Work in the adenovirus system led to the groundbreaking discovery of RNA splicing and alternative RNA splicing in 1977. These mechanisms are essential in mammalian evolution, as they increase the coding capacity of a genome. We used the Nanopore long-read sequencing platform to demonstrate how amazingly complex the human adenovirus type 2 (Ad2) transcriptome is, with a flexible splicing machinery producing a range of novel mRNAs from both the early and late transcription units. In total, we report more than 900 alternatively spliced mRNAs produced from the Ad2 transcriptome, more than 850 of which are novel mRNAs. A surprising finding was that more than 50% of all E1A transcripts extended upstream of the previously defined transcriptional start site. The novel start sites mapped close to the inverted terminal repeat (ITR) and within the E1A enhancer region. We speculate that novel promoter- or enhancer-driven transcription, so-called eRNA transcription, is responsible for producing these novel mRNAs. Their existence was verified by a peptide in the Ad2 proteome that was unique to the E1A ITR mRNA. Although we show a high complexity of alternative splicing from most early and late regions, the E3 region was by far the most complex when expressed at late times of infection. More than 400 alternatively spliced mRNAs were observed in this region alone. These mRNAs included extended L4 mRNAs containing E3 and L5 sequences and readthrough mRNAs combining E3 and L5 sequences. Our findings demonstrate that the virus has a remarkable capacity to produce novel exon combinations, which gives the virus an evolutionary advantage, i.e., the ability to change the gene expression repertoire and protein production in an evolving environment.IMPORTANCE Work in the adenovirus system led to the groundbreaking discovery of RNA splicing and alternative RNA splicing in 1977. These mechanisms are essential in mammalian evolution, as they increase the coding capacity of a genome. Here, we used a long-read sequencing technology to characterize the complexity of human adenovirus pre-mRNA splicing in detail. It is mind-boggling that the viral genome, which houses only around 36,000 bp, not being much larger than a single cellular gene, generates more than 900 alternatively spliced mRNAs. Recently, adenoviruses have been used as the backbone in several promising severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines. Further improvement of adenovirus-based vaccines demands that the virus be modified into an innocent carrier of foreign genes. This requires a full understanding of the components that govern adenovirus replication and gene expression. The split-gene concept, RNA splicing, and alternative RNA splicing were all discovered in the human adenovirus type Ad2) system almost 45 years ago , 2. In t system aThe adenovirus genome is compact, with essentially no surplus nucleotides that are not transcribed or that have regulatory functions  and VA RNAII. VA RNAI is crucial for the viral infection by inhibiting the interferon-induced protein kinase R (PKR). The VA RNAs are also processed by the microRNA (miRNA) machinery to viral miRNAs (mivaRNAs) (reviewed in reference Human primary lung fibroblast (IMR-90) cells were used in these experiments. The kinetics of infection in IMR90 cells is slower than in HeLa cells , with viFull-length cDNA-PCR-based sequencing relies on an efficient reverse transcription reaction, and the RNA direct sequencing relies on a highly processive Nanopore motor protein. These enzymes were not expected to function perfectly. Therefore, and because of the reduced raw base-calling accuracy of ONT sequencing, we were not expecting to be able to map transcriptional start sites and polyadenylation sites with nucleotide precision, only to identify regions involved in transcription initiation and poly(A) site usage. On a grander scale, the poly(A) sites are all located at the expected positions. However, a close examination of the poly(A) sites shows that all have heterogeneity at the 3′ ends. We refrain from a more in-depth discussion of transcriptional start sites and poly(A) sites, since these have to be experimentally verified before any conclusions can be drawn.J01917.1)  were observed compared to the published human Ad2 reference sequence , 12S (encoding the E1A-243R cell cycle regulator), and 11S, 10S, and 9S, of hitherto-unknown function(s)  reviewe, the so-A putative promoter within the inverted terminal repeat (ITR) produces three low-abundance mRNAs  , predomiThe classical E1B 13S and 22S mRNAs, containing one intron and the minor 14S and 14.5S mRNAs , each coWe also detected a low abundance of an unspliced E1B mRNA . In bothReadthrough transcripts of E1A and E1B have previously been reported . Here, wThe E2 transcription unit  encodes The complexity of UXP mRNA splicing was much higher than previously described  Fig. 3)Fig. 3). The E2 early and E2 late promoters   and encoOur analysis demonstrated a more complex usage of poly(A) sites than has previously been shown. Thus, we detected mRNAs initiated at the E3 promoter that use four alternative poly(A) sites : the L4,At the later stages of infection, a small portion of the E3 mRNAs extend beyond the E3B poly(A) site and instead use the L5 poly(A) site  and drives transcription in the reverse direction to MLP . This unThe pIX transcription unit is embedded in E1B  and 2. AThe MLTU encodes all the structural proteins of the virion except for pIX, which is encoded from its own transcription unit embedded within E1B . The MLTRecent work has shown a more complex splicing pattern within the tripartite leader region than was previously thought . The triA low frequency  of alternative splicing events within the leader region was detected at 36 hpi. Most conspicuous was the complex variability of alternative splicing events between the i leader and leader 3 . It is nL1 generates three mRNAs that were previously verified . The 52/Our Nanopore analysis demonstrated the existence of five additional 3′ splice sites within region L1 . InteresThe L2 unit encodes the penton base (III) and the three viral core proteins   would produce an in-frame shortened 100K protein (Table S1), adding to the repertoire of 100K truncated proteins . It is kIt is well established that adenovirus alternative splicing undergoes a regulated temporal shift in splice site usage  purchased from American Type Culture Collection (ATCC) were cultured in Eagle's minimum essential medium (EMEM) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin (PEST) at 37°C in 5% CO2, the medium was replaced with complete EMEM containing 10% FBS and incubated as described above. Infected cells were collected at 12, 24, and 36 hpi.Cells were infected with Ad2 at a multiplicity of 100 fluorescence-forming units (FFU) per cell (titrated on HeLa cells) in serum-free medium . After 12, 1% NP-40), followed by two rounds of phenol-chloroform-isoamyl alcohol extraction and one extraction with chloroform-isoamyl alcohol (https://www.protocols.io/view/one-pot-ligation-protocol-for-oxford-nanopore-libr-k9acz2e) was prepared from the pooled cDNA. The resulting library was sequenced on a FLO-MIN106 flow using a MinION sequencing device. Raw sequencing data were base called using the Guppy base caller (Oxford Nanopore Technologies). For Nanopore direct RNA sequencing, 500 ng poly(A) selected RNA was prepared from the total cytoplasmic RNA prepared at 24 and 36 hpi. An ONT library was generated by following the protocol “Direct RNA Sequencing Protocol for the MinION” (SQK-RNA001), available on the Oxford Nanopore website, according to the manufacturer’s instructions. RNA strands from the resulting adapter containing RNA/cDNA hybrid library were sequenced on a FLO-MIN106 flow using a MinION sequencing device. The raw sequencing data were base called using the Guppy base caller (Oxford Nanopore Technologies) with the direct RNA base calling algorithm.Cytoplasmic RNA was prepared by lysis with IsoB-NP-40 buffer  was used to identify full-length cDNA reads to select complete transcripts. In the demultiplexing step, first the updated and still supported qcat v1.0.1 (https://github.com/nanoporetech/qcat) was used. Unfortunately, qcat was not yet able to perform adapter trimming, so the unsupported tool porechop v0.2.4 (https://github.com/rrwick/Porechop) was used to trim adapters.Computational analysis of sequencing data. cDNA reads produced by ONT sequencing were first selected depending on the completeness of the transcripts and then demultiplexed. The tool pychopper v0.6.9 (https://nanopolish.readthedocs.io/en/latest/quickstart_polya.html for details of the process). The nanopore reads were aligned to HG38 (GRCh38) combined with the adenovirus 2 complete genome (J01917.1) with minimap2 (+ sequencing (24 and 36 hpi), and the splice junctions were verified using the Illumina data . Transcript variants were manually identified by examining the minimap2 alignments of the cDNA and RNA direct Nanopore reads to the adenovirus genome in the Integrated Genome Viewer (IGV) (SRX451094 to SRX451100) contained at least one read with a perfect k-mer spanning 9 nucleotides upstream and 9 nucleotides downstream of the junction. The k-mer test was made with a custom perl script scanning all reads in the forward and reverse directions and counting the reads having perfect matches to the 9+9 k-mers. The alignment coordinates were also extracted from the CIGAR strings in the SAM alignments and output into a table of aligned regions, reporting the coordinates of all alignment gaps equal to or larger than 20 nucleotides. This table was used to support identification of and quantify transcript variants. During transcript variant identification, the start and end of the alignments were allowed to deviate by 50 nucleotides from the expected start/end sites. When the start site was uncertain or was close to another start site or to a splice junction, this margin was adjusted appropriately. We also resequenced the Ad2 virus genome used in these experiments to eliminate artifacts by mutations in the k-mer analysis. The resequencing was done on an Illumina MiSeq machine using the Nextera XT library preparation kit. All splice junctions were compared to the mutations identified. None of the examined splice junctions were affected by the identified mutations.RNA direct fast5 files were base called using Guppy (version 3.0.3 + 7e7b7d0) 6 to trim the adapters for RNA while base calling. Then nanopolish , minimapminimap2 . For praer (IGV)  and by eer (IGV) , as prevgenome J017.1 withRaw data from our previously published work  was usedPRJNA678228. The SRA accession numbers are SRR13052884, SRR13052885, SRR13052886, SRR13052887, and SRR13052888.The Nanopore cDNA and direct RNA sequencing data have been deposited in the Sequence Read Archive (SRA) and are available via BioProject no.


