Puberty is an important developmental period marked by hormonal, metabolic and immune changes. Puberty also marks a shift in sex differences in susceptibility to asthma. Yet, little is known about the gene expression changes in immune cells that occur during pubertal development. Here we assess pubertal development and leukocyte gene expression in a longitudinal cohort of 251 children with asthma. We identify substantial gene expression changes associated with age and pubertal development. Gene expression changes between pre- and post-menarcheal females suggest a shift from predominantly innate to adaptive immunity. We show that genetic effects on gene expression change dynamically during pubertal development. Gene expression changes during puberty are correlated with gene expression changes associated with asthma and may explain sex differences in prevalence. Our results show that molecular data used to study the genetics of early onset diseases should consider pubertal development as an important factor that modifies the transcriptome. Puberty is an important developmental period marked by hormonal, metabolic and immunological changes. Here the authors report gene expression changes in immune cells associated with age and puberty, and that may be relevant for sex differences in susceptibility to asthma, in a longitudinal cohort of 251 children with asthma. The typical age for puberty onset is approximately 9–13.5 years in males and 8.5–13 years in females, yet there are fundamental differences between males and females in the dynamics of the reactivation of the gonadotropic axis at puberty onset2. Physiological changes during this period that are common to both sexes include growth spurts, increase in BMI3, growth of body hair, skin changes (increased oil production and acne), and the maturation of gonads4. Females experience breast growth and menarche, while males undergo deepening of the voice and develop facial hair4.Puberty is an important developmental period marked by hormonal, metabolic and immune changes, which have been implicated in disease predisposition later in life. Starting at puberty the physiology of men and women is influenced by different hormones - primarily androgens such as testosterone in males and estrogen and progesterone in females - with broad effects on all body systems8. In particular, incidence of many autoimmune diseases increases in the peri-pubertal period, with females bearing a higher disease risk than males12. Puberty also marks a shift in sex differences in susceptibility to asthma - males have higher asthma prevalence in childhood, but starting from young adulthood, females are more prone to asthma14 Sex hormones are hypothesized to play a role in the switch of sex skew, as asthma symptoms are also known to worsen around menstruation16. Recently, a Mendelian randomization study showed a protective effect of increased sex hormone-binding globulin (SHBG) levels on asthma onset, with a larger effect in females than males17. Furthermore, in a cross-sectional study of a large cohort of children with severe asthma, circulating levels of androgens were found to positively associate with improved lung function and symptom control in pubescent males, while late puberty in females was associated with increased estradiol levels and decreased lung function18 Despite sex differences in susceptibility to asthma before and after puberty onset13, earlier onset of puberty has been found to increase the risk of asthma in both sexes19. Age at menarche is also known to have substantial effects on growth and disease20. Earlier menarche has been associated with increased risk of asthma19, cardiovascular disease21, type 2 diabetes22 and higher BMI20. A thorough understanding of the longitudinal changes in the immune system that accompany puberty is needed to understand their impacts on asthma and other disease susceptibility.In addition to these physiological changes, certain pathological conditions also manifest during puberty or develop sex-specific symptom profiles28 in peripheral blood immune cells. DNA methylation is an important epigenetic mark regulating gene expression during development and aging29. Hormonal changes in puberty have been associated with changes in DNA methylation23. For example, differentially methylated regions between pre- and post-pubertal females were enriched for estrogen response element, reproductive hormone signaling and immune and inflammatory responses, while differentially methylated regions in males were enriched for genes involved in adrenaline and noradrenaline biosynthesis24. Prior studies found a larger number of genomic regions changing methylation status during puberty in females than in males30. Furthermore, regions experiencing changes in methylation status in both sexes show changes with higher magnitude in females26. In contrast, other studies on pre- and post-pubertal subjects found limited differences in DNA methylation patterns between sexes27. These inconsistencies may be due to age differences between studies and could be further investigated by employing a study design that focuses on all pubertal developmental stages rather than comparing pre- and post-pubertal stages only.The molecular mechanisms underlying the physiological and pathological changes in the immune system during puberty remain understudied and largely uncharacterized. Previous studies have explored the effects of puberty on DNA methylationAlthough DNA methylation findings support the hypothesis that blood gene expression during puberty undergoes reprogramming, little is known about the transcriptional changes accompanying human pubertal development, particularly from a genome-wide perspective. Most studies to date explored epigenetic differences between pre- and post-puberty, yet we don’t have a complete picture of the gene expression changes during pubertal development in healthy or asthmatic children.Here we analyze a longitudinal cohort of 103 females and 148 males aged 10-17 years old and diagnosed with childhood onset asthma. We investigate the associations between pubertal factors , asthma symptoms, and genome-wide gene expression in blood. We identify gene expression changes in peri-pubertal males and females for thousands of genes over a short time period, as well as gene expression changes associated with pubertal development. Substantial transcriptional changes detected between pre- and post-menarcheal females suggest a shift from predominantly innate to adaptive immunity as females sexually matured. To assess the contribution of genetic factors, we explore the effects of genetic variation on gene expression changes associated with puberty using an expression quantitative trait locus (eQTL) mapping approach, and show that genetic variation interacts with puberty to influence gene expression. Our work demonstrates that gene expression changes during puberty are correlated with gene expression changes associated with asthma and may explain differences in prevalence between males and females.Participants in this study were recruited as part of the longitudinal Asthma in the Lives of Families Today (ALOFT) study. The ALOFT study recruited males and females with childhood onset asthma and between 10 and 17 years old from Metropolitan Detroit. Children were followed annually for up to three years.31, were represented for both sexes.We collected gene expression, genotype, puberty and menarche data for a total of 251 participants. For 163 participants we collected data for two timepoints: baseline and follow-up , thus strengthening the evidence of gene expression changes associated with age in peri-pubertal children  to improve effect size estimates, increasing the power of discovery and enabling direct comparison between effects measured in different individuals. Applying this method to the results of the longitudinal analysis, we discovered over two thousand differentially expressed genes nes Fig. , 6. SpecND1 Fig. , which eOD2 Fig. , which e39, using a longitudinal study design. Here, we asked to what extent age effects on gene expression were similar at different life stages, i.e. during puberty and aging. Of the 1291 genes changing expression with age in the elderly, 188 were also differentially expressed in both sexes in our longitudinal peri-pubertal sample . Among the genes differentially expressed in both youth and elderly, 44 genes increased expression and 76 decreased expression as a function of age in both groups. These genes may be unrelated to ageing and puberty, specifically; instead they may be associated with age-related biological processes shared across the lifespan. For 68 genes, age was associated with opposite effects on gene expression in peri-pubertal children and the elderly.There are limited data available on immune gene expression changes throughout the lifespan. A recent study investigated gene expression changes between the eighth and ninth decade of lifeq-value = 3.1 × 10−16). Genes upregulated when the children grew older were most strongly enriched for genes involved in biological processes related to carbohydrate metabolism , neutrophil activation  and vacuole organization  , carbon metabolism , and glycan degradation   Fig. 40. We al01) Fig. .41. To directly investigate transcriptional changes in immune cells during pubertal development, we used the same longitudinal study design described above and considered a quantitative pubertal development index analogous to Tanner stages43 that ranged from 1 (no development) to 4 (completed development)31 (Table S2). We found 108 genes differentially expressed with advancement through puberty in females and none in males (10% FDR)  Fig. , 8. Thesp-value < 2.2 × 10−16) between the gene expression log fold changes from the longitudinal and cross-sectional models . Of these, ITGB3 was downregulated in children in later pubertal stages and also hypermethylated, while two others, TOX and HMG20A, were downregulated but hypomethylated in children that were in a later pubertal stage  and post-menarche females . A longitudinal analysis was not performed for menarche because this approach was only possible for 4 individuals in our cohort. We identified 667 differentially expressed genes, 604 genes had higher expression in pre-menarche females while 63 genes had higher expression in post-menarche females , and inflammatory response  and KEGG pathways related to bacterial infection . Genes with higher expression after menarche were enriched for the KEGG term T cell receptor signaling , suggesting a shift from predominantly innate to adaptive immunity as females sexually matured . All of these were downregulated post menarche, and six were hypermethylated in females post puberty  was expressed both in males and females. To investigate whether estrogen may directly regulate gene expression in immune cells during puberty, we performed a motif scan for estrogen receptor response elements in the cis-regulatory region of differentially expressed genes. We identified binding sites for the estrogen receptor in the regulatory region of 30 genes with transcriptional changes during puberty and 236 genes that were differentially expressed between pre- and post-menarcheal females. However, we observed no enrichment for genes with nearby estrogen receptor binding sites.Genes downregulated after menarche were enriched for GO biological processes involved