The M1 muscarinic acetylcholine receptor (M1-receptor) plays a crucial role in learning and memory and is a validated drug target for the treatment of Alzheimer’s disease (AD). Furthermore, M1-receptor ligands have been demonstrated to display disease-modifying effects in preclinical models of neurodegenerative disease. By employing a genetic mouse model expressing a G protein–biased M1-receptor in combination with a mouse model of terminal neurodegenerative disease, we demonstrate here that the M1-receptor exerts an inherent neuroprotective activity that is dependent on its phosphorylation status. Thus, in AD drug development programs, M1-receptor ligands that maintain the receptor phosphorylation status will be more likely to lead to beneficial neuroprotective outcomes. There are currently no treatments that can slow the progression of neurodegenerative diseases, such as Alzheimer’s disease (AD). There is, however, a growing body of evidence that activation of the M1 muscarinic acetylcholine receptor (M1-receptor) can not only restore memory loss in AD patients but in preclinical animal models can also slow neurodegenerative disease progression. The generation of an effective medicine targeting the M1-receptor has however been severely hampered by associated cholinergic adverse responses. By using genetically engineered mouse models that express a G protein–biased M1-receptor, we recently established that M1-receptor mediated adverse responses can be minimized by ensuring activating ligands maintain receptor phosphorylation/arrestin-dependent signaling. Here, we use these same genetic models in concert with murine prion disease, a terminal neurodegenerative disease showing key hallmarks of AD, to establish that phosphorylation/arrestin-dependent signaling delivers neuroprotection that both extends normal animal behavior and prolongs the life span of prion-diseased mice. Our data point to an important neuroprotective property inherent to the M1-receptor and indicate that next generation M1-receptor ligands designed to drive receptor phosphorylation/arrestin-dependent signaling would potentially show low adverse responses while delivering neuroprotection that will slow disease progression. The number of people living with dementia, of which Alzheimer’s disease (AD) is the most common form, is estimated to be ∼50 million worldwide . This nu4811To overcome these barriers, we and others have set out to define the optimal pharmacological properties of orthosteric and allosteric M1-receptor ligands that will deliver clinical efficacy while minimizing cholinergic adverse responses . To thisWhereas early studies have provided a framework for the design of M1-receptor ligands for the symptomatic treatment for AD, it has been the emergence of evidence that the M1-receptor might also modify neurodegenerative disease progression that has generated significant attention , 18–20. 18Here, we have extended these studies by asking if the M1-receptor inherently has neuroprotective properties. By employing the M1-PD mouse strain  in combiSI Appendix, Fig. S1 A–F). Using a BRET biosensor to measure Gq activation . Coupling to signaling pathways downstream of Gq activation, inositol phosphate accumulation . By fitting the ACh concentration response curves for Gq activation or arrestin recruitment to the operational model of agonism, we derived a transduction coefficient (τ) for each of these responses at the M1-PD receptor. We compared these values with the τ calculated for each of these pathways at the M1-WT and calculated a bias factor [ΔΔlog10(τ/KA)]. These analyses showed that M1-PD receptors show preferential signaling bias (bias factor = 20.66) toward Gq activation versus arrestin recruitment . Furthermore, quantification of receptor expression using Western blotting to detect the HA-tag revealed no significant (P > 0.05) difference in receptor expression when comparing M1-WT and M1-PD mice . Consistent with our findings in recombinant systems, we also show here that in primary hippocampal-cortical neurons prepared from M1-WT or M1-PD mice, agonist-stimulated inositol phosphate accumulation is equivalent .We have previously described the generation of genetically engineered mice where the coding sequence for the M1-PD variant was knocked-in into the natural gene locus of the M1-receptor (chrm1) . In addiuivalent . Finallyuivalent J. As a puivalent . StimulaSI Appendix, Fig. S5 A–C). Inoculation of mice with prion-infected brain homogenate induces accumulation of misfolded, insoluble prion protein that is resistant to digestion with proteinase K (PrPsc) . Since the time course of prion disease progression is dictated by the expression level of cellular prion protein (PrPc), whereby increased PrPc expression accelerates disease progression . These data show that prion disease had progressed faster in mice expressing a variant of the M1-receptor with reduced coupling to phosphorylation/arrestin signaling, suggesting that M1-receptor signaling through this pathway mediates a previously unappreciated neuroprotective effect.Our previous work demonstrated that M1-receptor PAMs can offer both symptomatic and disease-modifying properties in a mouse model of terminal neurodegenerative disease . Mice in (PrPsc) , 21, 24.SI Appendix, Fig. S6 C and D). These results provide further evidence that the M1-PD variant of the M1-receptor allows accelerated prion-induced pathological changes. Importantly, we show that acceleration of disease in M1-PD mice is not due to the detrimental activity of sustained ERK1/2 signaling . However, we did detect a nonsignificant trend for reduced phosphorylation of ERK1/2 in the hippocampus of prion-infected M1-PD versus M1-WT mice, which may warrant further investigations.We wanted to further test the possibility that uncoupling the M1-receptor from phosphorylation/arrestin signaling removed a neuroprotective component of receptor activity by monitoring biomarkers of disease severity. Recently, we have mapped the pathological changes in prion disease by conducting global transcriptomic and proteomic analyses of the hippocampus of prion-diseased mice. We have found several protein markers previously associated with human AD that are significantly up-regulated in prion disease. These included markers of neuroinflammation, glial fibrillary acidic protein (GFAP), clusterin, vimentin, and galectin-1 and markers of adaptive responses to neurodegeneration including apolipoprotein-E (APO-E) and the protease inhibitor serpinA3N . In thisignaling  since ERSI Appendix, Fig. S9A). Furthermore, Western blotting for GFAP and vimentin revealed an up-regulation of astroglia in the hippocampus and cortex of prion-infected M1-PD mice . Importantly, the levels of both GFAP and vimentin were significantly higher in the hippocampus of prion-inoculated M1-PD mice compared to M1-WT mice .Murine prion disease, similar to many human neurodegenerative diseases , is asso-WT mice A–C. ProtSI Appendix, Fig. S9D). Transcription of the proinflammatory cytokines TNF-α, IL-1β, and IL-6 was elevated in the hippocampus and cortex of prion-infected M1-WT mice compared to M1-WT mice inoculated with normal brain homogenate . Furthermore, we detected a significant increase in transcription of TNF-α in the hippocampus and cortex of M1-PD prion mice compared to M1-WT, whereas IL-1β levels were elevated in the M1-PD versus M1-WT in the cortex only .We further assessed the status of neuroinflammation by immunohistochemical staining of sections of the hippocampus and cortex. Staining for GFAP (astrocytes) and Iba-1 (microglia) demonstrated a profound increase in neuroinflammatory markers in the hippocampus and cortex of M1-PD, compared to M1-WT . Importantly, expression of transcripts for GFAP, CD86, and the battery of pro- and antiinflammatory cytokines tested previously were equivalent in noninfected M1-WT and M1-PD mice . Taken together, these data indicate that prion-infected M1-PD mice show increased neuroinflammation compared to M1-WT mice.There were no differences in the expression of transcripts for antiinflammatory cytokines, IL-4, IL-10, IL-11, and IL-13, in the cortex or hippocampus of prion-infected M1-WT or M1-PD mice (P = 0.048) and 15 w.p.i. (P = 0.017) in prion-diseased M1-PD mice when compared to the M1-WT . The appearance of at least two of the early indicator signs of prion disease occurred significantly earlier (P < 0.0001) in M1-PD mice compared to M1-WT mice (P < 0.0001) acceleration of confirmatory scrapie diagnosis, with median time for terminal illness of 25 w.p.i. for M1-WT mice and 21 w.p.i. for M1-PD mice  that has all potential phosphorylation sites removed . PreviouOur study has important implications for drug design. Like many members of the GPCR superfamily, muscarinic receptors mediate signal transduction in a bimodal fashion that involves both canonical G protein signaling and receptor phosphorylation/arrestin-dependent signaling. Whereas the molecular details of this phenomenon have been extensively studied in in vitro transfected cell systems, understanding the physiological importance of bimodal signaling and, further, how this might have a pathophysiological impact has been extremely challenging. We have approached this problem by genetically engineering mice to express variants of receptors where intracellular phosphorylation sites have been removed, thereby reducing coupling to arrestin but maintaining coupling to heterotrimeric G proteins. This approach has been successfully employed, for example, in mapping physiological processes downstream of the M3-receptor . ApplyinOur study also has important pathophysiological implications. AD is characterized by a loss of cholinergic neurons originating from the basal forebrain innervating the neocortex, amygdala, hippocampus, and entorhinal cortex –32. The Global proteomic and transcriptomic studies have established that murine prion disease shows many of the key hallmarks of AD including neuroinflammation, mitochondrial dysfunction, and oxidative stress . FurtherOne possibility is that lack of receptor phosphorylation could reduce the desensitization of GPCR signaling, resulting in excessive activation of signaling pathways , 36. For39Although receptor phosphorylation is important to stabilize the interaction with arrestins , β-arres4451In conclusion, we establish that the M1-receptor has inherent neuroprotective activity that results in the suppression of neuroinflammation and diminishes markers of neurodegenerative disease and is driven by