High-throughput transcriptomic profiling approaches have revealed that circular RNAs (circRNAs) are important transcriptional gene products, identified across a broad range of organisms throughout the eukaryotic tree of life. In the nervous system, they are particularly abundant, developmentally regulated, region-specific, and enriched in genes for neuronal proteins and synaptic factors. These features suggested that circRNAs are key components of an important layer of neuronal gene expression regulation, with known and anticipated functions. Here, we review major recognized aspects of circRNA biogenesis, metabolism and biological activities, examining potential new functions in the context of the nervous system. They were discovered by Sanger et al. in a virus infecting higher plants  and shorRemarkably, several studies in flies and mammals have demonstrated that circRNAs are preferentially enriched in neuronal tissues ,11,12,13In this review, we present an overview of the current knowledge of circRNA biogenesis and functions, indicating established experimental strategies for their study. In the second part of the review, we focus on neuronal circRNAs, describing their specific features and their known dysregulation in human neuronal disorders. The identification by in silico analysis of high-energy interactions between synaptic circRNAs and microRNAs (miRNAs), prompted us to hypothesize a miRNA degradation mechanism mediated by neuronal circRNAs.A large number of endogenous circRNAs have been identified in eukaryotic cells, which are stable and abundant, present both in the nucleus and in the cytoplasm. They show regulated expression both temporally, during development and differentiation, and spatially, in nuclear-cytoplasmic, soma-distal, and in different tissues ,13.The mechanism by which most circRNAs are generated depends on non-canonical splicing events, the so-called backsplicing, in which the 3′ end of a downstream exon is joined to the 5′ end of an upstream exon . BacksplThe mechanisms underlying circRNAs turn-over and degradation are still poorly understood. Due to the lack of 5′ and 3′ free ends, circRNAs are extremely resistant to the action of RNA degradation enzymes that process terminal nucleotides. In fact, the half-life of many circRNAs has been measured to be around 48 h and up to several days in some cases . ConsideVery recent studies showed that circRNAs respond to viral infections and participate in the antiviral host response ,31. Liu Nuclear circRNA molecules can act as regulators of their host genes expression. Through their complementarity to the parental genomic DNA, they might modulate and influence the processes of transcription and pre-mRNA splicing ,21. For circRNAs, by interacting with their parental locus, can decrease the activity of RNA pol II and induce transcriptional pausing. Conn et al. studied SEP3 exon6 circRNA, a nuclear circRNA derived from the exon 6 of the SEPALLATA3 gene (SEP3): in Arabidopsis, the overexpression (OE) of SEP3 exon6 circRNA promotes the expression of the cognate linear transcript lacking exon 6. The authors observed that SEP3 exon6 circRNA, by binding to the genomic parental DNA, forms an R loop (RNA:DNA hybrid), which, in turn, causes the stalling of RNA Pol II, then promoting the recruitment of splicing factors and AS .Moreover, circRNAs can affect gene expression by acting as modulators of chromatin epigenetic status. The FECR1 circRNA positively regulates the expression of its host gene FL1 . By immucircRNAs can act as functional competing endogenous RNA (ceRNA) molecules by binding to miRNAs and interfering with their functions.To date, the best example of a circRNA acting as miRNA sponge was described by Hansen et al., who studied the regulatory interaction between CDR1as and miR-7 . Human CNevertheless, genome-level analysis reported weak circRNA enrichment for miRNA binding sites . In addiApart from ceRNA activity, circRNAs binding to miRNAs might express other functions, for instance as regulators of miRNA stability, transport, degradation or storage. We will further discuss some of those potential circRNA activities.circRNAs can efficiently bind proteins in their mature sequence as well as in intronic sequences of the pre-RNA transcript ,51,52,53The ability of circRNAs to function as a protein scaffold is exemplified by circFoxo3 and circAmotl1 ,51. In hAs discussed above, circRNAs may act as molecular transporters recruiting macromolecules to specific cellular locations. An interesting example is the above-mentioned nuclear circFECR1, that recruits TET1 to the promoter of its host gene .Recognition sequence for RBPs might be identified bioinformatically, using algorithms initially developed for linear transcripts . HoweverSince circular RNA molecules lack 5′ cap and poly(A) tail, in order to be translated they require alternative signals to be recognized by ribosomal minor subunits. The translation of circRNAs through internal ribosome entry sites (IRESs) was initially demonstrated in 1995 on synthetic circular molecules  and receThe function of most circRNAs remain unknown. However, the growing interest of the scientific community in dissecting their biological activities has led to the development of various and sophisticated methodological approaches. In this review, we summarize the main biochemical approaches applied to date for the characterization and validation of circRNA molecular functions . Only thAltering the expression of circRNAs is a powerful strategy to investigate their function. As a matter of fact, loss-of-function studies by circRNA KD or KO, and gain-of-function studies by circRNA OE represent genetic approaches largely adopted to study their physiological activity.RNA silencing (RNAi) using synthetic small interfering RNAs (siRNAs) targeting the backsplicing junction (BSJ) is a common cellular approach to KD circRNAs in cells ,51,62. TTwo recent studies provided new approaches to KD circRNAs in vivo in animal models ,75. PamuIn vivo circRNA KO have been generated in animal models by using CRISPR-Cas9 technology ,76. The Considering that the majority of circRNAs are underrepresented in cells , their OTo study the coding potential of circRNAs, Yang and Wang developed a GFP-based mini-gene reporter, in which the GFP ORF can be reconstituted by the backsplicing reaction, allowing reporter expression .AS strongly contributes to the mammalian transcriptome complexity, especially in the nervous system . More thcircRNAs can be considered newly characterized splicing isoforms, with functional relevance. As already mentioned, the mammalian as well as the fly brains are unique among all other organs owing to the abundance of circRNAs ,12,13,23Neuronal circRNAs are developmentally regulated ,13,16,87circRNAs accumulate during brain ageing. This phenomenon, originally observed in Drosophila , has latChanges in the level of circRNAs have been observed in human neuronal disorders and human disease animal models Altered circRNA expression is often region-specific ,92 and iCis-acting elements are sequence motives and secondary structures in the precursor RNA, implicated in splicing regulation and circRNA formation. Therefore, mutations in cis-acting elements could be involved in disease-associated circRNA deregulation. Interestingly, Liu and co-authors have recently identified circRNA quantitative trait loci (circQTLs) by combining the expression level of human prefrontal cortex circRNAs with genetic SNPs. The authors suggest that partial circQTL SNPs could affect circRNA formation by altering splicing sites, and consequently circRNA biogenesis. A subset of these circQTL SNPs is highly associated with schizophrenia . Trans-fThe major question arising from the finding of circRNA dysregulations in neuronal pathologies is their direct implication in the disease etiology. The answer to this question implies the knowledge of circRNA molecular function. On the other hand, the study of disease-related circRNAs can be helpful for the characterization of their molecular function. It should be pointed out that circRNAs with a well-established molecular function are currently the minority. Interestingly, circHomer1, a highly expressed circRNA in the mammalian brain, was found to be significantly reduced in the prefrontal cortex of psychiatric patients suffering from schizophrenia and bipolar disorder, and in a patient iPS-derived neuronal culture . The in Most studies on circRNA expression in neuronal diseases correlates the circRNA expression profile with miRNA and mRNA profiles . DiseaseRNA structure and function studies taught us that high-order RNA structures confer extra layers of functionality to ribonucleotide sequences . Owing tAs a relevant example of a ceRNA, the cia-cGAS circRNA inhibits the cyclic GMP-AMP (cGAMP) synthase (cGAS) by blocking its enzymatic activity to maintain cellular homeostasis. cGAS is a DNA sensor and its catalysis is induced by binding to DNA, which ultimately elicits the production of type I IFNs. Since cia-cGAS harbors a stronger binding affinity to cGAS than self-DNA does, this circRNA consequently suppressed cGAS-mediated production of type I IFNs in long-term hematopoietic stem cells  Intra-doNeuronal circRNAs have been recently proposed as memory molecules. Chen and Schuman recently suggested that, in the brain, circRNAs might function as molecules for long-term memory storage . This famiRNAs with extensive complementarity to target RNAs are degraded through a mechanism called target-directed miRNA degradation (TDMD). In TDMD, the extensive complementarity of the 3′ region of the miRNA to the target RNA expose the miRNA 3′ end to tailing , trimming (shortening from the 3′ end) and degradation . This mehttp://www.circbase.org), [We reasoned that a circular RNA might trigger TDMD as well as linear RNA. Since the high complementarity of the binding of the miRNA to its target is a primary requirement for TDMD to be activated, we performed an in silico analysis searching for interactions between known circRNAs and miRNAs with extensive complementarities at the 5′ and 3′ miRNA ends. Bioinformatic predictions have been obtained by using RNA22 v2 tool (used with default parameters except “Minimum number of paired-up bases in heteroduplex” that was set to 15) , structuse.org), ,142 and se.org), ,144,145 se.org), ,146,147.Interestingly, we found various low free-energy interactions between miRNAs and circRNAs see , and othNotably, there are four miRNA/circRNA interactions with a low ΔG , close to the mir-7/Cyrano ΔG value , and other two (miR-129-5p/mmu_circ_0004245—Sipa1l1 and miR-146a-5p/mmu_circ_0007086—Fbxo11) with a higher ΔG but secondary structures compatible with the proposed model of interaction of miRNA and TDMD, inducing RNA targets ,148.According to the TDMD features, we hypothesize that even one single, highly complementary binding site on the circRNA molecule would trigger an efficient degradation of the complementary miRNA. In our model, the efficacy of the circRNA-mediated TDMD process could rely on a multiple turnover activity as well as on the high stability of the target RNA. However, circRNA conformational constraints should be considered, binding sites might not be accessible to miRNAs, and biochemical validation would be mandatory to test such hypothetical interactions and biological outcomes. In conclusion, circRNAs, hidden in their molecular complexity for many years and recently described as new regulatory RNAs, are still a secret garden to be discovered . Definit