with immune responses, such as myeloid leukocyte activation  is positively correlated with puberty and age, indicating that genes upregulated in older males are also upregulated during puberty in males that have high pulmonary function . A quantitative change in the genetic effect on gene expression during puberty would be defined instead as an allele that is associated with increased gene expression but the magnitude of the increase varies in different puberty stages. This type of analysis is also known as dynamic or context quantitative trait locus (QTL) mapping67.Timing of puberty is influenced by both environmentalIGKV1-27 and PGAP1 in males, and TBV30 and DSC1 in females. Additionally, in females, genetic effects on the expression of FLOT2 varied pre- and post-menarche 68.We first investigated whether genetic effects on gene expression changed across age. We did not find age to modify the effect of genetic variation on gene expression in either males or females. To find genetic variants whose effect on gene expression changes during puberty, we tested for interaction between puberty stage or menarche and genotype on expression of the nearby gene. We found that genetic effects on gene expression varied across puberty stages for four genes: che Fig. . We obseion Fig. . FLOT2 eIGKV1-27 in males, Fig. TRBV30 in females, Fig. TRBV30 in the same cohort69, which suggests that this association may be mediated by changes in blood cell composition that accompany puberty70. Interestingly, a sex-biased genetic effect on expression of TRBV30 had been previously reported71 with a significant effect in adult male but not female spleen. Our findings suggest that the genetic effect on TRBV30 expression may be active in pre-pubertal females but disappear due to different mechanisms of gene expression regulation in post-pubertal females.We also found interactions between genotype and puberty on expression of B cell . TWAS allows the integration of gene expression data with GWAS data to identify plausible candidate genes driving trait variability in the population. We performed TWAS for age at menarche by incorporating whole blood eQTL data from GTEx(v8) with a GWAS of age at menarche from 370,000 individuals51 using SPrediXcan. Among 7174 tested genes, we detected a total of 108 genes associated with age at menarche at a genome-wide significant level  is upregulated as females become older. Previous studies implicated this gene in endometrial cancer76 and preterm birth77, and a meta-analysis found a protective effect of late menarche on endometrial cancer risk78.We have limited information on the genetic underpinnings of pubertal development. As a consequence, our knowledge of the molecular mechanisms accompanying developmental changes in the different body sites during puberty is limited. Our study identified changes in the expression of several genes in immune cells. To confirm that these gene expression changes are markers of pubertal development in independent cohorts, we considered GWAS data for age at menarche from the UK Biobank79. A previous TWAS68 associated expression of 1621 genes with asthma risk. We investigated whether pubertal development affected the expression of these genes. In both sexes, we observed gene expression changes over time during the peri-pubertal period for 124 genes associated with asthma risk. In addition, the expression of 56 genes associated with asthma risk differed between pre- and post-menarcheal females, and four genes associated with asthma risk changed expression as females progressed through puberty . Our results thus suggest that in immune cells, gene expression changes during later pubertal stages are more pronounced than those occurring during earlier pubertal development. Our sample size should be adequate to detect large changes at any stage if those were to occur. Thus despite puberty being an important time for developmental changes in both sexes, expression changes associated with pubertal development in males during the time frame considered in our dataset were relatively smaller when compared to those of females. Furthermore, our results are in line with previous findings of more genes differentially methylated between pre- and post-pubertal females than males30. Accordingly, our study adds to the existing evidence of a stronger effect of pubertal development on the functioning of the immune system in females compared to males. However, we found only a small overlap between genes changing expression with pubertal development in females and those previously reported to change methylation status between pre- and post-puberty in females24. One biological explanation for this lack of enrichment may be that some genes are epigenetically poised for transcription even before puberty but are not expressed until activated by sex hormones. Alternatively, the small overlap between genes differentially expressed as females advance through puberty and genes associated with regions differentially methylated in pre- and post-pubertal females may suggest that some of the differentially expressed genes we discovered are specific to the pubertal transition period.While the epigenetic changes accompanying pubertal development have been explored previously in several studiesExpectedly, gene expression changes associated with pubertal development in females were well correlated with age effects. This finding helps further validate the use of self-reported pubertal development scores in research settings. However, we discovered more genes differentially expressed with age than pubertal development. This could be because some of the females in our sample had completed pubertal development and only differed by age, thus allowing us to discover additional genes for which age effects may be subtler but continue past puberty.26. In agreement with this finding, we found more genes whose expression differed between pre- and post-menarcheal females compared to genes discovered to change expression as females progress through pubertal development. This is likely because the onset of first menstruation is a less ambiguous marker of the shift into sexual maturity.A recent study of DNA methylation changes associated with the five markers of pubertal development used in our study found that menarche status was the puberty marker associated with the highest number of regions differentially methylated27, reproductive hormone signaling24, and other physiological changes related to growth and development26. Our results allowed a more fine-grained insight into the changes in the immune system that accompany the sexual maturation of females. We discovered an enrichment of processes related to innate immunity within genes more highly expressed in pre-menarcheal females and enrichment for T cell receptor signaling in genes more highly expressed in post-menarcheal females. The latter result corroborates a previous finding of T cell receptor signaling being overrepresented within genes near differentially methylated regions, which were co-expressed in post-menarcheal females24. Our results suggest a shift from innate to adaptive immunity past menarche. This is supported by the known effects of sex hormones on the immune system. For example, estrogen has been shown to promote adaptive immunity86 and may promote or restrict innate immunity87.Previous studies of DNA methylation changes between pre- and post-puberty in females found an overrepresentation of genes involved in immune and inflammatory responses14. However, a recent longitudinal study throughout adolescence found that asthma severity decreased equally in male and female subjects, probably due to an increase in androgens in both sexes47. Androgen levels were found to be positively associated with increased pulmonary function88, as well as suppression of airway inflammation90. Due to the demographics of our cohort, we captured late pubertal stages in females, which are associated with increased incidence of asthma14, thus resulting in the sex-specific patterns of gene expression that we observed. Indeed, we found that gene expression changes in late puberty in males are positively correlated with gene expression changes associated with pulmonary function and inversely correlated with changes associated with asthma symptoms, while in females, these patterns are reversed. Furthermore, the genes expressed in late pubertal stages in females are enriched for adaptive immunity processes, which have been found to be influenced by estrogen86. This is a potential mechanism for previously observed associations between estradiol and decreased pulmonary function in adolescent females with asthma18 and could be further investigated in conjunction with hormone level measurements in future studies.Previous cross-sectional studies of asthma severity and age in large cohorts have shown that prior to puberty, the incidence of asthma is higher in males, and then post-puberty, adults with asthma are more likely to be female71, previous studies included only adult participants. Here we demonstrate that genetic regulation of gene expression varies during pubertal development. For example, the genetic effect on the expression of the B cell antigen recognizing protein IGKV1-27 gene disappears by late puberty in males. We found hormone response elements for androgen and estrogen receptors near IGKV1-27, suggesting that the different hormonal context between early and late puberty may explain the observed interactions between genetic effect and puberty at this locus. In females, we discovered that the genotype at rs73170626 is associated with the expression of TRBV30 gene in early puberty, but this genetic effect disappears in late puberty. This gene encodes a T cell receptor. Interestingly, a sex-biased eQTL was previously reported for this gene, with genetic effects on gene expression present only in adult males but not females71. This implicates puberty in origins of sex-biased genetic effects on gene expression, which may translate into disease risk in women later in life.While sex-biased genetic effect on gene expression had been reported before51. Using a similar TWAS approach here we demonstrated an association between blood expression levels of 108 genes and age at menarche. For 31 of these genes, our results confirmed previous observations for causal or pleiotropic effects between blood gene expression and age at menarche that were based on Mendelian randomization. This overlap represents a significant enrichment over chance 51. We demonstrated that expression of 27 genes associated with age at menarche changed across puberty, including a gene implicated in endometrial cancer risk76, which may help explain the link between earlier age at menarche and increased endometrial cancer risk78. Additionally, we demonstrated that pubertal transition was associated with changes in expression of genes associated with asthma risk. Further investigations of genes that are both associated with asthma risk and differentially expressed between pre- and post-menarche females may provide clues to understanding increased asthma risk in females post puberty.A previous TWAS identified 205 genes whose expression in neural tissues is associated with age at menarcheOur study is the first genome-wide characterization of patterns of gene expression changes in peri-pubertal males and females, and complements the existing body of literature