a mechanism that requires receptor phosphorylation. Our data suggest that M1-receptor ligands that promote receptor phosphorylation signaling will not only act to improve memory deficits in neurodegenerative diseases such as AD but will deliver neuroprotection that will maintain normal behavior and extend life span.Generation of the M1-WT, M1-PD, and M1-KO strains used was described previously . All mic2O and incubated overnight at 37 °C. Plates were then washed three times using DEPC-treated H2O and dried for 2 h at room temperature.Tissue culture plates were coated using 4 µg/mL poly-D-lysine and 6 µg/mL Laminin Mouse Protein in diethyl pyrocarbonate (DEPC)–treated Hg for 5 min. The pellet was resuspended in neurobasal complete media to a final density of 5 × 105 cells/mL. Cells were then seeded onto precoated plates and maintained at 37 °C in a 5% CO2 humidified atmosphere.The hippocampal and cortical areas of the brain were isolated from E16 mouse embryos. The tissues were chopped into smaller pieces, washed three times in Hanks’ balanced salt solution, transferred to a 15 mL tube containing 4 mL of TrypLE Select 10X, and incubated at 37 °C for 10 min. TrypLE Select 10X was then inactivated by adding 8 mL of neurobasal complete media  to the tubes followed by centrifugation at 200 × Transgenic knock-in mice expressing HA-tagged M1-WT and M1-PD receptors were inoculated by intracerebral injection (under 3% isoflurane anesthesia) into the right parietal lobe with 1% brain homogenate infected with RML prion aged 3 to 4 wk as described previously . ControlAssessment of burrowing on control and prion-infected M1-WT or M1-PD mice was conducted from 9 w.p.i. The burrowing test involved mice being placed into individual cages (22 × 36 cm) with a plastic cylinder filled with 140 g of food pellets. Food remaining in the cylinders after 2 h was weighed and the amount displaced (“burrowed”) was calculated. Prior to the burrowing test, mice were placed in the burrowing cage for a 2-h period. The test was then repeated on a weekly basis.Prion-infected mice were scored according to the appearance of recognized early indicator and confirmatory signs of prion disease. Early indicator signs included piloerection, sustained erect ears, erect penis, clasping of hind legs when lifted by tail, rigid tail, unsustained hunched posture, mild loss of coordination, or being subdued. Confirmatory signs of prion disease included ataxia, impairment of a righting reflex, dragging of limbs, sustained hunched posture, and/or significant abnormal breathing. Survival times were calculated based on the presence of two early indicator signs plus one confirmatory sign or two confirmatory signs. At this time, mice would be humanely killed.Supplementary File


The gene-regulatory factors ATF5, CEBPB and CEBPD promote survival, growth, metastasis and treatment resistance of a range of cancer cell types. Presently, no drugs target all three at once. Here, with the aim of treating cancers, we designed novel cell-penetrating peptides that interact with and inactivate all three. The peptides Bpep and Dpep kill a range of cancer cell types in culture and in animals. In animals with tumors, they also significantly increase survival time. In contrast, they do not affect survival of non-cancer cells and have no apparent side effects in animals. The peptides work in combination with other anti-cancer treatments. Mechanism studies of how the peptides kill cancer cells indicate a decrease in survival proteins and increase in death proteins. These studies support the potential of Bpep and Dpep as novel, safe agents for the treatment of a variety of cancer types, both as mono- and combination therapies.IL6, IL8 and asparagine synthetase (ASNS), supporting their inhibition of transcriptional activation. Mechanisms by which the peptides trigger apoptosis included depletion of pro-survival survivin and a required elevation of pro-apoptotic BMF. Bpep and Dpep significantly slowed tumor growth in mouse models without evident side effects. Dpep significantly prolonged survival in xenograft models. These findings indicate the efficacy and potential of Bpep and Dpep as novel agents to treat a variety of cancers as mono- or combination therapies.Transcription factors are key players underlying cancer formation, growth, survival, metastasis and treatment resistance, yet few drugs exist to directly target them. Here, we characterized the in vitro and in vivo anti-cancer efficacy of novel synthetic cell-penetrating peptides (Bpep and Dpep) designed to interfere with the formation of active leucine-zipper-based dimers by CEBPB and CEBPD, transcription factors implicated in multiple malignancies. Both peptides similarly promoted apoptosis of multiple tumor lines of varying origins, without such effects on non-transformed cells. Combined with other treatments , the peptides acted additively to synergistically and were fully active on Taxol-resistant cells. The peptides suppressed expression of known direct CEBPB/CEBPD targets  A copious amount of literature links basic leucine zipper transcription factors, ATF5, CEBPB and CEBPD, to tumor formation, growth, metastasis and treatment resistance in a variety of cancers ,27,28,29In the past, we developed the strategy of generating an inducible or cell-penetrating dominant-negative (dn) peptide form of ATF5 for cancer treatment ,31,32,33CEBPB and CEBPD form homodimers and heterodimerize with one another via their leucine zippers ,35,36. OT98G, LN229, U251, MDA-MB-231, MCF7, MDA-MB-468, A549, HCT116, A375, MeWo, HEK293T and B16-F10 cells were from, and authenticated by, the ATCC. T98G Taxol-resistant cells were established by continuous exposure to incremental Taxol concentrations up to 1 nM. Mgpp3 cells were the kind gift of Dr. Peter Canoll, Columbia University . Cells wBpep, Dpep and mutated peptides were purchased as acetate salts either from CSBio or AlanScientific with the following sequences:Bpep: RQIKIWFQNRRMKWKKLETQHKVLELTAENERLQKKVEQLSRELSTLRNLFKQLDpep: RQIKIWFQNRRMKWKKLVELSAENEKLHQRVEQLTRDLAGLRQFFKDpep-mut: RQIKIWFQNRRMKWKKLVEGSAENEKGHQRVEQGTRDGAGGRQFFKThe peptides were dissolved in 10% glycerol in PBS, pH 7.2 and stored as 2 mM aliquots at −80 °C prior to final dilution for experiments.siRNA: Silencer™ Select (Invitrogen): Negative Control No. 2 siRNA (439084), BMF siRNA s40386 (#4392420), or BMF siRNA s40387 (#4392420) were transfected into 50% confluent cultures using Oligofectamine™  following the supplier’s protocol.4/well in 0.2 mL of growth medium. Following overnight incubation, cells were refreshed with DMEM with 2% FBS and the indicated concentrations of Bpep or Dpep and other indicated additives. Cell viability was assessed after 6 days or as indicated by cell counting using either a hemocytometer or a Countess II automated cell counter . Assays were performed in triplicate.Cells were seeded in 48-well plates at 1 × 103 cells were seeded with specified concentrations of Bpep or Dpep and indicated additives. After 12 days, total colonies were counted.Cells were seeded in 6-well plates (200 cells/well) and treated with indicated doses of Bpep or Dpep for 12 days. At the treatment end, colonies were fixed, stained with 0.5% crystal violet and 25% methanol in PBS for 30 min, washed with PBS and quantified. Colonies were defined as containing at least 50 cells. Soft agar colony-forming assays were established in 6-well plates as described . 5 × 103Fragments encoding wild-type and mutant DN-CEBPB or DN-CEBPD were subcloned into the 5′-BamHI site and the 3′-XhoI site of pCMV-3Tag-2A (Agilent Technologies) plasmid for in-frame 3xMyc-tagged expression. The sequences of the fragments were:pCMV-3Tag2A-DN-CREBP/B:MEQKLISEEDLEQKLISEEDLEQKLISEEDLARAGSMASMTGGQQMGRDPDLETQHKVLELTAENERLQKKVEQLSRELSTLRNLFKQLpCMV-3Tag2A-DN-CEBP/D:MEQKLISEEDLEQKLISEEDLEQKLISEEDLARAGSMASMTGGQQMGRDPDLVELSAENEKLHQRVEQLTRDLAGLRQFFKpCMV-3Tag2A-DN-CREBP/B-mut:MEQKLISEEDLEQKLISEEDLEQKLISEEDLARAGSMASMTGGQQMGRDPDGETQHKVGEGTAENERGQKKVEQGSREGSTGRNGFKQGpCMV-3Tag2A-DN-CEBP/D-mut:MEQKLISEEDLEQKLISEEDLEQKLISEEDLARAGSMASMTGGQQMGRDPDGVEGSAENEKGHQRVEQGTRDGAGGRQFFKCells were seeded in 48-well plates in triplicate overnight to reach 70–90% confluency. Cultures were transfected with 0.2 µg DNA/well using Lipofectamine™ 3000 (Invitrogen) following the supplier’s protocols. 48 h later, cells were fixed in 4% PFA for 10 min, treated with 0.3% Triton-X for 5 min and blocked with Superblock  for 30 min at room temperature. Cells were then incubated with mouse anti-myc  for 1 h, washed with PBS and incubated with the secondary antibody for another hour. Cultures were stained with Hoechst 33342 to detect total cells and apoptotic nuclei. Images were acquired using a Zeiss epifluorescence microscope with a digital camera and Axiovision software. The proportion of cells with apoptotic nuclei was quantified by scoring all transfected cells from 4 random fields for each sample.Apoptosis was evaluated using the FITC Annexin V Apoptosis Detection Kit I (BD Biosciences) using the supplier’s protocol, followed by analysis in the Columbia HICCC flow Cytometry Core.Cells were seeded in 6-well plates, treated with 20 µM Bpep or Dpep for 48 h and lysed. Total RNA purification, cDNA synthesis and qPCR were carried out as previously described . Primer 18S ribosomal RNA Forward primer: 5′-AGTCCCTGCCCTTTGTACACA-3′18S ribosomal RNA Reverse primer: 5′-GATCCGAGGGCCTCACTAAAC-3′IL6 Forward primer: 5′-CCAGAGCTGTGCAGATGAGTA-3′IL6 Reverse primer: 5′-TGGGTCAGGGGTGGTTATTG-3′IL8 Forward primer: 5′-CTCTTGGCAGCCTTCCTGATT-3′IL8 Reverse primer: 5′-TATGCACTGACATCTAAGTTCTTTAGC-3′BMF Forward primer: 5′-CCACCAGCCAGGAAGACAAAG-3′BMF Reverse primer: 5′-TGCTCCCCAATGGGCAAGACT-3′ASNS Forward primer: 5′-CTGTGAAGAACAACCTCAGGATC-3′ASNS Reverse primer: 5′-AACAGAGTGGCAGCAACCAAGC-3′Cells were seeded into 6-well dishes, treated with 20 µM Bpep or Dpep for 72 h and processed for Western immunoblotting and imaging as previously described . AntibodCells were seeded in 6-well plates to form monolayers and were either pretreated or not with 20 µM Bpep or Dpep for 20 h. Scratches were made with a 200 μL pipette tip. Cells were then treated for another 20 h. Images were taken at 0 and 20 h for determination of scratch widths.6 A375 HCT116 or B16-F10 cells were implanted subcutaneously into flanks of 6–8 week-old female NCR nude mice  or C57BL/6 mice , respectively. Each mouse received three implantations. When tumors reached 100–200 mm3, mice were randomized into different groups and treated intraperitoneally with 100 µL/mouse of vehicle or peptides 3–4 times/week as indicated. The amount of peptide per treatment ranged between 10–50 mg/kg as indicated in Figures and Figure legends. Tumor sizes were measured with calipers and calculated as (length × width2)/2. Body weights were monitored at each time point. For survival studies, mice were sacrificed once a single tumor reached a preset endpoint of 1000 mm3. H&E and TUNEL staining of harvested tumors were conducted by the Columbia University Molecular Pathology facility.Animal procedures were approved by the Columbia University IACUC (protocol AC-AABE2555) and followed guidelines set forth by the National Institutes of Health guide for care and use of laboratory animals. 