Chronic diseases and their inequalities amongst older adults are a significant public health challenge. Prevention and treatment of chronic diseases will benefit from insight into which population groups show greatest risk. Biomarkers are indicators of the biological mechanisms underlying health and disease. We analysed disparities in a common set of biomarkers at the population level using English national data . Blood-based biomarkers were HbA1c, total cholesterol and C-reactive protein. Non-blood biomarkers were systolic blood pressure, resting heart rate and body mass index. We employed an intersectionality perspective which is concerned with how socioeconomic, gender and ethnic disparities combine to lead to varied health outcomes. We find granular intersectional disparities, which vary by biomarker, with total cholesterol and HbA1c showing the greatest intersectional variation. These disparities were additive rather than multiplicative. Each intersectional subgroup has its own profile of biomarkers. Whilst the majority of variation in biomarkers is at the individual rather than intersectional level (i.e. intersections exhibit high heterogeneity), the average differences are potentially associated with important clinical outcomes. An intersectional perspective helps to shed light on how socio-demographic factors combine to result in differential risk for disease or potential for healthy ageing. Many of the current gaps in knowledge on healthy ageing and chronic disease inequalities centre around the complex interaction of sociocultural, political and biological factors2. However these inequalities have often been investigated according to single or at most a limited number of categories of difference at a time, such as gender, ethnicity or socioeconomic position (SEP). Such unidimensional approaches can inadvertently reinforce the intractability of inequalities and fail to provide evidence on how to intervene3. By focussing on single social attributes, there is a risk that the disadvantages faced by particular subgroups can be rendered invisible4.The huge and growing burden of chronic diseases and their inequalities amongst older adults is a significant public health challenge. Prevention and treatment of chronic diseases, and therefore attainment of the policy goal of healthy ageing, will benefit from insight into which population groups show greatest risk. Biological markers—objective measures of biological processes underlying healthy ageing and disease8. Its essence is that multiple social attributes overlap and interact with each other to drive health outcomes11. Different intersections defined by combinations of social attributes such as gender, ethnicity and SEP, are potentially associated with different (though overlapping) health outcomes and are subject to different causal processes driving these outcomes. An intersectional approach may help to advance understanding of the social processes and circumstances that create poor (or good) health as well as inform the design of policies and interventions for varied social contexts and for different population subgroups12.Intersectionality has increasingly been seen as a promising way to advance health inequalities research and policy13, body mass index14 and chronic obstructive pulmonary disease15. Here we extend this work by focussing on a key set of biomarkers of healthy ageing, elevated levels of which are associated with a range of chronic diseases and their longer term implications16. Intersectional mapping of biomarker outcomes illustrates how population health is mutually constituted by the social and biological17. Intersectional disparities in biomarkers would suggest that we can modify the social factors associated with intersectional positions/identities to reduce these disparities in order to reduce inequalities in healthy ageing. Currently, there is a lack of knowledge on the social distribution of biomarkers, as highlighted by the European Roadmap for Ageing Research2. By bringing an intersectionality lens to the analysis of biomarker data, we offer a novel approach to filling this gap in knowledge.A first step in intersectional health inequalities research is to socio-demographically ‘map out’ disparities according to multiple social attributes. So far, researchers have done this for various health outcomes, including depression6. There are a number of possibilities for intersectional patterning. For example, it might be that the disadvantage associated with certain social attributes such as ethnic minority status is offset by advantages in another attribute such as SEP. This offsetting might occur for men but not women. Such subgroup differences would suggest a need to understand the social forces driving outcomes for particular subgroups, and that remedial policies or interventions may be inefficient or even increase inequalities if they are based on a single axis of inequality.There are many unanswered questions regarding the way in which health, including its causes and consequences, is patterned intersectionally. Intersectional patterning may be present not only in the distribution of disease, but in risk factors and their social determinants, life course processes and dynamics, the lived experience of health and illness, healthy and functional ageing outcomes, and the social processes linking body processes to diagnosis. Researchers have only recent begun answering such questions, yet they might provide valuable knowledge for population health strategies19. For example, in the former, the effect of ethnic minority status on health is the same regardless of, say, SEP, and in the latter, the effect of ethnic minority status on health might be particularly pronounced for those with low SEP. Without testing for interaction effects, subgroup differences are assumed to be the result of additive effects only, which can lead to erroneous interpretations20. The necessity of testing for multiplicative effects to avoid such misinterpretation has often been taken to mean that intersectionality is falsified when only additive effects are present, when in fact, intersectionality is mainly a framework to understand heterogeneity and the social structures driving it18.An underlying dimension to intersectional patterning is whether dis/advantages are additive i.e. layer on top of each other, or have multiplicative effects i.e. have amplifying or attenuating effects, so that intersectional outcomes are different than expected given the referent attributes that comprise them16. Social disparities in the selected biomarkers are often analysed in order to unpack the social to biological processes leading to health inequalities21. We analyse data from older adults aged 50 or over as this when many chronic diseases emerge.In this paper, we examine patterns of key biomarkers of healthy ageing and chronic disease across intersectional subgroups and seek to answer two research questions: 1. What are the extent/nature of intersectional differences in later life (50 +) biomarker measures? 2. Are these intersectional differences multiplicative or additive? We use data from two national surveys containing comparable biomarker data. We focus on a set of six biomarkers which were available in both surveys and are commonly accepted as biomarkers of healthy and functional ageing and predictors of chronic disease morbidity and mortality22. Data from wave 6 (2012–2013) are analysed, including from the harmonised dataset23. Understanding Society: the United Kingdom Household Longitudinal Study (UKHLS) is a yearly household panel survey of people aged 16 + living in the United Kingdom. Data from the nurse health visit which took place across waves 2 and 3 in 2010–2012 are analysed. Data from the two surveys were pooled as shown in Fig. The English Longitudinal Study of Ageing (ELSA) is a biennial panel study of adults aged 50 + years living in England and includes biomarker collection every other wavewave 6 202–2013 arEthics approval including for biomarker collection and prospective analysis was sought by the respective studies. Ethics approval was not necessary for the present study as it uses secondary data only.Gender, ethnicity, education and income were used to define 24 intersectional subgroups. Given the interest in intersecting disparities, sample size limitations meant it was necessary to use relatively coarse categorisations. In ELSA, participants were asked to which ethnic group they felt they belonged with seven options. In UKHLS, respondents were asked their ethnic group from 18 options. Ethnicity was dichotomised into White/Black and Minority Ethnic (BME). For ELSA this was defined by the category ‘white’ versus any other response, and for UKHLS the categories ‘white British/English/Scottish/Welsh/Northern Irish’, ‘Irish’, and ‘Any other white’, versus any other response. Whilst the BME category contains substantial ethnic heterogeneity, and is the subject on ongoing debate, we are interested in minority ethnic status as a key dimension of social and health inequality in the English context. We were also restricted by sample size, which for specific ethnic groups is too small to expect meaningful differences to be uncovered.24) as our aim is to map out health disparities in relation to socioeconomic and socio-demographic factors. Further, the extent to which biomarkers are risk factors for disease is age variant.To capture both earlier and later life SEP, we analysed both education and income. Education sets in motion a (non-determinant) trajectory of socioeconomic dis/advantage at a relatively early stage of the life course. Income at age 50 is a result of dis/advantage accumulated through an individual’s life. Further, in the English context, retirement income is strongly linked to pre-retirement SEP. Education and income are themselves outcomes of intersectional processes, but nonetheless are strongly associated with particular social positions and identities. We represent the conceptual framework underlying the analysis in Fig. 25. It is associated with diabetes complications and cardiovascular events and stroke, including in patients without diabetes26. Total cholesterol is a well-established risk factor for cardiovascular disease (CVD)27. CRP is a marker of inflammation thought to respond to cumulative social adversity and is a predictor of CVD28 and diabetes29. Systolic, rather than diastolic, blood pressure was analysed as it is a predictor of CVD30. Resting heart rate is an independent risk factor for CVD and all-cause mortality31 as well as metabolic syndrome32. BMI is risk factor for a range of diseases33. We did not adjust for medication use (and therefore disease) as this is part of the underlying causal pathway of interest i.e. biomarkers as indicators of healthy ageing and disease. We present unstandardised results as we are interested in the clinical significance of intersectional disparities but use standardised outcomes when estimating model fit statistics as these are scale-dependent. We standardised outcomes so that outcomes had a mean of 0 and a standard deviation of 1. Measurement error with biomarker outcomes is likely to be minimal given the standardised measurement procedures implemented in ELSA and UKHLS.Biomarkers were derived from bloods ) and anthropometric/cardiovascular measures (systolic blood pressure (SBP), resting heart rate (RHR), body mass index (BMI)). HbA1c is a measure of blood glucose concentration over the past 2 to 3 months and is used to diagnose diabetes34. For non-blood measures, 522 (6.89%) respondents were missing values for SBP and 521 for RHR (6.88%). In 384 cases this was because they had engaged in an activity in the last 30 minutes that would affect their measures, with remaining cases due to invalid or incomplete measures. 