on the epigenetic reprogramming of immune cells during puberty. Our results also add to the literature on lifespan changes in gene expression by focusing on early adolescence. Overall, our findings suggest a shift from a more innate to a more adaptive immune response past puberty in females, which might be a mechanism for the higher incidence of autoimmune disease in adult women compared to men. Additionally, we demonstrate that the associations between gene expression and pubertal development can be modified by genetic variation, which may contribute to inter-individual variability in incidence and severity of diseases with peri-pubertal onset.69). Written assent and consent were obtained from the participating youth and their parent, respectively. Participants were compensated. The ALOFT study included three measurements at annual intervals, but only the first two sampled time points were included for individuals with more than two longitudinal data points in the ALOFT study. This strategy allowed us to maximize the sample size for the longitudinal analysis. For 65% of the participants, we were able to collect longitudinal gene expression data from at least two timepoints. The average time interval between the two-time points was 1.27 years (SD = 0.46 years).Participants were drawn from the Asthma in the Lives of Families Today study . The study was approved by Wayne State University Institutional Review Board, decision #0412110B3F. This multi-ethnic cohort was established to investigate the effects of psychosocial experiences on asthma symptoms in youth living in the Detroit Metropolitan Area. To be included in the study, youth were required to be between 10 and 17 years of age at the time of recruitment and diagnosed with at least mild to persistent asthma by a physician . The total sample included in the current study consisted of 251 participants  aged 10-17 at recruitment for whom we successfully collected RNA-seq and genome-wide genotype data  and only samples with RNA Integrity Number (RIN) of at least 6 measured on Agilent Bioanalyzer were included. Library preparation was done in batches of up to 96 samples following standard Illumina TruSeq Stranded mRNA protocol, with longitudinal samples from the same individual processed together in the same batch. Samples were sequenced on the Illumina NextSeq500 Desktop Sequencer to obtain 75 bp paired-end reads. A total of 21 million (M) to 76 M reads were collected per sample, with a mean of 41 M reads/sample. The RNA-seq data were aligned to the human genome version GRCh37 using HISAT291 and counted across all genes using HTSeq. Samples with excess PCR duplicate rate (>60%) were excluded and sample identity was confirmed by comparing the genotypes obtained from the RNA-seq data with those obtained from the DNA samples. For all gene expression analyses, genes on sex chromosomes and genes with expression below 6 reads or 0.1 counts per million in at least 20% of samples were dropped. Data for the first time point for each individual and longitudinal data for 120 of the participants have been previously published69, and together with additional new data included in this study are accessible at dbGaP accession #phs002182.v2.p1.Leukocyte gene expression data for all timepoints and genotypes imputed to the whole genome were obtained as described previouslyVCF files were generated from low-coverage (~0.4×) whole-genome sequencing of one blood-derived DNA sample per individual and imputed to 37.5 M variants using the 1000 Genomes database by Gencove . These data are accessible at dbGaP accession #phs002182.v2.p1.93. Pubertal development was assessed at each timepoint prior to the blood draw for gene expression measurement via a sex-specific self-report questionnaire31. The questions and possible responses can be found in Table S2. The overall pubertal development score is a mean of the responses to specific questions 1–5, given that at least four valid responses are provided. Pubertal development stages are analogous to the Tanner stages43 and were treated as a continuous variable in all analyses. For female participants, we also considered self-reported menarche status (pre-menarche/post-menarche).Puberty staging was self-reported as this has been shown to be reliable, less intrusive, and allows for large epidemiological studies69. Briefly, we used measures of lung function collected via spirometry  and peak expiratory flow meter (morning and evening peak expiratory flow averaged over four days), and self-reported measures of asthma symptoms, severity and inhaler use.Measures of asthma symptoms and severity were collected at each timepoint prior to the blood collection as described previouslyWe used DESeq2 to test for gene expression changes over time, across puberty stages and with asthma traits in the longitudinal sample. Specifically, we used the following models:gene expression ~ fraction of reads mapping to exons + fraction of high-quality reads + RIN + participant ID + agegene expression ~ fraction of reads mapping to exons + fraction of high-quality reads + RIN + participant ID + pubertal stagegene expression ~ fraction of reads mapping to exons + fraction of high-quality reads + RIN + participant ID + age + asthma measureIn the DESeq model, we corrected for fraction of reads mapping to exons, fraction of high-quality reads (after removing PCR duplicates), RNA Integrity Number (RIN), and individual effect.These analyses were run in each sex separately. A subset of 24 participants reported a lower pubertal development score in the second timepoint compared to baseline. To account for these mis-reported data, we corrected the second timepoint to match the pubertal development score measured for the same individual at baseline.Significance threshold for differentially expressed genes was set at 10% FDR using the Benjamini–Hochberg correction method.We used DESeq2 to test for gene expression associated with sex, age, puberty, and menarche status in each sex separately. To account for possible confounding factors, we corrected for library preparation batch, fraction of reads mapping to exons, fraction of high-quality reads, RNA Integrity Number (RIN), and the top three genotype PCs.agegene expression ~ batch + fraction of reads mapping to exons + fraction of high-quality reads + RIN + genotype PCs + pubertal stagegene expression ~ batch + fraction of reads mapping to exons + fraction of high-quality reads + RIN + genotype PCs + menarchegene expression ~ batch + fraction of reads mapping to exons + fraction of high-quality reads + RIN + genotype PCs + 33 using the mashr package v.0.2.40 in R v4.0. As input, we provided the log-fold change and corresponding standard error from the DESeq analysis in each sex, subset for genes tested in both sexes (18341 genes). We considered genes with local false sign rate (LFSR) < 0.1 to be differentially expressed.To investigate gene expression changes associated with age in both sexes, we used the multivariate adaptive shrinkage (mash) method94. We counted the number of genes with at least one strongly-predicted (log(2) odds over genomic background>10) binding motif within 10 kb of the transcription start site within the set of differentially expressed genes and the set of all tested genes for each binding motif separately. We tested for enrichment using Fisher’s exact test and used P-value < 0.05 as threshold for significance.To test for enrichment of estrogen receptor binding sites near differentially expressed genes, we downloaded position weight matrices (PWM) for ESR1 from the JASPAR Vertebrate Core Motifs database version 202095 package for R to run Gene Ontology enrichment analysis for the differentially expressed genes for each variable. We used as background all genes tested for differential expression for that same variable The significance threshold was set at 10% FDR, and we only considered categories that included at least three genes from the test set.We used the clusterProfilerTo investigate pathway-level changes between pre- and post-menarcheal females, we calculated pathway scores of interest as follows: (1) calculated residual expression values by regressing the effects of confounders used in differential gene expression analysis; (2) extracted the subset of differentially expressed genes between pre- and post-menarcheal females and belonging to a specific GO term or KEGG pathway; (3) scaled mean-centered gene expression for each gene across individuals; (4) calculated the average values as a specific pathway score across genes for each individual.limma . To remove the effect of technical confounders, we regressed the effects of: RIN, fraction of reads mapping to exons, fraction of high-quality reads, data collection batch, library preparation batch, genotype PC1, genotype PC2, genotype PC3, age, weight, and height. We first performed eQTL mapping in both sexes combined to identify genetic variants associated with gene expression levels. To do this, we used FastQTL96 with adaptive permutations . For each gene, we tested all genetic variants within 1 Mb of the transcription start site (TSS) and with cohort MAF > 0.1. We defined significant eGenes atFDR<10%. For interaction eQTL mapping, we tested the SNP with the lowest p-value for each eGene. We quantile-normalized the gene expression data in each sex separately using the voom(method = ”quantile”) function in limma and regressed the effects of: RIN, fraction of reads mapping to exons, fraction of high quality reads, library preparation batch, genotype PC1, genotype PC2, genotype PC3. To identify genetic variants that interacted with puberty stage, age or menarche status to influence expression of the nearby gene, we tested the interaction between genotype dosage and each of these variables while controlling for the marginal effects using the following models in each sex separately:To prepare the data for eQTL mapping, we quantile-normalized the gene expression data for the cross-sectional sample of 251 participants using the voom(method = ”quantile”) function in genotype dosage × ageNormalized gene expression ~ genotype dosage + age + genotype dosage × pubertal stageNormalized gene expression ~ genotype dosage + pubertal stage + genotype dosage × menarcheNormalized gene expression ~ genotype dosage + menarche + p-values as described previously69 before applying Storey’s q-value method to control for FDR.We used 1000 permutations to correct the 97. To perform TWAS, we first downloaded GWAS summary statistics of AAM from the ReproGen consortium (https://www.reprogen.org/)51, which were generated based on data from 329,345 women from 40 studies. This data set contains over 10 million genetic markers. After transferring marker name from “chr:position” to “rs id” using annovar pipeline98, over 4 million markers remained in the following TWAS analysis. As input for SPrediXcan, we also provided covariances file (single-tissue LD reference files) and gene expression model in the blood tissue from the GTEx (V8) project75, available from (https://predictdb.org/). We used gene expression models trained in the blood tissue based on the Elastic Net model from the GTEx (V8) project. To validate our results, we used Fisher’s exact test to consider the enrichment of genes associated with age at menarche through TWAS within genes previously implicated in age at menarche through Mendelian randomization51.We performed TWAS for age at menarche (AAM) by running the module SPrediXcan in the MetaXcan software v0.6.11Further information on research design is available in the Supplementary informationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Reporting Summary