1 × 10t-test or ANOVA (for multiple comparisons). IC50 values were calculated by non-linear regression.All experiments were performed at least in triplicate. Data are expressed as mean ± SEM. Statistical significance was calculated using a two-tailed Student’s To test the hypothesis that dn-decoy forms of CEBPB and CEBPD kill cancer cells, we transfected T98G glioblastoma cells with vectors expressing myc-tagged CEBPB or CEBPD leucine zipper sequences, or tag alone. Assessment of transfected cell nuclei after 3 days revealed that both dn constructs caused significant apoptosis compared with the control A. As an We next designed cell-penetrating peptide forms of dn-CEBPB and dn-CEBPD, containing a cell penetrating penetratin domain followed by the leucine zipper sequence of the corresponding proteins. We will henceforth refer to these peptides as Bpep and Dpep, respectively. While the peptides were freely soluble in PBS + 10% glycerol at 2 mM, in a culture medium containing 2% or greater fetal bovine serum, there was minor precipitation at 50 µM that increased with elevated concentrations. Dose-response experiments carried out for 6 days revealed that both peptides inhibited growth/survival of multiple cancer cell lines with 50% efficacy (IC50) generally in the range of 10–30 µM. Assessed cancer types included glioblastoma , melanoma , breast , lung adenocarcinoma (A549), colon cancer (HCT116), myelogenous leukemia (HAP1) and transformed embryonic kidney cells (293T) . A time Dose-responses for Bpep and Dpep were generally similar, but not identical across multiple lines, raising the question of whether the two might act additively or synergistically. Multiple lines were exposed to various concentrations of Bpep or Dpep alone or in a 1:1 combination and assessed for growth/survival. The outcome  indicateBpep and Dpep were designed to interact with targets by specific leucine-zipper interactions. To confirm the importance of this interaction and to rule out non-specific toxicity, parallel assays were carried out on multiple lines with unmodified peptides and with Dpep mutated to replace heptad repeat leucines in the leucine zipper with glycine residues. The outcome showed a greatly decreased potency of the modified peptides, indicating reliance on the intact leucine zipper for full activity .An important question is whether Bpep/Dpep effects on cell survival/growth are selective for cancer cells. To assess this as well as possible non-specific toxicity, non-transformed cells including primary astrocytes, MCF10A breast cells and HIEC-6 intestinal epithelial cells were treated with the peptides. No effects were observed on survival/growth at up to 50 µM for 6 days of treatment, indicating an absence of activity or toxicity on normal cells . These oIn addition to assessing peptide effects in monolayer cultures, we evaluated their actions in colony-forming assays in soft-agar and on culture dishes, conditions that may more closely model in vivo growth and metastasis. In both models, Bpep and Dpep suppressed colony formation by multiple lines with IC50′s in the 500 nM range and efficacies of 75–97% at 5 µM .While experiments with Bpep and Dpep indicated an apoptotic response, we directly evaluated this by flow cytometry of PI/Annexin V-stained cancer lines treated with or without 20 µM Bpep or Dpep for 3 days. This revealed substantial peptide-stimulated increases in apoptosis . In contATF5, CEBPB and CEBPD are reported to promote tumor cell migration and metastasis ,13,23,25We next assessed Bpep and Dpep actions in combination with multiple presently employed cancer therapeutics. We were particularly interested to learn whether the peptides might interfere with other therapies, or act additively or synergistically with them, and whether they would act on cells resistant to such therapies.ATF5, CEBPB and CEBPD have been reported to contribute to cancer cell radiation resistance ,14,24. TPast studies ,19,27 haTaxol resistance represents an important clinical challenge . To deteThe lysosomal inhibitor chloroquine is currently in clinical trials for various cancers  and has The anthracycline doxorubicin is widely used to treat hematopoietic and solid tumors . CombinaDue to its effects on chromatin structure and cell cycle, doxorubicin has been used to model cell senescence and cell cycle withdrawal ,47, thusIL6 and IL8 have been identified as direct CEBPB and CEBPD targets [IL6 and IL8 transcript levels in multiple cancer lines with or without 48 h of 20 µM Bpep or Dpep exposure. In each case, the peptides significantly reduced IL6 and IL8 expression  is an anti-apoptotic protein often over-expressed in various cancer types and absent or little-expressed in normal cells [Survivin  showed by far the largest elevation. To determine whether this was the case for Bpep and Dpep and occurred across multiple tumor cell types, we performed qPCR for BMF transcripts in multiple lines exposed to the peptides for 2 days. In all cases, there was a significant elevation of BMF expression  3–4 × /week . There w). While both doses significantly slowed tumor growth, there was no significant difference in their efficacy. There were also no evident side effects or differences in animal weight compared with controls over the 9-day treatment regimen for either dose. Next, using the A375 melanoma xenograft model, we asked whether Dpep (20 mg/kg) would affect survival in addition to tumor growth over a more extended treatment period  of animals at the endpoint of the study shown in Numerous studies have associated ATF5, CEBPB and CEBPD with various aspects of malignancy, including patient prognosis and survival ,31,32,33In past work, we characterized anti-tumor activity of a cell-penetrating dn-ATF5 ,32. A reKey features of Bpep and Dpep are the possession of the CEBPB and CEBPD leucine zipper sequence, respectively. Replacement of the heptad repeat leucines in these sequences eliminated or severely decreased anti-tumor cell activity, supporting the idea that Bpep/Dpep function via specific zipper–zipper interactions. Such dependence also reinforces the absence of non-specific toxicity by the peptides.Although not identical, Bpep and Dpep show quite comparable activities. This includes similar efficacy and potency across multiple cell lines and in combination treatments, additive effects when tested in combination with one another and comparable in vivo efficacy. These findings suggest similar mechanisms of action. However, given that the peptides did not act identically in some situations, one cannot not rule out the possibility that there may be conditions or cancer types in which one peptide is significantly more effective than the other.Bpep and Dpep show efficacy as monotherapies and in combinations with other cancer treatments. In each combination tested, the peptides did not interfere with partner activity and acted additively or synergistically. If, as appears, the peptides have little toxicity on their own, this supports the idea that they can be combined with therapeutics that have limiting side effects in order to achieve efficacy with partner doses below those causing toxicity.An important concept in cancer therapeutics is that tumor cells may temporarily withdraw from the cell cycle, potentially for extended times, and thereby escape treatments that target rapidly dividing cells to seed metastasis and recurrence ,65. HereIL6, IL8, ASNS), CEBPD  and ATF5 (ASNS) as well as of proteins (BCL2 and MCL1) encoded by ATF5 target genes. While further studies will be required to verify that the peptides reduce association of the three transcription factors with their target genes, these findings support the idea that Bpep and Dpep interfere with the expression of genes regulated by CEBPB, CEBPD and ATF5. Beyond representing markers for Bpep and Dpep activity, IL6, IL8 and ASNS have potential relevance for cancer treatment. IL-6 and IL-8, derived either from tumors or their niche , contribute to tumor growth, metastasis and therapeutic resistance [We found that Bpep and Dpep significantly reduced expression of known direct target genes of CEBPB  family with additional roles in cell proliferation and mitochondrial stress . Survivir agents . In addiThe aim of this study was to evaluate the potentials of Bpep and Dpep as novel prototypic drugs for cancer treatment. Our in vitro studies verified their selective efficacy on a range of tumor cell types. We further observed efficacy in suppressing tumor growth in 3 different in vivo models, and in two survival studies, there was highly significant prolongation of survival time. We found that doses of 20 and 50 mg/kg had similar efficacy in suppressing tumor growth. It remains to be determined whether lower doses or a different treatment regimen would be equally or more effective. Even with prolonged treatments lasting more than 6 weeks, we did not observe loss of weight or other evident side effects in the animal subjects. Although more detailed studies will be required to fully examine in vivo safety of the peptides, the current findings point to a high degree of tolerability.On the basis of the work described here, we conclude that the novel cell-penetrating peptides Bpep and Dpep promote apoptotic death of a wide range of cancer cells, both in vitro and in vivo without such effects on non-transformed cells. The distal part of the mechanism by which they do so includes depletion of the survival protein survivin and elevation of death protein BMF. Our findings support the idea that Bpep and Dpep or modified forms thereof have potential for treatment of a variety of cancer types. Our data also support their use in combination therapies in which they can augment the actions of other anti-cancer agents and/or reduce the levels needed to achieve efficacy.


Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings suggest biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results suggest that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these  Understanding of the diversity of immune responses to SARS-CoV-2 infections is critical for improving diagnostic and treatment approaches. Identifying which immune mechanisms lead to divergent outcomes can be clinically difficult, and experimental models and longitudinal data are only beginning to emerge. In response, we developed a mechanistic, mathematical and computational model of the immunopathology of COVID-19 calibrated to and independently validated against a broad set of experimental and clinical immunological data. To study the drivers of severe COVID-19, we used our model to expand a cohort of virtual patients, each with realistic disease dynamics. Our results suggest key processes that regulate the immune response to SARS-CoV-2 infection in virtual patients and suggest viable therapeutic targets, underlining the importance of a rational, multifaceted approach to studying novel pathogens using intra-host models. Clinical manifestations of SARS-CoV-2 infection are heterogeneous, with a significant proportion of people experiencing asymptomatic or mild infections that do not require hospitalization. In severe cases, patients develop coronavirus disease COVID-19) that may progress to acute respiratory distress syndrome (ARDS), which is frequently accompanied by myriad inflammatory indicators 9 that ma. An over+ T cell expansion under inflammatory conditions [+ T cell counts [Cytokines are critically important for controlling virus infections ,14 and anditions ; howevernditions  without l counts . Type I l counts ) also pll counts –21. Due l counts  as it isl counts . Interfel counts  and typel counts . The emel counts –26. Furtl counts ,28. Overl counts , and arel counts ,17,30.in silico trials enable the theoretical exploration of how multiple key underlying mechanisms simultaneously impact disease severity [Because the identification of immune mechanisms responsible for divergent disease outcomes can be difficult clinically, and experimental models and longitudinal data are only beginning to emerge, theoretical explorations are ideal . Quantitseverity . More reseverity ,36 have severity –42. Howein vitro, in vivo, and clinical data, analyzed to predict how early infection kinetics facilitate downstream disease dynamics, and used to create a virtual patient cohort with realistic disease courses. Our results suggest that mild and severe disease are distinguished by the rates of monocyte differentiation into macrophages and of IFN production by infected cells. In our virtual cohort, we found that severe COVID-19 responses were tightly correlated with a delay in the peak IFN concentration and that a large increase in IL-6 was the dominant predictor of CD8+ T cell depletion. These results provide insight into differential presentations of COVID-19 by suggesting key regulators of severe disease manifestation particularly related to monocyte differentiation and IL-6 concentrations. Importantly, these key mechanisms suggest promising avenues of experimental research into the mechanisms of immunopathology in COVID-19.In this study, we developed a mechanistic mathematical model to describe the within-host immune response to SARS-CoV-2. We explicitly modelled the interactions between epithelial cells, innate and adaptive immune cells, and cytokines. The model was fit to various Eqs. S1-S22, with variables and parameters summarized in S) that encounter virus (V) and become infected (I) before turning into damaged or dead cells (D) due to viral infection or immune involvement. The immune response is orchestrated by cytokines that act to stimulate the immune cell subsets present in the tissues and recruit cells from the bone marrow and circulation (F) that cause adjacent cells to become resistant to infection (R) and decrease the production of newly infected cells [MΦR) become inflammatory macrophages (MIΦ), which also arise through monocyte (M) differentiation following stimulation by GM-CSF (G) or IL-6 (L) [N) are recruited to the infection site by G-CSF and release reactive oxygen species (ROS) causing bystander damage to infected and susceptible cells [+ T cells (T) are subsequently recruited to the infection site following a delay to account for antigen presentation, with expansion modulated by type I IFN and IL-6 concentrations. See To study the dynamics of SARS-CoV-2 infection and the development of COVID-19, we constructed a computational biology model of host-pathogen interactions (culation . We haveed cells . ThroughIL-6 (L) . Neutrople cells ,46. CD8+Because the model has several parameters that are undetermined biologically and insufficient data exists to confidently estimate their values, we used a stepwise approach to parameter estimation see –S5 Figs.7 cells/ml to 6.5 × 106 cells/ml  using two different parameter sets. Mild disease dynamics were recreated using estimated parameter values (dV) and the infected cell death rate (dI) were recalculated to ensure that the maximum death rate of the virus and infected cells did not exceed the value obtained from the reduced viral dynamics model fit , high ratios of innate to adaptive immune cells, and a marked reduction in healthy viable lung tissue  and neutrophils (2 × 108 cells/ml versus 3 × 1013 cells/ml) over 30 days. Interestingly, inflammation remained high in the severe disease scenario despite the virus being cleared slightly faster (~1 day) than in the case of mild disease  to generate disparate dynamics, the immune cell and cytokine dynamics were qualitatively in line with clinical observations for IFN-α [In addition, there was a notable increase in the number of inflammatory macrophages , IL-6, G disease . FurtherCOVID-19 ,23. Despor IFN-α , IL-6 [5or IFN-α ,53, and p, δV,N, β, ϵF,I), CD8+ T cell induced epithelial cell apoptosis , macrophages, monocyte and CD8+ T cell production , IL-6  increased the peak concentration of both IL-6 and IFN  and decreasing the internalization and clearance rates  (β) and IL-6 binding kinetic  . Less siβ) in the model. To predict how increases to viral infectivity (β) values would impact disease trajectories, we varied (β) between 0% and 50% from its original estimated value and assessed predicted outcomes. Although we observed some heterogeneity in response, overall we found that increased infectivity alone is not sufficient to induce significant changes to disease severity  and also fixed the relevant production rates to be zero  on immunological trajectories. A comparison of the knockout experiment to severe disease simulations can be found in To better understand the individual contributions of innate immune cell subsets to disease trajectories, we performed a set of knockout . For eacin silico patient, seven patient-specific parameters were sampled from normal distributions with means corresponding to their respective fixed values and standard deviations inferred from clinical observations . For the production of IL-6 by macrophages and monocyte to macrophage differentiation via IL-6 stimulation, standard deviations were inferred from IL-6 levels in non-mechanically ventilated patients (mild) and from mechanically ventilated patients (severe) [α from Trouillet-Assant et al. [Parameters were chosen based on their impact on maximum IL-6 and IFN levels as well as tissue damage observed in the sensitivity analysis ((severe)   within 21 days of infection. Ordering patients by their value of Ψ and plotting the corresponding values for different characteristics showed a clear separation between those with mild disease (Ψ ≤ 3) and those with severe disease (Ψ > 3)  .V0 = 0.1, 1, 4.5, 8). By comparing outcomes with respect to several immunological biomarkers , we found that virtual patients who were predicted to experience mild COVID-19 in our base case  experienced mild disease irrespective of the exposure dose size (V0 = 4.5 log10(copies/mL) (i.e. patients 150 to 200) had little to no change in their IFN, IL-6 or neutrophil dynamics under changing inoculation size , with a distinct upper bound in the concentration (~80 pg/ml) achieved in 50% of the virtual cohort (4 cells/ml) compared to patients with more severe inflammation (Ψ ≥ 2.5) who had higher levels .Our model further predicted that patients with higher inflammation had higher IL-6, neutrophil, and inflammatory macrophage concentrations , which il levels  were str −9, see . IL-6 wal cohort . There a−5) was found to be correlated with inflammation (−5) and larger numbers of CD8+ T cells (p <1 × 10−5). Overall, delays in IFN peak did not cause significant changes to viral load but were sufficient to cause major tissue damage (100x reduction in viable tissue remaining) and over-heightened immune responses (4x increase in maximum IL-6 and GM-CSF concentrations).A distinct jump in the timing of the IFN peak in the virtual cohort  as opposed to with IL-6 . As expected, we also found a positive correlation  between the time to peak IFN concentration for each patient and the IFN production rate from infected cells  in comparison to the mild disease simulation. This finding is supported by recent clinical evidence suggesting that the rate of viral decline may be predictive of disease severity ,64. This+ T cells and infected cells remained virtually unchanged across the neutrophil, monocyte, and macrophage knockouts we considered, which is indicative of the link between IFN, infected cells, and CD8+ T cells in our model.Neutrophil and monocyte knockout simulations suggested that excessive suppression of neutrophils early in infection may lead to future hyperinflammation even in otherwise mild COVID-19. The peak ratio between CD8Evaluating SARS-CoV-2 infection in a cohort of 200 virtual patients revealed several immunological responses potentially leading to differential disease presentation. Notably, a distinct, emergent switch in the type I IFN response corresponded with late IFN peaks and more severe disease . This supports previous findings that connect a delay in type I IFN with more severe presentations of highly pathogenic coronaviruses infections including SARS-CoV, MERS-CoV, and SARS-CoV-2 ,13,14, aIn our cohort, virtual patients with mild disease tended to achieve peak IFN concentrations approximately 2 days after infection compared to those with severe disease who exhibited higher inflammation and later IFN responses peaking after 5 days. This switch in IFN timing was caused by a 3-fold increase in the rate of monocyte-to-inflammatory macrophage differentiation and decreased production rate of IFN by infected cells. The initial delay of IFN production was caused by increased monocyte-to-macrophage differentiation and this delay was exacerbated by reduced IFN production from infected cells, suggesting that the timing of the IFN peak in a patient may allow for improved stratification into treatment arms designed to target one or both of these responses. The finding that IFN binding was predictive of the duration of lung tissue damage suggests that virus-intrinsic properties and their ability to inhibit receptor mediated binding and endocytosis could delay IFN production and cause downstream increases in IL-6 and GM-CSF, resulting in severe disease. Our results further suggest that lymphopenia is tightly correlated with maximum IL-6 concentration and less dependent on the timing of IFN.Models by design require simplifying complex dynamics to highlight critical underlying structures, which should then be experimentally or clinically verified. For instance, some unmodeled cytokines have overlapping function and cellular sources to those explicitly modelled here; thus, model predictions may be representative of broader synergistic effects  and otheImportantly, our approach suggests future avenues of experimental studies through a hypothesis-generation and prediction paradigm, suggesting key mechanisms as promising avenues of investigation .Indeed, the ability