334 (4.41%) respondents were missing values for BMI nearly all due to measurement issues.For blood measures, in ELSA 1882 (24.85%) respondents were missing values for HbA1c, 1814 (23.95%) for cholesterol and 2142 (28.28%) for CRP. 602 (7.95%) respondents refused to give a blood sample and 364 (4.81%) were not eligible due to a clotting/blood disorder. In 829 (10.95%) cases there were problems with measurement for example due to poor veins or an incomplete or non-reactive sample. In 328 cases CRP was > 10 mg/L, indicating recent infection, and was recoded as missing following best practiceIn UKHLS, 3375 (38.08%) respondents were missing values for HbA1c, 3030 (34.18%) for cholesterol and 3500 for CRP (34.49%). 1380 (15.57%) respondents refused to give a blood sample, and 803 (9.06%) were ineligible for unspecified reasons. In 381 cases CRP was > 10 mg/L, and was recoded as missing. For non-blood measures, 1341 (15.13%) respondents were missing values for SBP and RHR due to 1130 (12.75%) engaging in an activity in the last 30 min that would affect their measures, with the remaining causes due to invalid/incomplete measures. 525 (5.92%) respondents were missing cases for BMI nearly all due to measurement issues.35. Given the included socio-demographics are likely to be highly associated with missingness, we would expect their inclusion to minimise bias. As a sensitivity test, missing data were imputed using multiple imputation following best practice guidelines35. Chained equations were used, with all biomarkers to be imputed included in the model, and all analysis variables included as predictors, as well as auxiliary variables known to be associated with missingness as stated in the user guides for ELSA and UKHLS. We used the pooled data for imputation as auxiliary variables were available across both surveys: marital status (married/not married), household size, home ownership (owner/non-owner), long-standing illness, self-rated health (5 categories). We used long-standing illness as a substitute for limiting long-standing illness as only the former was available across both datasets. We were also unable to include government office region, also known to be associated with missingness, as it is anonymous in ELSA and therefore cannot be pooled. The number of imputations was set to 34—the % missing for the variable (CRP) with most missing data for the pooled sample. As the differences between the imputed and non-imputed data were non-existent or negligible  according to combinations of social attributes. These intersections are taken to be analogous to the types of contexts traditionally studied in multilevel models such as neighbourhoods or countries37. For example, one intersection may be BME women with high education and low income. Given this method is explicitly testing multiple hypotheses (the differences between multiple intersections), readers might be concerned about multiple testing. Jones et al.38 argue that shrinkage in MAIHDA models solves the problem of multiple testing, but we have shown in previous work36 that this is not always the case. However, this should be seen as a sign of more certainty in our null finding of no multiplicative effects: there were no significant effects even though the test applied was potentially too anti-conservative.We first use the multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) method which is a novel multilevel approach developed to analyse intersectional inequalities9.First, a null model is specified with individuals at level one nested within intersections at level two. This allows for estimating the extent to which the variance in an outcome is explained by differences across intersections versus differences within them via the intraclass correlation coefficient (ICC)—in the MAIHDA framework termed the variance partition coefficient (VPC). A high VPC suggests that intersections have substantially different mean levels of an outcome and that individuals are fairly similar within them, whereas a low VPC suggests that individuals differ substantially within intersectional groups, which have similar mean levels of a particular outcome36, so we proceed with conventional linear regression analysis and use marginal effects to predict intersectional disparities, and we use MAIHDA to ascertain the discriminatory accuracy of intersectional clustering (i.e. the extent to which intersections are able to distinguish between different levels of a biomarker). The BIC values in Supplementary Table 11, confirm that linear regression models have no worse model fit than the multilevel main effects models. In the second step, main effects are added to the fixed part of the model. The VPC now represents the extent to which intersectional clustering is multiplicative. A reduction in the VPC to around 0% indicates that intersectional differences are fully explained by the main effects, and so are additive and not multiplicative. Given we find no evidence for multiplicative effects, the MAIHDA models offer no advantages over conventional models for estimating mean differences in outcomes between intersectional groups39 v2.36 (MLwiN v3.0440), called from Stata v14.0. Linear regression models were estimated directly in Stata and marginal effects were calculated using the margins command. Given that MAIHDA models are often estimated using the Markov Chain Monte Carlo (MCMC) method37 we tested whether the lack of multiplicative effects found was due to our use of IGLS estimation. Further, MAIHDA models often use age categories to define the intersectional subgroups37, so we also tested whether this strategy would result in multiplicative effects being found, by including age in 10 years bands to define intersectional subgroups. Supplementary Table MAIHDA models were estimated using runmlwin25, sample mean 5.53) and SBP . HbA1c was below the cut-point of 48 mmol/mol for diagnosis of diabetes25 (sample mean 40.32), CRP was below the suggested cut-point of > 3 mg/L for metabolic syndrome29 (sample mean 2.18); and RHR was below 120 bpm, which is considered high42 (sample mean 67.50). The sample mean for BMI (28.43 kg/m2) was above the cut-point for overweight but not obese. The intersectional distribution is shown in Supplementary Table ELSA and UKHLS were well-matched on nearly all variables Table . In termThe null multilevel intersectional models showed small overall intersectional variation advantages. We therefore plotted predicted results by intersectional subgroup Figs.  and 4, sIn terms of blood-based biomarkers, intersections ranged in mean predicted HbA1c from 38.73–45.73 mmol/mol, with a sample mean of 40.32 mmol/mol. The effect of ethnicity on HbA1c was apparent, and there was a difference of around 2.6 mmol/mol within both ethnic categories, according to the other intersectional attributes. The BME intersection with the lowest HbA1c still had a value around 1.5 mmol/mol higher than the white intersection with the highest HbA1c. Intersections ranged in predicted mean cholesterol from 4.71–5.95 mmol/L. BME male intersections had the lowest cholesterol, with small education/income variation within this group, whilst white female intersections had the highest, again with this group mostly homogenous. Intersections ranged in predicted CRP from 1.73–2.54 mg/L. Those with high income and education had the lowest levels, especially for men, regardless of ethnicity. Conversely the highest levels were seen amongst women in the lowest income and education categories.2, with a sample mean of 28.43 kg/m2. The effect of low or medium income was apparent across all gender/ethnic categories, and BME men and women with low or medium incomes had the highest BMI predictions. Conversely, those with high incomes in particular had the lowest BMIs, and again this was mostly invariant according to other categories.For non-blood biomarkers, intersections ranged in predicted SBP from 129.75–134.97 mm Hg, with a sample mean of 131.81 mm Hg. BME men, and especially those with low education had the highest SBP. White men with low education had a similar SBP to BME men with high education. White women had low SBP, and the lowest levels were found in those with high education. White men with high education and BME women with low education had around the sample mean. Intersections ranged in predicted RHR from 65.03–70.81 bpm. Despite the strong gender main effect, there was overlap between some male and female intersections, with BME men with low education and white women with high education near the sample mean of 67.50 bpm. White male intersections with high incomes had the lowest RHR while BME female intersections with low or medium incomes had the highest RHR. Intersections ranged in predicted BMI from 27.46–29.34 kg/mTable These heterogeneous patterns open up the possibility of biomarker health ‘profiles’ across different intersectional groups (which must nonetheless be viewed in the context of the majority of heterogeneity existing within intersections rather than between them). This suggests there may be differential intersectional drivers and healthy and functional ageing outcomes related to these profiles.37. We uncovered intersectional disparities both in terms of the intersectional range, as well as intersectional patterning, as a result of the additive (or layered) effects of social attributes. The intersections nonetheless exhibited low discriminatory accuracy. Methodologically, our analysis suggests that although the MAIHDA method is useful for distinguishing between additive and multiplicative effects and the discriminatory accuracy of intersectional subgroups, conventional main effects regression is a more parsimonious way to explore intersectional disparities in the absence of multiplicative effects.For the first time, we have intersectionally ‘mapped out’ the main social disparities in key biomarkers of healthy ageing using nationally representative English data. We found no evidence of multiplicative intersectional effects, consistent with other MAIHDA analyses which have generally found no or negligible effects in a range of health outcomes45. We agree with the position that actual intersectional disparities  are a key tenet of the intersectionality framework19. These disparities are more important than their statistical constitution46, and are relevant to policy interest in targeting and evaluating policies and interventions in relation to those facing multiple disadvantages47. Further, the social uniqueness of intersectional positions/identities will not necessarily translate into multiplicative effects13.The research and policy significance of additive vs multiplicative effects is a key theme in the intersectionality literature48 and a 17% increased risk of cardiovascular events in those with type 2 diabetes49. The intersectional range of 1 mmol/L in total cholesterol is associated with a ~ 20% increase in coronary heart disease (CHD) in women and a ~ 24% increase for men50. Mean intersectional CRP varied from 1.76–2.52 mg/L, which is associated with a ~ 50% increased risk of CHD and ~ 20% risk in vascular death51. The intersectional range of ~ 5 mm Hg in SBP is associated with a ~ 20% increase in the risk of stroke death and ~ 15% increase in risk of death from ischaemic heart disease and other vascular causes52. The intersectional range in RHR of 6 bpm is associated with an increased risk of CHD of ~ 4%, CVD of ~ 8%, and all-cause mortality of ~ 9%53. Finally the intersectional range in BMI of ~ 2 kg/m2 is associated with an increase in the hazard