AHC) (Sartorius, 18-5060) for 300 s at 0.5–1 µg ml−1. As an analyte, human CD45wt and variants, containing only domains 1 and 2, were titrated at seven different concentrations, from 1,000 nM to 15.6 nM or from 50 nM to 0.78 nM with a 1:2 dilution series. Association of the analyte to MIRG451 was monitored for 600 s and dissociation of the analyte from MIRG451 was monitored for 1,800 s. Reference subtraction was performed against buffer-only wells. AHC tips were regenerated using 10 mM Gly-HCl, pH 1.7. Data were analysed using the Octet data analysis software HT 12.0. Data were fitted to a 1:1 binding model. Kinetic rates Ka and Kd were globally fitted.

    To analyse binding to BC8, streptavidin biosensors (Sartorius, 18-5020) were first coated with CaptureSelect biotin anti-LC-κ (murine) conjugate (Thermo Scientific, 7103152100) for 600 s at 1 µg ml−1. BC8 was then captured by the coated streptavidin biosensors for 300 s at 0.5–1.0 µg ml−1. Analyte titration was performed as for MIRG451. Association of the analyte to BC8 was monitored for 300 s and dissociation of the analyte from BC8 was monitored for 900 s. Reference subtraction, regeneration and data analysis were performed as for MIRG451.

    Characterization of CD45 variants by nanoDSF

    The thermostability of CD45 D1–D2 variants was analysed by differential scanning fluorimetry and monitoring tryptophane fluorescence using Nanotemper Prometheus NT.48 NanoDSF or a Nanotemper Prometheus Panta (NanoTemper Technologies)56,57,58. Thermal denaturation was monitored by tryptophane/tyrosine fluorescence at 350 and 330 nm and an excitation wavelength of 280 nm was used. CD45wt and variants were prepared at 0.25–1.0 mg ml−1 in 20 mM HEPES, 150 mM NaCl, pH 7.4. Then 10 μl was put into the capillaries and placed into the sample holder. Each protein was measured in triplicates per experiment and the CD45wt was measured in four different experiments. The temperature was increased from 20 °C to 90 °C or 95 °C. The analysis was performed using the ratio of the fluorescent intensities at 350 and 330 nm. The software of the instrument was used to calculate Tonset and TM as well as the mean and s.d. of the triplicates. The melting temperature was determined as the inflexion point of the sigmoidal curve and compared with that of CD45wt.

    Flow cytometry analysis and sorting

    Flow cytometry was done on BD LSRFortessa instruments with BD FACSDiva software. Data were analysed with FlowJo software. Antibodies used for flow cytometry are listed in Supplementary Table 6. Cells were sorted with BD FACSAria or BD FACSMelody cell sorter instruments. Sorted cells were resuspended in 30 µl QuickExtract. PCRs were performed and sent for Sanger sequencing.

    CD45 expression of AML samples

    The CD45 expression of 27 people diagnosed with AML at University Hospital Basel was assessed using routinely acquired flow cytometry data as part of the diagnostic work-up. Gating for AML blasts, lymphocytes and erythrocytes was performed manually using FlowJo 10.10.0. Owing to the experimental set-up (threshold for SSC-A and FSC-A to exclude debris), a distinct erythrocyte population could not be distinguished in all samples (23 of 27). Data were analysed with GraphPad Prism 10 and statistical significance was calculated using mixed-effects analysis. All patients gave written informed consent to the analysis of clinical data for research purposes and the study was approved by the local ethics committee (BASEC-Nr 2023-01372).

    Animal experiments

    All animal work was done in accordance with the federal and cantonal laws of Switzerland. Protocols were approved by the Animal Research Commission of the Canton of Basel-Stadt, Switzerland. All mice were housed in a specific pathogen-free condition in accordance with institutional guidelines and ethical regulations. NBSGW (stock 026622) female mice were purchased from Jackson Laboratories. HSPCs were edited as described above. Two days after electroporation, cells were collected and frozen in CryoStor CS10 medium. Cells were thawed on the day of injection, washed and resuspended in PBS. Recipient NBSGW female mice (4 weeks old) were injected intravenously into the tail vein with HSPCs (the number of cells injected varied between 0.6 and 1.1 million and is indicated in each figure). Chimerism was analysed by flow cytometry in blood after ten weeks. Mice were treated with saline or CIM053–SG3376 at the dose(s) and intervals indicated in each figure. For tumour experiments in humanized mice, 1 × 106 MOLM-14–mCherry–luc cells were injected into the tail vein. Then, 10 or 12 days after tumour inoculation, the mice were treated with saline or 1 mg per kg CIM053–SG3376. The mice received a second antibody dose of 0.5 mg per kg CIM053–SG3376 10 or 25 days after the first dose. Mice were euthanized 43 or 45 days after tumour inoculation or when reaching the maximum allowed clinical score. To monitor tumour growth, mice were injected intraperitoneally with 100 μl d-luciferin (BioSynth, L-8220) and were subjected to Newton7.0 imaging (Vilber).

    For secondary transplant, NSG–SGM3 female mice (stock 013062) were purchased from Jackson Laboratories. Recipient mice were irradiated the day before the BM transplant with 200 cGy. Primary transplant mice were euthanized, the BM was isolated and 40% of it was re-injected into the new host. Mice from secondary transplants were euthanized 8 weeks after humanization.

    MOLM-14–mCherry–luc (1 × 106), OCI-AML-2–mCherry–luc (2 × 106), Jurkat–mCherry–luc (5 × 106) or NALM-6–mCherry–luc (0.5 × 106) cells were injected into the tail vein of NBSGW mice. After tumour inoculation, mice were monitored regularly (for behaviour, weight and imaging). Mice were treated with saline, control-SG3376 or CIM053–SG3376 at the dose and intervals indicated in the relevant figure and euthanized 21 days after tumour inoculation or when reaching the maximum allowed clinical score.

    Deidentified patient-derived AML samples were obtained from the PDX repository59,60 (Cancer Research Center of Toulouse, France). Signed written informed consent for research use in accordance with the Declaration of Helsinki was obtained from patients and approved by the Geneva Health Department Ethic Committee. PDX cells (0.6 × 106) were injected into the tail vein of humanized NBSGW mice (8 weeks after HSPC injection). The weight of the mice was monitored regularly. Mice were treated with saline or CIM053–SG3376 at the doses and intervals indicated in the figure and the mice were euthanized 54 days after tumour inoculation. Some control mice were euthanized 3 days before antibody treatment.

    Tissue collection and processing

    After the mice were euthanized, 0.2 ml blood, both hind legs (femur and tibia) and the spleen were collected from each mouse. Cell suspensions were generated, red blood cells were lysed using ACK lysis buffer and then the cell suspensions were filtered. For tumour experiments, organs were collected on the day of euthanasia, single cell suspensions were generated and frozen in cryo medium. Samples from all mice were thawed and stained on the same day to minimize experimental variability. Cells were stained for different antigens and 30 μl Accucheck counting beads (1,066 microspheres per μl; Invitrogen, PCB100) were added to each sample and the results were analysed by FACS using a BD LSRFortessa instrument.

    Statistics and reproducibility

    Statistical analyses were done using GraphPad Prism 9 and 10 software. In all figure legends, n refers to the number of experimental replicates. For multiple comparisons, two-way ANOVA tests were used with significance levels indicated. Data are presented as mean ± standard deviation. Survival curves were analysed using the log-rank Mantel–Cox test. rhAmpSeq was analysed using a χ2 test. The FDR was calculated using the Benjamini–Hochberg method.

    Some data points of the in vivo experiments were excluded after visual inspection of samples if the FACS time gate showed irregularities. One mouse that did not engraft HSPCs was excluded from Fig. 5 and Extended Data Fig. 10. Cell numbers in the sgNTC group treated with CIM053–SG3376 were so low that analysis of some assays became unreliable (NGS, genetic chimerism analysis). We therefore excluded this group from NGS.

    The number of biological replicates is specified for each experiment in the relevant figure legend. Several key experiments were performed by different people at times in different laboratories, and reagents were shared. For instance, identification of variants, characterization of recombinant variants and FACS validation were performed by different people. Some experiments were performed in the academic lab and validated in Cimeio labs and vice versa. To avoid unconscious bias when assigning mice to saline or the CIM053–SG3376 groups, we always assigned the mice with the largest tumour mass to the ADC group.

    The investigator who determined genetic chimerism (NGS and analysis) was blinded and provided the results to the investigator in charge of supervising in vivo experiments. The people who performed CHANGE-Seq_BE, rhAMPSeq and analysed the data were blinded and provided the results to the investigator in charge of supervising the in vivo experiments.

    Availability of materials

    Non-proprietary materials are freely available on reasonable request. Restrictions apply to proprietary, commercial material.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

    [ad_2]

    Source link

  • De Luca, V., Salim, V., Atsumi, S. M. & Yu, F. Science 336, 1658–1661 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Jacobowitz, J. R. & Weng, J.-K. Annu. Rev. Plant Biol. 71, 631–658 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Liu, Y. et al. Nature https://doi.org/10.1038/s41586-024-07345-9 (2024).

    Article 

    Google Scholar
     

  • Pifferi, C., Fuentes, R. & Fernández-Tejada, A. Nature Rev. Chem. 5, 197–216 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Kensil, C. R., Patel, U., Lennick, M. & Marciani, D. J. Immunol. 146, 431–437 (1991).

    Article 
    PubMed 

    Google Scholar
     

  • San Martín, R. & Briones, R. Econ. Bot. 53, 302–311 (1999).