of our model to recapitulate severe disease by, in part, regulating monocyte differentiation raises the possibility that patients with low monocyte levels  may beneEqs. S1-S22) was developed to examine heterogeneous SARS-CoV-2 infection dynamics and explore the immunological drivers of disease severity. The complete model is provided in the Supplementary Information Section S1 along with all variable and parameter descriptions provided in B is the interacting compound, γ its half-effect value, and h the Hill coefficient [X and cell population Y, the production (recruitment/differentiation) rate of X by Y was denoted by pX,Y and the rate of production of Y by X by pY,X. The half-effect concentration  as produced by infected cells at rate p and cleared via exponential clearance at rate dV, which accounts for all contributions to viral degradation except macrophage- and neutrophil-mediated clearance. Immune-mediated viral clearance via phagocytosis by inflammatory macrophages [δV,N, respectively. Susceptible epithelial cells (S) grow logistically with per capita proliferation rate λS and carrying capacity Smax, and become infected (I) at rate β. The damage inflicted on epithelial cells by neutrophils was modelled using a Hill function  was included to modulate bystander damage of uninfected cells (S and R).We modelled virus  asproteins ,46. The FU and FB) reduces the infectivity and replication capability of viruses by stimulating cells to become resistant to infection [R) proliferate at a rate equivalent to susceptible cells (λS). The concentration of bound IFN (FB) modulates the creation of infected and resistant cells [I). The potency of this effect is controlled by the half-effect parameter ϵF,I. Following the eclipse phase (which lasts τI hours), productively infected cells (I) were modelled to produce virus before undergoing virus-mediated lysis at rate dI. Although various immune cell subsets contribute to infected cell clearance, we limited our investigation to macrophages and effector CD8+ T cells which induce apoptosis at rates δI,MΦ and δI,T, respectively.For the purposes of our study, we only considered type I IFN dynamics . Type I IFN  was assumed to occur through infected cell lysis dI, neutrophil damage/killing of epithelial cells δN, macrophage phagocytosis of infected cells δI,MΦ, macrophage exhaustion δMΦ,D, and CD8+ T cell killing of infected cells δI,T. These dead cells disintegrate relatively quickly [dD, and are cleared through phagocytosis by macrophages [δD,MΦ.The accumulation of dead cells ( quickly  at rate rophages  at rate δRΦ) are considered to be replenished at a logistic rate inversely proportion to viral load with maximal rate of λMΦ and half-effect ϵV,MΦ . We modelled the transition of alveolar macrophages to inflammatory macrophages (MIΦ) as dependent on infected and dead cells, with a maximal rate of aI,MΦ. Resident macrophages die naturally at a rate δMΦ,D.Resident alveolar macrophages (austion)  at rate aI,MΦ, (2) GM-CSF-dependent monocyte differentiation, with maximal production pM,G and half effect ϵG,M, and (3) IL-6-dependent monocyte differentiation, with maximal production rate δMΦ,D.Inflammatory macrophages were modelled as produced by three distinct pathways : 1) stimulated tissue-resident macrophages FB and FU), IL-6 (LB and LU), GM-CSF (GB and GU), and G-CSF (CB and CU). In its general form, this pharmacokinetic relationship is expressed asYU and YB are free and bound cytokines, Yprod is the rate of endogenous cytokine production, kB and kU are the respective binding and unbinding rates, kint is the internalization rate of bound cytokine, and klin is the elimination rate. Here, POW is a stoichiometric constant, A is a scaling factor and X is the sum of all cells modulated by the cytokine withK is the number of receptors specific to each cytokine on a cell’s surface and 10n is a factor correcting for cellular units (see Eqs. S19-S22). The molecular weight was calculated in the standard way by dividing the cytokine’s molar mass (MM) by Avogadro’s number (YMW = MM/6.02214 × 1023).We have previously shown that endogenous cytokine concentrations are not at quasi-equilibrium at homeostasis . TherefoLU) to be produced from productively infected cells, inflammatory macrophages, and monocytes, with bound IL-6 (LB) resulting from binding to receptors on the surface of neutrophils, CD8+ T cells and monocytes. Unbound GM-CSF (GU) was assumed to be produced from inflammatory macrophages and monocytes and bind to receptors on monocytes to create bound GM-CSF (GB). GM-CSF can be produced by CD8+ T cells [CU) is secreted by monocytes, with bound G-CSF (CB) produced via binding to neutrophil receptors. Lastly, because unbound type I IFNs (FU) are known to be produced by multiple cell types in response to viral infection, including lymphocytes, macrophages, endothelial cells and fibroblasts [+ T cells and infected cells  were taken directly from Craig et al. [The pharmacokinetics and pharmacodynamics of G-CSF on neutrophils  was modelled to occur via the bound fraction of G-CSF [CBF = CB(t)/(ACN(t))) at rate pN,L and half-effect parameter ϵD,L. Neutrophils die at rate dN.Neutrophil recruitment of bone marrow reservoir neutrophils (of G-CSF  (CBF = Ctrophils . This waM) are recruited by bound GM-CSF [MR) recruited at a homeostatic rate pM,I with half-effect ϵI,M, and subsequently disappear through differentiation into inflammatory macrophages (as above) or death at rate dM.Monocytes  accounts for the time taken for dendritic cells to activate, migrate to the lymph nodes, activate CD8+ T cells, and the arrival of effector CD8+ T cells at the infection site. CD8+ T cell expansion occurs in response to bound IFN at a maximal rate pT,F with half-effect ϵF,T, and CD8+ T-cell exhaustion occurs with high concentrations of IL-6 [ϵL,T, and apoptosis occurs at rate dT.CD8 of IL-6 ,17, withEqs. S1-S22) to zero (MRΦ = MIΦ = M = N = T = LU = LB = GU = GB = CU = CB = FU = FB = 0). This givesIn an attempt to reduce the degrees of freedom during parameter estimation, we deployed a step-wise approach by isolating subsections of the model. This approach helps mitigate potential issues with parameter identifiability, given that ours is a large, nonlinear model –94 and aR = 0) due to the absence of an IFN equation. This resulted in a simplified ‘viral model’ that considers only virus (V) infection of susceptible cells (S) which creates infected cells (I) after τI days, which the die through lysis, creating dead cells (D).We also assumed there were no resistant cells , givingR) through IFN (FU and FB). The parameter pF,M and pF,MΦ in Eq. S17). Previously-fit parameters were then fixed to their estimated values (dFU/dt = 0 at homeostasis (i.e. V = I = 0), giving To study infection dynamics driven uniquely by IFN, we extended Eqs d values  and the Eq. S23), through fitting effect curves (Eqs. S24-S25) or sub-models (Eqs. S26-S56) to in vitro, in vivo, and clinical data, or by calculating the value that ensured that homeostasis was maintained (Eqs. S57-S70) in the absence of infection. All fitting procedures were performed using MATLAB 2019b functions fmincon or lsqnonlin [Model parameters  were obtsqnonlin . Full deLU,0, GU,0, CU,0 and FU,0), susceptible cells, resident macrophages, monocytes, neutrophils, and CD8+ T cells  were estimated from plasma and lung tissue concentrations in humans [MM), binding sites per cell (K), binding/unbinding rates (kB and kU), internalization rates for GM-CSF, G-CSF and IFN (kint), and cytokine clearance rates (klin), were estimated both from known values in the literature [POW and POW = 1.4608 and + T cell recruitment and expansion parameters were primarily estimated from previous mathematical modelling studies [Eq. S23 (Section S3.9).Initial concentrations of all unbound cytokines  in early infections, we initially fit Eqs 5 TCID50/ml (3 × 108 genome copies/ml) SARS-CoV-2 [To estimate the rates of virus production, decay, infectivity, and infected cell lysis p, dV, β RS-CoV-2  (n = 3) RS-CoV-2  (n = 5).p, as well as the estimates for dV, β, and dI from fitting the macaque date  were assumed to be negligible [dV) and cell lysis (dI) were used as an upper bound for parameter values in the full model to account for additional viral clearance and cell killing of the immune system.Given the heterogeneity in viral loads from human patients , Goyal et al.  estimateque date  to estimgligible . EstimatEqs. S24-S25) curves to in vitro and in vivo experiments. These include the IFN inhibition of viral infection and replication [ϵF,I; Section S4.1.1), the half-effect neutrophil concentration for epithelial cell damage [ICN50,; Section S4.1.2), and the half-effect concentrations for monocyte production and differentiation through GM-CSF signalling [ϵG,M and ηL,M and ϵL,M; Section S4.1.4), and the half-effect of IFN on CD8+ T cell [ϵF,T; Section S4.1.5) and IL-6 on CD8+ T cell expansion [ϵL,T; Section S4.1.6; A subset of parameters was obtained through fitting sigmoidal effect curves , the internalization rate of IL-6  were fit to corresponding time-series measurements [δI,MΦ and δV,MΦ; Section S4.2.4-S4.2.5) were fit to in vitro experiments [pF,MΦ; Section S4.2.6) was obtained by fitting to data measuring IFN-α production [λMΦ and ϵV,MΦ; Section S4.2.7) were estimated from our in vivo observations of resident macrophages during influenza virus infection , and IL-6 production by infected cells and macrophages  were all obtained from fitting reduced versions of Eqs. S1-S22 to in vitro experiments [These parameters were then fixed, and remaining parameters were estimated by fitting time-dependent sub-models of urements –129 usineriments ,130 Eqs. S1-Seriments ,131,132 ηC,M), the production rate of IL-6 and GM-CSF by inflammatory macrophages , the production rate of monocytes by GM-CSF , and the half-effect inflammatory macrophage concentration for IFN production . For some parameters it was not possible to obtain an estimation from the literature, and for these we either set their value equal to an already estimated parameter , epithelial cell proliferation (λS and Smax), and IFN  were fixed to those in For the ‘IFN model’ Eqs –16, paraode45 in MATLAB, and delay differentiation equations (i.e. Eqs. S1-S22) were solved using ddesd in MATLAB.All ODE models were solved using Eqs. S1-S22) by individually varying each parameter by ±20% from its estimated value and quantifying the effect on the model’s output. This change was recorded and used to evaluate different metrics representing the inflammatory response to SARS-CoV-2, namely maximum viral load, maximum number of dead cells, minimum uninfected tissue, maximum number of inflammatory macrophages, maximum number of CD8+ T cells, maximum unbound IL-6, maximum unbound IFN, the total exposure (AUC) to type I IFN, number of days the percent of damaged tissue was >80%, and time of unbound type I IFN peak. We quantified the fraction of undamaged tissue by (S + R)/Smax. For each parameter simulation, we recorded the value of the different metrics listed . For each metric, we then determined the maximum increase and decrease for that metric, and assigned the grid point for that parameter value a colour based on a linear grid of possible values for that metric.We performed a local sensitivity analysis for the full model  was designated as patient-specific after considering the results of the sensitivity analysis ..p from pui and lower li bounds for V, LU, FU and CU were based off these physiological ranges from Munster et al. [p, we used simulated annealing to minimize J(p), i.e.Mi(p) is the model output i evaluated at parameter set p corresponding to the upper and lower bound li and ui , neutrophils (N), and damaged tissue (S + R), each normalized by the virtual cohort’s average. In this way, Ψ measures an individual’s relative change from the cohort’s baseline, and quantifies the contributions of IL-6, neutrophils, and tissue damage on comparable scales. For a given patient j, the inflammation marker is given byn is the total number of