ratio for mortality of around 1.08; put another way, the intersectional level of just under 30 kg/m2 is associated with living around 4 years less compared to those with a BMI of < 2533. These comparisons are approximate given differing populations and confounder adjustment but nonetheless are indicative of the potential implications of wide-ranging intersectional disparities in biomarkers.Whilst further work is needed to understand whether intersectional disparities translate to differential risk of health events, existing work suggests that the mean differences in biomarker levels across intersections represent clinically important differences. For example, mean intersectional HbA1c varied from 38.82–45.72 mmol/mol, approximating a 5% increase in cardiovascular mortality in those with no known diabetes54. Studies on the relationship between ethnicity and biomarkers of healthy ageing seem to be lacking in the extant literature. We found that intersectional disparities according to gender, ethnicity, education and income did not follow a simple pattern but were different for different biomarkers. Consequently, different intersectional subgroups have different ‘biomarker profiles’, and although some intersections have a greater number of reduced or elevated biomarkers than others, no intersection has reduced or elevated levels of all biomarkers. Similarly, our findings also highlight that it is important to move away from mean differences between e.g. men and women, and between White and BME groups given the heterogeneity within these categories according to other social attributes. For example, we found evidence of compensatory mechanisms such that elevated ethnic minority HbA1c was somewhat but not completely compensated by socioeconomic advantage. For SBP, we found that the most advantaged men and the most disadvantaged women have approximately the same SBP, despite the overall strong gender effect.The main effects driving the intersectional patterning were approximately consistent with previous studies. For example, a study using UKHLS data found very similar effects for gender and income for the six biomarkers analysed here, despite also including younger adults37. In other words, intersections cannot discern which particular person is healthy or sick. This speaks to emerging findings from ‘precision medicine’55, where it is becoming evident that current statistical methods are unable to predict the health of any one individual with any reasonable degree of accuracy. Caution is therefore required in targeting interventions or policies, not only due to matters of efficiency, but also given the risk of stigmatising people who are assumed to have a particular level of health or illness by virtue of their social attributes18. Nonetheless action on particular intersections could still have important population health effects if they are effective at shifting the means for those subgroups.An important caveat to these findings emerging from the use of the MAIHDA is that the intersections exhibited low discriminatory accuracy. Most heterogeneity in biomarker outcomes exists within rather than between intersectional subgroups, consistent with the picture emerging of MAIHDA analyses of various outcomes56. However in the absence of this evidence caution is required in intersectionally targeting/tailoring interventions or policies at particular subgroups. An alternative more social epidemiologically-oriented research goal might be to uncover the social factors driving the individual distribution of risk9, in which case discriminatory accuracy matters less. Indeed, researchers are now turning towards answering the question, for example by investigating whether discrimination mediates intersectional patterning18.It remains to be seen whether intersections have better discriminatory accuracy with regards to other factors the theory suggests are patterned intersectionally such as differences in social discrimination, lived experience, or being subject to differential institutional or policy processes. In addition, differential life course drivers e.g. in terms of the social, political or geographical context in relation to an individual’s life trajectory, may be implicated in intersectional patterning, providing clues on how to design policies and interventions to address this57. Given that we analyse a range of biomarkers and social attributes it would be unfeasible to take into account the difference between levels of biomarkers (or biomarker profiles) and consequences for healthy and functional ageing in the current analysis. Instead we regard this as an important focus of future work. A potentially interesting interim step might be to investigate the pathways between biological processes and rates of diagnosed diseases, which might suggest areas of over or under-diagnosis, or point towards the social processes and mechanisms such as help-seeking and diagnosis mediating this relationship.In presenting our findings, we have deliberately used the term disparities to denote that the differences demonstrated do not necessarily represent inequalities because they may to some extent reflect natural underlying physiological differences. For example, men and women have different anthropometric profiles and so their different biomarker levels might be considered ‘normal’. Similarly, there is some debate whether there should be ethnic-specific cut-points in HbA1c for diagnosing diabetesOur study has a number of strengths. Pooling biomarker data from ELSA and UKHLS has allowed for a granular analysis of intersectional disparities in biomarkers of healthy ageing and disease across England. Whilst many health inequalities studies control for socio-demographics, for example controlling for socioeconomic status in explaining ethnic differences, we specifically focus on subgroup disparities in order to examine their co-constitution by multiple social attributes.Nonetheless, as with all studies ours has limitations. We did not take into account the use of medications which affect biomarker measures because medication use is confounded with diagnosis and severity, which are ultimately of interest in mapping out biomarker disparities. It is likely that intersectional differences vary according to whether people are diagnosed and on medication or not and this should be explored in further work. There may also be intersectional bias e.g. in terms of non-response which influences the patterns we found. Some intersectional effects may be due to how the meaning of some categories changes according to others e.g. educational qualifications and age. Ethnicity is notoriously difficult to measure in surveys, with a range of different meanings and constructs that impact on the way it is recorded. We found some differences between ELSA and UKHLS in terms of ethnic differences in biomarkers, including in sensitivity analyses. Nonetheless, we consider our results as broadly indicative of intersectional inequalities, but they should be seen as exploratory and in need of further investigation using larger samples, ideally with high quality ethnicity data. Our study would likely have been able to uncover greater ethnic heterogeneity using such data for example by focussing on particular ethnic groups. We cannot generalise out findings beyond the English setting. Given the minimal measurement error with biomarker outcomes, an interesting area of future research would be to see whether intersectional inequalities vary between different geospatial contexts.We present a complex picture of disparities in healthy ageing biomarkers to counter dominant simplistic narratives of health inequalities according to single or at most a limited number of social attributes e.g. SEP or ethnicity. Our results have a number of important implications. They show that certain intersections have particularly reduced, or elevated levels of particular biomarkers of healthy ageing and chronic disease, which should be taken into account in inequity policy. However, our results also show that intersections have substantial heterogeneity i.e. that they are relatively poor at discriminating who has an elevated or reduced level of any particular biomarker. This suggests that rather than using intersections to target interventions, which may be inefficient, policies might instead aim to affect the underlying social determinants of health that are responsible for overall intersectional differences. Our results also suggest that people who are typically disadvantaged according to multiple social attributes e.g. gender, income and ethnicity, are not always disadvantaged according to all health measures. Rather, a more nuanced picture emerges, suggesting intersectional groups exhibit a mixture of both protective as well as risk factors for illness.By opening up a granular view of inequalities, an intersectionality research agenda offers opportunities to unpack how the dynamics of power and social determinants might interact to drive variegated outcomes. This presents new puzzles and challenges on how and why biomarkers might translate into health and functional outcomes differently for different subgroups and the life course processes that lead to such differences. Such innovation may be helpful in tackling the huge and growing burden of chronic disease.Supplementary information 1.Supplementary information 2.


Penicillium digitatum is the main fungal postharvest pathogen of citrus fruit under Mediterranean climate conditions. The role of ethylene in the P. digitatum–citrus fruit interaction is unclear and controversial. We analyzed the involvement of the 2-oxoglutarate-dependent ethylene-forming enzyme (EFE)-encoding gene (efeA) of P. digitatum on the pathogenicity of the fungus. The expression of P. digitatumefeA parallels ethylene production during growth on PDA medium, with maximum levels reached during sporulation. We generated ΔefeA knockout mutants in P. digitatum strain Pd1. These mutants showed no significant defect on mycelial growth or sporulation compared to the parental strain. However, the knockout mutants did not produce ethylene in vitro. Citrus pathogenicity assays showed no differences in virulence between the parental and ΔefeA knockout mutant strains, despite a lack of ethylene production by the knockout mutant throughout the infection process. This result suggests that ethylene plays no role in P. digitatum pathogenicity. Our results clearly show that EFE-mediated ethylene synthesis is the major ethylene synthesis pathway in the citrus postharvest pathogen P. digitatum during both in vitro growth on PDA medium and the infection process, and that this hormone is not necessary for establishing P. digitatum infection in citrus fruit. However, our results also indicate that ethylene produced by P. digitatum during sporulation on the fruit surface may influence the development of secondary fungal infections. This pathway produces the highest ethylene rates, and has been described in Pseudomonas syringae and P. digitatum, among other microorganisms [Ethylene  Sacc. strain Pd1  [efeA gene. All the strains were grown on potato dextrose broth  or potato dextrose agar  plates, with or without the correspondent antibiotic. Cultures were incubated at 24 °C for 7–14 days. Conidia were scraped off the agar with a sterile spatula, suspended in sterile distilled water and titrated with a hemocytometer. CT20795)  was usedefeA gene expression during P. digitatum in vitro growth, PDA plates covered with a cellophane membrane were inoculated centrally with 5 µL of a conidia suspension (105 conidia/mL). Cultures were incubated at 24 °C for up to 14 