The design of minimum CRISPR RNA (crRNA) sets for detection of diverse RNA targets using sequence degeneracy has not been systematically addressed. We tested candidate degenerate Cas13a crRNA sets designed for detection of diverse RNA targets (Lassa virus). A decision tree machine learning (ML) algorithm (RuleFit) was applied to define the top attributes that determine the specificity of degenerate crRNAs to elicit collateral nuclease activity. Although the total number of mismatches (0–4) is important, the specificity depends as well on the spacing of mismatches, and their proximity to the 5’ end of the spacer. We developed a predictive algorithm for design of candidate degenerate crRNA sets, allowing improved discrimination between “included” and “excluded” groups of related target sequences. A single degenerate crRNA set adhering to these rules detected representatives of all Lassa lineages. Our general ML approach may be applied to the design of degenerate crRNA sets for any CRISPR/Cas system. Nucleic acid-based detection methods such as Polymerase Chain Reaction (PCR), being practically a gold standard for infectious disease diagnostics, offer high specificity and sensitivity but are limited by high cost, the requirement of expensive instrumentation and need for highly trained personnel. Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-based detection methods and CRISPR-Associated Protein (CAS) systems emerged recently as an alternative to PCR-based diagnostics with the potential to develop less complex isothermal detection assays but offering the diagnostic accuracy of PCR2 or RNA (SHERLOCK)3 targets using Cas12 and Cas13, respectively. Empirical design rules for RNA targets using Cas13 were originally based on observed experimental results of collateral cleavage of fluorescent molecular beacons following specific activation of Cas13 using a relatively small number of candidate crRNAs5. More recently, Metsky et al.6 reported a systematic approach for automated, target-specific crRNA design for large numbers of different pathogens using neural network-based machine learning (ML) algorithms trained on data obtained from massively parallel Cas13a crRNA collateral nuclease screens7. That approach depends mostly on selection of multiple specific guide RNAs, usually tolerant of ≤ 2 spacer region mismatches, in order to cover a broad phylogenetic target range.CRISPR detection applications invariably depend on the optimal “design rules” for CRISPR guide RNAs (crRNAs) against specific DNA (DETECTR)8 The authors designed and tested a small number of degenerate crRNAs for detection of highly variable Lassa virus (LASV) targets representing lineage II and lineage IV. The crRNA sets were found to be specific for the LASV lineages. That work did not involve testing of comprehensive sets of degenerate crRNAs nor exploration of cross-reactivity with other LASV lineages and with closely related viral species. In another, more recent study by Li et al.9 the researchers developed an assay for detection of multiple lineages of Crimean-Congo hemorrhagic fever virus (CCHFV), another highly variable RNA virus, using a single degenerate crRNA. That study demonstrated that the degenerate crRNA used was not cross-reactive with closely related viruses and could tolerate up to 3 mismatches and still activate the collateral RNAse activity of Cas13a enzyme. However, there was no attempt to develop general design rules for application to arbitrary phylogenetic groups.The concept of degenerate crRNAs for viral detection was reported recently by Barnes et al.10, or make use of convolutional neural networks11. The work presented here was directed at a ML determination of degenerate crRNA spacer sequence attributes that would prove easy to use for prospective design of Cas13a detection assays. This builds on our prior work using high-throughput crRNA screening aimed at finding the common characteristics of crRNA molecules for use in Cas13a based detection assays13. Our initial goal was to experimentally identify the smallest number of degenerate crRNAs that could activate Cas13a collateral activity to produce a simple binary result for the presence or absence of any member of a phylogenetically diverse group. Subsequently, we applied the RuleFit decision tree machine learning algorithm to find the spacer sequence attributes that determine Cas13a nuclease activity.Several groups have applied ML models to assist in guide design for Cas9-based CRISPR applications that range from simple linear regression models to more complex deep learning models including the bidirectional LSTM (long short-term memory networks) that is part of DeepHF prediction softwareSelection of conserved regions of LASV genomes,Tiled design of degenerate sequences in the complementary spacer regions of crRNAs across those conserved genomic regions,High-throughput screening of the candidate degenerate crRNAs against complementary synthetic targets,High-throughput screening of the selected high performing degenerate crRNAs against targets representing all lineages of LASV (our intended target) and target representing closely related viral species (near-neighbors), and,Use of a machine learning algorithm to analyze the datasets obtained in step (4) to identify the generalizable crRNA design rules for detection of highly variable targets.Using LASV as a model taxon, we approached this using the following steps:Our results demonstrate that besides total spacer region mismatches, the specificity of a degenerate guide set for a selected phylogenetic group depends as well on the spacing of the multiple mismatches relative to each other, the proximity of multiple mismatches to the 5’ end of the spacer and identity of the protospacer flanking sites (PFS). The generality of our ML approach may be applicable to the design of degenerate crRNA sets for other CRISPR/Cas systems.Our first step was to empirically observe which degenerate crRNAs might allow for high-performance Cas13a-based detection of LASV target sequences located in L and GPC genes. The spacer sequences for large sets of tiled crRNAs contained varying levels of degeneracy. Degenerate sequences are mixtures of all permutations of sequences with alternative nucleotides in degenerate positions. Two types of degenerate nucleotides were used: R (A/G) and Y (C/T). The number describing the degree of degeneracy reflected the number of distinct sequences present in the preparation of a specific degenerate crRNA. The degeneracy of each crRNA used is displayed in Supplementary Figure 13. The results of the testing are shown in Supplementary Figure 13, the majority of the crRNAs (from 82 to 93% depending on the target sequence) were classified as showing high or medium performance and only a small fraction (from 7 to 18%) of crRNAs produced no or low signal. The number of low performing crRNAs was higher for L targets (12–18%) than for GPC targets (7–11%) and for each of these target sequences the number of low performing crRNAs was higher for lineage IV than lineage II sequences. Importantly, these results indicate that degenerate crRNAs have similar performance to that observed for regular crRNAs (with non-degenerate spacer sequences).Performance of the crRNAs was assessed by measuring the total fluorescent signal produced in the Cas13a assay integrated over 2 h in the presence of 3 nM target RNA as described previouslyEach of the degenerate crRNA preparations contained multiple crRNA molecules with variable spacer sequences, where some of the preparations potentially contain over a thousand of different sequences. We conducted multiple experiments in order to find out if such a high level of multiplexing results in detection of non-specific sequences and how it affects the limit of detection (LOD) of the assay.Yersinia pestis lcrV target RNA) and LASV GPC, lineage IV target RNA at 3 nM. The results of these experiments are shown in Supplementary Figure Four high-performing crRNAs designed for detection of L target  were tested for background cross-reactivity and limit of detection (LOD) determination. The degeneracy of these crRNAs ranged from 16 to 1024 . To test for potential cross-reactivity with non-specific targets, the Cas13a activity assays were performed using high molecular weight human total genomic RNA at 25 ng/µL or high copy short synthetic RNA targets . These were tested against a panel of 12 target sequences representing all currently known LASV lineages (I through VII) and eleven near-neighbor Old World arenaviruses (OWA) closely related to LASV . Figure 11.Figure The RuleFit classifier was trained to predict whether a guide/target pair would yield a signal above or below threshold  using the three mismatch datasets (based on Watson–Crick pairing or two versions of G-U wobble pairing). The output accuracy after tenfold cross-validation was 95.0% ± 2.0% for the Watson–Crick pairing dataset. The model had similar accuracy using the G-U wobble datasets (95.4% ± 1.4% for asymmetric G-U wobble and 93.6% ± 1.9% for symmetric G-U wobble). A representative confusion matrix of the predictions for Watson–Crick pairing dataset is shown in Fig. 14. While the exploration of mechanism of target RNA binding and Cas13a RNase activation was outside of the scope of this study, the obtained results were generally in line with the previous findings. For example, no spacer/target pair with three consecutive mismatches in the “seed region” was found among pairs classified as positive based on Cas13a activity assay. Overall, the properties of the analyzed features suggest that, while overall mismatch count (n) was the most important, the location and distribution of those mismatches and identity of a nucleotide at PFS #1 had the strongest relative impact on classification .Using this model, we interrogated the rules for feature importance see Fig.  shows thThe RuleFit classifier model identifies the prediction rules associating the most important features. For some crRNAs like #5, the classification is almost perfectly correlated with number of mismatches (n). For crRNA #5 spacer/target pairings, 17 out of 18 with n < 4 are positive while for pairings with n ≥ 4, all 30 out of 30 are negative. However, more discriminating rules involving more features are needed for predicting classification of other spacer/target pairing. For example for all spacer/target pairs with 3 mismatches, only 73% have positive signals. After filtering this group using IQR ≥ 6.25, 89% are positive. Finally, after selecting only the guides where PFS_1 is not G, a 100% positive rate is achieved.The top ten rules identified for the Watson–Crick dataset are provided in Supplementary Table An example decision tree used to generate prediction rules is shown in Fig. 15. One way to solve this issue may include multiplexing or use of degenerate primers17. The goal of this study was to assess the utility of crRNAs with degenerate spacer sequences for detection of highly variable RNA targets using the Cas13a based CRISPR detection assay. We used LASV sequences as a model of a highly diverse taxon. LASV, a viral pathogen and an etiologic agent of a highly lethal hemorrhagic fever (Lassa fever—LF)18, has always posed difficulty for the development of nucleic acid based assays due to its high variability with close to 30% divergence on the RNA level20. An alternative approach using “universal” crRNA for Cas9-based editing of polymorphic genes was shown by Krysler et al.21 That approach utilized nucleotides such as inosine . In comparison, our approach uses degenerate crRNA sets comprised of standard RNA nucleotides. Our work is confirmatory of other Cas13a crRNA design assumptions that 2 and 3 spacer mismatches are tolerated for specific  and degenerate  crRNAs, respectively. We show for the first time that in order to rigorously extend the use of degenerate crRNAs for inclusion of diverse phylogenies, other determinants of spacer sequence effectiveness and specificity must be considered.Nucleic