One of the hallmarks of antiviral responses to infection is the production of interferons and subsequently of interferon stimulated genes. Interferon stimulated gene 15 (ISG15) is among the earliest and most abundant proteins induced upon interferon signalling, encompassing versatile functions in host immunity. ISG15 is a ubiquitin like modifier that can be conjugated to substrates in a process analogous to ubiquitylation and referred to as ISGylation. The free unconjugated form can either exist intracellularly or be secreted to function as a cytokine. Interestingly, ISG15 has been reported to be both advantageous and detrimental to the development of immunopathology during infection. This review describes recent findings on the role of ISG15 in antiviral responses in human infection models, with a particular emphasis on autophagy, inflammatory responses and cellular metabolism combined with viral strategies of counteracting them. The field of ISGylation has steadily gained momentum; however much of the previous studies of virus infections conducted in mouse models are in sharp contrast with recent findings in human cells, underscoring the need to summarise our current understanding of its potential antiviral function in humans and identify knowledge gaps which need to be addressed in future studies. Upon infection, viral pathogens are detected by an array of innate immune sensors distributed across various intracellular compartments. Depending on the type of virus and their genetic makeup  these pathogen recognition receptors are activated to initiate a signalling cascade culminating in the production of a range of cytokines ,2. AmongISG15 is initially synthesised as a 17 kDa precursor protein and processed into a 15 kDa form . While iIsg15 gene or those encoding associated enzymes. Frameshift mutations in the ubiquitin like domain of ISG15 have been linked with recurrent skin lesions, cerebral calcification and lung disease [The importance of the ISGylation pathway is underscored by consequences of mutations in either the  disease . Autosom disease . More re disease .In vivo mouse models have yielded variable and often contradictory results on the role of ISGylation in antiviral responses. For example, initial studies with Usp18−/− mice underscored the importance of ISGylation in antiviral responses [−/− mice were protected from lethal LCMV and VSV infection, displaying increased ISG15 conjugates, diminished virus replication and virus-specific T cell responses, implicating ISGylation as the basis for this phenotype [−/− mice, which displayed no effect on interferon signalling, or resistance to LCMV and VSV infection [−/− mice, but reduced replication in Usp18−/− mice [esponses . Usp18−/henotype . This wanfection . These f−/− mice ,24. Much−/− mice  has shed−/− mice ,27.While ISG15 has appeared to be an antiviral effector from studies in mouse models, data from ISG15-deficient patients has shown otherwise. For example ISG15-deficient patients have been found to be more susceptible to mycobacterial infections; however, they display increased resistance to viral infections on account of sustained IFN signalling and presence of ISGs . The folSeveral human cell-based studies have shown that ISG15 conjugation or binding to host and viral factors can directly inhibit the lifecycle of several viruses. The first evidence came from influenza virus infections, where ISG15 was shown to bind the viral non-structural protein 1 (NS1) of influenza B . SubsequBesides affecting virus replication by directly interacting with or modifying viral factors, ISGylation was also found to inhibit virus release by modifying components of the multivesicular pathway. Experiments performed in influenza virus infected semi-permeabilised cells revealed Tsg101 as one such component modified by ISG15 upon immune activation, resulting in impaired transport of virus glycoproteins and budding of viral progenies . These sAutophagy functions as a cell intrinsic pathway to dispose intracellular pathogens - a process often manipulated by viruses to their own advantage . ISGylatListeria monocytogenes) resulted in oligomerisation of Rnf213 on lipid droplets [An interesting recent study designed to capture ISG15 interactors point toward concentration of ISGylated proteins on lipid droplets . One of droplets ,58. Thesdroplets . Altereddroplets . How thedroplets .ISG15-deficient patients were more resistant to HIV infection, underscoring the confounding effects of ISGylation on individual stages of the viral lifecycle versus host immune responses to them [An emerging theme from recent studies on ISG15 is its impact on innate immune responses to infection, both in its free and conjugated forms. Initial proteomics studies conducted with expression of tagged ISG15 revealed many substrates that participate in innate immune signalling cascades. These include STAT1 , JAK1 6, RIG-I , IFIT1,  to them .ISG15 or USP18 deficiency [ISG15 deficiency are consistent with chronic hyperinflammation, originating from innate immune cells such as macrophages. ISG15−/− fibroblasts, keratinocytes and iPSC-derived endothelial cells also display hyperinflammation, consistent with increased production of proinflammatory cytokines [M Tb infected cells were found to contain free ISG15. When these were added to naïve monocytes, it triggered production of proinflammatory cytokines such as IL-8, MIP-1α and IP-10 [Several studies over the past few years have reported on altered secretion of cytokines emanating from ISG15 deficiency. For instance, monogenic type I interferonopathies are a group of autoinflammatory and autoimmune disorders characterised by persistently elevated levels of type I interferons (IFN-I). The underlying molecular mechanisms include enhanced IFN-I gene transcription due to gain of function of STAT2 and defective negative regulation of IFN-I as a result of either ficiency . Clinicaytokines . Most prnd IP-10 . Subseqund IP-10 .−/− cells, supporting its role in suppressing glycolysis [We recently reported that in virus-infected macrophages enzymes of the glycolytic pathway and mitochondrial enzymes are major targets of ISGylation , and in ycolysis . WhetherISG15−/− macrophages displayed the expected hyperinflammatory responses, but normal phagocytic function [ISG15−/− cells also displayed defective expression of genes involved in mitochondrial biogenesis and respiratory chain complexes II–V, resulting in diminished mitochondrial respiration. This phenotype was rescued upon reconstitution with wild-type ISG15, but only partially by a conjugation-deficient variant, suggesting that at least some of these phenotypes were driven by ISGylation to cellular targets [In a separate study with induced pluripotent stem cell-derived macrophages and endothelial cells as models, function . Their pfunction . ISG15−/ targets . Pharmac targets .−/− macrophages were found to exert reduced activity, phagocytic capacity and programmed cell death upon vaccinia virus infection. Peritoneal macrophages from ISG15-deficient mice too were unable to phagocytose infected cells nor block virus infection in co-cultures with infected fibroblasts [−/− bone marrow derived macrophages upon IFN treatment, rendering them more susceptible to vaccinia virus infection [Some of these data are in agreement with previous findings with vaccinia virus infection models in peritoneal macrophages. ISG15roblasts . Furthernfection . CollectWhile cells mount an ISG15-dependent response to trigger defences against viral infection, many viruses have in turn co-evolved strategies to counter them. Particularly interesting in this regard are viral proteases that are able to function as de-ISGylases and remove ISG15 modifications from substrates to their own advantage. OTU-domain containing proteases expressed by several RNA viruses e.g nairoviruses, arteriviruses, are able to hydrolyse both ubiquitin and ISG15 modifications , with bAn obvious consequence of de-ISGylation activity is an increased unconjugated, monomeric form of ISG15, which can either exist intracellularly, or be secreted out of cells. Early studies had indicated that extracellular ISG15 functioned as a signalling molecule to trigger release of IFNγ from lymphocytes . The rec−/− macrophages display a complex pathological phenotype of increased glycolysis, oxidative stress and apoptosis, but decreased oxidative phosphorylation, β-oxidation and NADH-dependent oxidoreductase activity, underscoring its importance in mitochondrial function [Proteomics analyses revealed glycolytic enzymes as major targets of PLpro. In addition, components of purine metabolism and immune signalling were found to be PLpro substrates. Metabolic reprogramming by altering glycolysis has been consistently observed in different experimental and disease models. Reduced thermogenic gene expression and oxygen consumption in adipose tissue resulted from ISG15-mediated inhibition of glycolytic enzymes . iPSC-defunction .While ISGylation has been studied extensively in the context of viral infections to identify potential mechanisms of virus restriction, emerging data from several independent studies indicate that a significant number of substrates are enzymes of various metabolic processes. This is particularly interesting since ISG15-driven metabolic reprogramming may explain many of the phenotypes observed during stress conditions, including those generated during either viral or bacterial infections. Given the link between cellular metabolic states and immune responses, it may also be a major determinant of inflammatory responses accompanying infection or other stress conditions such as DNA damage .Despite increased momentum in ISG15 research, several knowledge gaps remain. For example, delineating the contribution of free versus conjugated ISG15 in pathological conditions is one such area. Till date, three E3-ligases have been identified for ISG15 conjugation to substrates . Of thesISG15 has emerged as an abundant and versatile post-translational modification not only in the context of infection, but also in a range of stress conditions such as metabolic disorders, DNA damage, autoimmunity and inflammatory diseases. Understanding the fundamental principles of ISGylation therefore has far-reaching implications.Our current understanding specifically on the antiviral role of ISG15 indicates that it does not directly inhibit the viral lifecycle. Instead it is part of the host response that alters the immuno-metabolic network to restrict availability of resources necessary for viral amplification, thereby limiting virus spread. Further studies in simpler organisms such as bacterial defence against phages may shed more light on the fundamental and evolutionarily conserved role of ISG15.Several questions remain outstanding in this field, not the least of which is whether ISG15 can be targeted for therapeutic applications. Future studies on developing small molecule or antibody-based inhibitors to ISG15 and its associated enzymes will not only address the potential for therapy, but also resolve the confounding effects of free versus conjugated forms of ISG15.