days. Mycelium and spores were collected by scraping the surface of plates with a sterile spatula on days 1, 2, 3, 4, 5, 6, 7, 8, 11, and 14. Three biological replicates were collected per analysis day. Biomass was frozen in liquid nitrogen and kept at −80 °C until RNA extraction.For the determination of efeA was performed by RT-qPCR  as previously described [Total RNA was isolated from frozen spores and mycelia as previously described . DNase tescribed , and folescribed . Gene-spescribed . For eacefeA gene expression during the infection process, sequence reads of P. digitatum spores and P. digitatum-infected oranges at 12, 24, and 48 h post inoculation (hpi) obtained using the Roche 454 GS FLX Titanium system [P. digitatum Pd1 (PDIP) genome using default parameters, i.e., the number of mismatches allowed was two, minimum length fraction 0.5, and minimum similarity fraction 0.8. Gene expression values were calculated as reads per kilobase of exon model per million mapped reads (RPKM). For the determination of m system  were retefeA gene was obtained based on the article by Johansson et al. [P. digitatum EFE protein sequence by modifying the start codon 147 base pairs upstream of the originally predicted start codon [efe genes were obtained from the NCBI. A preliminary multiple protein sequence analysis for eight fungal selected sequences was performed with the MUSCLE algorithm [The sequence of the n et al. . These art codon . Sequenclgorithm . Pfam dolgorithm  and the lgorithm . The phyP. digitatum efeA gene (PDIP_08660) was performed by PCR from the genomic DNA of the two gene flanking sequences using primers O1-O2 to amplify the upstream flanking region (amplicon of 1389 bp), and A3-A4 to amplify the downstream flanking region (amplicon of 1547 bp)  . The resescribed , and traescribed . All theP. digitatum Pd1, the ectopic efeA mutant, and two ΔefeA knockout mutants, 5 µL of the conidial suspensions adjusted to 106 conidia/mL were centrally inoculated onto PDA plates and incubated in the dark at 24 °C. Mycelial growth was determined by measuring two perpendicular diameters of growing colonies up to 6 dpi. The sporulation assessment was done as described in To quantify the mycelial growth and sporulation of 6 conidia/mL) of parental strain P. digitatum Pd1, the ectopic mutant and two knockout mutants. Inoculated plates were incubated at 24 °C for up to 14 days. To determine ethylene production, each PDA plate was incubated without the lid in a 1 L sealed glass jar at 24 °C for 1 h. Then 1 mL of headspace gas was drawn from each jar with a hypodermic syringe and injected into a 7820A gas chromatograph system 7820A , equipped with a HayeSep Q pre-conditioned column  and a flame ionization detector [Ethylene production was determined in three replicate samples of PDA plates centrally inoculated with 5 µL of the conidial suspensions  were obtained from a packinghouse in Liria, Valencia (Spain) on the same harvesting day before receiving any postharvest treatment. Fruit were washed for 5 min in a 5% commercial bleach solution, thoroughly rinsed with tap water, and allowed to dry until the next day. Orange peel discs were obtained with a 5 mm cork-borer and subjected to three flash-freezing cycles in liquid nitrogen and thawing at ambient temperature. This procedure inactivated orange cells, as noted by a lack of respiration. Five discs were placed inside 15 mL glass tubes. Then orange discs were inoculated with 500 µL of a conidia suspension (106 conidia/mL) of parental strain Pd1 or the Δ1f knockout mutant. Five replicate tubes were incubated at 24 °C for up to 21 days. Ethylene production was determined by sealing the tubes containing the inoculated discs and incubating them at 24 °C. After 1 h of incubation, a 1 mL headspace gas sample was withdrawn from each tube, and ethylene production was determined as previously described.To determine ethylene production by efeA knockout mutants, pathogenicity assays were conducted with citrus fruit. C. sinensis ‘Navelina’ oranges were harvested at the beginning of the season and used for experiments 1 and 2. The ‘Lane-Late’ oranges employed for experiment 3 were harvested at the end of the season. Oranges were obtained from a packinghouse in Liria, Valencia (Spain) on the same harvest day and taken to the laboratory, where they were washed as described before. The next day, four equidistant wounds (3 mm deep) were made along the equatorial axis and were immediately inoculated with 10 µl of a conidial suspension, adjusted to 104 conidia/mL, from the parental P. digitatum or the ectopic or the ΔefeA knockout mutant strains. Five fruit constituted a replicate, with three replicates per treatment. Fruit were incubated in a thermostatic chamber at 20 °C and 90% relative humidity (RH) for up to 7 days. The incidence, measured as the percentage of infection, and infection severity, measured as decayed area (cm2) for all inoculated wounds, were determined up to 7 dpi. An analysis of variance (ANOVA) was performed to test the differences among samples. Means were separated using Tukey’s test with p < 0.05 by employing Statgraphics Stratus .To analyze the virulence of the generated ΔP. digitatum, fully mature oranges (C. sinensis cv ‘Navel’) were obtained and processed as described before. In this experiment, the inoculum was raised to 106 conidia/mL to obtain a more synchronous infection. Unwounded and mock-inoculated samples were also used as controls. Fruit were kept at 20 °C and 90% RH. Ethylene production from whole fruit was measured periodically by incubating three biological replicate samples containing five fruit with four inoculated wounds each in 3.8 L sealed glass jars for 1 h at 24 °C. Two technical replicate samples of the 1 mL headspace gas sample were withdrawn from each glass jar, and ethylene production was determined as previously described.To determine the possible involvement of ethylene biosynthesis in the pathogenicity of P. digitatum [efeA gene should be located 147 base pairs upstream of the predicted start codon in PDIP_08660 in the published annotated genome of P. digitatum [P. syringae (P32021.1) and P. digitatum (PDIP_08660), as well as similar genes from Penicillium expansum , Penicillium griseofulvum , Penicillium rubens (XP_002562422.1), Penicillium camemberti , Aspergillus clavatus , Fusarium oxysporum (PCD42226.1), and the scanning of these protein sequences against the Pfam database showed they all contained the non-haem dioxygenase in morphine synthesis N-terminal  and the 2OG-Fe (II) oxygenase superfamily  domains, located between amino acids 63-181 and 202-345 in the P. digitatum EFE protein, respectively  involved in ethylene biosynthesis has been previously identified in igitatum . These aigitatum . The MUSectively A. The seer clade B.efeA gene during the in vitro P. digitatum growth (5 conidia/mL) of P. digitatum and incubated at 24 °C for up to 14 days. Mycelia and spores were scraped from the surface of plates, and RNA was extracted and cDNA synthesized. The efeA gene expression was low in spores and increased during radial P. digitatum growth on PDA plates by up to 25- and 39-fold, with maximal values at 8 and 11 days, respectively. Later, the relative efeA gene expression decreased by 14 days.We analyzed the expression of the m growth A. PDA plefeA gene expression in P. digitatum spores and P. digitatum-infected oranges at 12, 24, and 48 hpi using available RNA-seq data  [efeA gene in spores (S), and at 12, 24, and 48 h post inoculation (hpi). The efeA gene expression was detected only in spores.We determined the A059533) . Figure A. tumefaciens-mediated transformation of P. digitatum was conducted to obtain efeA deletants. Two ΔefeA gene knockouts, named Δ1f and Δ23c, and an ectopic mutant were selected after the analysis of putative transformants (6 conidia/mL) of parental strain Pd1, the ectopic mutant (ect), and two knockout mutants (Δ1f and Δ23c) was point inoculated in the center of PDA plates and incubated in the dark at 24 °C. No differences were observed in mycelial growth among the different strains at 6 dpi  of the parental Pd1 strain and the Δ1f knockout mutant, and incubated for up to 21 days at 24 °C. Development of parental strain Pd1 on orange discs led to a sharp increase in the ethylene levels, starting at 3–4 dpi, with a maximum production of 280 nL/g h at 11 dpi  the ACC pathway, mainly used by higher plants and uncommon in microorganisms; (ii) the KMBA pathway, employed by bacteria and fungi; (iii) the 2-oxoglutarate pathway, described in pathways  based onsize ACC  through size ACC . Besides species , P. citreaminase . Moreove oranges . The addigitatum . These rP. digitatum by using the KMBA and EFE pathways, depending on growth conditions [P. digitatum and showed its capability to mediate ethylene production in yeast. Our data reveal that the expression of the efeA gene of parental strain P. digitatum Pd1 increased concomitantly with pathogen development on PDA plates. Moreover, the parental strain produced ethylene during in vitro growth on PDA, and gradually increased in parallel to fungal growth and sporulation. However, the non-ethylene producing ΔefeA knockout mutant did not produce ethylene above the background levels in any fungal development stage. Previous studies by Yang et al. [P. digitatum uses the 2-oxoglutarate pathway for ethylene synthesis when grown in PDA tubes with low oxygen levels and limited nutrient resources. However, these authors also suggested that when the fungus was grown on PDA plates, the KMBA pathway was mainly responsible for ethylene synthesis. This conclusion was built upon the induction effect exerted by methionine, a precursor in the KMBA pathway. Our results clearly show that under the assayed conditions, and with the PDA medium incubated in the dark and no potential ethylene precursor added, most, if not all, the ethylene produced by P. digitatum was via the 2-oxoglutarate/EFE pathway. However, we cannot rule out the possibility of the KMBA pathway making a minor contribution to ethylene synthesis under our experimental conditions.There are reports that ethylene can be formed by nditions . This funditions ,9. Regarnditions  identifig et al.  have sugAlternaria alternata is able to produce ethylene via the KBMA pathway and to utilize it for enhanced virulence during grape berry infection [B. cinerea also produces ethylene via the KMBA pathway, and the emission of the hormone follows the hyphal growth pattern [B. cinerea–grape system [Verticillium dahliae produces ethylene during in vitro growth and the role of the hormone during the infection process depends on timing: ethylene presence upon infection inhibits disease development, whereas it enhances symptoms in tomato [P. digitatum–citrus fruit interaction, the ethylene detected in initial infection stages has been described to be of fruit origin, while the ethylene burst observed in advanced development stages originates from the fungus [P. digitatum and used it to test the role of ethylene in the pathogenicity on citrus fruit. He showed that the natural isolate did not produce ethylene during in vitro growth and was as pathogenic as the ethylene-producing isolate. However, as the mutations present in the non-ethylene producing isolate were not known, this result does not constitute formal proof to rule out any role of ethylene in P. digitatum pathogenesis. During the infection of Citrus reticulata fruit by P. digitatum, at least five fungal genes associated with ethylene synthesis were up-regulated [P. digitatum as a gene involved in ethylene biosynthesis by using 2-oxoglutarate