acid amplification based detection technologies  rely on recognition of target sequences by specific primers. In the case of highly variable targets, finding the primers sets without mismatches to at least some variants of the target may be challengingThe immediate utility of degenerate crRNAs lies in the minimization of guides needed whenever fine discrimination of a diverse taxon is not required and the number of crRNAs (with corresponding sensor bandwidth) must be minimized, most notably in handheld or lateral flow assay formats. Our results show that this can be accomplished without loss of sensitivity or cross-reactivity with high levels of background RNA. The specific feature ranges will likely be dependent on tradeoffs between specificity and signal levels. In this study, we arbitrarily defined threshold signal as > 20% of maximum at 2 h. In practical terms, the design feature ranges should be comparable when using initial signal rates versus final signal levels and different threshold signal percentages.Assemble and align representative set of sequences for both “inclusion” (taxon on interest) and “exclusion” ;Scan genome assemblies of “inclusion” sets to locate candidate nucleotide regions that can be populated with degenerate nucleotides to cover the entire inclusion set while still satisfying degenerate guide RNA design rules. If multiple guides are needed for a given inclusion taxon, identify those.Apply guide design rules to identify any possible false positive signal generation by scanning “exclusion” taxon sequences. Eliminate those candidate guides if detected.Perform limited high-throughput screening of remaining candidate degenerate guides to experimentally confirm signal generation and lack of cross-reactivity.A generalized approach for predictive application of our design rules to Cas13a activation . Application of ML to different CRISPR/Cas protein combinations can in principle be readily achieved using analogous experimental and ML methods described herein.The RuleFit based classifier presented in this study was validated using sequences of only one highly variable target represented by LASV and its close neighbors. To ascertain that the algorithm is not biased towards specific patterns present in LASV sequences and is more generally applicable, testing with other highly variable targets will be needed. Another limitation of the study is that only guides having relatively fixed composition (constant spacer and hairpin lengths) were screened for their ability to activate LwaCas13a collateral nuclease activity at greater than an arbitrarily-defined threshold, as measured following the completed reactions with molecular beacons. Specific applications may require shorter reaction times as well as different arbitrary cut-off thresholds for collateral activation. These parameters can be explored using the complete reaction data set , C/T: Y (pyrimidine)) were manually identified in the aligned sequences. While other types of degenerate nucleotides are available : A/T, S (strong): C/G and several others) there was no need to use other kinds of degenerate bases for the consensus sequences obtained in this study. Consensus target sequences for the crRNA spacer sequence design were created by using the isolate Nig08-A37 sequences for lineage II targets and isolate SL15 sequences for lineage IV targets as the base sequences. The polymorphic positions identified in the alignments were replaced with degenerate bases either R (A/G) or Y (C/T)—(Supplementary Table 13. The degeneracy of crRNAs used in this study ranged from 2 (1 degenerate base) to 4096 (12 degenerate bases), Supplementary Figure The degenerate consensus sequences for crRNA spacer design were generated as follows. For the lineage II, four partial GPC and L sequences from human isolates from Nigeria were aligned. For the lineage IV, 35 partial GPC and L sequences obtained from both human and rodent LASV isolates from MRU were aligned  were selected for testing the selected highly performing crRNAs. Synthetic DNA fragments of the L gene covering the same region as the DNA fragment were used for synthesis of lineage II L target RNA described earlier. PCR primers with T7 RNA polymerase promotor built-in were also designed for each of these sequences as described above. See Supplementary Table 12. The individual transcription reactions were performed in 25 μL total volume. This included 0.5 μL of 100 μM T7 forward primer, 1.5 μL of 100 μM crRNA-encoding DNA oligonucleotide, 1.25 μL of T7 RNA polymerase, 9.25 μL of 2 × NTP buffer and 12.5 μL of nuclease-free ddH2O. The reactions were carried out for 2 h at 37 °C. The obtained crRNAs were used in Cas13a activity assays without additional purification. This approach has been validated in two prior studies13.The degenerate crRNA molecules were obtained by conducting in vitro transcription of crRNA encoding DNA oligonucleotides described above. The oligonucleotides were purchased from Eurofins Genomics , and listed in Target RNA sequences were also prepared using HiScribe™ in vitro transcription system. Fragments of LASV GPC or L genes were amplified, using primers listed in 2O . The transcription reactions were incubated at 37 °C for 2 h after which 5 μL Turbo DNAse  and 15 μL of nuclease-free ddH2O were added  and incubated further 30 min at 37 °C to remove the template DNA. The obtained transcript preparations were cleaned up using using RNA Clean and Concentrator 25 kit  according to the manufacturer instructions. The RNA concentration was determined using Qubit fluorometer and RNA BR (broad range) assay kit (ThermoFisher). The template solutions were diluted to 150 mM for use in Cas13a activity assays.The transcription reactions were set up using 2 μL of unpurified DNA amplicon preparation, 2 μL of T7 RNA polymerase, 10 μL of 2 × NTP buffer and 16 μL of nuclease-free ddH12, Supplementary Figure Both crRNA synthesis and Cas13a activity assays were conducted using a high throughput workflow in 384 well plates with fluid transfer handled by Echo 525 acoustic liquid handler  using the Plate Reformat software provided by the manufacturer as described earlierg to bring all the liquid to the bottom of the wells and remove air bubbles. The plate was sealed using MicroAmp Clear Adhesive Film sealer (ThermoFisher) and incubated for 2 h at 37 °C. After incubation, the plates with transcribed crRNAs were stored in − 80 °C. For long term storage MicroAmp sealers were replaced with Adhesive PCR Sealing Foil .In order to generate crRNAs, 27–50 crRNA transcription reactions were set up using reagent volumes as described above for an individual reaction. The reactions included the tested crRNAs and a negative control (crRNA template oligo replaced with TE buffer). First, master mix containing all reaction components except for template oligonucleotide were distributed using Echo instrument from 6 well Echo qualified reservoir plate (cat# ER-0050) to Echo qualified 384 well microplate (cat# ER-0050). 23.5 μL of the master-mix were transferred to each well. Subsequently 1.5 μL of the crRNA template oligonucleotides were added to each well containing master-mix using Echo instrument from a previously prepared Echo qualified 384 well microplate. The plates were spun briefly in a centrifuge at approximately 1500 L. wadei4 which was synthesized and purified by GenScript Biotech . The enzyme was stored and diluted using the storage buffer . Each nuclease activity assay was performed in 20 μL reaction that included 1 μL of 1 μM Cas13a, 1 μL of 2 μM RNase alert v.2 , 17.2 μL of nuclease assay buffer , 0.4 μL of crRNA (from unpurified transcription reaction) and 0.4 μL of 150 mM target RNA. For each crRNA a total of six reactions were set up, with three target negative reactions and three target positive. First, master mix containing all reaction components except for crRNA and target RNA were distributed using Echo instrument from 6 well Echo qualified Reservoir plate to a 384 well assay plate . A total volume of 19.2 μL of the master-mix was transferred to each well. Next, 0.4 μL crRNAs from previously prepared 384 well microplate were transferred using Echo instrument to the wells containing the master-mix in such a way that each crRNA was added to 6 subsequent wells in the reaction plate. Finally, 0.4 μL of the target RNAs (previously placed in the area of the crRNA plate not occupied by transcribed crRNAs) were added to three of the wells for each crRNA. The Cas13a reaction plates were spun briefly in a centrifuge at approximately 1500 × g to bring all the liquid to the bottom of the wells and remove air bubbles. Immediately after spinning, the reaction plates were sealed using the MicroAmp sealers. The plates were incubated in Biotek Synergy Neo2 plate reader  at 37 °C and fluorescence was read from the bottom of the wells every 5 min for 2 h using excitation at 490 nm, emission at 520 nm and gain set at 100.To determine the efficacy of each crRNA, Cas13a nuclease activity assays were conducted using Cas13a enzyme from The integrated background corrected final fluorescence values reflecting the Cas13a RNase activation for each of the crRNAs was calculated by subtracting the sum of averages of fluorescence measured for template negative samples over the course of the experiment (25 measurements) from sum of averages for template positive samples.The crRNAs were classified into three groups based on the integrated, background subtracted, fluorescent signal relative to the highest signal obtained. Thee performance classes were defined: high performance , intermediate  and low .24, and then applying binary labels for match/mismatch for each base and each spacer/target pairing.A dataset was constructed based on the results of a series of assays testing the performance of eight selected crRNAs  designed for detection of LASV L target with a panel of targets containing 12 LASV sequences representing all known LASV lineages and 11 viral species closely related with LASV. Each data entry included a list of positions of mismatches between the crRNA spacer and the corresponding target sequence together with a fluorescent signal obtained in the Cas13a activity assay using this crRNA spacer/target combination. To identify the mismatch positions the target sequences (converted to DNA sequence) and reverse complements of the crRNA spacers containing degenerate residues  were compared. Mismatches were identified by first decomposing R and Y degenerate bases to A/G and C/T, respectively, based on IUPAC definitions25. However, the results of a recent study on spacer/target interaction in CRISPR-Cas13a system the G-U pairs functioned as a match only in the configuration in which G was in the spacer and U in the target sequence6. For the above reasons we created two additional mismatch datasets: one treating all the pairings corresponding to G-U pairs in RNA sequences as a match (symmetric G-U wobble dataset) and another in which the pairings corresponding to the G-U wobble were treated as a match only in the cases when G was in the crRNA spacer and U was in the target (asymmetric G-U wobble dataset). Each of the final datasets had 192 entries.For the baseline mismatch dataset the mismatch positions were determined according to the standard Watson–Crick pairing rules. However, while we used the DNA versions of the spacer and target sequences for the mismatch determination the actual molecules interacting in the Cas13a activity assays are RNA molecules. It was found that in RNA-RNA pairing the so called “G-U wobble” pairing has a similar thermodynamic stability to the Watson–Crick base pairs (G-C and A-U in case of RNA)14. Another set of the features describes distribution of the mismatches across the spacer . Spacer/target pairs with a single mismatch were given position values of 0 for calculations of features related to mismatch distribution. To calculate the interquartile range (IRQ) for a set of mismatch positions corresponding to a particular spacer/target pair