    Article 

    Google Scholar
     

  • Perrot, T. et al. Curr. Opin. Biotechnol. 87, 103098 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Reed, J. et al. Science 379, 1252–1264 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Martin, L. B. B. et al. Nature Chem. Biol. 20, 493–502 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Srinivasan, P. & Smolke, C. D. Nature 585, 614–619 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Galanie, S., Thodey, K., Trenchard, I. J., Interrante, M. F. & Smolke, C. D. Science 349, 1095–1100 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Luo, X. et al. Nature 567, 123–126 (2019).

    Article 
    PubMed 

    Google Scholar
     

[ad_2]

Source link

  • Unveiling Secrets of Early Life With Synthetic Models

    Unveiling Secrets of Early Life With Synthetic Models

    [ad_1]

    By

    Membrane Cell Biology Concept

    Researchers from the Okinawa Institute of Science and Technology created synthetic droplets to study chemotaxis, mimicking cellular movement by responding to chemical gradients which could explain early life movement and inspire future technologies. Their findings, using droplets that migrate toward each other due to changes in pH and surface tension, contribute to understanding fundamental biological processes and the potential development of new biotechnological applications.

    A synthetic droplet could provide researchers with insights into how the most basic forms of life on Earth might interact with their environment.

    Our bodies consist of trillions of diverse cells, each performing a specific role to sustain our lives.

    How do cells move around inside these extremely complicated systems? How do they know where to go? And how did they get so complicated to begin with? Simple yet profound questions like these are at the heart of curiosity-driven basic research, which focuses on the fundamental principles of natural phenomena. An important example is the process by which cells or organisms move in response to chemical signals in their environment, also known as chemotaxis.

    Three Figures Showing the Principles of Droplet Movement Caused by the Marangoni Flow

    The synthetic droplets contain the enzyme urease which catalyzes the breakdown of urea into ammonia, which has a high pH-value. Droplets migrate due to the pH gradient, from low to high, because of the Marangoni effect. Credit: OIST

    A constellation of researchers from three different research units at the Okinawa Institute of Science and Technology (OIST) came together to answer basic questions about chemotaxis by creating synthetic droplets to mimic the phenomena in the lab, allowing them to precisely isolate, control, and study the phenomena. Their results, which helps answering questions about the principles of movement in simple biological systems, have now been published in the Journal of The American Chemical Society.

    “We have shown that it is possible to make protein droplets migrate through simple chemical interactions,” says Alessandro Bevilacqua, PhD student in the Protein Engineering and Evolution Unit and co-first author on the paper. Professor Paola Laurino, head of the unit and leading author, adds that they “have created a simple system that mimic a very complex phenomenon, and which can be modulated through enzymatic activity.”


    Numerical models showing what happens when the halos of two synthetic droplets interact. pH in the space between the droplets is higher (and surface tension lower), which causes the droplets to migrate towards each other while keeping their spherical shape, as pH is lower within the droplets, until they meet and merge. Larger droplets attract smaller droplets.
    Credit: OIST

    Tensions on the surface

    While the process of creating droplets might not sound like the most complicated task, mimicking biological processes as close to reality as possible while keeping accurate control over all the variables certainly is. The synthetic, membrane-less droplets contain a very high concentration of the bovine protein BSA to mimic the crowded conditions inside cells, as well as urease, an enzyme that catalyzes the breakdown of urea into ammonia.

    Ammonia is basic, meaning it has a high pH-value. As the enzyme gradually catalyzes the production of ammonia, it diffuses into the solution, creating a ‘halo’ of higher pH around the droplet, which in turn enables droplets to detect other droplets and migrate towards each other.

    The researchers found that the key to understanding the chemotaxis of the droplets is the pH-gradient, as it facilitates the Marangoni effect, which describes how molecules flow from areas of high surface tension to low. Surface tension is the measure of energy required to keep molecules at the surface together, like glue. When pH increases, this glue weakens, causing molecules to spread out and lowering surface tension, which in turn makes it easier for molecules to move. You can see this by adding soap, which has a high pH, to one end of a bathtub of still water: the water will flow towards the end with soap because of the Marangoni effect.


    How do the droplets move, and what determines their direction? Each green droplet is densely packed with proteins as well as an enzyme that increases the pH-value within and around the droplet, which may lead to the answer to these questions. Credit: OIST

    When two synthetic droplets are close enough, their halos interact, raising the pH in the environment between them, which makes them move together. Because the surface tension is still strong on the opposite ends of the droplets, they keep their shape until the surfaces touch, and the cohesive forces within the droplets overcome the surface tension, causing them to merge. As larger droplets both produce more ammonia and have a larger surface area (which decreases surface tension), they attract droplets smaller than themselves.

    Collaborating on ancient soup and future biotech

    Thanks to the development of these droplets, the researchers have made headway in answering basic questions about biological movement – and in doing so, they have gained insight into the directed movement of the earliest forms of life in the primordial soup billions of years ago, as well as a lead on creating new biologically inspired materials.

    Our knowledge of life as it looked billions of years ago is fuzzy at best. A prominent hypothesis is that life originated in the oceans, as organic molecules gradually assembled and became more sophisticated in a ‘primordial soup’ – and this could have been facilitated by chemotaxis through the Marangoni effect. “It would have been beneficial for droplets to have this mechanism of migration in the hypothetical origin of life scenario,” as Professor Laurino puts it. This migration could have triggered the formation of primitive metabolic pathways whereby enzymes catalyze a variety of substances that ultimately produce a chemical gradient that drives the droplets together, leading to larger and more sophisticated communities.

    The research also points ahead in time, providing leads on new technology. “One example is the creation of responsive materials inspired by biology,” suggests Alessandro Bevilacqua. “We have shown how simple droplets can migrate thanks to a chemical gradient. A future application of this could be technologies that sense or react to chemical gradients, for example in micro-robotics or drug delivery.”

    The work to produce and analyze the synthetic droplets is the result of a combination of deeply integrated interdisciplinarity and the human factors undergirding scientific work. The project began during the coronavirus pandemic, when a member of the Protein Engineering and Evolution Unit was in quarantine with a member of the Complex Fluids and Flows Unit. The two began talking, and though the two units are from two disparate fields – biochemistry and mechanics, respectively – the project evolved in tandem. Eventually, members from the Micro/Bio/Nanofluidics Unit joined the project with sophisticated measurements of the droplets’ surface tension.

    The unique non-disciplinary research environment at OIST catalyzed the collaboration. As Professor Laurino puts it, “this project could never have existed if we were separated by departments. It hasn’t been an easy collaboration, because we communicate our field in very different ways – but being physically close made it significantly easier.”

    Alessandro Bevilacqua joins in: “The coffee factor has been very important. Being able to sit down with other unit members made the process much faster and more productive.” Their cooperation doesn’t stop here – rather, this paper is the beginning of a fruitful partnership between the three units. “We see a lot of synergy in our work, and we work effectively and efficiently together. I don’t see a reason why we should stop,” as Professor Laurino states it. It’s thanks to the combined efforts of the three units that we now know more about the minute movements of life at the smallest, earliest, and possibly future scale.

    Reference: “Chemotactic Interactions Drive Migration of Membraneless Active Droplets” by Mirco Dindo, Alessandro Bevilacqua, Giovanni Soligo, Vincenzo Calabrese, Alessandro Monti, Amy Q. Shen, Marco Edoardo Rosti and Paola Laurino, 15 April 2024, Journal of the American Chemical Society.
    DOI: 10.1021/jacs.4c02823

    The study was funded by the Japan Society for the Promotion of Science, the Okinawa Institute of Science and Technology Graduate University, the Takeda Foundation, and the High-Performance Computing Infrastructure.



    [ad_2]

    Source link

  • The beauty of what science can do when urgently needed

    The beauty of what science can do when urgently needed

    [ad_1]

    A woman sits in an office room with a blue wall. A chart is shown on the glowing screen behind her.

    Cultivarium chief scientific officer Nili Ostrov works to make model organisms more useful and accessible for scientific researchCredit: Donis Perkins

    Nili Ostrov has always been passionate about finding ways to use biology for practical purposes. So perhaps it wasn’t surprising that, when the COVID-19 pandemic hit during her postdoctoral studies, she went in the opposite direction from most people, moving to New York City to work as the director of molecular diagnostics in the Pandemic Response Lab, providing COVID-19 tests and surveilling viral variants. She was inspired by seeing what scientists could accomplish and how much they could help when under pressure.

    Now the chief scientific officer at Cultivarium in Watertown, Massachusetts, Ostrov is bringing that sense of urgency to fundamental problems in synthetic biology. Cultivarium is a non-profit focused research organization, a structure that comes with a finite amount of time and funding to pursue ‘moonshot’ scientific goals, which would usually be difficult for academic laboratories or start-up companies to achieve. Cultivarium has five years of funding, which started in 2022, to develop tools to make it possible for scientists to genetically engineer unconventional model organisms — a group that includes most microbes.

    Typically, scientists are limited to working with yeast, the bacterium Escherichia coli and other common lab organisms, because the necessary conditions to grow and manipulate them are well understood. Ostrov wants to make it easier to engineer other microbes, such as soil bacteria or microorganisms that live in extreme conditions, for scientific purposes. This could open up new possibilities for biomanufacturing drugs or transportation fuels and solving environmental problems.

    What is synthetic biology and what drew you to it?