as a precursor [P. digitatum, we constructed a knockout mutant of the efeA gene. The Δ1f knockout mutant and parental strain Pd1 are isogenic strains with similar morphological characteristics and growth rates both in vitro and in vivo. Moreover, the knockout mutant was as pathogenic as the parental high ethylene-producing isolate during citrus fruit infection, which confirms previous results obtained with a natural non-ethylene-producing isolate [efeA knockout mutant resulted in a slight increase in ethylene production in the initial infection phases. This enhanced production of ethylene originates mainly from the citrus fruit as a response to the pathogen. Marcos et al. [P. digitatum infection resulted from the coordinated distinct spatio-temporal up-regulation of the three ethylene biosynthetic genes ACS1, ACS2, and ACO in citrus fruit. This initial rise was followed by a more pronounced increase in the fruit infected with the P. digitatum parental strain. The increased ethylene production in advanced infection states is likely to stem mostly from the fungus because this ethylene burst was observed only in the fruit infected by parental strain Pd1. The ethylene burst was not detected in the fruit infected by the non-ethylene-producing knockout mutant, which reinforces the idea that the ethylene generated in advanced infection stages is produced mainly by the fungus and not by citrus fruit. Moreover, as the knockout mutant only lacks the efeA gene, we can affirm that the ethylene produced by parental strain Pd1 in advanced infection stages of citrus fruit is synthesized through the 2-oxoglutarate/EFE pathway. Although it would seem clear that ethylene synthesis by P. digitatum is dispensable for establishing the colonization of citrus fruit, the role of ethylene in advanced development stages of infection is still unclear. Our results suggest that the inoculation of oranges with an ethylene-deficient mutant facilitates the development of secondary pathogens on fruit surfaces in advanced infection stages, when fruit surfaces are completely colonized by spores. This secondary colonization was present to a much lesser extent when oranges were infected with the ethylene-producing parental strain. We hypothesize that the ethylene produced by the parental strain could serve to deter the development of opportunistic pathogens that could compete with P. digitatum for the nutrients released by the killed fruit tissue. Further experiments should be performed to explore this possible new role of ethylene produced by P. digitatum during orange fruit infection.Ethylene may play a dual role in fungus–plant interactions by affecting both the plant and the pathogen. This hormone has been implicated in biotic stresses as both a virulence factor of fungal pathogens and a signaling compound in disease resistance ,17,47. Tnfection . B. cine pattern ,8. Howeve system . Verticin tomato . In the e fungus ,19. Chale fungus  identifiegulated . As far recursor . In orde isolate . Accordi isolate ,19,24, is et al.  showed tP. digitatum ΔefeA knockout mutant allowed us to analyze the role of the 2-oxoglutarate pathway in ethylene synthesis in this citrus postharvest pathogen. A lack of ethylene production observed in the mutant when grown on PDA medium in the dark led us to conclude that this pathway is responsible for most, if not all, the produced ethylene. During citrus fruit infection with the wild-type strain, a marked rise in ethylene production was observed at the time of mycelia and spore colonization on the fruit surfaces. However, this ethylene burst was not observed when infection was carried out by the ΔefeA knockout mutant. This result indicates that the 2-oxoglutarate pathway also constitutes the main route for ethylene synthesis in this fungus during the infection process. Finally, we observed a widespread presence of bluish spores on the surfaces of the fruit colonized by the ΔefeA knockout mutant, which is indicative of the development of secondary infections. These secondary infections were less abundant when fruit were colonized by the parental strain. This result suggests a new possible role for ethylene in the infection process as a tool to deter the development of secondary pathogens on the surface of already dead fruit tissue, and precludes other organisms from taking advantage of the presence of the readily available source of nutrients. Nevertheless, further work is necessary to explore the feasibility of this hypothesis.The construction of a


Mobile elements comprise a large fraction of metazoan genomes. Accumulation of mobile elements is bound to produce multiple putative double-stranded RNA (dsRNA) structures within the transcriptome. These endogenous dsRNA structures resemble viral RNA and may trigger false activation of the innate immune response, leading to severe damage to the host cell. Adenosine to inosine (A-to-I) RNA editing is a common post-transcriptional modification, abundant within repetitive elements of all metazoans. It was recently shown that a key function of A-to-I RNA editing by ADAR1 is to suppress the immunogenic response by endogenous dsRNAs.Here, we analyze the transcriptomes of dozens of species across the Metazoa and identify a strong genomic selection against endogenous dsRNAs, resulting in their purification from the canonical transcriptome. This purifying selection is especially strong for long and nearly perfect dsRNAs. These are almost absent from mRNAs, but not pre-mRNAs, supporting the notion of selection due to cytoplasmic processes. The few long and nearly perfect structures found in human transcripts are weakly expressed and often heavily edited.Purifying selection of long dsRNA is an important defense mechanism against false activation of innate immunity. This newly identified principle governs the integration of mobile elements into the genome, a major driving force of genome evolution. Furthermore, we find that most ADAR1 activity is not required to prevent an immune response to endogenous dsRNAs. The critical targets of ADAR1 editing are, likely, to be found mostly in non-canonical transcripts. Most invading viruses give rise to long double-stranded RNAs (dsRNAs) in the cytoplasm of the host cells . These sIt was pointed out recently that the main function of the essential ADAR1 enzyme, conferring adenosine to inosine (A-to-I) RNA editing, is to prevent activation of cytosolic immune response –10. A-toFurthermore, editing levels are usually quite low within paired inverted Alu repeats, the archetypal examples of human endogenous dsRNAs –17. AlthHere, we study the repertoire of putative dsRNA structures in mRNA and pre-mRNA molecules. We analyze the transcriptomes of 49 organisms, from yeast to human, and find that dsRNAs in mRNA molecules are strongly depleted from the genome. This demonstrates a strong selective force that controls the integration of repetitive elements into genes in order to minimize the number of endogenous dsRNAs. Notably, in pre-mRNA molecules (including the introns), such depletion is much weaker. This indicates that the main driving forces of this selection process are cytoplasmic, presumably the need to avoid activation of innate immunity sensors by endogenous RNA. Moreover, long and nearly perfect RNA duplexes, reported to be the prime targets of MDA5 , 20, areIn order to explore the extent to which mature RNA molecules and pre-mRNA molecules form long and stable dsRNA structures, we employ a cross-species whole transcriptome approach, studying the full transcriptome of 49 organisms included in the Ensembl database , from yeDespite important recent technological advancements, predicting the detailed structure of RNA molecules, including structural motifs, protein-binding regions, and RNA-RNA interactions, is yet a major challenge. Numerous computational tools are available for predicting RNA secondary structures, but their reliability in predicting full-length molecule structure is limited . Thus, fp < 1e−100; see Additional file p < 1e−100). Limiting the gap between the 2 duplicated sequences to 2000 bp does not change the results, qualitatively . Moreover, depletion of almost perfect hits is noticeable even for pre-mRNAs (1.07M TDSs vs. 0.78M IDSs (p < 1e−100); 205 Mbp vs. 81 Mbp, respectively; Additional file p < 1e−100). In pre-mRNA molecules, one finds only 1.57M long IDSs compared with 2.31M long TDSs    transcripts . GenomesPurifying selection is a major driving force of evolution, weeding out of the genome harmful alleles. Here, we demonstrated a general evolutionary principle that provides a fundamental layer of defense against false triggering of the viral infection response: Endogenous dsRNA-forming sequences that pose a risk of self-intolerance leading to cell death are either rooted out of the genome or silenced transcriptionally.What is then the critical role of ADAR1 editing? As mobile element activity goes on, more and more repeats integrate into the transcribed part of the genome, and putative dsRNAs accumulate in the transcriptome . Some ofStrong depletion is observed for long and nearly perfect dsRNAs, consistent with previous studies of MDA5/MAVS specificity. However, short (< 300 bp) and imperfect (identity < 96%) dsRNAs are also purified from mRNAs. Why would these be depleted? Possibly, even shorter and imperfect RNA duplexes may be recognized by dsRNA sensors to some extent. Alternatively, an overload of ADAR1-binding transcripts might have a detrimental effect on ADAR1 protection, even if these transcripts themselves do not pose a risk. In addition, endogenous dsRNAs might have additional detrimental effects on RNA processing and translation –39.Integration of mobile elements is one of the major mechanisms for genomic innovations . To dateFinally, we would like to note that most of ADAR1 editing is not at all required for preventing the innate immune response. For example, most editing events in human exons occur due to exon-intron pairing. These exonic Alu elements do not have any complementary Alu sequence in the hosting mRNA sequence after splicing and thus do not pose any threat of dsRNA formation at the mRNA level. Moreover, these are typically edited in about one adenosine per Alu sequence , which iEnsemble  transcriFor each gene, we used BLAST  to look Often, the same region appears in multiple hits. We thus created a bed file from all reversely oriented or same-strand hits in an organism (within a given range of identity and length), merged the regions using bedtools sort and merge, and summed the number of genomic nucleotides in the merged regions. A nucleotide belonging to several hits was assigned to the longer of them.http://prinseq.sourceforge.net/index.html). RNA-seq data were aligned (as single-end reads) to the genome (hg19) using BWA [We compiled a pool of 30 GTEx RNA-seq samples originating from 15 different human tissues (2 samples each) and another pool of GTEx muscle samples (representing a lowly edited tissue) . We useTo calculate the fraction of paired regions belonging to genomic repeats, we used RepeatMasker annotation as downloaded taken from UCSC (when available). Tetraodon was excluded from this analysis, as there was no RepeatMasker annotation for this organism.To test for significance of the disparity between numbers of IDS and TDS, we look at the observed fraction of IDSs among all duplicated sequences found, i.e., #IDS/(#IDS+#TDS), and use a single proportion test to reject the null hypothesis that IDS and TDS are equally probable .Additional file 1: Supplementary tables.Additional file 2: Supplementary figures.Additional file 3: Review history.