the quartiles Q1 and Q3 were determined and IRQ was obtained by subtracting Q1 value from Q3 (IQR = Q3 – Q1). The IRQ value reflects the uniformity of distribution of mismatches across the length of the spacer. Values of IRQ close to 14 indicate a uniform distribution of mismatches along the spacer while values much lower than 14 corresponding to mismatches arranged as a single cluster and values much higher than 14 corresponding to mismatches arranged in two separate clusters. Final set of features was related to the protospacer flanking site (PFS) identity  and these features reflected the bases present at the FPS locations of the target sequences.For each of the dataset entries 22 features were extracted or calculated from the mismatch data and target sequences. The features describe various aspects and properties of the specific spacer/target pair. The detailed description of the features is summarized in the Supplementary Table The results of the Cas13a activity assays using a particular crRNA spacer/target combination were designated as either positive or negative based on the following criteria: a sample was evaluated as negative if the cumulative fluorescent, background subtracted, signal was less or equal of 20% of the maximum signal obtained in the experiment (low performing crRNAs), and positive if it was greater than 20% of the maximum signal obtained in the experiment (medium and high performing crRNAs). According to these criteria, 108 spacer/target pairs were labeled negative and 84 were labeled positive.26. However, their main advantage is in their interpretability, as each rule in the ensemble is a simple statement related to the individual features in the input dataset. This property of RuleFit allows for clear ranking of the relative importance of each feature, and allows to better understand their data and the predictions.Models were built to classify the spacer/target combinations as producing positive or negative assay outcomes. Rule-based models such as RuleFit use ensembles of linear models to construct either classification or regression predictions that have been shown to be comparable in accuracy as the best alternatives27. Rule based Learning Ensembles (RuleFit) were assembled with the XRF package26. The number of trees contained in the ensemble was set to 2, maximum depth of the tree was set to 3, and the L1 regularization parameter was set to 0.01; all other parameters were set to defaults.The classification model was generated in R using the Tidymodels series of packagesSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Yersinia pseudotuberculosis is regulated by quorum sensing (QS) and dependent on the haemin storage locus hms, required for the extracellular polysaccharide poly-N-acetylglucosamine (poly-GlcNAc) production. In Escherichia coli NagC regulates both GlcNAc biosynthesis and metabolism with GlcNAc acting as a signal that co-ordinates these and other activities. However, the contribution of NagC and GlcNAc to biofilm development in Y. pseudotuberculosis is not known. Here we show that a Y. pseudotuberculosis nagC mutant is impaired for biofilm production on abiotic (glass) and biotic (Caenorhabitis elegans) surfaces. Genetic complementation restored poly-GlcNAc production and biofilm formation on C. elegans. Using lux-based promoter fusions, hmsHFRS expression was found to be nagC dependent. Given that NagC and QS both regulate aggregation and biofilm formation, we investigated their regulatory relationship using lux-based promoter fusions. These revealed that (i) nagC is negatively autoregulated, but expression can be partially restored in the nagC mutant by exogenous GlcNAc, (ii) NagC negatively regulates the ytbI and ypsI QS genes and (iii) nagC expression is reduced in the ytbI, ypsI and ypsR mutants but not the ytbR mutant. These data establish the existence of a reciprocal regulatory relationship between NagC and QS, which in the case of the luxRI pair ytbRI, is also GlcNAc-dependent. NagC and GlcNAc are therefore components of a regulatory system involving QS that modulates biofilm formation and aggregation.Biofilm formation by  Yersinia pseudotuberculosis is a highly adaptable psychrotrophic pathogen which, in humans causes self-limiting enterocolitis but can adapt to survive in a range of hosts and external environments. Part of this successful survival strategy relies on the production of a self-generated extracellular polymeric (EPS) adhesive matrix, usually referred to as a biofilm    were alshmsHFRS promoter fusion to luxCDABE a 687 bp PCR product upstream to hmsH (primers VG-Phms-F2-ApaI and VG-Phms-R-XhoI) was cloned into pBluelux [hmslux:: was cloned into pDM4 as an ApaI/SpeI fragment for conjugation. Unfortunately, the bioluminescent signal was too low for detection and therefore the vector was linearized (ApaI), Klenow treated and the cassette excised with BamHI and cloned (SmaI and BamHI) into the low copy number vector pHG327 to give pHG::Phmslux:: which was cloned into the nagC mutant and its complement.To construct the pBluelux  as an Ap600 of 0.006. Then 300 µl samples were transferred (×3) into a 96-well microtitre plate and luminescence as relative light units (RLU) and OD measurements were taken every 30 min for 18 h at 37 °C or 26 °C using a Tecan GeniusPro luminometer/spectrophotometer. The expression profiles were plotted as RLU/OD against time and the curves were used to calculate the area under the curve (AUC) for each strain [Lux-based reporter fusion assays were performed by diluting overnight cultures (30 °C) 1 : 100 and re-growing to mid-log phase, washed twice and diluted to ODh strain . For simY. pseudotuberculosis parent, nagC mutant and the genetically complemented mutant were grown in 5 ml YLB-MOPs  to an OD600 of 1.0 at 37 °C. Cells were harvested and 1 ml of cell free supernatant was mixed with an internal standard (d9-C5-AHL) at a final concentration of 0.1 µM. The deuterium component of the internal standard allows differentiation from C5-AHL by mass spec and allows the resulting concentration to be used as a control marker. Then 0.5 ml of 0.1 % acetic acid in ethyl acetate was added to the supernatant before the sample was vortexed for 30 s and centrifuged at 2000 g for 5 min. The upper organic layer containing solubilised AHLs was pooled in a fresh tube and the process repeated twice more on the lower inorganic layer. The pooled sample was then dried using a centrifugal evaporator and resuspended in 10 µl acetonitrile.AHL analysis was performed using an adapted protocol described by . The Yet al., (2011) [−1. After a 5 µl sample injection, the binary gradient increased from 0 % B to 40 % B over 0.5 min. This then increased further to 99 % B over 6.5 min. After 1 min at this composition, percent B decreased to 0 over 0.1 min and remained at this composition for another 1.9 min with a total run time per sample of 10 min. The mass spectrometry (MS) system was an AB Sciex Instruments Qtrap 6500+ hybrid triple-quadrupole linear ion trap mass spectrometer, equipped with an electrospray ionisation (ESI) interface. Instrument control, data collection and analysis were conducted using Analyst software (v 1.7.3). Source parameters were set as: curtain gas: 20.0, ion source potential: 5500 V, source temperature: 450 °C, GS1 : 50.0, and GS2 : 50.0. AHL Synthetic standards were synthesised in house and dried, extracted samples were stored at −20 °C. Prior to analysis, each sample extract was redissolved in 50 µl of MeOH. LC-ESI-MS/MS methods were used for the analysis of AHLs in bacterial supernatant extract samples. Analysis was conducted with the MS operating in MRM (multiple reaction monitoring) mode, screening the LC eluent for the specific analytes of interest.AHL analysis was conducted using a modified version of the method reported by Ortori , (2011)  whereby C. elegans, biofilm infection assays, calculation of the biofilm severity index (BSI) and confocal laser scanning microscopy (CLSM) were performed as previously described [Yersinia spp. biofilms [C. elegans or on cells grown in planktonic cultures [Y. pseudotuberculosis were fluorescently labelled by transforming with pSB2019 [Routine culturing of escribed . The lecbiofilms  and whencultures . Y. ps pSB2019  which coYersinia defined minimal media (YDMM) , 0.4 % glucose, 0.4 % casamino acids, 10 mM MgCl2, 5 mM K2SO4 and 38 µM Thiamine) [600 of 0.5 in freshly prepared YDMM 1 ml was transferred (×3) to a 24-well glass bottomed micro-titre plate , sealed with plastic film and statically incubated for 24 h. The wells were washed twice (dH2O), air dried and the biofilm was stained with 300 µl of 5 μg ml−1 wheat germ agglutinin (WGA) conjugated to fluorescein (Flu-WGA) (Vector Laboratories). Plates were then covered and incubated at 4 °C for 2 h and washed twice with sterile dH2O and air-dried before quantitative analysis of total biofilm production was performed by recording the absolute fluorescence  of a 15×15 circular scan area (omitting the outer 2.0 mm) with a Tecan SPARK plate reader.To culture biofilms on glass each strain was grown in hiamine)  and afteet al. [For Rho-WGA labelling a modified method described by Yoong et al.  was perfnagC mutant, poly-GlcNAc production in planktonic cells was examined at 37 and 22 °C in a Gfp-labelled Y. pseudotuberculosis parent, nagC mutant and genetically complemented nagC mutant grown overnight in the presence of Rho-WGA. Y. pseudotuberculosis and that the amount of red fluorescence as a marker of poly-GlcNAc was severely reduced in the nagC mutant at 37 °C when compared with the parent and complemented mutant. There were no differences at 22 °C. When the 37 °C confocal images were quantified, the mean fluorescence was reduced greater than three-fold in the nagC mutant when compared with the parent  at 37 °C in the nagC mutant when compared with the parent and complemented mutant whereas at 22 °C the reduction was less only ~2-fold with the overall levels much reduced when compared to 37 °C  as a measure of biofilm formation, was almost abolished in the nagC mutant . nagC complemented strain were embedded in a red fluorescent Rho-WGA labelled biofilm matrix which was severely reduced on worms that had been infected with the nagC mutant  was constructed and transformed into the parent, nagC mutant and complemented mutant. hmsHFRS was reduced by ~12-fold in the nagC mutant when compared with the parent with the complemented mutant partially restoring levels back to those of the parent suggesting that NagC acts as an activator of hmsHFRS at this temperature. At 26 °C a similar trend emerges with a small but not significant reduction (<2-fold) in the expression of the nagC mutant compared with the parent (data not shown).Since Y. pseudotuberculosis nagC possesses −10 and −35 sequences as well as a putative nagC binding site with only two mismatches to the consensus [nagC promoter fusion (PnagC::lux) to this region which was integrated into the parent and nagC mutant. At 37 °C nagC expression increased approximately eight-fold in the absence of GlcNAc in a nagC mutant background when compared with the parent suggesting NagC can repress its own expression . The expression profiles over 18 h from which the AUC data was generated are shown in Fig. S2A–D.Given the role of NagC as a repressor of GlcNAc metabolism, the contribution of poly-GlcNAc to the composition of biofilms and our previous observations showing that QS and biofilm formation on ependent  suggestetbR::lux ) were inC mutant  and a sinagC mutant and we therefore extracted AHLs from culture supernatants taken from the Y. pseudotuberculosis parent, nagC mutant and the complemented nagC mutant. LC-ESI-MS/MS revealed that, contrary to expectations, the concentrations of the major Y. pseudotuberculosis AHLs, namely C6-HSL, C8-HSL, 3-oxo-C6-HSL, 3-oxo-C8-HSL and 3-oxo-C10-HSL were each reduced by ~50 % in cell-free supernatant extracts taken from the nagC mutant when compared to the parent and the nagC mutant