patients in the cohort, and Rj are the unbound IL-6, neutrophils, and susceptible and resistant epithelial cell count, respectively. The threshold value for severe diseases (Ψ = 3) was determined given the distinct “jump” in the delay in peak concentrations  to account for the combined changes in IL-6  and z-tests (number of patients ≥ 40) at the α < 0.05 significance level to test the hypothesis that there were no differences between sample means.The Pearson correlation coefficient (R) was used to measure the degree of interaction between two variables, with a significance level of S1 Text(PDF)Click here for additional data file.S1 FigA) Using the measurements by Knaapen et al. [Eq. S25) was fit to the cell viability of RLE cells under various concentrations of H2O2. B) The stimulatory effect curve E (Eq. S24) was fit to the dose response measurements of blood monoculture cells (3 × 103 cells/dish) with various concentrations of murine recombinant GM-CSF (IU/ml) [C) The stimulatory effect curve E (Eq. S24) was fit to measurements for the monocytic myeloid cell count as a function of GM-CSF [D) Eq. S27 fit to time course data of IL-6 production from monocytes [E) IL-6 stimulation of monocyte differentiation to macrophages modelled by the inhibitory effect curve E (Eq. S24) fit to the percentage of CD14+ cells (macrophages) as a function of the number of fibroblasts measured by Chomarat et al. [F) Stimulatory effect curve E (Eq. S24) for IFN-γ stimulation on CD8+ T cells fit to measurements of the signalling in CD8+T cells for varying doses of IFN-γ [A-C&F); solid blue line: corresponding fit.n et al. , the inh (IU/ml) . C) The f GM-CSF  D) Eq. Sonocytes . E) IL-6t et al. . F) Stimof IFN-γ . Data (b(TIF)Click here for additional data file.S2 FigA) Effect curve (Eq. S24) for the IL-6 effect on T cell expansion fit to measurements CD4+ T cells from dilutions of IL-6 by Holsti and Raulet [B) Exponential growth curve fit to the growth of A549 cells [C) The internalization rate of IL-6 (Eq. S30) fit to the fraction of internalized IL-6 [2O2 administration [E) The macrophage clearance of apoptotic material (Eqs. S31-S33) was fit to the percentage of macrophages that had engulfed material over 25 hours [F) The phagocytosis rate of extracellular virus by macrophages was obtained by fitting Eqs. S34-S35 to the uptake of virus by macrophages measured by Rigden et al. [A & F) or mean and standard deviation error bars (B-E); solid blue line: corresponding fit.d Raulet . B) Expo49 cells  C) The ized IL-6 . D) Expostration . E) The 25 hours . F) The n et al. . Data (b(TIF)Click here for additional data file.S3 FigA) Eq. S37 fit to time course of proliferation of monocytes in culture [B) Fit of Eqs. S38-S39 to the production of IFN-α by monocytes after 24 hours with RSV as a function of the number of days of pre-culturing  [C) Correlation between infectious virus titre and RT-PCR copy number for influenza A and B measured by Laurie et al. [50 compared to the RNA copies is plotted for each virus strain and the mean as a black dashed line. D-E) Fit of Eqs. S40-S42 to viral loads [F) The production of GM-CSF from stimulated monocytes was recorded by Lee et al. [Eqs. S43-S46), we obtained the production rates for monocytes and GM-CSF. Data (black) is plotted as either circles/stars (B&F) or mean and standard deviation error bars ; solid blue line: corresponding fit. culture . B) Fit  4 or 7) . C) Corre et al.  The relaal loads  and alvee et al.  Using a (TIF)Click here for additional data file.S4 FigA) Concentration of IFN-β released by alveolar epithelial cells in response to stimulation with influenza virus recorded at 8, 16 and 24 hours [B-C) IL-6 production by infected cells in response to A) H5NA and B) H7N9, measured by Ye et al. [Eqs. S51-S54) in solid blue. D) IL-6 production by macrophages (Eq. S56) in response to stimulation with LPS of varying dosage sizes. Shibata et al. [L,MΦ, ηL,MΦ.24 hours . B-C) ILe et al.  Data (blges Eq. S in respo(TIF)Click here for additional data file.S5 FigA) To confirm that parameters in the model represented realistic immunocompetent individuals in the disease-free scenario, Eqs. S1-S22 were simulated where V0 = 0 and parameters were given by the homeostasis Eqs. S57-S70. The initial concentration of G-CSF was perturbed and compared to simulations of the model at homeostasis. Simulations at homeostasis are represented by solid lines (purple) and perturbed simulations as dashed lines (pink). B) The maximum residual between variables and their initial conditions at day 50 was measured to confirm that the system was stable for perturbations in all immune cells and cytokines.(TIF)Click here for additional data file.S6 FigA) Virus (V) infects susceptible cells (S) making infected epithelial cells (I) which then die to produce dead cells (D) and new virus. B) Comparison of predicted viral dynamics compared to observations from 6 animals, with susceptible cell kinetics (left) with predictions of infected and dead cells over time (right). We estimated β, p, dI, V0 and dV from the reduced model in A) fit to data from Munster et al. [ A reducer et al.  measurin(TIF)Click here for additional data file.S7 FigA) IFN dynamics of the reduced model (Main Text) overlaid with patient IFN-α2 plasma concentrations from Trouillet-Assant et al. [Eqs. 27–33). Individual patient IFN-α2 measurements are plotted as grey circles for IFN-positive patients (n = 21), patients with no IFN measurements  have not been plotted. Healthy volunteer IFN-α2 concentrations are indicated by a grey area. B-F) Mild (solid line) and severe (dashed line) dynamics (Eqs. 27–33 corresponding to simulations in Main Text and U(t)) and mild and severe unbound IL-6 (LU(t)) IFN-negative patients (yellow stars) had no IFN-α measurements and IFN-positive patients (grey points) had non-zero IFN-α measurements. Healthy volunteer concentrations are indicated by a grey area. D) IL-6 levels in patients requiring (“Yes”) and not requiring mechanical (“No”) ventilation obtained by Herold et al. [E) IL-6 concentration in Moderate (“M”) and severe (“S”) COVID-19 patients obtained by Lucas et al. [F) G-CSF plasma concentration obtained by Long et al. [G-I) Neutrophils, monocytes and CD8+ T cells in moderate and severe COVID-19 patients normalized by health care worker (HCW) baseline measurements obtained by Lucas et al. [t et al.  The solid et al.  overlaidg et al.  in sympt(TIF)Click here for additional data file.S8 FigMain Text. Mild disease (solid lines) dynamics obtained by using baseline parameter estimates  Lung cells concentrations (susceptible cells S(t), resistant cells R(t), infected cells I(t), dead cells D(t) and virus V(t)). Solid black line with error bars indicates macaque data (see Main Text). B) Immune cell concentrations (resident macrophages MΦR(t), inflammatory macrophages MΦI(t), monocytes M(t), neutrophils N(t) and T cells T(t)). C) Bound and unbound cytokine concentrations (IL-6 unbound LU(t) and bound LB(t), GM-CSF unbound GU(t) and bound GB(t), G-CSF unbound CU(t) and bound CB(t), type I IFN unbound FU(t) and bound FB(t)).Extension of results of mild and severe disease dynamics in stimates  while se(TIF)Click here for additional data file.S9 FigΦI)), maximum number of CD8+ T cells (max(T)), maximum concentration of IL-6 (max(LU)), maximum concentration of type I IFN (max(FU)), the total exposure to type I IFN (FU exposure), the number of days damaged tissue was >80% (time (S + R)/Smax)<0.2), and the day type I IFN reached its maximum (day max(FU)). The heatmaps show the fold change of each metric, where blue signifies the minimum value observed and red signifies the maximum value observed, or by the number of days, where light to dark pink signifying increasing number of days from zero. The most sensitive parameters are shown in A local sensitivity analysis was performed by varying each parameter ±20% from its originally estimated value and simulating the model. Predictions were then compared to baseline considering: Maximum viral load (max(V)), maximum concentration of dead cells (max(D)), minimum uninfected live cells (min(S+R)), maximum concentration of inflammatory macrophages (max(M(TIF)Click here for additional data file.S10 FigA) viral load, B) uninfected cells, C) inflammatory macrophages, D) neutrophils, E) CD8+ T cells relative to uninfected cells and F) unbound IL-6. This figure is an extension of We performed in silico knockout experiments in the mild disease scenario ((TIF)Click here for additional data file.S11 FigVirtual patients were generated by sampling from normal distributions for a subset of model parameters and optimizing parameters using simulated annealing to confirm realistic disease trajectories see . Resulti(TIF)Click here for additional data file.S12 FigA) human SARS-CoV-2 viral loads [B) IL-6 concentrations from patients [Virtual patients were generated so that viral load, IFN, G-CSF and IL-6 concentration were within physiological ranges obtained in the literature. The physiological ranges  of A) hual loads , B) IL-6patients  plotted (TIF)Click here for additional data file.S1 Table9 cells/ml. Time t is in days. The final sub-table (m) is a list of the variables in the model.Parameters have been grouped into: (a-e) cell related, (f-k) cytokine related parameters, and (l) initial conditions. Relevant references are given for parameters directly estimated from the literature (“Direct estimate”). Parameters obtained through fitting to data in the literature have the appropriate figure noted and data referenced . Remaining parameters were estimated from homeostasis calculation (“Homeostasis”) or qualitatively estimated (“Estimated”). Parameters whose value was taken from another parameters estimation has that parameter noted. Viral load is reported as log(virion copies/ml) and cells have been noted in 10(PDF)Click here for additional data file.


Because genetic deficiency of Fas and Fasl causes the accumulation of double-negative  T cells that have been proposed to derive from CD8+ cells, we decided to explore the role of Fas and FasL in self-antigen-induced CD8 downregulation. To this end, we quantified Fas and FasL induction by different stimuli and analyzed the effects of Fas/FasL deficiency during a protective immune response and after exposure to self-antigens. Our data describes how Fas and FasL upregulation differs depending on the setting of CD8 T cell activation and demonstrates that Fas/FasL signaling maintains CD8 expression during repetitive antigen stimulation and following self-antigen encounter. Together, our results reveal an unexpected role of Fas/FasL signaling and offer a new insight into the role of these molecules in the regulation of immune tolerance.Activation of self-reactive CD8 Fas deficiency causes abnormal accumulation of antigen-specific T cells during chronic (but not acute) viral infections and after T cell activation under steady state conditions (Fas CD95) and FasL play essential roles in immune function that include induction of apoptosis and modulation of T cell activation . In micenditions –4. In ad and FasLFas or Fasl LOF mutations is the accumulation of an unusual population of CD3+ TCR-αβ+ T cells that lack CD4 and CD8   . Becauseive; DN) , 13. How T cells ; (b) a p T cells . Consequ T cells , 16, all T cells , and ant T cells .