First, we obtained antigenic distance estimates derived from serum neutralization assays cross-reacting PRRSV monospecific antisera with virus isolates from 27 PRRSV1 viruses circulating in Europe. Antigenic distances were weakly to moderately associated with ectodomain amino acid distance for open reading frames (ORFs) 2 to 4 (ρ < 0.2) and ORF5 (ρ = 0.3), respectively. Dividing the antigenic distance values at the median, we then categorized the sera-virus pairs into two levels: low and high antigenic distance (dissimilarity). In the machine learning models, we used amino acid distances in the ectodomains of ORFs 2 to 5 and site-wise amino acid differences between the viruses as potential predictors of antigenic dissimilarity. Using mixed-effect gradient boosting models, we estimated the antigenic distance (high versus low) between serum-virus pairs with an accuracy of 81% ; sensitivity and specificity were 86% and 75%, respectively. We demonstrate that using sequence data we can estimate antigenic distance and potential cross-protection between PRRSV1 strains.The control of porcine reproductive and respiratory syndrome (PRRS) remains a significant challenge due to the genetic and antigenic variability of the causative virus (PRRSV). Predominantly, PRRSV management includes using vaccines and live virus inoculations to confer immunity against PRRSV on farms. While understanding cross-protection among strains is crucial for the continued success of these interventions, understanding how genetic diversity translates to antigenic diversity remains elusive. We developed machine learning algorithms to estimate antigenic distance IMPORTANCE Understanding cross-protection between cocirculating PRRSV1 strains is crucial to reducing losses associated with PRRS outbreaks on farms. While experimental studies to determine cross-protection are instrumental, these in vivo studies are not always practical or timely for the many cocirculating and emerging PRRSV strains. In this study, we demonstrate the ability to rapidly estimate potential immunologic cross-reaction between different PRRSV1 strains in silico using sequence data routinely collected by production systems. These models can provide fast turn-around information crucial for improving PRRS management decisions such as selecting vaccines/live virus inoculation to be used on farms and assessing the risk of outbreaks by emerging strains on farms previously exposed to certain PRRSV strains and vaccine development among others. Betaarterivirus suid 1 (PRRSV1) and Betaarterivirus suid2 (PRRSV2) in the order Nidovirales (–Porcine reproductive and respiratory syndrome (PRRS) continues to be one of the leading causes of economic losses and reduced pork production in most major pork-producing countries , 2. PRRS3–1–ovirales –11. Evolvirales –, 12.The genetic and antigenic diversity in PRRSV has greatly contributed to challenges in controlling PRRS outbreaks on farms using vaccines or live virus inoculation. Relatively suboptimal/poor cross-protection between PRRSV isolates, especially against heterologous strains, is a common issue in immunization , 14, yet15–20–Although numerous efforts have been made to correlate specific genetic differences to the clinical presentation of disease , 23–26 a23–36–in vitro studies, cross-neutralization assays  have been used to quantify the level of heterologous and homologous cross-reactivity between different viruses and antibodies generated against them  assays have been used extensively in influenza studies to estimate antigenic differences between strains based on the hemagglutinin (HA) gene of the virus, which codes for an immunogenic glycoprotein . Given t viruses , 47. Gen viruses .in silico will support decision-making by producers, practitioners, and vaccine developers on the selection and application of vaccines or live virus inoculation for PRRS management.Therefore, the objectives of this study were to develop an algorithm to estimate the likelihood of serum-virus cross-protection between PRRSV-1 viruses and identify important amino acid sites that influence antigenic variability between viruses. Further, we investigated how specific differences in those amino acid sites contributed to antigenic variability between the viral isolates. The ability to distinguish between antigenic traits of PRRSV cocirculating in the swine population and estimate potential cross-protection between viruses P < 0.01) (n = 612), antigenic distance was categorized into high antigenic distance (n = 305) and low antigenic distance (n = 307), splitting at the median (3.9). When ORF2 sequence data were added, the total number of pairs was 314, with 146 classified as high and 168 low antigenic distance.Pairwise adjusted antigenic distance ranged from −2.7 to 7.5 units, with a mean (±SD) 3.8 ± 1.5, respectively. Compared to ORFs 2 to 4, the mean pairwise amino acid distance was highest in ORF5 (0.19 ± 0.05) and was moderately correlated with antigenic distance between the serum-virus pairs  . After eAmong the ORF-specific models, ORF5 performed with the highest accuracy of 0.75  and specificity (0.81); other ORF-specific models were not distinctly different from each other. For models combining different ORFs, the model combining ORFs 2 to 5 performed best . The pairwise amino acid distance between all three isolates was 10.7 to 12.9%, thus all would typically be considered genetically heterologous from one another. However, two of the viruses (A and B) cross-reacted with each other, whereas virus C did not cross-react with either of the others. Using the ORF4 model (only ORF4 sequences were available), we correctly classified antigenic distance between the isolates corresponding to the study’s in vitro assay findings .n = 25), which included 21 amino acid sites as well as ectodomain amino acid distance of each ORF (n = 314) , we identified predictors with importance ≥0.01 (each ORF . InteresP < 0.05). Similar statistically significant effects were observed on ORF3 site 79 (H-H versus R-Y and H-H versus H-R) and ORF2 site 137 (V-V versus A-V) (P < 0.05)  versus the reference . From the multimodal inference analysis, seven amino acid sites were ranked highest from the averaged models . Specific site-wise amino acid differences in ORFs 2, 3, and 5 were significantly associated with classification into high or low antigenic distance . For exa < 0.05) .in silico methods can be used to approximate differences in antigenicity for PRRSV. Additionally, we reaffirm that there are some immunodominant sites in the PRRSV ectodomain regions. The ability to estimate differences in antigenicity between viruses in silico will be beneficial for vaccine and live virus inoculum selection by swine producers and pharmaceutical companies, as well as aid our ability to understand how immunity plays into the emergence and cocirculation of multiple strains in swine populations  at certain sites of the genome  may result in changes in epitope structure or complete hiding of an epitope, hence limiting or impairing host immune response and translating to a change in immunogenic phenotype , we could not elucidate the biological impacts of those differences. While other methods such as mutagenesis are applicable to investigating cross-protection and amino acid interactions, our approach is inexpensive, and the predictive model can be used for in vitro cross-reactivity insights to in vivo cross-protection in the field; our model could not account for these factors.All isolates used in this study belonged to one of four subtypes of PRRSV1 (subtype 1), and 63% of the isolates were from Spain. As a result, it is yet unclear whether our models can be generalized to other subtypes whose genetic profiles may still differ. In addition, we were only able to interrogate the impact of amino acid changes observed in our data; thus, our predictive model does not account for all changes in potentially antigenic sites that could exist in natural PRRSV1 populations but were not in our data. However, as demonstrated in 71–in silico estimation of antigenic distance of PRRSV1 can be