complement grown under the same conditions used for the Lux-based assays . The expression profiles over 18 h are shown in Fig. S4E–H. A similar dose response to that observed at 37 °C was also noted at 26 °C for ytbI and ytbR in the parent and nagC mutant . However, ypsI or ypsR expression in the parent or nagC mutant showed no response to exogenous GlcNAc . The expression profiles over 18 h are shown in Fig. S6A–H.Since  by NagC  we examie parent  whereas sponsive . IncreasC mutant . These dnagC expression and NagC regulates QS gene expression we investigated whether there was a reciprocal regulatory relationship between the QS system and NagC. nagC expression was examined in the parent and QS mutants containing PnagC::lux at 37 and 26 °C. At 37 °C nagC expression was reduced in the ypsI, ypsR and ytbR mutants when compared to the parent suggesting QS activates nagC expression  surface biofilm assays. The loss of poly-GlcNAc and the reduction in biofilm on glass at 37 °C in the nagC mutant may be a result of GlcNAc degradation and/or reduced hms expression and in addition, the considerable difference in hms expression in the nagC mutant at 37 °C compared to 26 °C may help to explain why the difference in poly-GlcNAc and biofilm at the lower temperature is also reduced. However, it is entirely feasible that temperature acts as an important environmental trigger for NagC functionality, initiating a differential response depending on the local conditions such as those found in an infection context. Although it is not possible to make a direct comparison at 37 °C because C. elegans infection assays cannot be carried out at this temperature, at 22 °C the nagC mutant behaves differently in the C. elegans biofilm model when compared with planktonic cells and biofilms on glass, since biofilm formation is almost completely abolished on the worm cuticle.To investigate how the observations relating to poly-GlcNAc production might link to biofilm formation we conducted abiotic (glass) and biotic (ypsI/ytbI), AHL regulator (ypsR/ytbR) and motility mutants are attenuated for biofilm on the surface of C. elegans [nagC mutant was used to infect C. elegans the degree of attenuation was in excess of anything we had previously observed. Crucially adding back GlcNAc at a concentration of 5 mM was sufficient to restore biofilm to the levels observed in the parent, which suggested a link between biofilm formation and the activity of NagC. These data suggest that as with the observations at 37 °C, the absence of NagC may trigger GlcNAc metabolism which reduces the amount available for poly-GlcNAc production and hence biofilm formation, possibly through the activities of NagC-dependent hmsHFRS expression. However, if an excess of GlcNAc is available there may be sufficient to overcome the metabolic consequences of an unregulated nag operon and so sufficient GlcNAc is again available for biofilm formation on C. elegans.We have previously reported that the double AHL synthase , an alternative explanation is required. This could involve mRNA transcript processing or inhibition of translation of the AHL synthase genes in the nagC mutant. Alternatively, a homeostatic control mechanism(s) preventing AHL concentrations increasing beyond the physiological concentrations required would ensure that key cellular metabolites such as S-adenosylmethionine (SAM) and acyl-CoA intermediates that are substrates for the YpsI and YtbI AHL synthases are not exhausted. Such a mechanism has been described in P. aeruginosa in which the transcriptional regulator RsaL represses expression of both the synthase (lasI) and the response regulator (lasR) genes so limiting N-(3-oxododecanoyl) homoserine lactone (3-oxo-C12-HSL) production [rsaL mutant, 3-oxo-C12-HSL concentrations continue to increase substantially with growth rather than reaching a plateau. Alternatively, AHL homoeostasis could be achieved via the regulated expression or activity of AHL-degrading enzymes such as lactonase, acylases or oxido-reductases whose expression may be under the control of NagC [Yersinia spp. It is also noteworthy that in contrast to most LuxR/I-based QS systems, for Y. pseudotuberculosis and Yersinia enterocolitica, the addition of exogenous AHLs to AHL synthase mutants does not restore QS phenotypes despite the AHLs being confirmed (through reporter assays and by using tritiated AHLs) as entering the cells [Chromobacterium violaceum CV026 cviI mutant in which violacein pigment production cannot be restored with exogenous AHLs. Restoration of violacein required a second mutation in a repressor in order to enable the cviI mutant to respond [Given that AHL synthase gene expression was elevated in the C mutant  we may halidated , and as . coli  where a oduction . In an r of NagC , althoughe cells . This is respond .ypsI and ytbI, it clearly involves NagC either directly or indirectly. Further experimental work will be required to unravel the molecular basis of these observations.However, whatever the mechanism that leads to reduced AHL levels despite the increased transcription of  °C and 37 °C nagC expression is reduced in the ypsI, ypsR and ytbR systems indicating that population-wide monitoring can activate nagC expression. However, nagC expression remains unaffected in the ytbI mutant. As a signal synthase YtbI is responsible for the synthesis of a range of AHLs as well as several which are also produced via YpsI [ypsI mutant due to the synthase activity of YtbI, it is interesting that mutation of ypsI rather than ytbI has an effect on nagC expression. To our knowledge there are no reports of an AHL synthase fulfilling an additional role as an activator/repressor autonomously and therefore this observation remains intriguing.We have also uncovered a reciprocal regulatory relationship between NagC and the QS system since at 26via YpsI . Given tnag, hms and QS operons where there are reciprocal regulatory relationships between the systems which presumably enables a population-wide response to fluctuations in available GlcNAc. Conceptually it seems logical that the metabolism of such an abundant carbon source such as GlcNAc, which is integral to the biofilm lifestyle would be linked to QS to ensure that a bacterial population can coordinate its efforts to metabolize the available substrate to accommodate fluctuating environmental challenges. nagBACD operon, QS, hmsHFRS and biofilm formation. Y. pseudotuberculosis [It is evident from our findings that the availability of GlcNAc and NagC-mediated GlcNAc catabolism can impact on the production of poly-GlcNAc and biofilm on biotic and abiotic surfaces. This is linked to the regulatory network involving the ulosis .Y. pseudotuberculosis which couples population sensing with environmental cues to facilitate adaptation in a changing environment. This study indicates that NagC and hms are additional components within this elegant system providing the bacterial population with the capacity to monitor and metabolise GlcNAc as part of this wider interconnected regulatory network.We have uncovered a sophisticated regulatory system based around QS, T3S, biofilms and motility in Click here for additional data file.


DMD gene are causative for Duchenne muscular dystrophy (DMD). Antisense oligonucleotide (AON) mediated exon skipping to restore disrupted dystrophin reading frame is a therapeutic approach that allows production of a shorter but functional protein. As DMD causing mutations can affect most of the 79 exons encoding dystrophin, a wide variety of AONs are needed to treat the patient population. Design of AONs is largely guided by trial-and-error, and it is yet unclear what defines the skippability of an exon. Here, we use a library of phosphorodiamidate morpholino oligomer (PMOs) AONs of similar physical properties to test the skippability of a large number of DMD exons. The DMD transcript is non-sequentially spliced, meaning that certain introns are retained longer in the transcript than downstream introns. We tested whether the relative intron retention time has a significant effect on AON efficiency, and found that targeting an out-of-frame exon flanked at its 5’-end by an intron that is retained in the transcript longer (‘slow’ intron) leads to overall higher exon skipping efficiency than when the 5’-end flanking intron is ‘fast’. Regardless of splicing speed of flanking introns, we find that positioning an AON closer to the 5’-end of the target exon leads to higher exon skipping efficiency opposed to targeting an exons 3’-end. The data enclosed herein can be of use to guide future target selection and preferential AON binding sites for both DMD and other disease amenable by exon skipping therapies.Mutations in the  DMD gene, which encodes the dystrophin protein. The full-length transcripts are 2.21 Mb long, and introns make up 99.3% of the transcript size, varying in size between 107 bp and over 248 kb.Most gene transcripts undergo pre-mRNA splicing. In this process, the coding sequences of gene transcripts (exons) are joined together, while the non-coding intronic sequences are removed by the spliceosome in the form of lariat RNA. Introns are generally longer than exons and intron size ranges anywhere from less than 100 base pairs to over 100 kb. A notable example is the DMD gene that abolish production of functional dystrophin protein cause Duchenne muscular dystrophy (DMD), which manifests as progressive muscle wasting and loss of muscle function starting in young boys and leads to premature death, generally in the 3rd or 4th decade of life [DMD deletions is located between exon 43 and 55 [DMD can also cause Becker muscular dystrophy (BMD), which is characterized by a milder phenotype than DMD, but BMD mutations generally maintain the reading frame of DMD [DMD mutations in DMD patients, it would be feasible to restore the reading frame by skipping a neighbouring exon using antisense oligonucleotides (AONs) [DMD, can restore the reading from to generate a shorter, partially functional dystrophin protein as seen in BMD. However, with the currently approved AONs only about 30% of DMD patients can potentially be treated, meaning that a majority of DMD patients do not yet benefit from the current therapies. The development of different AONs necessary to skip the entire range of skippable DMD mutations is however a time consumable and difficult endeavour.Mutations in the  of life . The maj3 and 55 ,3. Mutate of DMD . For aboDMD exons [While AON design is guided by practical knowledge available in the scientific community , it is sMD exons , these rMD exons . Based oMD exons . TherefoDMD as a model gene to study AON targeting is convenient as ample data is available about dystrophin transcript processing. These data indicate that DMD is non-sequentially spliced [DMD exons flanked at either their 5’- and 3’-side by these ‘slow’ or ‘fast’ introns, and assess whether this characteristic influences the skippability of the exon. This model also allows us to assess whether targeting the AON itself more proximal or distal within the target exon is beneficial for exon skipping strategies, and whether this targeting location is dependent on the intron-class that flanks it on the target site.Next to clinical necessity, using  spliced , meaningin vitro study where a large set of phosphorodiamidate morpholino oligomers (PMOs) with similar physical characteristics, such as melting temperature, GC content and free energy to form secondary structures, are used to determine optimal targeting of an exon to induce exon skipping. Our data indicates that exon skipping strategies have a significantly higher chance of successfully skipping an out-of-frame exon when the preceding 5’-intron is retained in the transcript for a longer amount of time. The speed at which the downstream 3’-intron is spliced does not seem to influence skipping efficiency. Furthermore, in the vast majority of targeted exons, there is a significantly higher efficiency of exon skipping when the AON is designed to