+ T cells: (a) CD8+ and DN T cells share Vβ usage and CDR3 sequences  micequences –22; (c) activity , 24; (d) as self , 26; (e) as self . Importa as self , primary as self , and pso as self . Therefo as self –32, partlprFas/J (B6.lpr), B6Smn.C3-gldFasl/J (B6.gld), C57BL/6-Tg(TcraTcrb)1100Mjb/J (OT-I), and C57BL/6-Tg(CAG-OVAL)916Jen/J (Act-mOVA) mice were purchased from The Jackson Laboratory . B6.lpr OT-I, B6.gld OT-I, and B6.lpr/gld OT-I were generated by breeding. Mice were housed in SPF conditions on a 12 hour light/dark cycle and had ad libitum access to food and water. All experiments involving mice were approved by the Animal Care and Use Committee of the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (IRE-1725).B6.MRL-lpr, or B6.gld bone marrow cell suspensions by culturing them in full RPMI (10% FBS) in the presence of GM-CSF  during 8 days. BM-DCs (5 x 104), loaded with the indicated concentration of SIINFEKL, were cultured in U-bottom 96-well plates with 2 x 105 CD8+ T cells isolated using CD8α+ T Cell Isolation Kit II (Miltenyi Biotec) from OT-I mice in B6 (WT), B6.lpr, or B6.gld background. For activation with antibodies, 1.25 x 106 OT-I cells were cultured in 48-well plates coated with anti-CD3 and anti-CD28 (2 µg/mL). For quantification of gene expression by RT-qPCR, live T cells were sorted and lysed in TRIzol. Total RNA was reverse transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems) and qPCR was performed using SYBR Green PCR Master Mix (Applied Biosystems). Results were normalized using Actb and are expressed as ΔCt.Bone marrow-derived dendritic cells (BM-DCs) were differentiated from WT, B6.4 c.f.u. of Listeria monocytogenes expressing recombinant OVA  were injected i.v , at room temperature, for 30 min. Cells were washed twice and resuspended in PBS + 2% FCS and acquired in a FACS Aria II instrument (BD Biosciences). Data was analyzed using FlowJo software.Labeled antibodies were purchased from Tonbo, Biolegend, and eBiosciences.  or functional FasL (B6.gld). By crossing OT-I mice with B6.lpr and B6.gld mice, we obtained OT-I cells deficient in Fas and FasL. These cells allowed us to analyze CD8+ T cell activation in cell culture systems devoid of signaling through Fas and FasL  and as CD8 abundance per cell . As shown in gld OT-I cells in the presence of B6.gld BM-DCs, or B6.lpr OT-I cells with B6.lpr BM-DCs  BM-DCs, indicating that BM-DC→T cell signaling through Fas or FasL does not play an essential role in the modulation of CD8 expression during T cell activation or re-stimulation  and B6.gld or B6.lpr OT-I (CD45.2) into CD45.1/2 Act-mOVA mice in a 1:1 ratio  OT-I mice and co-transferred WT and DM OT-I cells . At Day 7, we quantified the abundance of WT and DM cells and analyzed their expression of CD8. As shown in P=0.016) at the peak of clonal expansion (DM : WT ratio 1.45 ± 0.22), consistent with the role of Fas/FasL in re-stimulation-induced cell death  , and allowed us to observe the behavior of OT-I cells during a protective and a tolerogenic immune response and to determine whether the absence of Fas/FasL signaling affects CD8 expression in those circumstances.In vitro experiments showed that CD8+ T cell activation via BM-DCs induced higher expression of Fas (but not FasL) than activation of the same cells with plate-bound anti-CD3 and anti-CD28. Although these two systems are different and it is not possible to weigh the influence of TCR affinity, the fact that BM-DCs induced much higher expression of Fas, suggests that signals different to CD28 and CD3 (e.g. DC-derived cytokines and/or surface molecules) may promote transcription of Fas  or trans signaling (Fas and FasL on different T cells) could avoid CD8 downregulation because the OT-I cells did not require Fas or FasL signals originated from the APC in the presence of cis signaling. The importance of cis engagement for signaling or as competition for ligands presented in trans, has been observed in other T cell co-receptors, for example PD-1, PD-L1 and CD80, HVEM and BTLA, or Notch and Delta  did not cause an accumulation of OT-I cells. Moreover, in concordance with Hao et al., that demonstrated that FasL blockade is necessary for the accumulation of DN T cells in the presence of T cell-specific ficiency , we obseficiency .cis signaling through this receptor pair may protect self-reactive cells from deletion and from CD8 downregulation, perhaps by annulling Fas/FasL signaling from APCs to T cells as has been proposed in other systems  OT-I cells, we completely blocked all Fas and FasL signaling in T cells. However, it would be important to distinguish between the effects of Fas and FasL engagement. Theoretically, Fas-deficient OT-I cells could be adoptively transferred into FasL deficient recipient mice, to observe the effects of T cell FasL signaling in the absence of Fas signaling . The main caveat of these systems is that Fas-deficient cells express FasL and thus induce apoptosis in Fas-bearing cells. Therefore, adoptive transfer of Fas-sufficient T cells into Fas-deficient animals could result in apoptosis of the transferred cells and alter the process of CD8 downregulation. On the other hand, an advantage provided by our system is the opportunity to dissect the individual contribution of Fas/FasL signaling exclusively in the context of the CD8 T cell and avoid the implications of its deficiency in other cell types.Because humans and mice with In summary, Fas and FasL expression are differentially induced on CD8 T cells depending on the conditions that prevail during their priming. High expression of Fas and FasL induced during self-antigen presentation could regulate CD8 expression and cell survival and therefore contribute to the regulation of T cell responses to self-peptides.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was reviewed and approved by the Comité Interno para el Cuidado y Uso de Animales de Laboratorio (CICUAL), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán.Conceptualization, GF-M, NR-R, FR, and JCC. Methodology, GF-M, NR-R, RMR, IM-S, FR, and JCC. Investigation, GF-M, NR-R, RMR, IM-S, and FR. Writing GF-M and JCC. Funding Acquisition JCC. Supervision, FR and JCC. All authors contributed to the article and approved the submitted version.This work was supported by grants from CONACYT  to JCC (CB-2015-256752). GF-M was supported by a doctoral fellowship (CONACYT) and this work will allow her to contend for a doctoral degree .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


E. coli and mammalian cells. We compare protein yields and amino acid specificity for these Acd RSs to identify an optimal construct. We also demonstrate the use of Acd in FLIM, where its long lifetime provides strong contrast compared to endogenous fluorophores and engineered fluorescent proteins, which have lifetimes less than 5 ns.Acridonylalanine (Acd) is a fluorescent amino acid that is highly photostable, with a high quantum yield and long fluorescence lifetime in water. These properties make it superior to existing genetically encodable fluorescent amino acids for monitoring protein interactions and conformational changes through fluorescence polarization or lifetime experiments, including fluorescence lifetime imaging microscopy (FLIM). Here, we report the genetic incorporation of Acd using engineered pyrrolysine tRNA synthetase (RS) mutants that allow for efficient Acd incorporation in both  Acridonylalanine (Acd) is photostable, with a high quantum yield and long fluorescence lifetime in water. An evolved tRNA synthetase (RS) enables genetic incorporation of Acd in mammalian cells and its use in fluorescence lifetime imaging microscopy. One challenge for monitoring these dynamics is the ability to fluorescently label the protein of interest without disrupting its folding and function.E. coli using these amber codon suppression methods.13–18 Acd is a small fluorescent amino acid (222 Å3), with an emission maximum at 420–450 nm, an extinction coefficient of 5700 M−1 cm−1 and a quantum yield of 0.98 in aqueous buffer  pair.13,14,18,21 One key advantage of Acd is its long fluorescence lifetime (τ), which varies with the polarity of the environment and has been used in fluorescence polarization (FP) assays to investigate the dynamics of protein–protein interactions.17 Compared to other visible wavelength fluorescent ncAAs used for in cellulo genetic incorporation,22–25 hydroxycoumarin ethylglycine (Hco), coumarinyl lysine (CoulLys), dansylalanine (DanAla), and acetylnaphthalenylaminoalanine (Anap), Acd has the highest quantum yield, longest fluorescence lifetime, and greatest photostability in water  in al probe . Acd's fpy FLIM, .28E. coli protein expression, as our engineered RSs were based on an Methanocaldococcus jannaschii (Mj) tyrosyl RS and thus were only orthogonal to the translation machinery in prokaryotes. In order to use Acd in mammalian cell imaging experiments, including FLIM, we needed an RS that is orthogonal in mammalian cells.To date, all Acd studies relied on Methanosarcina bakeri (Mb),29Methanosarcina mazei (Mm),29 and Methanomethylophilus alvus (Ma)30,31 have been shown to be orthogonal in both E. coli and mammalian cells. Using engineered Mb PylRS variants that specifically and efficiently incorporate Acd in both E. coli and mammalian cells, we also demonstrate that this new labelling platform can be used for FLIM studies in mammalian cells.Here, we report the development of an Acd incorporation method that relies on an engineered pyrrolysine (Pyl) RS for site specific protein labeling. PylRS variants from Mb PylRS/tRNACUA pair capable of site-specifically incorporating Acd into proteins in response to an amber stop codon (TAG) we screened a library of Mb PylRS variants in which five active-site residues were randomized to all 20 amino acids  using a standard life/death selection in E. coli.32,33 Following a round of positive selection in the presence of Acd and a round of negative selection against canonical amino acids, 96 colonies were assessed for their ability to suppress a TAG codon interrupted sfGFP gene (sfGFP-TAG150) in the presence of Acd and suppressor tRNACUA. The top 25 performing clones were sequenced and 13 unique RS clones were identified with highly similar active site sequences , an undesired side product of Acd synthesis that was recognized by first-generation AcdRSs.13,14 Three of these clones showed permissivity for Npf, and were not considered further.To identify a equences . These 1311S and V366A, as well as W382T or W382V). Mb AcdRS 32 and 82 also had an additional active site mutation of C313A or C313G, respectively, and Mb AcdRS 82 also contained a non-active site mutation, L155V. AcdRS 41 and AcdRS 82 were selected for further evaluation as 82 was most active and 41 featured the same active site mutations, except it retained Cys313 of the parent RS. The two RSs were cloned into both pDULE2  showed activity without the mutation. If necessary, Npf incorporation concerns can be eliminated by using an alternative Acd synthetic route.13The most active and selective variants  had similar mutations in the active site .34 With the exception of one loop region, there is very high active site sequence homology between the Mm and Mb PylRS  and calmodulin (CaM) to investigate the RSs in the context of an intrinsically disordered protein (αS) where the tertiary structure of the protein should not affect incorporation, and a structured protein (CaM) where destabilization by Acd incorporation could decrease protein expression.18 We have previously studied conformational changes and protein/protein interactions in both proteins using Acd.13,16 Each protein construct was fused to a C-terminal GyrA intein containing a six histidine (His6) tag. This fusion construct provides a way to eliminate potential protein truncations, as the intein with the His6 tag is only produced for the full length protein and can be cleaved tracelessly using β-mercaptoethanol.42From the initial screen, Mb AcdRS 41 Mb AcdRS 82 and the cognate tRNACUA. We compared incorporation efficiency, based on isolated yields, and amino acid specificity, based on matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) data. Phe94 and Glu114 were chosen as the incorporation sites as they have been shown to tolerate a variety of bulky fluorophore substitutions, including both ncAA incorporation and Cys modification.16,43–50 As a positive control, we expressed the same protein construct using an Mj RS previously optimized for Acd incorporation in E. coli (Mj AcdRS A9).14 As an additional comparison of anticipated masses and yields, we also expressed the wild-type (WT) αS sequence fused to the His6 intein tag. This serves as an approximate reference mass for any mis-incorporation at the TAG site due to charging of canonical