    Synthetic biology melds biology and engineering — it is the level at which you say, “I know how this part works. What can I do with it?” Synthetic biologists ask questions such as, what is this part useful for? How can it benefit people or the environment in some way?

    During my PhD programme at Columbia University in New York City, my team worked with the yeast that is used for brewing beer — but we asked, can you use these yeast cells as sensors? Because yeast cells can sense their environment, we could engineer them to detect a pathogen in a water sample. In my postdoctoral work at Harvard University in Cambridge, Massachusetts, we investigated a marine bacterium, Vibrio natriegens. A lot of time during research is spent waiting for cells to grow. V. natriegens doubles in number about every ten minutes — the fastest growth rate of any organism.

    Could we use it to speed up research? But using V. natriegens and other uncommon research organisms is hard work. You have to develop the right genetic-engineering tools.

    How did the COVID-19 pandemic alter your career trajectory?

    It pushed me to do something that I otherwise would not have done. During my postdoctoral programme, I met Jef Boeke, a synthetic biologist at New York University. In 2020, he asked me whether I wanted to help with the city’s Pandemic Response Lab, because of my expertise in DNA technology. I’m probably one of the only people with a newborn baby who moved into Manhattan when COVID-19 hit.

    That was an amazing experience: I took my science and skills and used them for something essential and urgent. In a couple of months, we set up a lab that supported the city’s health system. We monitored for new variants of the virus using genomic sequencing and ran diagnostic tests.

    Seeing what science can do when needed — it was beautiful. It showed me how effective science can be, and how fast science can move with the right set-up.

    How did that influence what you’re doing now with Cultivarium?

    COVID-19 showed me how urgently needed science can be done. It’s about bringing together the right people from different disciplines. Cultivarium is addressing fundamental problems in science, which is usually done in academic settings, with the fast pace and the dynamic of a start-up company.

    We need to make progress on finding ways to use unconventional microbes to advance science. A lot of bioproduction of industrial and therapeutic molecules is done in a few model organisms, such as E. coli and yeast. Imagine what you could achieve if you had 100 different organisms. If you’re looking to produce a protein that needs to be made in high temperatures or at an extreme pH, you can’t use E. coli, because it won’t grow.

    How is Cultivarium making unconventional microbes research-friendly?

    It took my postdoctoral lab team six years to get to the point where we could take V. natriegens, which we initially didn’t know how to grow well or engineer, and knock out every gene in its genome.

    At Cultivarium, we’re taking a more systematic approach to provide those culturing and engineering tools for researchers to use in their organism of choice. This kind of topic gets less funding, because it’s foundational science.

    So, we develop and distribute the tools to reproducibly culture microorganisms, introduce DNA into them and genetically engineer them. Only then can the organism be used in research and engineering.

    Developing these tools takes many years and a lot of money and skills. It takes a lot of people in the room: a biologist, a microbiologist, an automation person, a computational biologist, an engineer. As a non-profit company, we try to make our tools available to all scientists to help them to use their organism of choice for a given application.

    We have funding for five years from Schmidt Futures, a non-profit organization in New York City. We’re already releasing and distributing tools and information online. We’re building a portal where all data for non-standard model organisms will be available.

    Which appeals to you more — academic research or the private sector?

    I like the fast pace of start-up companies. I like the accessibility of expertise: you can bring the engineer into the room with the biologists. I like that you can build a team of people who all work for the same goal with the same motivation and urgency.

    Academia is wonderful, and I think it’s very important for people to get rigorous training. But I think we should also showcase other career options for early-career researchers. Before the pandemic, I didn’t know what it was like to work in a non-academic set-up. And once I got a taste of it, I found that it worked well for me.

    This interview has been edited for length and clarity.

    [ad_2]

    Source link

  • Bioengineering edible mycelium to enhance nutritional value, color, and flavor

    Bioengineering edible mycelium to enhance nutritional value, color, and flavor

    [ad_1]

    In a recent study published in Nature Communications, researchers developed a modular synthetic biology toolkit for Aspergillus oryzae, an edible fungus used in fermented foods, protein production, and meat alternatives.

    Study: Edible mycelium bioengineered for enhanced nutritional value and sensory appeal using a modular synthetic biology toolkit. Image Credit: Rattiya Thongdumhyu/Shutterstock.comStudy: Edible mycelium bioengineered for enhanced nutritional value and sensory appeal using a modular synthetic biology toolkit. Image Credit: Rattiya Thongdumhyu/Shutterstock.com

    Background

    Food production is estimated to account for a third of greenhouse gas emissions worldwide, contributing to biodiversity loss, environmental degradation, and new diseases.

    Transitioning from industrial animal agriculture to alternatives is necessary to mitigate the planetary impact and sustainably feed the global population. Microbial food production offers improved safety and efficiency, more precise production control, and reduced animal suffering.

    Filamentous fungi are a diverse group of microbes, including mushrooms and molds, and are highly advantageous for microbial food production.

    Besides, their naturally high secretion capacity makes them potent hosts for protein production. In addition, owing to its filamentous structure that mimics the animal muscle structure, fungal biomass (mycelia) can be formulated into alternatives to meat (mycoprotein).

    The study and findings

    In the present study, researchers developed a modular synthetic biology toolkit for A. oryzae, a safe and edible fungus with a history of palatable consumption.

    They created an alternative, easy-to-use clustered, regularly interspersed short palindromic repeats (CRISPR)–CRISPR-associated protein 9 (Cas9) approach, compatible with existing reagents.

    This approach involved transforming CRISPR-Cas9 ribonucleoprotein complexes directly instead of encoding single-guide RNAs (sgRNAs) and Cas9 from a plasmid.

    Moreover, the DNA template used to fix double-strand breaks contained an orotidine-5′-phosphate decarboxylase gene (pyrG) marker for positive and negative selection.

    The system was designed such that a successful loop out of pyrG could only occur upon integrating the fixing template at the site of interest, wherein identical 300 bp sequences will flank it.

    Ectopic integrations due to non-homologous end joining (NHEJ) in this system cannot loop out or survive on media with 5-fluoroorotic acid. A vital feature of this design was the recyclability of the pyrG marker upon insertion at the correct locus.

    Further, candidate-neutral loci in A. oryzae were investigated to integrate genes for overexpression. The researchers explored the intergenic regions in the A. oryzae RIB40 genome and ranked the expression of two genes surrounding them.

    A list of candidate loci predicted for high gene expression was generated, and ten regions were selected for further analysis.

    Next, the team integrated green fluorescent protein (GFP) cassettes under the control of a strong, constitutive promoter (pTEF1) and examined fluorescence on the conidia of looped-out strains.

    Of the ten loci, nine exhibited highly efficient integration, and GFP expression was detected from eight of these. All loci demonstrated higher expression than the positive control.

    Next, the researchers aimed to establish a synthetic expression system (SES) in A. oryzae. To this end, they evaluated the ability of a characterized synthetic transcription factor (sTF) to drive the expression of mCherry from a core promoter (Cp).

    They genetically integrated the sTF and induced a low basal expression under a Cp from A. niger. Separately, an mCherry cassette with 6x upstream activating sequences (UAS) was integrated at a different genomic location upstream of the Cp.

    The team observed mCherry expression in conidia and mycelia. Both the sTF and UAS were required for the activity. Next, the team aimed to bioengineer an edible mycelium, focusing on the bioactive amino acid ergothioneine.

    They speculated that its production could be increased by modulating the expression of endogenous ergothioneine biosynthetic genes in A. oryzae.

    Orthologs of Egt1 and Egt2, enzymes from Neurospora crassa implicated in ergothioneine biosynthesis, were identified in A. oryzae.

    The orthologs were then inserted at neutral loci; both genes were expressed under a bidirectional promoter or separately at different locations. Ergothioneine levels in the mycelium were low in RIB40, the background strain.

    However, its levels were 11- and 21-fold elevated in bidirectional and separate promoter strains compared to RIB40. Ergothioneine levels in the bidirectional promoter stain were similar to those in oyster mushrooms. By contrast, its levels were 1.5-fold higher in the separate promoter strain.

    There were no differences in protein content between engineered and wild-type strains. Nevertheless, a slight growth defect was observed with ergothioneine overproduction.

    Next, the researchers applied these tools to enhance the sensory properties of the edible biomass. They targeted heme biosynthesis, as heme gives meat its (red) color and flavor upon cooking.

    They identified potential heme biosynthetic genes in A. oryzae and targeted the expression of five predicted rate-limiting enzymes. Additionally, two copies of soy leghemoglobin were expressed as a potential heme sink, as high levels of free heme could be cytotoxic.

    The biomass of the engineered strain was four-fold higher than that of the non-engineered strain.

    Upon harvesting, the biomass was red compared to off-white in RIB40. This color difference persisted after cooking, enhancing the meat-like appearance of the fungal biomass.

    The engineered mycoprotein contained all essential amino acids. Protein content or growth yield was not lower in the engineered strain.

    Conclusions

    The researchers developed a synthetic toolkit to integrate and regulate genes and pathways. They leveraged this toolkit and engineered A. oryzae mycoprotein to (over)produce ergothioneine at levels far greater than in natural dietary sources, i.e., mushrooms.

    Additionally, the mycelia were engineered to overproduce heme for enhanced color and flavor. Notably, this work represents an early prototype; further evaluations of sensory attributes, food safety, consumer acceptance, and regulatory landscape are required.