target the proximal (5’) region of the exon. The data and AON sequences contained herein can be used to guide exonic target selection for Duchenne and our observation adds another design feature that can be taken into consideration when targeting various exons in diseases which are amenable for AON mediated therapies.We present here the first DMD gene [DMD are flanked on their upstream 5’-end splice site by an intron, which is retained in the transcript relatively longer (slow intron) or shorter (fast intron). From here on, these intron classes will be referred to as 5’-Slow and 5’-Fast. Similarly, we denote the nature of the downstream 3’-end intron as 3’-Slow and 3’-Fast to indicate that these introns were found to be retained relatively longer or shorter, respectively.Non-sequential splicing of the DMD gene , as wellDMD gene for which we designed PMO AONs , we selected 3 out-of-frame exons of the PMO AONs . To prevDMD expressing myotubes, as determined by RT-qPCR for myogenic markers MYOG and MYH3 . Expression of MYOG was reduced for all conditions using the P1 buffer system compared to unpulsed controls, although cell survival seemed largely unaffected. We also compared the efficiency of nucleofection with other methods of antisense oligonucleotide delivery such as gymnosis [To facilitate reliable and reproduceable delivery of the PMOs to the control myoblast line we used electroporation in an Amaxa 4D nucleofector X unit with 16-well nucleocuvette strips . We valigymnosis , calciumgymnosis  and endoWe electroporated the set of PMOs targeting the 12 selected exons outlined above in HC myoblasts and allowed cells to differentiate to myotubes. After RT-PCR and analysis of exon skipping efficiency , we observed that there was a clear, statistically significant, difference in skipping efficiency between the different exon classes . The 5’SDMD exons for each class from both out-of-frame  and in-frame exons .To test whether the optimal target within an exon was determined by the presence (5’ or 3’) of slow introns, we designed another set of PMOs, now only using 5’Slow-3’Fast  and 5’Fast-3’Slow  exon classes ). For exon 10 (exon length 189 bp) this means that a 25-mer PMO on either position 1 of the scale (H10A(+01 + 25)) or position 100 of the scale (H10D(25 + 01)), respectively, bind nucleotides 1 through 25 or 165 through 189 of this exon. When analysing the skipping efficiency of PMOs in the 5’Slow-3’Fast category , a cellular mechanism used to degrade mRNA molecules which contain premature stop-codons. Specifically, degradation of the ed cells ,18. DegrFinally, it is known that there are various types of exonic splicing enhancers (ESEs), which can influence the ability of the spliceosome to correctly identify and excise introns . HoweverDMD transcript, which could be a target for NMD. We electroporated sets of exon 51 or exon 53 targeting PMOs in two DMD patient derived cell lines. These cells harbour a deletion of DMD exon 48–50 (line 8036) [DMD transcript as measured by RT-qPCR, using primers up- and downstream of the targeted exons . As an example, we observe that in control cells, the more efficient AONs skipping exon 51   or exon ne 8036) , making  exon 51 , such asDMD is to generate a shorter, but functional dystrophin protein in patients muscle cells. As a proof of principal, we tested whether the exon skipping efficiency we observed on mRNA level for our set of exon 51 skipping PMOs, also translated to the recovery of dystrophin protein in the DMD exon Δ48–50 patient cell line. Using a capillary western immunoassay system (Wes), we visualized dystrophin protein recovery in myotubes after electroporation of exon 51 targeting PMOs . RT-qPCR of the DMD gene in these samples show a similar, stabilizing effect on the DMD transcript, with a modest increase in the total detected expression level . Similarly, exon skipping of exon 51 and 53 in the HC  and the two DMD lines  and treatment with CHX yielded inconclusive results. While HC cells showed a slight increase of detectable skipped DMD after CHX, DMD cells showed lower detectable skipping efficiency for some PMOs. This result is not dissimilar to previous reports claiming that blocking NMD in DMD cells does not stabilize the DMD transcript [We noticed that skipping of exon 51 and exon 53 was more efficient in DMD patient cells, where exon skipping restores the reading frame, than in HC cells, where exon skipping disrupts the reading frame. To test whether NMD might play a role in this observation, both HC and DMD myocytes were nucleofected with PMOs, after which cells were treated with cycloheximide (CHX), an inhibitor of NMD. As a proof of principle, we also included samples nucleofected with an equimolar mix of all 12 available exon 65 targeting PMOs, at the same end concentration . We aimed to see whether a combination of multiple PMOs that are individually ineffective at inducing exon skipping could still work when combined for a more skipping-resistant exon such as exon 65. The mix of a dozen PMOs leads to minor levels of skipped exon 65, while none of the individual PMOs resulted in detectable levels of exon skipping. Treatment of exon 65 targeting PMO nucleofected HC cells with CHX causes an increase in detected exon 65 skipped anscript .DMD gene transcripts in human muscle cells, as there is great potential for therapeutic use of AONs in treating DMD. For this purpose we used a library of 153 custom designed and individually tested DMD targeting PMOs with similar physical properties. We found that targeting an out-of-frame exon of which the upstream (5’-end) intron is retained in the transcript longer (a ‘slow intron’) leads to generally higher efficiency of exon skipping than when the exon is preceded by a rapidly spliced (‘fast’) intron. There seems to be no correlation between the splicing speed (‘slow’ nor ‘fast’) of the downstream (3’-end) intron on an exons skippability. Furthermore, we analysed whether targeting an AON closer to a 5’- or 3’-end of either a ‘fast-slow’ or ‘slow-fast’ exon would lead to a difference in exon skipping efficiency. We observed a clear indication that targeting a sequence closer to the 5’-end of the exon leads to more efficient exon skipping than targeting its 3’-end. Our study is the first to systematically assess optimal exons and optimal target sites within exons for AON-mediated exon skipping. Other (retrospective) studies [In this study we aimed to elucidate the connection between the efficiency of AON mediated exon skipping, and the splicing characteristics of the introns neighbouring the target exon. Our study focused on the  studies  on exon A discrepancy was observed in the efficiency of skipping ‘slow-fast’ exons, depending on whether the resulting transcript is in-frame or out-of-frame. Here, out-of-frame exons abide by the observation that skipping efficiency is correlated with a slow 5’-flanking intron while in-frame exons do not. It would be of interest for future studies to assess, whether this observation is circumstantial or correlated with induced frameshifts in the transcript upon splicing, especially as most therapeutic exon skipping of interest for Duchenne concerns out-of-frame exons.DMD targeting AONs have been FDA approved for the treatment of Duchenne: Eteplirsen (exon 51), Golodirsen (exon 53), Viltolarsen (exon 53) and Casimersen (exon 45) [th nucleotide of the 176 bp exon 45. Hence, the targeting sites of these therapeutic AONs largely are in accordance with our findings that targeting the 5’-end of an exon would be a preferential targeting site over a 3’-end targeting AON. However, our AON design never included intronic sequences, so the possibility remains that AONs spanning the intron-exon junction are more efficient than pure exonic targeting AONs.Currently, four different sen exon 1, GolodiDMD reading frame in currently untreatable cases, it does lead to the generation of out-of-frame transcripts in the healthy control cell line used. It is therefore likely that nonsense mediated decay could degrade skipped transcripts, leading to an underestimation of exon skipping potential of an NMD-triggering AON. Indeed, we observe that inhibition of NMD by a six hour CHX treatment in HC cells treated with certain exon 51 or 53 targeting PMOs increases the observed amount of exon skipping as determined by RT-PCR. Levels of DMD transcript are also increased in HC-cells treated with CHX as seen by RT-qPCR, confirming previous work suggesting that DMD is also degraded through NMD in a wildtype situation [DMD stabilizing effect was not observed in muscle cells from DMD patients that would be amenable to exon skipping therapies with exon 51 or 53 targeting PMOs. In these cells, while skipping levels were markedly higher than in HC cells, no additional benefit was seen from a combination of CHX and AONs, suggesting that the DMD transcript in DMD patients is not stabilized in our treatment window of six hours. Previously published data on inhibition of NMD by CHX treatment in DMD patient cells used a treatment time of 24 hours [Our initial screening method only sought to include out-of-frame exons to test our hypothesis on the influence of intronic retention times. While this approach leads to more potential to use the dataset in the development of AONs to restore the ituation ,18. IntrDMD gene as far as our described design constraints allowed. Transcription of DMD is suggested to take as long as 16 hours, and there is a reported imbalance between the abundance of the 5’- and 3’-end of the transcript [DMD being underrepresented indicates that potentially, transcription is properly initiated but unfinished. As splicing of DMD occurs co-transcriptionally, there might be a correlation between the transcription of the RNA molecule and the possibility to skip an exon, as a proximal exon would be more abundantly present in cells. However, we did not see a correlation between exon skipping efficiency of our tested PMOs and the location of the exon in the gene. e.g.: the skippability of exon 17 and 70 is relatively high, while the skippability of exon 10 and 65 was low.The exons included in this study were positioned over the entirety of the anscript . The 3’-DMD gene were based on results we previously generated in HC cells [The splicing maps indicating the splicing order of introns of the HC cells . It is yHC cells ,4. WhethIn conclusion, our data and tested AON sequences can provide new insights into preferred selection of exon skipping target exons. The observations of splicing speed-related exon skipping efficiency can be taken into consideration when selecting the next exon skipping therapy candidate in DMD and other applicable diseases. Moreover, we show in an unbiased manner that generally, the optimal exon targeting region is the 5’-end, which can guide future design for AON based exon skipping therapies.2. Cells were regularly tested for mycoplasma infection by use of the mycoalert  kit. For cycloheximide (CHX) treatment experiments, cells were exposed to 100 µM CHX  for 6 hours, or DMSO as control.Healthy control (HC (KM155)) and DMD patient cells (8036 and 6311) were kindly provided by Prof. Vincent Mouly (Institute de Myology) and have been described before ,20,23. CDMD exons, while exhibiting similar physical properties such as G/C content, number of nucleotides and melting temperature. For determination of the free energy of potential secondary structures (intramolecular and homodimer formation), RNAstructure 6.2 was used [DMD exons outlined below was designed and analysed . The average G/C content and melt temperature were calculated for the entire set, and AONs that deviated >1× standard deviation were excluded. Exons where less than 25 potential AONs remained were excluded from further consideration, as diversity of targeting sites would be too low. PMOs were synthesized and quality controlled in-house at Sarepta Therapeutics inc. Cambridge, MA, USA. PMOs were dissolved in sterile saline and diluted as stocks of 1 