achieved, although further research with diverse data sets could lead to more robust and generalizable models. Inferences of antigenic distance and possibly cross-protection from genetic sequence data using these methods present valuable opportunities to (i) reduce the cost and need for live animal/in vivo experiments for cross-protection/cross-neutralization assays; (ii) identify of immunodominant sites in different strains of PRRSV1 for further research; (iii) develop a prediction tool for in silico estimation of antigenic distance for tailoring immunization protocols in farms to match field outbreaks ; and (iv) advance understanding of PRRSV1 ecology, trends of emergence, and immune escape. Additionally, providing the option to classify viruses based on comparative antigenic traits could complement the currently available PRRSV1 classification methods based on the genetic distance for a more accurate characterization of isolates. By identifying specific amino acid sites of more antigenic importance, we provide a basis for further investigation and characterization of the PRRSV1 genome and biological implications of specific amino acid substitutions in those sites.Overall, this study reaffirms the association between viral antigenicity and genetic differences. We demonstrate that using machine learning techniques and sequence data, 2 of the reciprocal of the serum dilution that completely inhibited viral replication in 50% of the wells. This values were recorded for each sera-virus pair in the SN panel  – log 2(ijH), with a one unit difference representing a 2-fold loss in neutralization ability between the homologous and heterologous pair  assay outputs published by Martinez-Lobo et al. . In thatSN panel . From thous pair , 64.JF730883 to JF730921 for ORF3; JF730922 to JF730960 for ORF4 and JF730961 to JF730999 for ORF5). ORF2 sequences that were not part of the GenBank sequences have been submitted and assigned accession numbers (OP643789 to OP643820). We aligned the 27 amino acid sequences using Muscle in Aliview 1.26  was less than those available for ORFs 3 to 5 (n = 612) after excluding 10 and 12 pairs with negative antigenic distances, respectively. Negative antigenic distances occurred in some cases, indicating sera-virus pairs where the heterologous sera neutralized a virus better than the homologous sera. This could occur if the heterologous sera were broadly neutralizing, if the homologous serum had low avidity, or potentially lab error, although the last possibility is unlikely because all assays with unexpected results were repeated in a separate assay to confirm the data. These were excluded from further analysis. Compared to ORFs 3 and 4, slightly higher correlations between ectodomain amino acid distance and antigenic distance were observed for ORFs 2 and 5. Different combinations of the specific ORFs were used to build separate models and their performance was compared. The model combinations were ORFs 2 to 5, 3 to 5, and 2 and 5.Subsequently, we set up a gradient-boosting classification model (XGBoost ). While We used 200 trees and 10-fold cross-validation as part of the model tuning to train and make predictions with the model. We based model performance on accuracy , sensitivity (percentage of high observations correctly classified), specificity (percentage of low observations correctly classified), and predictive values (proportion [%] of times the classification [high or low] is the true antigenic distance class). Apart from model predictions, XGBoost models generate several other outputs. In this study, we used gain  to rank the importance of model predictors . In our GU737264 and GU737266) isolated form Belgian farms. The viruses were propagated in 19 naive wean piglets divided into groups of 6, 6, and 7, from which serum for neutralization assays was collected at day 44 postinoculation. Only ORF4 sequence data were available from this study, thus we used our ORF4-specific model to predict cross-neutralization across these serum-virus pairs and compared the predicted results to the experimental results reported in the paper.To test the performance of the machine learning algorithm’s predictions on a completely external set of sequences, we used data from Vanhee et al. . In thisglmulti package in R to select the best models. The best models were considered the simplest models that were ΔAICc ≤2 than the model with the lowest AICc . One hundred top ranked models (from 4 million iterations) based on AICc and Akaike weights were stored for comparison . Final summary statistics are reported for model covariates with an estimated importance of ≥0.8, and a full summary across all averaged models is presented in Table S4.Using variable importance rankings from the best XGBoost model, we selected the amino acid sites whose gain was estimated at ≥0.01 (21 sites) and the effect of these differences on antigenic dissimilarity was further analyzed via multimodal regression. We modeled the categorical pairwise antigenic distance as the outcome and amino acid sites as factor covariates to estimate the contribution of specific site-wise amino acid differences. Unlike in the above machine learning model , the specific amino acid changes were explored here . For the purpose of logistic regression, the same-same amino acid pair with the highest frequency was set as the reference, and model coefficients can be interpreted as a comparator to the reference. We used exhaustive-screening multimodal logistic regression inference (2 million iterations) in mparison . Due to mparison , and allOP643789-OP643820).The nucleotide sequences of the PRRSVs sequenced in the present study have been deposited in GenBank (


This paper illustrates a study of the thermal oxidation of several epoxy amine networks. Oxidation was followed at the molecular scale using Fourier Transform InfraRed spectroscopy (FTIR) and at the macromolecular scale using tensile tests. FTIR showed the major formation of amides, while tensile tests showed the progressive increase in the elastic modulus (~0.5 GPa for room temperature Young modulus) and decrease in ultimate strain and volumic energy for failure . Both ultimate strain and volumic energy were divided by more than two. Linear correlations between oxidation trackers (amide concentration) and changes in mechanical parameters are presented and discussed. Epoxy-amine networks are easily obtained from the condensation reaction between polyglycidyl molecules and amines. Since there is a quasi “infinity” of networks differing by the flexibility of monomers and crosslink densities, epoxy-amine networks are thus undoubtedly the most investigated family of thermoset polymers in terms of structure–properties relationships ,2,3,4. Fg) changes and « antiplasticization » induced by the destruction of small groups (α-hydroxypropylether) and the loss of sub-glassy mobility  ~0.6 mol L mol L−1 . This meAccording to Model epoxy-amine networks consisting of DGEBA cured with trioxa-tridecanediamine, or ethylene diamine, or diethylene triamine, or commercial system consisting triethylenetetramine and a cycloaliphatic polyamine hardener was cured. They were submitted to thermal ageing at various temperatures (from 80 to 200 °C). At the molecular scale, and despite differences in chemical structure, all networks were shown to undergo fast oxidation (occurring in less than 24 h at 160 °C) with the predominant formation of amides as stable chemical trackers. At the macroscopic scale, mechanical properties were followed by classical stress-strain curves in tensile mode. The elastic modulus increased mainly to the previously documented effect of antiplasticization associated with the destruction of small groups responsible for sub-glassy mobility. Ultimate strain and volumic energy for failure  progressively decreased. A correlation between chemical trackers and mechanical parameters was observed for all systems under investigation. Building a kinetic model for the prediction of degradation in service conditions is possible by extrapolating data obtained by accelerated ageing at high temperature.