    [ad_2]

    Source link

  • Proteome-scale discovery of protein degradation and stabilization effectors

    [ad_1]

  • Békés, M., Langley, D. R. & Crews, C. M. PROTAC targeted protein degraders: the past is prologue. Nat. Rev. Drug Discov. 21, 181–200 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Deshaies, R. J. Multispecific drugs herald a new era of biopharmaceutical innovation. Nature 580, 329–338 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Takahashi, D. et al. AUTACs: cargo-specific degraders using selective autophagy. Mol. Cell 76, 797–810.e10 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Banik, S. M. et al. Lysosome-targeting chimaeras for degradation of extracellular proteins. Nature 584, 291–297 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cotton, A. D., Nguyen, D. P., Gramespacher, J. A., Seiple, I. B. & Wells, J. A. Development of antibody-based PROTACs for the degradation of the cell-surface immune checkpoint protein PD-L1. J. Am. Chem. Soc. 143, 593–598 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Henning, N. J. et al. Deubiquitinase-targeting chimeras for targeted protein stabilization. Nat. Chem. Biol. 18, 412–421 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lim, S. et al. bioPROTACs as versatile modulators of intracellular therapeutic targets including proliferating cell nuclear antigen (PCNA). Proc. Natl Acad. Sci. USA 117, 5791–5800 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Caussinus, E., Kanca, O. & Affolter, M. Fluorescent fusion protein knockout mediated by anti-GFP nanobody. Nat. Struct. Mol. Biol. 19, 117–121 (2012).

    Article 
    CAS 

    Google Scholar
     

  • Liang, F.-S., Ho, W. Q. & Crabtree, G. R. Engineering the ABA plant stress pathway for regulation of induced proximity. Sci. Signal. 4, rs2 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Winter, G. E. et al. Phthalimide conjugation as a strategy for in vivo target protein degradation. Science 348, 1376–1381 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shin, Y. J. et al. Nanobody-targeted E3-ubiquitin ligase complex degrades nuclear proteins. Sci. Rep. 5, 14269 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Alerasool, N., Leng, H., Lin, Z.-Y., Gingras, A.-C. & Taipale, M. Identification and functional characterization of transcriptional activators in human cells. Mol. Cell 82, 677–695.e7 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kawai, Y. et al. LAPTM5 promotes lysosomal degradation of intracellular CD3ζ but not of cell surface CD3ζ. Immunol. Cell Biol. 92, 527–534 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • MacLennan, M. et al. Mobilization of LINE-1 retrotransposons is restricted by Tex19.1 in mouse embryonic stem cells. eLife 6, e26152 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mund, T. & Pelham, H. R. B. Regulation of PTEN/Akt and MAP kinase signaling pathways by the ubiquitin ligase activators Ndfip1 and Ndfip2. Proc. Natl Acad. Sci. USA 107, 11429–11434 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yip, M. C. J., Bodnar, N. O. & Rapoport, T. A. Ddi1 is a ubiquitin-dependent protease. Proc. Natl Acad. Sci. USA 117, 7776–7781 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, W. et al. TRAF family member-associated NF-κB activator (TANK) inhibits genotoxic nuclear factor κB activation by facilitating deubiquitinase USP10-dependent deubiquitination of TRAF6 ligase. J. Biol. Chem. 290, 13372–13385 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bennett, R. D. & Strehler, E. E. Calmodulin-like protein enhances myosin-10 translation. Biochem. Biophys. Res. Commun. 369, 654–659 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Celen, A. B. & Sahin, U. Sumoylation on its 25th anniversary: mechanisms, pathology, and emerging concepts. FEBS J. 287, 3110–3140 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Segal, D. et al. A central chaperone-like role for 14-3-3 proteins in human cells. Mol. Cell 83, 974–993.e15 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    Article 
    PubMed 

    Google Scholar
     

  • Yen, H.-C. S., Xu, Q., Chou, D. M., Zhao, Z. & Elledge, S. J. Global protein stability profiling in mammalian cells. Science 322, 918–923 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Swatek, K. N. & Komander, D. Ubiquitin modifications. Cell Res. 26, 399–422 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cowan, A. D. & Ciulli, A. Driving E3 ligase substrate specificity for targeted protein degradation: lessons from nature and the laboratory. Annu. Rev. Biochem. 91, 295–319 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • King, E. A. et al. Chemoproteomics-enabled discovery of a covalent molecular glue degrader targeting NF-κB. Cell Chem. Biol. 30, 394–402.e9 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hibbert, R. G., Huang, A., Boelens, R. & Sixma, T. K. E3 ligase Rad18 promotes monoubiquitination rather than ubiquitin chain formation by E2 enzyme Rad6. Proc. Natl Acad. Sci. USA 108, 5590–5595 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kumar, P. et al. Role of a non-canonical surface of Rad6 in ubiquitin conjugating activity. Nucleic Acids Res. 43, 9039–9050 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • David, Y., Ziv, T., Admon, A. & Navon, A. The E2 ubiquitin-conjugating enzymes direct polyubiquitination to preferred lysines. J. Biol. Chem. 285, 8595–8604 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Morreale, F. E. et al. Allosteric targeting of the Fanconi anemia ubiquitin-conjugating enzyme Ube2T by fragment screening. J. Med. Chem. 60, 4093–4098 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • St-Cyr, D. et al. Identification and optimization of molecular glue compounds that inhibit a noncovalent E2 enzyme–ubiquitin complex. Sci. Adv. 7, eabi5797 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, C. et al. Peptidic degron in EID1 is recognized by an SCF E3 ligase complex containing the orphan F-box protein FBXO21. Proc. Natl Acad. Sci. USA 112, 15372–15377 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lanier, L. L., Yu, G. & Phillips, J. H. Analysis of Fc gamma RIII (CD16) membrane expression and association with CD3 zeta and Fc epsilon RI-gamma by site-directed mutation. J. Immunol. 146, 1571–1576 (1991).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Maxfield, K. E., Macion, J., Vankayalapati, H. & Whitehurst, A. W. SIK2 restricts autophagic flux to support triple-negative breast cancer survival. Mol. Cell. Biol. 36, 3048–3057 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, F.-C. et al. Interaction between salt-inducible kinase 2 (SIK2) and p97/valosin-containing protein (VCP) regulates endoplasmic reticulum (ER)-associated protein degradation in mammalian cells. J. Biol. Chem. 288, 33861–33872 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kinoshita, T. Biosynthesis and biology of mammalian GPI-anchored proteins. Open Biol. 10, 190290 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lemus, L., Hegde, R. S. & Goder, V. New frontiers in quality control: the case of GPI-anchored proteins. Nat. Rev. Mol. Cell Biol. 24, 599–600 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Luka, Z., Pakhomova, S., Luka, Y., Newcomer, M. E. & Wagner, C. Destabilization of human glycine N-methyltransferase by H176N mutation. Protein Sci. 16, 1957–1964 (2007).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Clague, M. J., Urbé, S. & Komander, D. Breaking the chains: deubiquitylating enzyme specificity begets function. Nat. Rev. Mol. Cell Biol. 20, 338–352 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhang, Y.-H., Zhou, C.-J., Zhou, Z.-R., Song, A.-X. & Hu, H.-Y. Domain analysis reveals that a deubiquitinating enzyme USP13 performs non-activating catalysis for Lys63-linked polyubiquitin. PLoS ONE 6, e29362 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Walden, M., Masandi, S. K., Pawłowski, K. & Zeqiraj, E. Pseudo-DUBs as allosteric activators and molecular scaffolds of protein complexes. Biochem. Soc. Trans. 46, 453–466 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Juang, Y.-C. et al. OTUB1 co-opts Lys48-linked ubiquitin recognition to suppress E2 enzyme function. Mol. Cell 45, 384–397 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Garg, A. et al. KLHL40 deficiency destabilizes thin filament proteins and promotes nemaline myopathy. J. Clin. Invest. 124, 3529–3539 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Reitsma, J. M. et al. Composition and regulation of the cellular repertoire of SCF ubiquitin ligases. Cell 171, 1326–1339.e14 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nguyen, T. V. et al. p97/VCP promotes degradation of CRBN substrate glutamine synthetase and neosubstrates. Proc. Natl Acad. Sci. USA 114, 3565–3571 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ganji, R., Mukkavalli, S., Somanji, F. & Raman, M. The VCP–UBXN1 complex mediates triage of ubiquitylated cytosolic proteins bound to the BAG6 complex. Mol. Cell. Biol. 38, e00154–18 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dikic, I. & Elazar, Z. Mechanism and medical implications of mammalian autophagy. Nat. Rev. Mol. Cell Biol. 19, 349–364 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bensimon, A. et al. Targeted degradation of SLC transporters reveals amenability of multi-pass transmembrane proteins to ligand-induced proteolysis. Cell Chem. Biol. 27, 728–739.e9 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Donovan, K. A. et al. Mapping the degradable kinome provides a resource for expedited degrader development. Cell 183, 1714–1731.e10 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zeng, M. et al. Exploring targeted degradation strategy for oncogenic KRASG12C. Cell Chem. Biol. 27, 19–31.e6 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bery, N., Miller, A. & Rabbitts, T. A potent KRAS macromolecule degrader specifically targeting tumours with mutant KRAS. Nat. Commun. 11, 3233 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Götzke, H. et al. The ALFA-tag is a highly versatile tool for nanobody-based bioscience applications. Nat. Commun. 10, 4403 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gupta, A. et al. Facile target validation in an animal model with intracellularly expressed monobodies. Nat. Chem. Biol. 14, 895–900 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wojcik, J. et al. Allosteric Inhibition of Bcr–Abl kinase by high affinity monobody inhibitors directed to the Src homology 2 (SH2)–kinase interface. J. Biol. Chem. 291, 8836–8847 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Dowling, J. J., Weihl, C. C. & Spencer, M. J. Molecular and cellular basis of genetically inherited skeletal muscle disorders. Nat. Rev. Mol. Cell Biol. 22, 713–732 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ahn, G. et al. LYTACs that engage the asialoglycoprotein receptor for targeted protein degradation. Nat. Chem. Biol. 17, 937–946 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dey, S. K. & Jaffrey, S. R. RIBOTACs: small molecules target RNA for degradation. Cell Chem. Biol. 26, 1047–1049 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Liu, X. & Ciulli, A. Proximity-based modalities for biology and medicine. ACS Cent. Sci. 9, 1269–1284 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Modell, A. E., Lai, S., Nguyen, T. M. & Choudhary, A. Bifunctional modalities for repurposing protein function. Cell Chem. Biol. 28, 1081–1089 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Domostegui, A., Nieto-Barrado, L., Perez-Lopez, C. & Mayor-Ruiz, C. Chasing molecular glue degraders: screening approaches. Chem. Soc. Rev. 51, 5498–5517 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Spradlin, J. N., Zhang, E. & Nomura, D. K. Reimagining druggability using chemoproteomic platforms. Acc. Chem. Res. 54, 1801–1813 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Röth, S. et al. Identification of KLHDC2 as an efficient proximity-induced degrader of K-RAS, STK33, β-catenin, and FoxP3. Cell Chem. Biol. https://doi.org/10.1016/j.chembiol.2023.07.006 (2023).