Our group and other researchers have developed SDRE methods utilizing adenosine deaminases acting on RNA (ADARs) and guide RNAs recruiting ADARs to target RNAs bearing point mutations. In general, efficient SDRE relies on introducing numerous guide RNAs relative to target genes. However, achieving a large ratio is not possible for gene therapy applications. In order to achieve a realistic ratio, we herein developed a system that can introduce an equal number of genes and guide RNAs into cultured cells using a fusion protein comprising an ADAR fragment and a plasmid vector containing one copy of each gene on a single construct. We transfected the single construct into HEK293T cells and achieved relatively high efficiency (up to 42%). The results demonstrate that efficient SDRE is possible when the copy number is similar for all three factors . This method is expected to be capable of highly efficient gene repair  Genome editing technologies such as the clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-associated proteins (Cas) system are expected to provide novel therapeutics for a range of diseases ,2,3. GenHowever, all DNA editing technologies, including those mentioned above, suffer from problems that hinder gene therapy applications. Notably, errors (off-target mutations) in genomic DNA caused by DNA editing remain permanently in treated cells due to the persistence of DNA, and they can cause cell death or oncogenic transformation ,12. For Instead of DNA editing, in the present work we decided to edit disease-related genes at the RNA level. When targeting RNA, a nucleotide change is transient ,14,15; hTo achieve gene editing at the RNA level, we developed a site-directed RNA editing (SDRE) technology based on adenosine deaminases acting on the RNA deaminase domain (ADAR DD) and phage-derived MS2 (MS2-ADAR) system ,17,18. MAlthough many studies on SDRE have been carried out, most rely on introducing numerous copies of genes responsible for SDRE  relative to target genes. Our ultimate goal is gene therapy for patients suffering from genetic diseases, and achieving such a large ratio is not compatible with clinical applications. For example, in a clinical trial on gene delivery of a functional dystrophin transgene to skeletal muscle in patients with Duchenne’s muscular dystrophy using adeno-associated virus (AAV), the delivery efficiency was 0.01 to 2.56 vector copies per diploid genome . In anotHerein, we report the development of a system that can introduce similar copy numbers of genes encoding guide RNAs, fusion proteins, and target genes (EGFP reporter) into cultured cells using a plasmid vector containing one copy of each gene in a single construct. We employed a guide RNA that includes six copies of the MS2 RNA driven by the cytomegalovirus immediate-early 94 (CMV IE94) promoter described in our previous studies ,17,18. SFokI nuclease, the one with an XTEN linker had the highest activity [Our previously developed MS2-ADAR  did not activity , so we cIn order to perform SDRE under more realistic conditions, the copy number ratios of the three factors  had to be equal in all transfected cells. In the conventional method introducing the three factors by separate plasmid vectors, even if each plasmid is transfected in an equal molar ratio, each plasmid is not uniformly introduced into each cell. Therefore, in the above method, the interpretation of the result may be difficult. Instead, we used a single construct containing a copy of each of the genes, so that the copy number ratios of the three factors were equal in cell units.For the SDRE target, we used enhanced green fluorescent protein (EGFP) W58X bearing a nonsense mutation in the codon for tryptophan 58 (TGG) that turns it into a stop codon (TAG). Since full-length EGFP is not synthesized due to the mutation, EGFP W58X does not emit green fluorescence. If this stop codon (TAG) is repaired by A-to-I editing (I is recognized as G during translation) to regenerate the tryptophan (TGG) codon, full-length EGFP is synthesized, and green fluorescence should be observed.After transfecting HEK293T cells with EGFP W58X, MCP-ADAR1 DD (fusion protein), and guide RNA using three separate plasmids (each carrying a different factor), or transfecting with a single construct carrying all three factors a, we carHaeIII. Cleavage of the fragment was detected both when the three factors were introduced separately and when a single construct was used  analysis was performed to detect the single base difference. If SDRE occurred, the 329 bp fragment was cut into 228 bp and 101 bp fragments by the restriction enzyme was used c. HoweveTo determine the editing efficiency, Sanger sequencing of RT-PCR products was performed d, and thSDRE using MS2-ADAR is dependent on MCP binding to MS2 RNA. Therefore, the binding affinity of MCP to the RNA might affect the editing efficiency. MCP harboring the N55K mutation at amino acid position 55 (MCP N55K) has higher binding affinity to MS2 RNA (a lower Kd for MCP-MS2 RNA interaction) than wild-type MCP (MCP WT) ; hence, These three MCPs  were compared using single constructs. The editing efficiencies of MCP WT, MCP N55K, and 2MCP N55K after 48 h were 35%, 33%, and 23%, respectively a,b.A nuclear localization signal (NLS) is a short stretch of amino acids that mediates the transport of nuclear proteins into the nucleus ,37. MCP-In order to investigate the editing efficiency in the nucleus under conditions in which the copy number of the three factors is equal, we added an NLS to MCP N55K-ADAR1 DD in the single construct (NLS+). In addition to MCP N55K-ADAR1 DD  and NLS+, NLS- and NLS+ were co-transfected at the same molar ratio to validate the utility of simultaneous editing in the cytoplasm and nucleus (NLS±). The editing efficiencies of NLS-, NLS+, and NLS± after 48 h were 36%, 6%, and 39%, respectively . The ediACTB, GAPDH) were selected as candidates, and MCP N55K-ADAR1 DD and the guide RNA  co-expressing plasmids were constructed and transfected into HEK293T cells. The editing efficiencies at ACTB and GAPDH targeting sites after 48 h were 53% and 32%, respectively  are present in an equal number of copies. Using a single construct containing a copy of each of the factors, we achieved relatively high efficiency (up to 42%). In addition, we showed MS2-ADAR worked not only on the reporter gene but also on several endogenous genes, suggesting its applicability to actual gene therapy.In previous studies, expression levels of the guide RNA were extremely high relative to the target gene, which is far from ideal for gene therapy applications. In one recent study on SDRE using λN, although a very high editing efficiency (up to 57%) was achieved, the amount of guide RNA expression plasmid (U6 pENTR guide RNA vector) was 1.5 μg while that of the target gene (CFTR Y122X) expression plasmid was 25 ng, meaning that the guide RNA was 60-fold more abundant than the target gene . In anotp = 0.27). This indicates that the binding affinity of MCP WT was sufficient for efficient editing by MS2-ADAR. The editing efficiency of 2MCP N55K was 23%, approximately 10% lower than that of MCP WT/N55K (p < 0.05). This reduced editing efficiency for 2MCP N55K might be because the two MCPs form a dimer that binds to MS2 RNA [We also compared the editing efficiencies of MCP WT, MCP N55K, and 2MCP N55K in MS2-ADAR. The editing efficiencies of MCP WT and MCP N55K were almost the same . However, the efficiency of NLS+ was reported to be the same as that of NLS- [We verified the nuclear localization of MCP-ADAR1 DD by adding an NLS to the protein in the single construct. The editing efficiency of NLS+ was 6%, which was much lower than that of NLS- . Future studies are needed to determine whether highly efficient gene repair can be achieved As a reporter gene of SDRE, a plasmid expressing EGFP W58X  based on backbone vector pcDNA3  was prepared by site-directed mutagenesis .acatgaggatcacccatgtctgggccagggcacgggcagctaacatgaggatcacccatgtctttt-3′), in which the MS2 RNA is underlined and bold letters indicate antisense RNA recognizing the target, was designed so that MS2 RNAs are on both sides of the antisense RNA to enable efficient SDRE [For the guide RNA, the CMV IE94 promoter of pCS2+ (Addgene) was replaced with the human U6 promoter (hU6 promoter), and the guide RNA was then inserted downstream of the hU6 promoter. The guide RNA sequence  driven by the CMV IE94 promoter within the pCS2+MT vector (Addgene), MCP is at the N-terminus and ADAR1 DD is at the C-terminus. These two proteins were linked via a glycine–serine (GS) + XTEN linker (GGSGSGAGSGSSGSETPGTSESATPES). Wild-type MCP (MCP WT) was prepared by site-directed mutagenesis (AAG > AAC) by PCR using MS2-HB plasmid (Addgene) harboring the MCP N55K sequence. MCP N55K was prepared from MS2-HB. 2MCP N55K, comprising two copies of MCP N55K in tandem, was prepared by inverse PCR using the MCP N55K-ADAR1 DD construct as a template and inserting another MCP N55K fragment amplified by PCR. The amino acid sequence of 2MCP N55K is the same as that of the 2MCP N55K part of MS2-HB. ADAR1 DD was amplified by PCR from a plasmid used in our previous study  as a temFor the single construct, the guide RNA expression gene and MCP-ADAR1 DD expression gene were inserted into the EGFP W58X expression plasmid using standard restriction enzyme cloning. In the single construct, the woodchuk hepatitis virus posttranscriptional regulatory element (WPRE) was inserted into the 3′-untranslated region (UTR) of MCP-ADAR1 DD.To validate the utility of the NLS for SDRE, three copies of the NLS of the SV40 Large T Antigen (APKKKRKVDPKKKRKVDPKKKRKV) ,41 were Escherichia coli DH5α competent cells , then extracted using NucleoSpin Plasmid Transfection-grade . The DNA concentration was measured using a NanoDrop 1000 Spectrophotometer .Plasmids used in this work were amplified using Detailed information for all plasmids used in reporter gene editing assay is included in 2 at 37 °C. Cells were used in experiments after at least three passages from frozen stocks.HEK293T cells from RIKEN BRC CELL BANK were maintained on 60 × 15 mm Falcon Cell Culture Dishes  in Dulbecco’s modified Eagle’s medium  supplemented with fetal bovine serum (Thermo Fisher Scientific) at a volume ratio of 10:1 DMEM:FBS under 5% CO5 to 7.0 × 105 cells/well. Before transfection, the medium was removed so that the volume per well was 0.15 mL, and 0.15 mL of fresh DMEM + FBS was added. Next, 25 µL of Opti-MEM (Thermo Fisher Scientific), 1.25 µL of 1.0 µg/µL PEI MAX , and 250 ng of plasmid DNA were mixed, incubated for 20 min, and added to each well. At 24 h after transfection, 0.18 mL of medium was removed from each well, and 0.18 mL of fresh DMEM + FBS was added. At 48 or 72 h after transfection, total RNA from transfected cells was collected immediately after green fluorescence was observed using a BZ-8000 fluorescence microscope .At 16–24 h before transfection, HEK293T cells were seeded in Falcon 48-well Multiwell Cell Culture Plates (Corning) at a density of 3.9 × 10Details of the conditions of reporter gene editing assay are included in At 48 or 72 h after transfection, total RNA was extracted using TRIzol Reagent (Thermo Fisher Scientific) at 0.1 mL/well according to the manufacturer’s protocol. Extracted total RNA was treated with 1.5–5 units of Recombinant DNaseI (Takara Bio) for 30 min at 37 °C , and 1 µL of 20 mM ethylenediaminetetraacetic acid (EDTA) was added and incubated for 10 min at 75 °C.Using 300–500 ng of total RNA treated with DNase I, cDNA was synthesized with 50 units of ReverTra Ace  by incubating for 10 min at 50 °C, then incubating for 5 min at 99 °C. Primer No. 1 employed for cDNA synthesis is listed in PCR was performed with GoTaq Flexi DNA Polymerase  using 0.2–10 ng of cDNA  as template to amplify the DNA region containing the target base in EGFP W58X. Primers No. 2 and No. 3 used for PCR are listed in Sanger sequencing of purified samples was performed by Eurofins Genomics . For sequencing, a reverse primer was employed as described previously ; primer HaeIII, which recognizes and cuts the 5′-ggcc-3′ sequence. In order to detect target base conversion, HaeIII (Takara Bio) digestion was performed for 1 h at 37 °C, and the presence or absence of fragment cleavage was observed by polyacrylamide gel electrophoresis. For nucleic acid gel staining, SYBR Green I  was employed according to the manufacturer’s protocol.After cDNA synthesis using primer No. 8 , nested 5 cells/well. A total of 1.5 μg of each plasmid was transfected into the cells. At 48 h after transfection, editing efficiencies were determined in the same way as 4.7. Detailed information for plasmids used in this assay is included in ACTB and GAPDH mRNA targeting guide RNAs  were designed and constructed MCP N55K-ADAR1 DD and gRNA-1/gRNA-2 co-expressing plasmids. On the day before transfection, HEK293T cells were seeded in Falcon 12-well Multiwell Cell Culture Plates (Corning) at a density if 4.0 × 10t-test followed by the Bonferroni correction method.In all experiments for determining editing efficiency, three independent experiments were performed. Multiple comparisons were made by performing a paired