EDIL3 has been described to be a negative regulator of IL-17-dependent pathologies [+ regulatory CD4+ T-cell differentiation during inflammation [2 integrins and modulation of αvβ3 integrin function [Hypertension is a very frequent chronic disease associated with significant cardiovascular morbidity and mortality. Pathological findings in patients with chronic hypertension include aortic fibrosis and loss of elastic fibers leading to arterial stiffening which further increases systolic blood pressure. Chronically elevated blood pressure eventually also affects the heart with the development of left ventricular hypertrophy and fibrosis . An essehologies . In addiammation . These afunction .vβ3 integrin and activated MMP2 playing major roles), opening new avenues in treating patients with chronic hypertension. This might be true for both the early and later phases of the disease when vascular and cardiac remodeling has already progressed to substantial changes in vascular and left ventricular tissue architecture. Although the microcirculation was not the focus of the study by Failer et al. [Hypothesizing that therapeutic use of DEL-1 might be an interesting and effective way to attenuate hypertension-induced cardiovascular remodeling, Failer and colleagues performed an excellent study to address a potential beneficial effect of therapeutic DEL-1 in hypertension-induced cardiovascular disease . The autr et al. , arteriar et al. . AdditioTaken together, Failer and colleagues present convincing evidence that DEL-1-dependent modulation of the inflammatory response during chronic hypertension might offer an interesting new treatment strategy to complement conventional antihypertensive therapy . Applied


Asymmetric allylic substitution of Morita–Baylis–Hillman (MBH) carbonates with less-nucleophilic phenols mediated by nucleophilic amine catalysis was successfully developed. A variety of substituted aryl allyl ethers were afforded with moderate to high yields with excellent enantioselectivities. The chiral MBH alcohol could be easily accessed from the corresponding aryl allyl ether. Asymmetric allylic substitution of Morita–Baylis–Hillman (MBH) carbonates with less-nucleophilic phenols mediated by nucleophilic amine catalysis was successfully developed. Reactions with nucleophilic oxygen result in formation of chiral MBH alcohols or ethers, which are synthetically useful building blocks1 and bioactive molecules.5 Despite a few nucleophilic O-substitution reactions of MBH carbonates and acetates under the catalysis of palladium6 or organocatalysts7 that have been developed, there are still many limitations for the enantioselectivity, chemical yields and generality of the substrates, especially when the less-nucleophilic phenols were used.7 Therefore, the development of efficient protocols to access enantiomerically pure MBH alcohols and ethers is still in demand.Morita–Baylis–Hillman (MBH) reactions provide efficient highly functionalized synthons to be used in the total synthesis of natural products and biologically relevant compounds.8 we herein present an asymmetric allylic O-substitution reaction of MBH carbonate with less-nucleophilic phenols mediated by nucleophilic amine catalyst. After a careful investigation of the reaction conditions, asymmetric allylic substitution of Morita–Baylis–Hillman (MBH) adducts with less-nucleophilic phenols was successfully developed. An array of substituted chiral aryl allyl ethers were obtained with excellent enantioselectivities. Chiral aryl allyl ethers are valuable substructures and synthetic intermediates of many naturally occurring molecules and pharmaceutical compounds.5,9 Moreover, chiral MBH alcohol could be directly accessed from the final product.As part of our on-going program on developing novel and practical catalytic asymmetric transformations,2 The results are outlined in 2PHAL (1h) was tested as the catalyst, the product was obtained in 84% yield and 41% ee , the pseudo enantiomer of 1h, was also tested for the reaction, and gave the enantiomer of 4a in moderate yield and ee value  and the valuable chiral MBH alcohol 6 was isolated with the ee value retained  with excellent enantioselectivity (up to 95%). In addition, the MBH phenyl carbonate was synthesized and found to be able to generate the same chiral aryl allyl ether with excellent enantioselectivity by nucleophilic catalysis. The synthetic potential of the disclosed methodology was demonstrated by the synthesis of chiral MBH alcohol and the 1, 3-dipolar cycloaddition reaction of the aryl allyl ether with chlorobenzaldoxime.In conclusion, we have successfully developed an asymmetric allylic There are no conflicts to declare.RA-009-C9RA00155G-s001


Motor neuron diseases (MNDs) are neuromuscular disorders where the spinal motor neurons–either the cell bodies themselves or their axons–are the primary cells affected. To date, there are 120 different genes that are lost or mutated in pediatric-onset MNDs. Most of these childhood-onset disorders, aside from spinal muscular atrophy (SMA), lack viable therapeutic options. Previous research on MNDs has focused on understanding the pathobiology of a single, specific gene mutation and targeting therapies to that pathobiology. This reductionist approach has yielded therapeutic options for a specific disorder, in this case SMA. Unfortunately, therapies specific for SMA have not been effective against other pediatric-onset MNDs. Pursuing the same approach for the other defined MNDs would require development of at least 120 independent treatments raising feasibility issues. We propose an alternative to this this type of reductionist approach by conceptualizing MNDs in a complex adaptive systems framework that will allow identification of common molecular and cellular pathways which form biological networks that are adversely affected in early-onset MNDs and thus MNDs with similar phenotypes despite diverse genotypes. This systems biology approach highlights the complexity and self-organization of the motor system as well as the ways in which it can be affected by these genetic disorders. Using this integrated approach to understand early-onset MNDs, we would be better poised to expand the therapeutic repertoire for multiple MNDs. Neuromuscular diseases (NMDs) result in chronic, severe disability as well as early mortality and a high burden of disease. They also display clinical and genetic heterogeneity; in fact, there are currently 1,079 distinct NMDs and 608 known genes associated with these disorders . Those MSMN expression, either by SMN1 gene replacement or modulating the alternative splicing of the orthologous SMN2 gene. While these options are likely to have significant positive impacts on the quality of life of many patients with SMA, not all SMA patients respond positively to these clinically approved therapies and the length of therapeutic benefit in these patients is not known , genes associated with vesicular trafficking, endosome trafficking and cell proliferation were identified as modifiers of disease phenotype  4 (DHRS4) was identified as a hub gene for ALS1 by comparing the transcriptomes of SOD1 (G93A) mouse spinal cords using WGCNA  is an approach to identify disease-related networks and hubs from different datasets . When cong WGCNA . This ansenataxin (SETX) (SETX mutation against those with an AOA2-specific SETX mutation identified networks which are unique to either disorder (SETX (R2136H) or SETX (L389S) mutant transgenes] identified overrepresentation of pathways involved in synaptic signaling and nervous system development  are clinical distinct early-onset neurodegenerative diseases disorders but are caused by mutations in n (SETX) , 28. WGCdisorder . Specifielopment . InteresIn addition to gene regulatory network analysis, important insights into the pathogenesis of early-onset motor neuron diseases can be gained from the analysis of disease-associated protein-protein interaction (PPI) networks. When comparing the protein-protein interactomes between ALS and ALS linked with frontotemporal dementia (ALS-FTD), there were common hub proteins between these two types of motor neuron disease but the enrichment profiles around these hubs were distinct between ALS and ALS-FTD . This PPMFN2(R94Q)], CMT2E [NEFL(P8R)], CMT2F [HSPB1(G84R) and HSPB1(P182L)] and CMT2L [HSPB8(K141N)]} revealed overrepresentation of mitochondrial respiratory chain and axon guidance networks . Network analysis of all CMT-related genes–including those linked to demyelinating peripheral neuropathy–identified pathways involved in myelination, axonal function, mitochondrial metabolism and autophagy as key pathways in CMT pathogenesis . Bis-Brenetworks . InteresThere is strong overlap and interconnectivity between the SMA and ALS PPI networks . One of SMN2 is an extremely well characterized modifier gene for SMA as increasing SMN2 copy number lessens the disease severity  motor neurons (FUS [FUS (R521H) and FUS (P525L)] motor neurons cultured in microfluidic devices (TARDBP (A382T); TARDBP (G287S) and TARDBP (N390S)] ] . The subMFN2 (R94Q) mouse model for CMT2A1 (HSPB1 (S135F) mouse model for CMT2F (HSPB1 (S135M)] mice  CMT motor neurons (GARS (R234KY) as well as in Gars (C201R) transgenic mouse models for CMT2D ] mice , 129. HD neurons . Tubastaor CMT2D . Electroor CMT2D . Tubastaleotides .SMN2 expression (D156844) alongside a myoprotectant (follistatin)  . CoadminSMA mice . In addinhibitor  or a pronhibitor  have shoLower motor neurons and their connections to skeletal muscles as well as to interneurons–or the motor unit–can be thought of as a network of agents  organized into aggregate clusters that interact with each other non-linearly in a hierarchical organization. These agents and their interactions have distinct, adaptable specializations that respond to selective environmental pressure. The complex adaptive system mediates the flow of information or materials from the external input from the lower motor neuron to the neuromuscular junction. Early-onset MNDs can be viewed as consequences of a breakdown of complexity within the motor unit network. A genetic perturbation within an agent in this network would pathologically alter the entire network and the flow of information from the input to the output.Current strategies for therapeutics development for early-onset MNDs focus on amelioration or replacement of a single perturbation. This reductionist approach has been successful in improving the quality of life and disease presentation in SMA by SMN replacement. While very successful, targeted SMN replacement therapy has provided variable therapeutic benefit within the SMA patient population. Using a complex system approach to understand early-onset MNDs would provide a path for better understanding how loss of a specific agent affects the entire motor system network as well as multiple avenues for therapeutic interventions. These neuroprotective interventions potentially would provide therapeutic benefit for many early-onset MNDs as opposed to a single genetic disorder.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.This perspective article has been supported by the Nemours Foundation to MB and RS.