  • Weng, G. et al. PROTAC-DB 2.0: an updated database of PROTACs. Nucleic Acids Res. 51, D1367–D1372 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Tanaka, T., Williams, R. L. & Rabbitts, T. H. Tumour prevention by a single antibody domain targeting the interaction of signal transduction proteins with RAS. EMBO J. 26, 3250–3259 (2007).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tanaka, T., Sewell, H., Waters, S., Phillips, S. E. V. & Rabbitts, T. H. Single domain intracellular antibodies from diverse libraries: emphasizing dual functions of LMO2 protein interactions using a single VH domain. J. Biol. Chem. 286, 3707–3716 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • O’Shea, J. M. et al. Generation of photocaged nanobodies for intracellular applications in an animal using genetic code expansion and computationally guided protein engineering. ChemBioChem 23, e202200321 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • [ad_2]

    Source link

  • INTEGRA supports the next generation of synthetic biologists

    INTEGRA supports the next generation of synthetic biologists

    [ad_1]

    INTEGRA Biosciences awarded 3 PIPETBOY acu 2 serological pipette controllers to the 2023 iGEM Competition team at Technische Universität Braunschweig (TU_BS) to aid their pioneering research into lithium therapy toxicity testing. The pipette controllers enhanced the team’s pipetting precision and streamlined the multiple liquid handling tasks involved in this exciting project, helping them to achieve their project goals.

    The iGEM Competition is an annual synthetic biology event open to groups of high school, undergraduate and graduate students from all over the world, and is dedicated to advancing discoveries that will help to solve some of the problems currently facing the environment and human health. Lithium-based therapies are highly effective mood stabilizers that are commonly used to treat mental health conditions, such as bipolar disorder. However, lithium can be toxic at higher concentrations, making regular monitoring of blood lithium level extremely important. Unfortunately, these tests must be performed by medical professionals in a healthcare environment, requiring frequent and inconvenient hospital visits and blood tests.

    The TU_BS team’s Li+onSwitch project aimed to explore the possibility of creating a novel self-testing method that can be performed at home. INTEGRA supplied the lab with 3 PIPETBOY acu 2 serological pipette controllers in June 2023, as Daniel, a member of the 2023 iGEM team, explained: “A large part of our work included designing our own plasmid constructs and testing potential reporter systems, and we worked extensively with liquid cultures. We were very excited to receive the PIPETBOY controllers, and they quickly became essential daily tools in our lab, significantly speeding up our repetitive pipetting protocols. This huge cumulative time saving led to greater productivity, and freed us up to focus on other aspects of our research. The PIPETBOYs are also extremely precise instruments, so helped to improve the reproducibility and consistency of our many manual liquid handling tasks.”

    The iGEM Grand Jamboree took place from November 2-5 in Paris, and the TU_BS team was awarded Best Diagnostics Project, Overgrad, as well as a gold medal. They were also ranked in the top 10 postgraduate teams, and were nominated for Best New Composite Part, Overgrad. These achievements are a testament to the group’s diligence over the preceding months.

    Learn more about the Li+onSwitch project.

    [ad_2]

    Source link

  • Synthetic reversed sequences reveal default genomic states

    [ad_1]

    Design of synthetic loci

    The synthetic HPRT1 locus has been described previously18. The synthetic HPRT1R locus was designed by reversing (but not reverse-complementing) the sequence of the human HPRT1 locus corresponding to hg38 chromosome X:134429208-134529874. HPRT1RnoCpG was designed starting with the HPRT1R sequence, using a Python script to scan the sequence for occurrences of CG and randomly delete either the C or the G. As this sequence transformation can result in the formation of new CG instances, the script was reiterated until no CG sequences remained. We used software developed in house to split the synthetic loci into smaller DNA segments for commercial DNA synthesis. HPRT1R was split into 28 segments, 27 of ~4 kb and one of ~2 kb, and HPRT1RnoCpG was split into 36 segments, 35 of ~3 kb and one of 1,300 bp. Each synthetic segment had overlaps of ~300 bp, in both termini, with the neighbouring segments. MenDEL69 was used to design primers for junction PCR screening of yeast clones harbouring the correct assembly. Synthetic DNA segments were ordered from Qinglan Biotech, and junction PCR primers were ordered from IDT.

    Synthetic loci sequence features

    Dinucleotides were counted across each synthetic locus. Expected CpG number was calculated as (no. of C × no. of G)/sequence length and CpG ratio was calculated as observed CpG/expected CpG. Yeast TFBSs were predicted by scanning the DNA sequences with the YEASTRACT+ database65. Mouse TFBSs were predicted using FIMO66 in the MEME suite using the JASPAR vertebrate motif database67.

    Yeast assembly and BAC recovery

    All yeast work was performed starting with the parental strain BY4741 using standard yeast media. HPRT1R was assembled from 28 synthetic DNA segments, first as two half-assemblies that were then combined using eSwAP-In18. HPRT1RnoCpG was assembled from 36 synthetic segments in one step. For both HPRT1R and HPRT1RnoCpG assemblies, ~50 ng each of linearized and gel-purified yeast assembly vector (YAV) (pLM1110 (ref. 17), Addgene #168460) backbone DNA and purified assembly fragments were transformed into yeast using the high-efficiency lithium acetate method70. Transformants were plated on synthetic complete media lacking uracil or leucine (SC–Ura, SC–Leu) depending on the selectable marker (URA3 for HPRT1R segments 1–15 half-assembly, and LEU2 for HPRT1R segments 15–28 half-assembly and for HPRT1RnoCpG full assembly). Successful assemblies were screened by junction quantitative PCR (qPCR) on crude yeast genomic DNA (gDNA) prepared from 48 colonies from each assembly transformation. Crude yeast gDNA was prepared by performing three cycles of boiling in 20 mM NaOH at 98 °C for 3 min, followed by cooling at 4 °C for 1 min. Junction qPCRs were set up using an Echo 650 liquid handler (Labcyte) by dispensing 20 nl crude gDNA and 10 nl premixed junction primer pairs (50 µM) into a LightCycler 1536 Multiwell Plate (Roche 05358639001) containing 1 µl 1× LightCycler 1536 DNA Green mix (Roche 05573092001). qPCR reactions were performed using a LightCycler 1536 Instrument (Roche 05334276001) and successful assemblies were identified based on positive results for all junctions, defined as a having a Ct value lower than 30 (with exceptions for primer pairs determined to be consistently poor). Candidate assemblies were verified by next-generation sequencing. Libraries were prepared from 100 ng of DNA using the NEBNext Ultra II FS DNA Library Prep Kit for Illumina (NEB E7805L) with NEBNext Multiplex Oligos for Illumina (E7600S), according to the manufacturer’s protocol for FS DNA Library Prep Kit with Inputs ≤100 ng. Sequencing reactions were run on a NextSeq 500 system (Illumina SY-415-1001). Sequence-verified assemblons were recovered from yeast using the Zymoprep Yeast Miniprep I kit (Zymo Research D2001) and electroporated into TransforMax EPI300 Electrocompetent E. coli (Lucigen EC300150), recovered in LB + 5 mM MgCl2 at 30 °C for 1 h and then selected on LB + kanamycin agar plates. Bacteria colonies were screened by colony PCR for one or two assembly junctions to confirm that they contained the assemblon, then assemblon DNA was isolated from overnight cultures using ZR BAC DNA Miniprep kit (Zymo Research D4048) and verified by next-generation sequencing. eSwAP-In18 was used to combine the two HPRT1R half-assemblies. The sequence-verified assembly of segments 15–28 was purified from E. coli and digested with I-SceI and NotI to release the HPRT1R portion along with the LEU2 marker. This digested segment was transformed into yeast harbouring the assemblon with segments 1–15, along with a Cas9–guide RNA (gRNA) expression vector, pYTK-Cas9 (ref. 71), with a URA3-targeting gRNA. The Cas9-induced break in the URA3 marker was repaired with the HPRT1R-15–28-LEU2 segment using homology provided by the common segment 15 and common sequence downstream of the selection markers. eSwAP-In transformants were selected on SC–Leu and colonies were picked to screen by junction PCR using a subset of primers spanning the entire locus. Candidate clones were verified by next-generation sequencing and recovered into E. coli as previously described.