amino acids. Given the structural similarity of Acd to the aromatic amino acids Phe, Trp, and Tyr we anticipated that there was a potential for mis-incorporation of these amino acids. Indeed, Phe mis-incorporation has been previously observed in PylRS mutants selected for incorporation of aromatic unnatural amino acids,52 although the selectivity of engineered PylRSs has been reported to be generally good.53To compare RS efficiency and specificity, we first expressed αS using a plasmid encoding αS with a TAG codon replacing Phe94 or Glu114 as well as a plasmid encoding either E. coli were performed with either pDULE2 or pUltra plasmids, using BL21 DE3 or C321.deltaA cells ,54 respectively. Protein levels were monitored via gel throughout cell lysis, nickel purification and intein cleavage. SDS-PAGE analysis confirmed that full-length Acd-containing αS was produced for both Mb AcdRS 41 and Mb AcdRS 82 using either the pDULE2/BL21 and pUltra/C321 expression methods. However, MALDI MS analysis of purified whole protein constructs demonstrated that significant mis-incorporation of a canonical amino acid occurred with pUltra/C321 with either Mb AcdRS 41 and Mb AcdRS 82  protein quantification assay, showing that AcdRS 41 . The misither RS . Proteine active . TogetheMb AcdRS variants was examined in mammalian cells. Mb AcdRS 41 and Mb AcdRS 82 were separately cloned into the pAcBac1 tR4-MbPyl vector, which contains two copies each of cognate Mm PylRS tRNACUA and Desulfitobacterium hafniense PylRS tRNACUA.55 These plasmids were transfected into HEK293T/17 cells together with a dominant negative eukaryotic release factor one construct (DN-eRF1) that enhances ncAA expression yields in mammalian cells56 as well as a vector encoding enhanced GFP (EGFP) with a TAG codon at position 40 (EGFP-Y40δ). RS efficiency and specificity were tested by measuring GFP fluorescence in the presence or absence of Acd for each Mb AcdRS. Transfections in the absence pAcBac1 tR4-AcdRS provided an additional negative control for read-through of the TAG codon in the absence of a cognate RS and tRNACUA. The trend of incorporation efficiency for the RSs observed in E. coli held in that GFP fluorescence for Mb AcdRS 82 was 3-fold higher than Mb AcdRS 41. Additionally, both RSs showed low EGFP fluorescence in the absence of Acd, indicating little to no mis-incorporation of natural amino acids  MS. We found no evidence of mis-incorporation of Npf with either RS, but trace incorporation of a natural amino acid using AcdRS41  at a 1 : 5 DNA to Lipofectamine 2000 ratio, with imaging after 24–48 h.To determine optimal Acd concentrations for imaging experiments, we expressed EGFP-Y0 μM Acd . We also0 μM Acd . Cell im0 μM Acd . Finally0 μM Acd . Compari676δ-GFP), hyperpolarization-activated cyclic nucleotide–gated ion channel fused to yellow fluorescent protein (spHCN-W355δ-YFP), and maltose binding protein (MBP-E322δ). The GFP and YFP fusions are C-terminal, so production of the fluorescent protein ensures that suppression of the stop codon was successful. Similar to the GFP screening transfection, Mb AcdRS 82 and DN-eRF1 plasmids were co-transfected into HEK293T/17 cells along with a vector encoding the protein of interest with a TAG codon at a site previously shown to permit bulky amino acids.57–59 After washout of unincorporated Acd, we imaged Acd-containing insulin IR-K676δ-GFP, spHCN-W355δ-YFP, and MBP-E322δ in live cells with confocal microscopy.To investigate whether Acd can be used as a fluorescence reporter for protein localization in mammalian cells, we incorporated Acd at TAG mutants in three fusion proteins of varying localization, insulin receptor fused to GFP  in HEK293T/17 cells using FLIM. FLIM spatially resolves the fluorescence decay rate of a sample to produce an image based on its fluorescence lifetime. Endogenous fluorophores typically found in a cell have a fluorescence lifetime in the range of 0.3–7.5 ns.28 Since Acd has a fluorescence lifetime of 15–16 ns, our imaging method is free of background from endogenous cellular fluorophores. For this study, we transfected vectors containing Mb AcdRS 82, DN-eRF1, and IR-K676δ-GFP into HEK293T/17 cells. Imaging the cells using intensity alone while exciting at 375 nm  in blue reveals the autofluorescence in all of the cells. The long lifetime pixels (red) compare well with emission from the GFP tag (excitation at 488 nm). The pronounced autofluorescence signal in 676δ-GFP in different stages of maturation where it is observed in what appears to be ER and/or Golgi in addition to the plasma membrane. Additional FLIM data with spHCN-W355δ-YFP highlighting long lifetime Acd emission are shown in Fig. S28 in ESI.†Since Acd is particularly useful as a FLIM probe, as a proof of concept, we monitored the localization of Acd labeled insulin receptor .61,62 The other genetically-encodable ncAAs all have shorter lifetimes, except DanAla in hydrophobic environments. However, the variation of the DanAla lifetime from 3 ns in water to 10–12 ns in MeOH, DMSO, and THF  or BODIPY derivative (Acd FRET acceptor) could be attached.13 Finally, while Acd's properties are valuable, its low extinction coefficient and blue wavelength emission are not ideal for some microscopy applications, and we will attempt to obtain Mb AcdRS 82 variants that can encode brighter and/or red-shifted Acd analogs such as aminoacridonylalanine.21 We expect that the PylRS may be better for this than the Mj TyrRS as it has been shown to accommodate many large amino acids.53Beyond FLIM applications, E. coli and these proteins were used for in vitro biophysical experiments. Here, we have demonstrated the capability of two PylRS variants to efficiently and selectively incorporate Acd into several proteins in E. coli and mammalian cells. Notably, transfection and imaging experiments were independently conducted in the investigators' laboratories and consistent results were observed. Mb AcdRS 82 afforded higher levels of protein expression in both cell types with excellent fidelity, as confirmed by MALDI and ESI MS, and was used for proof-of-concept fluorescence microscopy and FLIM studies. FLIM has advantages over confocal microscopy because it provides an additional dimension with which to discriminate changes in intensity due to the number of proteins from intensity changes due to solvatochromic effects resulting from conformational changes or interactions with other proteins. To our knowledge, the work described here is the first example of the direct use of a fluorescent ncAA in FLIM (the lone previous example used FRET with GFP).69 The long lifetime of Acd provides a large dynamic range for FLIM measurements, making it superior to GFP variants or the other genetically encodable ncAAs. This, in addition to Acd's high quantum yield and photostability, make Mb AcdRS 82 a valuable new tool for the protein imaging community, enabling facile labelling of proteins in mammalian cells with a small, non-perturbing fluorophore that has unique properties.Acd has been previously demonstrated to be useful as a fluorescent marker and as a biophysical probe to monitor protein conformations without perturbing protein structure. Prior to this study, Acd labelling was limited to proteins expressed in Mb PylRS mutants a life/death selection as previously described.70 Briefly, RS library members in a pBK screening plasmid were transformed into E. coli cells with a positive selection plasmid, pREP-pylT, containing an amber codon-disrupted chloramphenicol acetyltransferase gene and pylRS tRNA (pylT). Cells containing the pBK-RS library and pREP-pylT were cultured in the presence of chloramphenicol and Acd. Surviving pBK RS library members were isolated and the pBK plasmid was transformed into cells containing a negative selection plasmid, pYOBB2-pylT, encoding an amber codon-disrupted barnase gene under control of an arabinose promoter as well as pylT. Cells containing pBK-RS library and pYOBB2-pylT were cultured in the presence of arabinose, but in the absence of Acd. The remaining pBK-RS library from the surviving colonies was transformed into cells containing a GFP reporter plasmid, pALS-GFP-TAG150. Cells containing pBK-RS library and pALS-GFP-TAG150 plasmids were grown in 96 well plates in autoinducing media  analogue (PDB 2ZIN) was used as the basis for Rosetta modeling.34 Firstly, the ATP analogue was removed, and the amino acid ligand was replaced with Ala. Subsequently, missing loops were modeled using Rosetta Remodel and the structure was relaxed using the beta_nov16 scorefunction.71 Upon active site mutations, Acd was initially positioned using DARC and several relaxations of the structure were performed.72 The final structure was analyzed in PyMol.73Mj AcdRS A9, Mb AcdRS 41, or Mb AcdRS 82) in the previously described pDULE2 vector,35 containing tRNACUA, were separately transformed into BL21 cells that also contained the pTXB1_αS-TAG114_Mxe-H6 plasmid. After growth in auto-inducing media  and pEGFP-Y40TAG-C1 to evaluate suppression efficiency. Transfections were also performed in the absence of pAcBac1 tR4-MbAcdRS41 or pAcBac1 tR4-MbAcdRS82 as negative (−RS) controls. Transfections with DN-eRF1 and pEGFP-C1 (WT EGFP) were used as positive controls. Transfections were performed with and without DN-eRF1 as well as with varying Acd concentrations, incubation times, and Lipofectamine 2000/DNA ratios to identify optimal expression conditions. Transfections were evaluated by imaging using an Olympus CKX53 microscope and by measuring the EGFP fluorescence of cell lysates using a Tecan M1000 plate reader.74 Transfection and imaging conditions are described in detail in the ESIpAcBac1-tR4-MbAcdRS82 and DN-eRF1 plasmids were co-transfected into HEK293T/17 cells along with either pIR-TAG676-GFP, pcDNA3.1HE-spHCN-TAG355-YFP, or pcDNA3-FLAG-MBP1-E322TAG (FLAG-MBP1-E322TAG-W340W.pcDNA3-k) plasmids, using a total of 1.6 μg DNA. Confocal imaging. was performed on a Zeiss LSM 710 microscope in and analysed using Fiji software.Cells were prepared and transfected as described for confocal microscopy experiments. FLIM experiments were performed at room temperature in DMEM supplemented with 10% FBS, penicillin, and streptomycin using an ISS Q2 laser scanner and ISS A320 FastFLIM system mounted on a Nikon TE2000U microscope. Acd was excited using a 375 nm laser and GFP and YFP were excited using bandpass filters to select lines of a YSL SC-PRO 7 supercontinuum laser. FLIM data were acquired and analysed using ISS VistaVision software.E. coli, and pAcBac1-tR4-MbAcdRS82, for expression in mammalian cells, are given in the ESI.via Addgene. Acd is available upon request to Prof. Petersson and is commercially available from Watanabe Chemicals.Sequences of the pDULE2-MbAcdRS82, for expression in Primary data for photophysical measurements and synthetase screening, as well as computational modeling code and atomic coordinates, are provided as ESI.†E. coli incorporation studies, and optimization of mammalian cell incorporation. R. J. B. screened Mb PylRS mutants. A. J. E. performed mammalian cell mass spectrometry analysis of Acd incorporation. M. M., W. N. Z. and S. E. G performed confocal imaging and FLIM experiments. D. M. R. assisted in the synthetase screen and performed the initialE. coli experiments in C321 cells. T. S. M. provided computational docking models. Y. V. synthesized Acd. C. M. J and E. J. P. wrote the manuscript with input from all authors.Experiments were designed collaboratively by C. M. J., E. J. P., W. N. Z., S. E. G., and R. A. M. C. M. J. performed the photophysical characterization of all Uaas, There are no conflicts to declare.SC-012-D1SC01914G-s001SC-012-D1SC01914G-s002SC-012-D1SC01914G-s003SC-012-D1SC01914G-s004SC-012-D1SC01914G-s005SC-012-D1SC01914G-s006SC-012-D1SC01914G-s007SC-012-D1SC01914G-s008SC-012-D1SC01914G-s009SC-012-D1SC01914G-s010SC-012-D1SC01914G-s011SC-012-D1SC01914G-s012SC-012-D1SC01914G-s013SC-012-D1SC01914G-s014SC-012-D1SC01914G-s015SC-012-D1SC01914G-s016SC-012-D1SC01914G-s017SC-012-D1SC01914G-s018SC-012-D1SC01914G-s019