mM. Sequences and properties of all PMOs are listed in supplementary table 1.PMOs were designed to specifically target selected was used . Each po6 cells per 20 µl of buffer. Then, 20 µl of P1 cell suspension was transferred to each well of the 16 well nucleocuvette, after which 1 µl of 1 mM PMO was added to the well, for a final concentration of 50 µM. Cells were electroporated in the X unit using program CM-137, and allowed to recover for 10 min at room temperature (RT). After recovery, cells were carefully resuspended in 200 µl growth medium and transferred to 6-well plates containing equilibrated growth medium. Cells were grown for at least 48 h before confluent cultures were allowed to differentiate to myotubes for 72 h as described.For delivery of PMOs to myoblasts cells, a Lonza Amaxa 4D nucleofector with accompanying X unit was utilized, as described in . In brie260/A230 and A260/A280 ratios. For cDNA synthesis, 1 µg of total RNA was used using bioscript Tetro (bioline BIO-65050), according to the manufacturer’s instructions in a 20 µl reaction. Samples were diluted to 100 µl final volume with MQ.For RNA isolation, myotubes in each well were lysed in 500 µl Tri-sure lysis reagent, after which 200 µl chloroform was added and phases were separated by centrifugation at 16.200 relative centrifugal force (RCF) for 15 min at 4°C. The aqueous phase was transferred to 500 µl of 2-propanol and RNA was precipitated by centrifugation for 15 min at 4°C at 16.200 RCF. The pellet was washed twice with 70% ethanol, air-dried and resuspended in 25 µl RNAse-free milli-Q (MQ). RNA concentration and purity was determined using a ND-1000 Nanodrop (thermo-scientific) and provided as ADMD exon skipping efficiency. Ten percent of generated cDNA was used per reaction using specific sets of intron spanning primers (See supplementary table S2), and Dreamtaq polymerase (Thermo scientific EP0713). Reactions of 25 µl total consisted of 2.5 µl 10× reaction buffer (green), 0.2 µl DreamTaq polymerase (1 Unit), 1 µl 10 µM forward primer, 1 µl 10 µM reverse primer, 1 µl DNTP mix (10 µM each nucleotide) and 9.3 µl MQ. Amplification was performed in T-100 thermal cyclers (Bio-Rad) using the following parameters: 1: 95°C 2 min; 2: 95°C 30 sec; 3: 60°C 30 sec; 4: 72°C 45 sec; 5: Go to step 2, 34 additional cycles; 6: 72°C 5 min. A fraction of each sample was analysed using standard agarose TRIS-Borate-EDTA (TBE) gel-electrophoresis. DNA quantity in the PCR samples was assessed using Qubit dsDNA broad range reagent, measured in a Spectramax ID3 instrument. Samples were diluted to 0.2 ng/µl and analysed using an Agilent Femto Pulse with NGS separation gel. Peaks were called using the accompanying Prosize Data analysis software version 4.0.2.7. Skipping efficiency was calculated by determining the ratio of concentration (in nmole/L) of the skipped product over the total concentration (in nmole/L) of the sum of the full-length and skipped product. Statistical analysis was performed in Graphpad Prism Version 8.RT-PCR analysis was used to determine GAPDH and GUSB, using the ΔΔCt method, after which statistical analysis and visualization was performed using Graphpad Prism Version 8.For gene expression analysis, we used 2% of the generated cDNA samples described above per reaction. The RT-qPCR reaction consisted of 4 µl SensiMix 2× SYBR master mix (bioline QT605–05), 2 µl cDNA, 1 µl forward primer (10 µM) and 1 µl reverse primer (10 µM) (see supplementary table S2). Each sample/primer combination was measured in a technical triplicate. Samples were manually pipetted in 384-well plates  and run in a CFX-384 Real-time PCR system (Bio-Rad). Cycling conditions were as follows: 1: 95°C 5:00; 2: 95°C 0:10; 3: 60°C 0:30 (plate read); 4: Go to step 2 39 additional times; 5: melt curve 60°C to 95°C, 0.5°C increase per cycle of 0:05. Data was analysed using the CFX-Maestro software version 2.0, with baseline and Cq value calculation set to ‘auto calculation mode’. CFX-Maestro was also used to determine run quality by studying the melt curves generated for each product, as well as internal QC functions. Expression values were normalized to the housekeeping genes For dystrophin protein expression analysis, cells were electroporated with PMOs as described above. After 72 hours of differentiation, cells were harvested in Laemmli buffer  and boiled to fully denature the protein samples. Samples were adjusted to 1 µg/µl after quantification with Pierce BCA protein assay kit . Protein samples were further prepared for Wes analysis by diluting to a final concentration of 0.5 µg/µl with Wes standard pack 3 (Bio-Techne PS-ST03EZ-8), and run in a Wes 25-capillary electrophoresis system  as per the manufacturer’s instructions. Antibodies specific for dystrophin  and vinculin  were diluted 1:100 in antibody diluent 2 for a duplex assay. QC and analysis of generated data was performed with Compass for Simple Western software (V6.1.0), after which data was exported and subsequently normalized in Excel 365 (Microsoft office).Click here for additional data file.


XDH have been shown to alter xanthine oxidoreductase (XOR) activity leading to impaired purine catabolism. However, XOR is a multi-functional enzyme that depending upon the environmental conditions also expresses oxidase activity leading to both O2·- and H2O2 and nitrite (NO2−) reductase activity leading to nitric oxide (·NO). Since these products express important, and often diametrically opposite, biological activity, consideration of the impact of XOR mutations in the context of each aspect of the biochemical activity of the enzyme is needed to determine the potential full impact of these variants. Herein, we show that known naturally occurring hXDH mutations do not have a uniform impact upon the biochemical activity of the enzyme in terms of uric acid (UA), reactive oxygen species (ROS) and nitric oxide ·NO formation. We show that the His1221Arg mutant, in the presence of xanthine, increases UA, O2·- and NO generation compared to the WT, whilst the Ile703Val increases UA and ·NO formation, but not O2·-. We speculate that this change in the balance of activity of the enzyme is likely to endow those carrying these mutations with a harmful or protective influence over health that may explain the current equipoise underlying the perceived importance of XDH mutations. We also show that, in presence of inorganic NO2−, XOR-driven O2·- production is substantially reduced. We suggest that targeting enzyme activity to enhance the NO2−-reductase profile in those carrying such mutations may provide novel therapeutic options, particularly in cardiovascular disease.Several rare genetic variations of human   •Mutations of xanthine oxidoreductase modulate both its expression and activity.•The His1221Arg natural mutation increases xanthine oxidoreductase activity.•2- substrate leads to increased NO provision and reduced O2·- levels.Raised XOR activity coupled with increased level of NO However, under pathological conditions such as ischemia-reperfusion, inflammation and hypertension XDH can be reversibly or irreversibly converted to the oxidase form of the enzyme (XO) via oxidation of cysteine residues  or proteolytic cleavage of lysine  respectively  is the concentration of the isoxanthopterin (μM) added at the end of the assay. FIPXT is the immediate increase of fluorescence obtained after isoxanthopterin addition. Vc is the final volume used in mL, Vs is the volume of sample added in mL, and T indicate the concentration of the homogenate (mg ml−1).To determine the relative proportions of XDH versus XO activity for each mutant we used a modified protocol as described by Beckman et al. (1989) . 1000 μg2.112·- quantification via LECL 100 μg protein was added to each well in a 96-well plate, solid white with a flat and transparent bottom (Greiner Bio-One™). Briefly, red phenol-free Hank's Balanced Salt Solution  supplemented with HEPES (1 mM) pH 7.4 was mixed in a 1:1 v/v ratio with Na2CO3/NaHCO3 0.1 M (4:1 v/v) buffer, pH 11.0; final pH was adjusted with NaOH or HCl 1 M to 10.4. In some cases, protein lysates were incubated with pharmacological inhibitors, febuxostat (1 μM) or DPI (10 μM) for 30 min at 37 °C. Experimental substrate(s) xanthine, NADPH or NADH were added at 100 μM per well, followed by 10 μM lucigenin  as previous studies , eductase . Thus, t2−-reductase activity in the presence of NADH, or xanthine. These substrates did not influence ·NO generation from NO2− in HEK-293, Asn909Lys or Trp336Ala/Phe337Leu cell lysate. In contrast for cells expressing hXDH WT, His1221Arg or Ile703Val, NO2− reduction was facilitated in the presence of both reducing agents, with His1221Arg showing a preference for xanthine over NADH. Taken together these findings suggest that in His1221Arg, the two redox centres Fe/S I and II are still functional and efficiently shuttling electrons between the Mo–Pt and FAD sites; both xanthine- and NADH-derived electrons are able to catalyse dioxygen and NO2− reductions; and finally, the electrons passed to the enzyme are preferentially used at the same site of production and thus His1221Arg showing a tendency of more efficiently using xanthine over NADH and vice versa for Ile703Val. These observations, albeit exploratory, suggest that non-synonymous mutations of the enzyme may change its affinity for Mo–Pt or FAD specific substrate. Considering that purines and NADH concentrations change dramatically before and after a pathological insult, such as ischemia, this aspect warrants further investigations.To interrogate more closely the possibility that non-synonymous mutations may alter the enzymatic tertiary structure and consequently the intramolecular shuttling of electrons, we investigated the NOWe acknowledge that our study utilises an in vitro expression system and thus whilst the cell system is of human origin further in vitro and in vivo analysis of the consequences of these mutations is needed. This can be addressed by purification of proteins and creation of transgenic mouse models as well as greater scrutiny of large genome-wide-association-study (GWAS) for further confirmation through replication. Since many known mutations of XDH are rare, recent advances in testing the impact of rare mutations upon key disease traits and markers is warranted.52−-reductase potential. Moreover, in the presence of inorganic NO2−, these variants lead to increased generation of bioavailable NO coupled with a concomitant reduction of ROS and potentially UA. We also suggest that specific mutations such as Ile703Val, with a similar oxidase activity of WT but potentially increased NO2−-reductase activity, may exert a biologically important effect in preventing aspects of CVD (3−). With emerging evidence suggesting elevated sUA, ROS, and ·NO depletion as key markers of CVD, identification of pro-oxidative and uricaemic genotypes would allow potential stratification to treatments that might be useful in elevating NO levels leading to health benefits.This study has shown for the first time that a naturally occurring non-synonymous mutation of human XDH, His1221Arg leads to an altered XDH/XO proportion and an increased oxidase activity; effects typically seen in numerous pathological conditions. However, we have also demonstrated for the first time that the upregulation of XOR activity is accompanied by a substantial rise in its NOs of CVD . CollectThis article contains supplementary materials.10.13039/501100014337Menarini Ricerche, Italy (awarded to Prof Claudio Borghi) and the 10.13039/501100015272Italian society of Pharmacology (SIF). This work and GM were also supported by The 10.13039/100015652Barts Charity Cardiovascular Programme (MRG00913). Nicki Dyson is funded by a 10.13039/501100000274BHF MRes/PhD Studentship (FS/19/62/34901).GM was partially supported by The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Amrita Ahluwalia is a Co-Director of a small start-up Heartbeet Ltd seeking to identify therapeutic opportunities for dietary nitrate.