    The HPRT1 locus was transplanted from its original assembly vector18 by restriction digestion of purified assemblon DNA with NotI and NruI to release the HPRT1 locus, followed by co-transformation of the digested locus (~1.5 μg) along with the new, linearized, pLM1110 assembly vector (~100 ng) and linker DNAs that included loxP and loxM sites flanked by 200 bp of homology to the assembly vector and HPRT1 locus (~50 ng each). Forty-eight colonies were picked following transformation and selection and crude yeast gDNA was screened by PCR using primers spanning the vector-HPRT1 junctions. Candidate clones were verified by next-generation sequencing and recovered into E. coli as described above.

    Assemblons were recovered from TransforMax EPI300 E. coli for delivery to mouse ES cells. Cultures of 250 ml cultures were grown at 30 °C with shaking overnight in LB + kanamycin + 0.04% arabinose to induce copy number amplification of the assemblon BAC. DNA was purified using the NucleoBond XtraBAC kit (Takara Bio 740436.25) and stored at 4 °C for less than one week before delivery to mouse ES cells.

    Integrating loci into the yeast genome

    A landing pad containing a URA3 cassette flanked by loxM and loxP sites was installed at YKL162C-A21 in yeast strains harbouring either HPRT1 or HPRT1R assemblons. The landing pad was co-transformed, along with linker DNAs with terminal homologies to the yeast genomic locus and to the landing pad cassette (~200 ng each), into yeast as described above. Colonies were selected on SC–Ura plates, and 4 colonies were picked from each transformation and screened by PCR using primers spanning the genome–landing pad junctions. Landing pad integration was verified by Sanger sequencing of PCR products spanning the genome–landing pad junctions. The synthetic HPRT1 and HPRT1R loci were integrated by Cre-mediated recombination. A HIS3 plasmid expressing Cre-recombinase from a galactose-inducible promoter (pSH62 (ref. 72), Euroscarf P30120) was introduced by yeast transformation, single colonies were picked and grown to saturation in SC–His–Leu with raffinose, subcultured 1:100 in SC–His media with galactose, and plated on SC + 5-Fluoroorotic acid (5FOA) plates after 2 days of growth. 5FOA-resistant colonies were picked, screened by PCR using primers spanning the yeast genome–HPRT1 or HPRT1R junctions, and verified by next-generation whole-genome sequencing as described above. Engineered yeast strains are available upon request.

    Sphis5 insertion and transcription factor knockouts

    The His5 gene, including 5′ and 3′ untranslated regions, was cloned by PCR using Q5 high-fidelity DNA polymerase (New England Biolabs M0494L) from S. pombe genomic DNA. PCR primers were designed to add 40 bp of homology on each side for the desired target location in the synthetic HPRT1 or HRPT1R sequence, or in the yeast genome. Sphis5 PCR products were purified using the DNA Clean and Concentrator 5 kit (Zymo Research D4004) and transformed into HPRT1 or HPRT1R episome-harbouring yeast strains, as described above. Transformations were selected on SC–His–Leu plates and correct insertions were determined by PCR using a forward primer annealing in the in the predicted promoter regions within the HPRT1 or HPRT1R locus or yeast genome, outside of the homology arm, and a reverse primer annealing inside of the Sphis5 sequence.

    Select transcription factor genes were knocked out of His+ yeast strains by cloning the URA3 expression cassette from pAV116 (Addgene #63183) using primers designed to add 40-bp homology arms targeting the genomic region upstream and downstream of the transcription factor coding sequence. URA3 PCR products were purified using the DNA Clean and Concentrator 5 kit (Zymo Research D4004) and transformed into His+ yeast strains as above. Transformations were selected on SC–Leu–Ura and correct knockouts were verified by PCR using two sets of primers spanning the URA3–genome junctions.

    Yeast spot assays

    Fitness of yeast strains following Sphis5 insertions and transcription factor knockouts was assessed by spot assay. Yeast strains were grown to saturation in selective media and diluted to OD600 of 1 in sterile water. Five tenfold serial dilutions were made of each strain, and 5 μl of each dilution was spotted on agar plates using a multichannel pipette. Plates were incubated at 37 °C for 2 days before imaging. 3-AT, a competitive inhibitor of the Sphis5 gene product, was used to better identify small magnitude changes in expression.

    Mouse ES cell culture

    C57BL6/6J × CAST/EiJ (BL6xCAST) ΔPiga mouse ES cells, which enable PIGA-based Big-IN genome rewriting, have been described previously17. Mouse ES cells were cultured in 80/20 medium, which consists of 80% 2i medium (1:1 mixture of Advanced DMEM/F12 (ThermoFisher 12634010) and Neurobasal-A (ThermoFisher 10888022) supplemented with 1% N2 Supplement (ThermoFisher 17502048), 2% B27 Supplement (ThermoFisher 17504044), 1% GlutaMAX (ThermoFisher 35050061), 1% penicillin-streptomycin (ThermoFisher 15140122), 0.1 mM 2-mercaptoethanol (Sigma M3148), 1,250 U ml−1 LIF (ESGRO ESG1107l), 3 μM CHIR99021 (R&D Systems 4423), and 1 μM PD0325901 (Sigma PZ0162)), and 20% mouse ES cell medium (KnockOut DMEM (ThermoFisher 10829018) supplemented with 15% FBS (BenchMark 100106), 0.1 mM 2-mercaptoethanol, 1% GlutaMAX, 1% MEM non-essential amino acids (ThermoFisher 11140050), 1% nucleosides (EMD Millipore ES-008-D), 1% penicillin-streptomycin, and 1,250 U ml−1 LIF). Mouse ES cells were maintained on plates coated with 0.1% gelatin (EMD Millipore ES-006-B) at 37 °C in a humidified incubator with 5% CO2. C57BL6/6J × CAST/EiJ (BL6xCAST) mouse ES cells were originally provided by D. Spector, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY. The BL6xCAST cell line was authenticated in next-generation capture-sequencing experiments, confirming cells as C57BL6/6J × CAST/EiJ hybrids on the basis of species-specific single-nucleotide polymorphisms. Cell lines were verified to be mycoplasma free prior to the study. There was no indication of contamination of any kind.

    Integrating synthetic loci into mouse ES cells

    Integration of synthetic loci was performed using the Big-IN method17. First, a landing pad, LP-PIGA2, containing a polycistronic cassette, pEF1 α-PuroR-P2A-PIGA-P2A-mScarlet-EF1αpA, for selection and counterselection and flanked by loxM and loxP sites, was modified with homology arms for targeting the landing pad to the mouse Hprt1 locus. Specifically, ~130-bp homology arms (amplified from a mouse Hprt1 BAC) flanked by gRNA sites for the Hprt1-targeting gRNAs (see below) and protospacer adjacent motifs were cloned flanking the lox sites using BsaI Golden Gate Assembly. LP-PIGA2 was delivered to BL6xCAST ΔPiga mouse ES cells, along with Cas9–gRNA-expression plasmids (pSpCas9(BB)-2A-GFP, Addgene #48138) expressing gRNAs that target sites flanking the Hprt1 locus, by nucleofection using the Neon Transfection System (ThermoFisher) as described17. One million cells were used per transfection with 5 μg of the landing pad plasmid and 2.5 μg each of Cas9–gRNA-expression plasmids. Cells were selected with 1 μg ml−1 puromycin starting day 1 post-transfection, with 6-thioguanine (Sigma-Aldrich A4660) starting day 7 post-transfection to select for the loss of Hprt1, and with 1 µM ganciclovir (Sigma PHR1593) to select against the landing pad plasmid backbone that contained a HSV1-ΔTK expression cassette. Candidate clones were picked on day 10, screened by qPCR using primers spanning the mouse genome–landing pad junctions and with primers for validating the loss of the endogenous Hprt1 gene and the absence of landing pad backbone or pSpCas9 plasmid integration. Mouse ES cell clones were further verified by next-generation baited Capture-seq17 that the Hprt1 locus was deleted and the landing pad was present on target. Genomic integration of a landing pad at Sox2 has been described20, replacing only the BL6 allele in the hybrid BL6xCAST cell line, leaving the CAST Sox2 allele intact. Engineered mouse ES cell lines are available upon request.

    Delivery of the synthetic locus payloads was performed as described17 using the Amaxa 2b nucleofector (program A-23). In brief, 5 million cells were nucleofected with 5 μg pCAG-iCre (Addgene #89573) and 5 μg of assemblon DNA. Nucleofected mouse ES cells were treated with 10 µg ml−1 blasticidin for 2 days starting 1 day post-transfection to transiently select for the presence of the synthetic assemblons, and then with 2 nM proaerolysin for 2 days starting day 7 post-transfection to select for loss of PIGA in the landing pad cassette. Cells delivered with HPRT1 were also selected with HAT medium (ThermoFisher Scientific 21060017) starting day 7 post-transfection. Clones were picked on day 9 post-transfection, expanded, and screened first by qPCR aided by an Echo 550 liquid handler (Labcyte) as described20 using primers spanning the junctions between the mouse genome and HPRT1 or HPRT1R synthetic loci, and verified by Capture-seq17. For each locus integration we established two clonal cell lines from independent integration events.