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Inadequate sleep and physical activity are common during and after hospitalization, but their impact on patient-reported functional outcomes after discharge is poorly understood. Wearable devices that measure sleep and activity can provide patient-generated data to explore ideal levels of sleep and activity to promote recovery after hospital discharge.This study aimed to examine the relationship between daily sleep and physical activity with 6 patient-reported functional outcomes  at 13 weeks after hospital discharge.This secondary analysis sought to examine the relationship between daily sleep, physical activity, and patient-reported outcomes at 13 weeks after hospital discharge. We utilized wearable sleep and activity trackers (Withings Activité wristwatch) to collect data on sleep and activity. We performed descriptive analysis of device-recorded sleep (minutes/night) with patient-reported sleep and device-recorded activity (steps/day) for the entire sample with full data to explore trends. Based on these trends, we performed additional analyses for a subgroup of patients who slept 7-9 hours/night on average. Differences in patient-reported functional outcomes at 13 weeks following hospital discharge were examined using a multivariate linear regression model for this subgroup.P=.02), community mobility , and perceived physical health , compared with those who were more physically active (≥5000 steps/day).For the full sample of 120 participants, we observed a “T-shaped” distribution between device-reported physical activity (steps/day) and sleep  with lowest physical activity among those who slept <7 or >9 hours/night. We also performed a subgroup analysis (n=60) of participants that averaged the recommended 7-9 hours of sleep/night over the 13-week study period. Our key finding was that participants who had both adequate sleep (7-9 hours/night) and activity (>5000 steps/day) had better functional outcomes at 13 weeks after hospital discharge. Participants with adequate sleep but less activity (<5000 steps/day) had significantly worse symptom burden  and physical activity (>5000 steps/day) reported better functional outcomes after 13 weeks compared with participants outside the “sweet spot.” Wearable sleep and activity trackers may provide opportunities to hone postdischarge monitoring and target a “sweet spot” of recommended levels for both sleep and activity needed for optimal recovery.ClinicalTrials.gov NCT03321279; https://clinicaltrials.gov/ct2/show/NCT03321279 Inadequate sleep and physical activity are common during and after hospitalization, but little is known about how long these disruptions continue after discharge and the consequences on functional outcomes such as mobility and overall physical health. Disruptions of normal patient activities  may be traumatic for acutely ill patients and lead to a high-risk, generalized condition that has been described as “posthospital syndrome” -3. The rPrevious work has examined the impact of disrupted in-hospital sleep and physical activity on short–term recovery -6, but mGiven these gaps in the existing literature, we evaluated sleep and activity data from wearable devices worn by patients for 90 days (13 weeks) after hospital discharge. The purpose of this study was to examine the relationship between daily sleep and physical activity with 6 patient-reported functional outcomes at 13 weeks after hospital discharge. We used evidence-based recommendations to define ranges for healthy sleep as 7-9 hours per night ,18 and fOur specific objectives for this study were to (1) observe the distribution of sleep and step data in our sample and describe any patterns, (2) explore differences between any observed patterns and functional outcomes , and (3) explore associations of functional outcomes with sleep and activity patterns using evidence-based guidelines for sleep and activity as an a priori subgroup. Our overarching hypothesis was that participants with both adequate sleep and activity would have better functional outcomes than those with only one (sleep vs activity).This was a secondary analysis from the Mobility and Outcomes for Validated Evidence Incentives Trial . Full details of the primary trial have been previously reported ,23. BrieIn addition to steps per day , we assessed patient-reported outcomes at baseline (enrollment) and at 13 weeks after hospital discharge. All participants completed standardized assessments that quantified basic and instrumental activities of daily living , and all participants provided informed consent. Additionally, to acknowledge the time and effort contributed by participants to this study, we offered US $300 for completing all the surveys required for the study. Furthermore, we monitored closely for adverse or unexpected events and provided clear contact information to participants to reach study staff and prompt responses (within 24 hours) to any questions or concerns raised. Four participants experienced a rash on their wrist where the device was worn and were provided with cloth-based replacement bands, which resolved the issue.Daily steps and sleep were quantified via a wrist-worn wearable device (Withings Activité). The Activité is a wearable activity tracker designed to look and function like a wristwatch. The face of the device has hour, minute, and second hands like a traditional wristwatch with an additional dial in the center that indicates progress towards a preset step goal. The device is water-resistant to 50 meters/5 atmospheres and uses a traditional watch battery (CR2025), which does not require frequent charging and lasts about 8 months. These features help to increase consistent wear by users. Motion is detected by a high-precision MEMS 3-axis accelerometer and translated into steps or sleep using a proprietary algorithm. Synchronization between the device and user smartphone occurs automatically every 6 hours and is also triggered by each 1000 steps taken since the last sync. Previous studies by our group and others have demonstrated the reliability and validity of step counts from commercially available devices for activity studies , and theWe also leveraged the Way to Health platform, a National Institutes of Health (NIH)–funded research technology platform at the University of Pennsylvania that has been used for more than 150 clinical trials in 45 states using smartphones and wearable devices previous studies ,34. The Step data calculated by summing the number of steps in each 24-hour period (12 am to 12 am). Step data were missing in 37.7% of the current sample. Step data were considered missing for any day the participant did not wear their device, sync their data to the Way to Health platform, or reach 1000 steps. We have used this method in prior work ,35,36, bDaily sleep was calculated by summing the number of sleep minutes between 12 pm and 12 pm the following day (24-hour period). Sleep data were missing in 39.8% of the current sample. Sleep data were considered missing if the participant did not wear or sync their device, did not wear the device at night, or registered an errant number. We used multiple imputation for missing sleep data with the classification and regression trees (CART) method, which we have used in previous studies .To create our study sample for this paper from the original data set, we performed analyses in 2 steps. First, we tested for differences in the 6 patient-reported outcomes at 13 weeks by activity category (less active vs active) using all participants with available data at week 13 (n=120). Second, in an effort to disentangle the impact of sleep and physical activity on patient-reported outcomes, we then restricted the full sample to a subgroup of participants (n=60) who averaged the recommended 7-9 hours of sleep/night ,18 over The primary outcome for this analysis was the patient-reported outcomes on 6 standardized assessments administered at 13 weeks postdischarge. We calculated each participant’s average daily steps and total sleep minutes over the 12-week intervention period. Participants who averaged <5000 steps/day were classified as less active, and those who averaged >5000 steps/day were classified as active -21. BecaP<.05 significance level.Since our outcomes were scores from clinical assessment scales (not binary “yes/no” outcomes), we used multivariate linear regression models with each outcome as a continuous primary (dependent) variable and activity category (less active or active) as the primary predictor variable. We also adjusted for other factors as covariates, which were selected because they were different between the 2 categories at baseline: age, gender, Charlson comorbidity index, and study arm. We tested for differences in the 6 patient-reported outcomes at 13 weeks between those who were less active and those who were active but averaged between 7 hours and 9 hours of sleep per night. All analyses were completed in R  with a POf the 232 participants in the primary trial, 120 had device-recorded sleep and step data over the entire study period and patient-reported follow-up survey data at week 13. As shown in As shown in P=.004) compared with those who were active at 13 weeks after hospital discharge. This difference, however, was present at baseline with the less active group reporting worse physical health compared with the active group . There were no other differences in patient-reported outcomes among the full sample of 120 participants.To further explore these clusters, we first defined participants as “less active”  versus “active” . Among the 120 participants, those who were less active reported significantly worse physical health  had a mean age of 41.7 (SD 13.5) years and a mean hospital length of stay of 4.4 (SD 2.4) days; in addition, 81% (29/36) were female . Active P=.02), poorer physical health , and significantly higher symptom burden  at 13 weeks postdischarge. These differences were not observed at baseline  and healthy (Charlson Comorbidity Index score range: 1-2) compared with a typical population of adults hospitalized for general medicine services. Although this likely represents selection bias (see the Limitations section) for our sample, we were surprised by these findings in a relatively young and healthy cohort. The relationship between sedentary levels of physical activity and adverse outcomes in older, multimorbid adults is very well-known, especially during and after acute illness ,46. IndeThese findings add to the growing body of literature exploring the effects of sleep and physical activity on functional outcomes after hospital discharge. Previous work has primarily focused on older adults or critically ill patients ,5,13,49,Several limitations should be considered when interpreting these data. We examined patient-reported outcomes at a single time point. First, given that our sample was younger and healthier than the general population of hospitalized patients, it is likely that older and sicker patients opted to wear their devices less consistently, thus limiting our ability to describe the effects of poor sleep or low activity levels on this vulnerable population. Discontinuation of device use among high-risk populations after discharge has been observed in other studies and represents an important challenge for the field of mHealth . Second,In conclusion, these findings are an important step in understanding how posthospitalization sleep and physical activity impact longer-term functional outcomes. Participants within the “sweet spot” that balances recommended sleep (7-9 hours/night) and physical activity (>5000 steps/day) reported better functional outcomes after 13 weeks compared with participants outside the “sweet spot.” Future interventions to improve functional outcomes posthospitalization should leverage wearable devices to further explore the effects of the “sweet spot” of sleep and activity on functional outcomes.