The temperature dependence of infection reflects changes in performance of parasites and hosts. High temperatures often mitigate infection by favoring heat-tolerant hosts over heat-sensitive parasites. Honey bees exhibit endothermic thermoregulation—rare among insects—that can favor resistance to parasites. However, viruses are heavily host-dependent, suggesting that viral infection could be supported—not threatened—by optimum host function. To understand how temperature-driven changes in performance of viruses and hosts shape infection, we compared the temperature dependence of isolated viral enzyme activity, three honey bee traits, and infection of honey bee pupae. Viral enzyme activity varied <2-fold over a > 30 °C interval spanning temperatures typical of ectothermic insects and honey bees. In contrast, honey bee performance peaked at high (≥ 35 °C) temperatures and was highly temperature-sensitive. Although these results suggested that increasing temperature would favor hosts over viruses, the temperature dependence of pupal infection matched that of pupal development, falling only near pupae’s upper thermal limits. Our results reflect the host-dependent nature of viruses, suggesting that infection is accelerated—not curtailed—by optimum host function, contradicting predictions based on relative performance of parasites and hosts, and suggesting tradeoffs between infection resistance and host survival that limit the viability of bee ‘fever’. Deformed Wing Virus best infects honey bee pupae at temperatures optimal for pupal development—not for viral enzymes—suggesting host facilitation of virus replication, and tradeoffs between infection resistance and bee survival. Metabolic theory provides a framework for explaining the temperature dependence of physiological and ecological phenomena, including host-parasite interactions5. This approach uses models derived from enzyme kinetics to describe the temperature dependence of organismal performance 7. The ‘thermal mismatch hypothesis’ postulates that the outcome of antagonistic bipartite interactions reflects the relative performance of the interacting parties8. Hence, temperature is predicted to impact infection when hosts and parasites respond differently, or in extreme cases, oppositely, over a given temperature range, with parasite growth maximized at temperatures where parasites outperform hosts and minimized where hosts outperform parasites10. In general, hosts are expected to be most resistant to infection at temperatures within the preferred host range, resulting in lower infection of warm-adapted hosts at warm temperatures. In diverse endo- and ectothermic plants and animals, including insects13, infection can be reduced by high body temperatures  that compromise the essential functions of parasites and/or potentiate the immune function of hosts19.Temperature is a fundamental driver of biological rates and a strong predictor of infection outcome in many systems where host temperature is variable22, expending considerable energy to maintain brood temperatures within a remarkably narrow, 34–36 °C range close to mammalian body temperature23. Honey bee physiology is also highly temperature-sensitive, with peak function achieved within a narrow range of high temperatures. Adults are unable to fly at body temperatures below 30 °C, with muscle force peaking at 38 °C24, and resting metabolic rate increasing 5-fold between 20 and 35 °C25. The immature life stages, which develop entirely within the thermoregulated colony core, are even more temperature-sensitive than are adults. Successful development occurs only between 29 and 37 °C, with mortality and developmental defects occurring outside of the 32–36 °C region and optimal brain development only at 33.5–35 °C26. Nevertheless, honey bees can experience temperatures as low as 5 °C in broodless winter colonies23—although the 20–37 °C range is more typical—and tolerate temperatures of >40 °C during flight22.The thermal biology of honey bees offers a unique opportunity to study the effects of body temperature on infection. These social bees are endothermic at both the individual and colony level28. High temperatures achieved by colonies during the brood-rearing season can decrease infection with Varroa mites29, Ascosphaera apis30, Nosema apis and N. ceranae31, and viruses34. These results are consistent with mismatched responses to temperature between parasites adapted to diverse ectothermic hosts and endothermic honey bees, which appear to gain a relative advantage over these parasites as temperatures increase towards the peak performance range of bees.Honey bee body temperature has the potential to affect infections by a well-documented assortment of parasites and pathogens, which can adversely affect individual and colony health. These include invertebrate animals, eukaryotic fungi and protozoa, prokaryotic bacteria, and a suite of viruses, many of which also infect less endothermic species36. Although historically DWV has not been a major threat to bee health, its virulence has been augmented by the global spread of the Varroa destructor mite, which readily transmits the virus among developing pupae and adult bees, resulting in adults with wing deformities and drastically reduced performance and longevity35. Outside of honey bees, the virus has been found in eight orders of arthropods, including a diversity of wild bees in which its pathogenicity and spillover from honey bees is of potential concern38. Yet no antiviral treatments currently exist for honey bee colonies, motivating studies of virus traits, bee defenses, and environmental factors—including temperature—that affect infection39. Adult bees reared at 37 °C show reduced DWV proliferation but also dramatically higher host mortality relative to lower temperatures33. However, effects of temperature on viral infection of susceptible pupae, and the relative contributions of hosts and viruses to such effects, remain unknown. Although more stable than that of honey bee adults, temperature in this life stage can vary by several degrees temporally across the season20, spatially within the brood cluster40, and between female (worker) and male (drone) brood, the latter of which are preferred by virus-vectoring mites;41 much greater temperature ranges occur in DWV-associated, non-honey bee host species that do not thermoregulate during development.One parasite that has emerged as a formidable honey bee antagonist is Deformed wing virus (DWV). This RNA virus is likely the most prevalent virus among managed bees worldwide43. Initial evaluation of the first cloned virus enzyme—a protease that cleaves the whole-genome viral polyprotein into individual proteins—showed less than 20% variation in rates of activity across a > 20 °C temperature range43, suggesting that the virus itself is relatively temperature-insensitive. To evaluate the effects of temperature on host-parasite interactions in virus-infected, developing honey bees and the extent to which these dynamics are explained by mismatches between the thermal performance curves of hosts and parasites, we modeled and compared the temperature dependence of performance between a cloned viral protease, three honey bee traits, and pupal infection with a luciferase-tagged clone of DWV.The characterization of isolated parasite enzymes offers an opportunity to understand the intrinsic traits of unculturable viruses outside of hosts, while the use of reporter-tagged virus clones allows for controlled inoculations with traceable parasites that can be distinguished from preexisting infections. Both approaches have recently been applied to DWVThe preliminary evidence for a contrast between the temperature sensitivity of honey bee physiology—which is optimized at relatively high temperatures—with the relatively constant function of a key viral enzyme across a wide temperature range suggested three alternative hypotheses with divergent predictions for the effects of temperature on infection across the temperature range of the colony: First, if infection is governed primarily by the activity of virus enzymes, then a temperature increase from that typical of an ectothermic insect host to that of an endothermic honey bee colony should have weak effects on infection, reflecting low temperature sensitivity of the virus. Second, if infection is limited by efficient function of the host immune system, then as temperature approaches the high levels over which honey bee metabolism is optimized, infection should rapidly decrease. Third, if rapid proliferation of the virus is instead dependent on strong host performance, then as temperature rises into the range of optimum honey bee function, infection should increase. This differs from the second hypothesis in that the virus is dependent on optimal host function, rather than limited by it, such that infection correlates directly (rather than inversely) with host performance.Th (estimate 30.2 ± 2.6 °C SE) was at least 7 °C lower than that of any of the bee traits , a measure of temperature sensitivity, was also more than two-thirds lower than the average of the three bee traits  to 9.43 ∙ 103 ± 2.91 ∙ 103 SE at 37 °C  much closer to that of the bee traits than the activity of the viral protease  at the low end of the usual range observed for biochemical reactions of metabolism . The maintenance of protease function at >50% of maximum activity over a > 30 °C range is consistent with infectivity in honey bees across a range of season- and host caste-related temperatures23, and associations of the virus with a wide range of arthropod hosts that exhibit varying degrees of endothermy36. In contrast, the high (>35 °C) temperatures of honey bee peak performance—well above the 25–29 °C reported for most insect traits3—and high thermal sensitivity of honey bee traits (e > 0.9 eV) are consistent with this species’ endothermically controlled body temperature during key periods and activities 45. This control is exemplified by maintenance of temperatures within < 1 °C of 35 °C at the colony core20, site of the highly temperature-sensitive pupal life stage.Our experiments with the DWV protease are qualitatively consistent with this protein’s initial characterization46.Our thermal performance curves of the viral protease and honey bee traits identified a relatively large temperature window—spanning the typical range of colony temperatures—over which viral protease activity declined while honey bee performance rapidly increased. These contrasting responses of host and parasite to temperature allowed us to test the thermal mismatch hypothesis using pupal infection. Specifically, the higher temperature sensitivity and warmer temperatures of peak activity of bee traits relative to the virus enzyme suggested that the relative performance of parasites vs. hosts would decline steeply throughout the range of temperatures typical of the colony, resulting in a corresponding reduction in infection intensity. However, we observed the opposite result, with the temperature dependence of infection—which exhibited a high temperature sensitivity and a well-defined peak in the region of brood incubation temperatures—corresponding to host performance rather than to the relative performance of viruses and hosts. The breakdown in viral proliferation occurred only over the >34.5 °C range where pupal development also becomes threatened, as manifested by decreased emergence success, alteration of brain development, and disruption of subsequent behavior as adults33, in which a 37 °C rearing temperature resulted in 8- to 17-fold reductions in infection intensity relative to 28 and 34 °C. The latter