    Whole-genome sequencing and Capture-seq

    Whole-genome sequencing and Capture-seq were performed as previously described17. Biotinylated bait DNA was generated by nick translation from purified BACs and plasmids of interest: the mouse Hprt1– and Sox2-containing BACs (RP23-412J16, RP23-274P9 respectively, BACPAC Resources Center), the synthetic HPRT1, HPRT1R, and HPRT1RnoCpG BACs, LP-PIGA2, pCAG-iCre and pSpCas9(BB)-2A-GFP.

    Sequencing and initial data processing were performed according to as previously described17 with modifications. Illumina libraries were sequenced in paired-end mode on an Illumina NextSeq 500 operated at the Institute for Systems Genetics. All data were initially processed using a uniform mapping pipeline. Sequencing adapters were trimmed with Trimmomatic v0.39 (ref. 73). Whole-genome and Capture-seq reads were aligned using BWA v0.7.17 (ref. 74) to a reference genome (SacCer_April2011/sacCer3 or GRCm38/mm10), including unscaffolded contigs and alternate references, as well as independently to HPRT1 and HPRT1R custom references for relevant samples. PCR duplicates were marked using samblaster v0.1.24 (ref. 75). Generation of per base coverage depth tracks and quantification was performed using BEDOPS v2.4.35 (ref. 76). Data were visualized using the University of California, Santa Cruz Genome Browser. On-target, single-copy integrations are validated using DELLY77 call copy number variations, and bamintersect17 to identify unexpectedly mapping read pairs. Using these quality control steps, DELLY will identify duplications or deletions, and bamintersect will identify duplications based on read pairs mapping either between the end and the start of the synthetic locus (if duplicated in tandem) or between the synthetic locus and an unexpected genomic location (if duplicated by off-target integration). The sequencing processing pipeline is available at https://github.com/mauranolab/mapping.

    ATAC-seq

    For yeast, two independent clones for each strain were inoculated into 5 ml of SC–Leu (for assemblon strains) or YPD (for integration strains) for overnight culture at 30 °C. Saturated overnight cultures were diluted to an OD600 of 0.1 and cultured for 6 h at 30 °C, until OD600 reached ~0.6. Around 5 × 106 cells were taken from each culture, pelleted at 3,000g for 5 min, washed twice with 500 μl spheroplasting buffer (1.4 M sorbitol, 40 mM HEPES-KOH pH 7.5, 0.5 mM MgCl2), resuspended in 100 μl spheroplasting buffer with 0.2 U μl−1 zymolyase (Zymo Research E1004), then incubated for 30 min at 30 °C on a rotator. Spheroplasts were washed twice with 500 μl spheroplasting buffer then resuspended in 50 μl 1× TD buffer with TDE (Illumina 20034197). Tagmentation was performed for 30 min at 37 °C on a rotator and DNA was purified using the DNA Clean and Concentrator 5 kit (Zymo Research D4004). PCR was performed as previously described78 using 11 total cycles. The libraries were sequenced with 36-bp paired-end reads on a NextSeq 500 for ~1 million reads per sample.

    For mouse ES cells, two independent cultures of each cell line were grown to medium confluency in 6-well plates. Cells were harvested by washing once with PBS, dissociated into single-cell suspension with TrypLE Express (ThermoFisher 12604013) and then neutralizing with equal volume mouse ES cell medium. Cells were counted and 50,000 were taken for tagmentation. Cells were pelleted at 500g for 5 min at 4 °C, washed with 50 μl cold PBS, resuspended in 50 μl cold ATAC lysis buffer (10 mM Tris-HCl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% IGEPAL CA-630), spun down at 500g for 10 mins at 4 °C, resuspended in 50 μl TDE mix, and incubated at 37 °C on rotator for 30 mins. DNA was purified using the DNA Clean and Concentrator 5 kit (Zymo Research D4004). PCR was performed as previously described78 using 10 total cycles. The libraries were sequenced with 36-bp paired-end reads for ~50 million reads per sample.

    Illumina libraries were sequenced on an Illumina NextSeq 500 operated at the Institute for Systems Genetics. Sequencing adapters were trimmed with Trimmomatic v0.39 (ref. 73). Reads were aligned using bowtie2 v2.2.9 (ref. 79) to custom references in which the synthetic locus sequences were present on separate chromosomes or inserted at their specific integration sites in the SacCer_April2011/sacCer3 or GRCm38/mm10 genomes (produced using the reform tool; https://gencore.bio.nyu.edu/reform/). Coverage tracks were produced in bigWig format using bamCoverage (deepTools v3.5.0)80 with bin size 10 and smooth length 100, normalized using RPGC to an effective genome size of 12,000,000 for sacCer3 and 2652783500 for mm10, and visualized using IGV v2.12.3 (ref. 81). Peaks were called using macs2 v2.1.0 (ref. 82) with the parameters: –nomodel -f BAMPE –keep-dup all -g 1.2e7 (sacCer3)/1.87e9 (mm10). Relative coverage analysis was performed as described below.

    RNA-seq

    For yeast, the remaining culture that was not used for ATAC-seq was centrifuged at 3,000g for 5 min to pellet cells, washed once with water, pelleted again at 3,000g for 5 min, and cell pellets were frozen at −80 °C. Frozen pellets were resuspended in 200 μl lysis buffer (50 mM Tris-HCl pH 8, 100 mM NaCl) and lysed by disruption with an equal volume of acid washed glass beads, vortexing 10× 15 s. 300 μl lysis buffer was added and samples were mixed by inversion followed by a short centrifugation to collect all liquid in the tube. Supernatant (450 μl) was mixed with an equal volume of phenol:chloroform:isoamyl alcohol, vortexed for 1 min, and centrifuged at maximum speed for 5 min. 350 μl of the aqueous layer was then mixed with an equal volume of phenol:chloroform:isoamyl alcohol, vortexed for 1 min, and centrifuged at maximum speed for 5 min. RNA was precipitated from 300 μl of the aqueous phase by adding 30 μl of 3 M sodium acetate and 800 μl of cold 99.5% ethanol, briefly vortexing, and centrifuging at maximum speed for 10 min. The pellet was rinsed with 70% ethanol and dried at room temperature before dissolving in 100 μl of RNase-free DNase set (Qiagen 79254) and incubating at room temperature for 10 min to remove DNA. RNA was purified using the RNeasy Plus Mini kit (Qiagen 74136) and eluted in 30 μl RNase-free water. RNA-seq libraries were prepared from 1 μg total RNA using the QIAseq FastSelect -rRNA Yeast kit (Qiagen 334217) and QIAseq Stranded RNA Library kit (Qiagen 180743) according to the manufacturer’s protocol. The libraries were sequenced on a NextSeq 500 with 75 bp paired-end reads for ~45 million reads per sample.

    For mouse ES cells, the remaining cells that were not used for ATAC-seq were pelleted at 500g for 5 min and RNA was isolated using Qiagen RNeasy Plus Mini kit, resuspending in 350 μl buffer RLT Plus + β-mercaptoethanol, with homogenization using QIAshredder columns (Qiagen 79654). RNA-seq libraries were prepared from 1 μg total RNA using QIAseq FastSelect -rRNA HMR (Qiagen 334386) and QIAseq Stranded RNA kits (Qiagen 180743) according to the manufacturer’s protocol. The libraries were sequenced with 75-bp paired-end reads for ~50 million reads per sample.

    Illumina libraries were sequenced on an Illumina NextSeq 500 operated at the Institute for Systems Genetics. Sequencing adapters were trimmed with Trimmomatic v0.39 (ref. 73). STAR (v2.5.2a)83 was used to align reads, without providing a gene annotation file, to custom references in which the synthetic HPRT1 and HPRT1R sequences were present on separate chromosomes or inserted at their specific integration sites in the SacCer_April2011/sacCer3 or GRCm38/mm10 genomes (produced using the reform tool; https://gencore.bio.nyu.edu/reform/). Coverage tracks were produced in bigWig format using bamCoverage (deepTools v3.5.0)80 with bin size 10 and smooth length 100, filtering by strand, normalizing using TMM84, and visualized using IGV v2.12.3 (ref. 81). Relative coverage analysis was performed as described below.

    CUT&RUN

    For yeast, two independent colonies for each strain were inoculated into 5 ml of SC–Leu (for assemblon strains) or YPD (for integration strains) for overnight culture at 30 °C. Saturated overnight cultures were diluted to OD600 of 0.1 and cultured for ~6 h at 30 °C, until OD600 reached ~0.6. Cells were pelleted at 3,000g for 5 min, washed twice with water, and resuspended in spheroplasting buffer (1.4 M sorbitol, 40 mM HEPES-KOH pH 7.5, 0.5 mM MgCl2, 0.5 mM 2-mercaptoethanol). Spheroplasting was performed by adding 0.125 U μl−1 Zymolyase (Zymo Research E1004) and incubating at 37 °C for 45 min on a rotator. Nuclei were prepared as previously described85. Resuspended nuclei were split into aliquots of ~108 nuclei each and snap frozen in liquid nitrogen.

    For mouse ES cells, two independent cultures for each engineered cell line cells were harvested from tissue culture dishes using TrypLE Express (ThermoFisher 12604013), dissociated into single-cell suspension, and quenched with mouse ES cell medium. Crosslinking was performed by adding formaldehyde to a final concentration of 0.1% (v/v) and incubating at room temperature for 5 min with occasional mixing by inversion. Crosslinking was stopped by quenching with 125 mM glycine and incubating at room temperature for 5 min with occasional mixing by inversion. DMSO was added to a final concentration of 10% (v/v) and cells were frozen in aliquots of ~106 cells.

    Isolated yeast nuclei (~108 per sample) or crosslinked mouse ES cells (~106 per sample) were thawed and processed for CUT&RUN using the CUTANA ChIC/CUT&RUN kit (EpiCypher 14-1048) according to the manufacturer’s protocol. Antibodies were all used at 0.5 μg: rabbit IgG negative control (EpiCypher 13-0042), H3K4me3 (EpiCypher 13-0041), H3K27ac (EpiCypher 13-0045), H3K27me3 (Active Motif 39055, RRID: AB_2561020), RNAP2 (Santa Cruz Biotechnology sc-56767). Sequencing libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs E7645L) and sequenced with 75 bp paired-end reads for ~15 M reads for H3K4me3 and Pol II samples, and ~20 M reads for H3K27ac and H3K27me3 samples.

    Illumina libraries were sequenced on an Illumina NextSeq 500 operated at the Institute for Systems Genetics. Sequencing adapters were trimmed with Trimmomatic v0.39 (ref. 73). Reads were aligned using bowtie2 v2.2.9 (ref. 79) to custom references in which the synthetic HPRT1 and HPRT1R sequences were present on separate chromosomes or inserted at their specific integration sites in the SacCer_April2011/sacCer3 or GRCm38/mm10 genomes (produced using the reform tool; https://gencore.bio.nyu.edu/reform/). Coverage tracks were produced in bigWig format using bamCoverage (deepTools v3.5.0)80 with bin size 10 and smooth length 100, normalized using RPGC to an effective genome size of 12,000,000 for sacCer3 and 2,652,783,500 for mm10, and visualized using IGV v2.12.3 (ref. 81). Peaks were called using macs2 v2.1.0 (ref. 82) with the parameters: –nomodel -f BAMPE –keep-dup all -g 1.2e7 (sacCer3)/1.87e9 (mm10). Relative coverage analysis was performed as described below.

    CAGE-seq

    RNA was isolated as described above for RNA-seq, using two replicate colonies for each yeast strain. CAGE libraries were prepared as previously described24,starting with 5 μg RNA, with the following modifications. SuperScript IV Reverse Transcriptase (Invitrogen 18090010) was used for the reverse transcription step. AMPure XP beads (Beckman Coulter A63881) were used for all bead cleanup steps. We also used custom-made linker and primer oligonucleotides so that linkers are universal to all samples and primers contain sample-specific barcodes. Libraries were amplified using universal forward and reverse primers with 20 cycles of PCR. Libraries were sequenced on with 75 bp paired-end reads for ~22 million reads per sample.

    Illumina libraries were sequenced on an Illumina NextSeq 500 operated at the Institute for Systems Genetics. Sequencing adapters were trimmed with Trimmomatic v0.39 (ref. 73). The 5′ reads only were aligned using bowtie2 v2.2.9 (ref. 79) to custom references in which the synthetic HPRT1 and HPRT1R sequences were present on separate chromosomes or inserted at their specific integration sites in the SacCer_April2011/sacCer3 or GRCm38/mm10 genomes (produced using the reform tool; https://gencore.bio.nyu.edu/reform/). Coverage tracks were produced in bigWig format using bamCoverage (deepTools v3.5.0)80 with bin size 1, filtering by strand, normalized using RPGC to an effective genome size of 12,000,000, and visualized using IGV v2.12.3 (ref. 81). Peaks were called using macs2 v2.1.0 (ref. 82) with the parameters: –nomodel -f BAM –keep-dup all -g 1.2e7.

    Locus copy number estimation

    For copy number estimation in yeast strains, coverage depth was calculated from whole-genome sequencing data for the synthetic HPRT1 and HPRT1R loci as well as the entire yeast genome (excluding chrM) using samtools v1.9 depth86, and the calculated depth of the synthetic loci was divided by the genome average.

    Sequencing coverage analysis

    Relative coverage analysis was performed for yeast ATAC-seq, RNA-seq, and CUT&RUN experiments. Average coverage depth was calculated over the synthetic HPRT1 and HPRT1R loci, 100-kb sliding windows of yeast genome using samtools v1.9 bedcov86, which reports the total read base count (the sum of per base read depths) per specified region, and then dividing the total read base count by the region size − 100,735 bp for the HPRT1/HPRT1R loci or 100,000 bp for the 100-kb windows. Coverage was corrected for estimated copy numbers of the HPRT1 and HPRT1R episomes. The yeast genome was split into 100-kb sliding windows with 10-kb step size using bedtools v2.29.2 makewindows87. The average of the 100-kb windows was then calculated. The average coverage depth over the synthetic loci was then divided by the relevant genome average to determine relative coverage depth in each context (that is, HPRT1 average coverage/average coverage of yeast 100-kb windows = relative coverage of HPRT1 compared to the yeast genome). For peak analysis, total peaks were counted across the HPRT1 and HPRT1R loci, or averaged over the yeast genome 100-kb windows.

    For mouse genome RNA-seq read analysis, the mouse genome was split into 100-kb sliding windows with 10-kb step size using bedtools v2.29.2 makewindows87. The windows were then filtered to exclude ENCODE blacklist regions88, centromeres, telomeres, and annotated transcripts based on Gencode comprehensive gene annotation, release M10 (GRCm38.p4). RNA-seq reads were counted for the synthetic loci and for the 100-kb genomic windows using samtools v1.9 (ref. 86) view with arguments -c -F 2308 -L (reference bed file).

    Replicate correlation

    Correlation between sequencing assay replicates was assessed using deepTools v3.5.0 (ref. 80) multiBigwigSummary to first calculate average bigWig scores for each dataset across the mouse genome in 10-kb bins, and across the yeast genome in 100-bp bins. Biological and technical replicates were compared using plotCorrelation with the following arguments: –corMethod pearson –whatToPlot scatterplot –skipZeros –removeOutliers –log1p.

    Metaplots analysis

    TSSs were defined as the 5′ coordinate of the experimentally identified CAGE-seq peaks. Metaplots were produced using deepTools v3.5.0 (ref. 80) computeMatrix and plotProfile, with argument –plotType se. Matrices were computed for ATAC-seq and H3K4me3 CUT&RUN signals and profiles were plotted for TSSs across the HPRT1 and HPRT1R loci and across the rest of the yeast genome.

    Motif analysis

    Putative promoter regions in the synthetic HPRT1 and HPRT1R loci were defined as 200 bp upstream and 100 bp downstream of the TSSs identified based on CAGE-seq peaks (above). Motif discovery was performed on the putative promoter regions, ATAC-seq peaks, and ATAC-seq peaks that intersect with putative promoters, identified with bedtools v2.29.2 intersect87. Regions of interest were combined from HPRT1 and HPRT1R for motif analysis using MEME v4.102 (ref. 25) with a maximum motif width of 10 bp. This width was determined empirically by observing that increasing widths did not result in the predicting of any more informative motifs. Tomtom27 was performed to scan the identified motifs for matches to motifs in the YEASTRACT database65. GOmo89 was performed to identify gene ontology terms linked to gene promoters containing the identified motifs.

    Public sequencing data

    We obtained UCSC browser data for CpG islands90,91, as well as the following ENCODE data92. DNase-seq from ES-E14 mouse embryonic stem cells, ENCSR000CMW93. Chromatin immunoprecipitation with sequencing (ChIP-seq) from ES-Bruce mouse embryonic stem cells, ENCSR000CBG, ENCSR000CDE, ENCSR000CFN94, ENCSR000CCC. RNA-seq from ES-E14 mouse embryonic stem cells, ENCSR000CWC, ENCSR000CWC. ATAC-seq data from embryonic day (E)11.5 mouse embryonic tissue, ENCSR282YTE, ENCFF936VGM28. ChIP-seq data from E11.5 mouse embryonic tissue, ENCSR427OZM, ENCFF952ZWD, ENCSR531RZS, ENCFF033UPR, ENCSR240OUM, ENCFF179QWF28. DNase-seq from H1 human ES cells ENCSR000EJN, ChIP-seq from H1 human ES cells ENCSR443YAS, ENCSR880SUY, ENCSR928HYM, RNA-seq from H1 human ES cells ENCSR000COU95. Long RNA-seq data from H1 human ES cells, ENCSR000COU, ENCFF563OKS, ENCFF501KFP, ENCFF407PJY, ENCFF761BKF2.

    We obtained public sequencing data for yeast from the following datasets (Gene Expression Omnibus (GEO) accession numbers): ATAC-seq (GSM6139041), H3K4me3 ChIP-seq (GSM3193266), RNA-seq (GSM5702033) and yeast CAGE-seq (ref. 96).

    DNA reagents

    Sequences and identifiers, where applicable, for all DNA reagents used in this study are available as supplementary material, including all oligonucleotides, synthetic DNA segments, plasmids, landing pads, homology arms and yeast strains.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

    [ad_2]

    Source link

  • Mammalian cells repress random DNA that yeast transcribes

    Mammalian cells repress random DNA that yeast transcribes

    [ad_1]

    Nature, Published online: 06 March 2024; doi:10.1038/d41586-024-00575-x

    In experiments dubbed the Random Genome Project, researchers have integrated DNA strands with random sequences into yeast and mouse cells to find the default transcriptional state of their genomes.

    [ad_2]

    Source link