temperatures also spanned the range of optimal adult survival, the 28 °C preferred honey bee sleeping temperature25, and the 32 °C temperature in which honey bee thermoregulatory energy expenditure is minimized47. This concordance between the performance of hosts and viral infection could reflect the high host dependence of virus production, which exploits the resources and machinery of the host cell, resulting in viral growth rates that are limited by the efficiency of the host as a viral factory. Our results with DWV are also comparable to that of honey bee infection with Nosema spp. microsporidia, which like viruses are intracellular, host energy-dependent pathogens. Infection intensity was increased by 10-fold at 33 °C relative to 25 °C, then decreased by over two orders of magnitude at 37 °C31, mirroring the pattern found here and possibly reflecting niche overlap between these host-dependent parasites, which appear to compete with one another in bees48. Infection with these types of parasites may be less well-predicted by the thermal mismatch hypothesis relative to other parasites that can reproduce independently of host cells9, or whose proliferation is more effectively controlled by a temperature-dependent host immune response.Although this result contrasts with predictions based on the expected relative performance of hosts and parasites, it is consistent with a previous study that examined naturally occurring DWV infection in adult bees34, suggesting a negative relationship between host performance and infection. In mosquito vectors of mammal viruses, infection with most species developed fastest at 37 °C, some 5 °C to 9 °C higher than the optimum temperatures for most of the mosquito traits6. Although these viruses are an exception to the pattern of low infectivity among ectotherm-hosted viruses above 35 °C19 —a necessary adaptation for infection of the mammalian bloodstream—they provide additional examples of peak proliferation rates in temperature ranges where insect host physiology is compromised. Hence, their temperature-dependent development in the insect vector is consistent with the predictions of the thermal mismatch hypothesis for performance of a high temperature-adapted parasite in a less heat-tolerant host—i.e., the mammal-hosted viruses are expected to perform well at high, mammal-like temperatures, whereas the mosquito vectors are expected to resist infection most strongly at the lower (near-ambient) temperatures to which these insects are accustomed. Our finding of concordance between host and parasite performance also contrasts with that of another arthropod host-microsporidian parasite system (Daphnia magna and Ordospora colligata), in which parasite growth rate increased over a gradient of temperature that reduced host lifespan by 10-fold (among both parasite-exposed and control hosts)4. Examination of additional virus proteins and processes, and comparison of proliferation in identified hosts37 or compatible cell lines39 with different temperature sensitivities and thermal optima, should allow further evaluation of the relationship between host and virus performance and infection with this widespread virus, testing the generality of and examining the mechanisms underlying our result.Despite this host-dependence of intracellular parasites, a match between the temperature dependence of host and parasite performance is certainly not a foregone conclusion; there are numerous counterexamples of arthropod-parasite systems where parasite proliferate fastest at temperatures that are not ideal for hosts. In honey bees, Bee virus X was reported to proliferate in young bees incubated at a below-optimal 30 °C, but not in those incubated at the 35 °C found in their colony50, the 37 °C upper limit of pupal development suggests either a narrow thermal safety margin or a heavy cost of high temperature-based resistance for hosts. Even in adults, resistance to the virus came at the expense of host survival33, implying that elevation of body temperature up to or beyond 37 °C would not be considered an adaptive “self-medication” response51. Nevertheless, although viral protease function is preserved at higher temperatures than can be tolerated by pupae, it is possible that other key elements of the virus life cycle are disrupted as temperatures approach 37 °C. As honey bees clearly can regulate temperatures between 35 and 37 °C with better than 1 °C precision23, at least in the brood at the colony center, we cannot rule out a temperature window over which host health is preserved but virus replication is inhibited. It would be of interest to see whether virus inoculation leads to differences in thermal preference, as seen in bees exposed to the similarly heat-sensitive infection with Nosema52.Regarding the potential for high colony temperatures to counteract DWV proliferation, although our finding of strongly reduced infection above 34 °C is consistent with high temperature-mediated reductions in insect-, plant-, and bacterium-infecting viruses53—that exhibit a range of life history strategies and thermoregulatory abilities37. By elucidating factors governing virus replication, including host temperature, this study system has great potential for testing the thermal mismatch hypothesis of host-parasite interactions. Such investigations can evaluate the immunological value of endothermy in predominantly ectothermic host-parasite communities, further elucidating parasite host range, seasonal dynamics, and spillover potential and consequences across seasons, climates, and host communities. This work may also add nuance to the thermal mismatch hypothesis by considering both the parasite-facilitating and -inhibiting aspects of host performance.DWV infects honey bees throughout the year as well as diverse arthropods—including some hosts where infection may be virulent43 . Net reaction rate was computed by subtracting the maximum rate of increase of a control sample incubated in parallel at the corresponding temperature in the absence of protease, to control for possible differences in spontaneous fluorescence of the substrate across temperatures.We conducted three runs with a temperature range of 5–30 °C (in 5 °C increments) and one run from 15 to 30 °C (in 3 °C increments) using parallel incubation in six thermocyclers, and three runs of eight temperatures each spanning 30–50 °C using the thermocycler’s temperature gradient feature, yielding 48 observations of reaction rate over a 25 °C temperature range. The reaction was sampled at 0, 15, and 30 min of incubation  by transferring two aliquots per temperature (pooled volume: 80 µL) to a 96-well assay plate, which was read immediately in a Biotek Synergy H1 spectrophotometer . The maximum rate of increase in fluorescence over either 15-min period was calculated for each sample  of 3.5-day-old larvae over a 20 min time interval in 4 °C increments from 18 to 38 °C45. Pupal development rate (d−1) was measured by incubating combs of freshly capped brood in 1 °C increments from 28 to 38 °C and recording the number of days until adult bees emerged26. Adult force production (mN) was recorded from flight muscles of tethered bees at 7 thoracic temperatures from 27.5–47 °C24. We elected not to focus on adult respiration rates because even in isolation, bees may be endothermic at low temperatures and become agitated at high temperature, making it difficult to estimate true resting metabolic rates55.To compare the temperature sensitivity of the virus protease to that of bee traits, we gathered data for larval respiration (18–38 °C), pupal development rate (28–38 °C), and adult muscle force production during flight (thoracic temperatures 27.5–47 °C) from existing literature42. Infection with this reporter clone can be rapidly quantified by luminescence—which has a nearly 1:1 correlation with virus copy number over four orders of magnitude —using a spectrophotometer42. Purple-eyed pupae were removed from brood frames using scalpel and forceps. We injected 32 pupae per temperature with 107 virus particles of reporter-tagged virus-containing inoculum in 4.0 µL of sterile phosphate buffer saline (PBS). Pupae were incubated at six temperatures from 22 to 37 °C (3 °C increments). We chose this temperature range because it spans the typical temperature range experienced by bee brood and adults21 and corresponds to the region where increases in temperature have opposite effects on viral protease activity and bee performance , suggesting that small changes in temperature could have large and biologically relevant effects on infection.To examine the temperature sensitivity of pupal infection, we used a nanoluciferase-tagged reporter clone of DWV previously developed in our laboratoryInjected pupae were incubated for 48 h on filter paper in covered petri dishes. Incubators were manually humidified by placing shallow, water-filled containers on the bottom. After 48 h, pupae were frozen at −80 °C. For luciferase activity-based quantification of infection intensity, samples were individually homogenized for 30 s with glass microbeads in 100 µL PBS. A 5 µL aliquot of homogenate was diluted 10-fold in PBS in a black 96-well microplate. To each sample, we added 50 µL of 2x luciferase substrate-containing buffer from a commercial kit . Total luminescence was measured immediately using a spectrophotometer (100 ms integration time). The raw luminescence value was used as the response variable to model the effects of temperature on infection intensity. Sixteen samples frozen immediately post-injection were assayed in parallel to estimate virus replication.56.The effects of temperature on each trait , and pupal infection) were modeled using a Sharpe–Schoolfield equation modified for high temperaturesrate refers to the observed response variable; rTref is the rate at the calibration temperature Tref ; e is the activation energy (in eV), which primarily affects the upward slope of the thermal performance curve  at suboptimal temperatures; k is Boltzmann’s constant (8.62 ∙ 10−5 eV∙K−1); eh is the deactivation energy (in eV), which determines how rapidly the thermal performance curve decreases at temperatures above the temperature of peak rate; Th is the high temperature (in K) at which rate is reduced by 50% ;7 and T is the experimental incubation temperature (in K). Models were fit using the rTPC package of R58. Confidence intervals on parameter values were defined as the range ±2 standard errors from the model estimate; estimates were considered significantly different when these intervals did not overlap for different traits. We focus our discussion on the parameters e and Th, as these were deemed most biologically relevant to this system and could be accurately estimated for all traits given the range of temperatures for which data were available. Because we measured a single virus trait, we could not conduct formal statistical tests to compare parameter estimates between bees and viruses; however, we highlight instances where 95% confidence intervals were non-overlapping in virus and host. Confidence intervals on predicted rates were obtained by bootstrap resampling of the model residuals  using R package “car”59. To display the temperature responsiveness of all traits on the same scale, we scaled each thermal performance curve to the trait’s maximum model-predicted value to yield a proportion of peak activity at each temperature.In Eq. , rate reNumbers of replicates are described in the details of each experiment. All computed statistics are described in “Methods: Estimation of thermal performance curves”.Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary