Chronic primary insomnia is a prevalent sleep disorder that is associated with adverse effects on health outcomes. Sleep disturbance is usually associated with abnormal level of systemic inflammation biomarkers.The aim of this study was to detect changes in sleep quality and inflammatory markers following weight loss among subjects with chronic primary insomnia.Eighty previously sedentary subjects with chronic primary insomnia subjects enrolled in this study, their age ranged from 32–51 year were randomly assigned to life style intervention group  or control group . Polysomnographic recordings for sleep quality assessment, IL-6, IL-10 and TNF-α were measured before and at the end of the study after six months.There was a significant increase in the total sleep duration, sleep efficiency, sleep onset latency and IL-10 in addition to significant reduction in awake time after sleep onset, REM latency, IL-6 and TNF-α after 6 months of in group(A) as a result of weight loss program; while the results of the control group (group B) were not significant. Also, there were significant differences between both groups at the end of the study.Life style intervention modulates systemic inflammatory parameters and sleep quality among subjects with chronic primary insomnia. The global prevalence of chronic insomnia symptoms is about 19% of peopleSleep impairment leads to abnormal level of inflammatory cytokines and elevation in blood pressure To the best of our knowledge, the present may be the first study for effect of life style intervention on sleep and systemic inflammation parameters among obese subjects with chronic insomnia syndrome. Therefore, the purpose of this investigation was to measure changes in inflammatory cytokines and sleep parameters following weight loss among subjects with chronic primary insomnia.Eighty previously sedentary subjects having Chronic Primary Insomnia for longer than six months, their age ranged from 32–51 years and participated in this study. Inclusion criteria included difficulty initiating sleep, difficulty maintaining sleep, early morning awakenings at least 3 nights during the previous 3 months. However, exclusion criteria included other sleep disorders include moderate or severe apnea, major depression, mental illness, cognitive impairment, cardiovascular disease, heart failure, liver disease, cancer, chronic pain conditions, shift work, regular use of sedatives, hypnotics and painkillers. The participated subjects were enrolled in two groups; group (A) received weight reducing program included training on treadmill and diet regimen. While, group (B) received no weight reducing program included training or treadmill and diet regimen and was the control group. The CONSORT diagram display the essential details of randomization . InformeA. Sleep measures: Recording of sleep parameters were measured by polysomnographic (PSG)  which was measured over 2 nights before the study and after six months at the end of the study B. Inflammatory cytokines: An overnight fasting venous blood samples were drained from the antecubital vein, the centrifuged blood samples were used to analyzed Interleukin-6 (IL-6) and Interleukin-10 (IL-10) levels with “Immulite 2000” immunassay analyzer . While, ELISA microplate reader  was used to measure tumor necrosis factor-alpha (TNF-α).Participants were enrolled randomly into the following groups:1. Group (A): received weight reducing program included training on treadmill and diet regimen. The aerobic exercise was conducted on treadmill , The training program composed of warming-up for 5 minutes, aerobic exercise for 30 minutes of 60%–70% of maximal heart rate in addition to cooling down for 1o minutes, three sessions/ week for six months. While, the diet control provided 1200 Kilocalories/day for six months.2. Group (B): received no weight reducing program included training or treadmill and diet regimen and was the control group.All the data were collected and entered in IBM SPSS version 21 and analyzed through its statistical package. All the variables were worked out and results were analyzed and presented in tables. The paired “t” test was used to compare the mean values of the investigated parameters in both groups. While Independent “t” test was used for comparison between the two groups (P<0.05).The two groups were considered homogeneous regarding the demographic variables . The meaThere was a significant reduction in BMI, CD3, CD4 and CD8 awake time after sleep onset, REM latency, IL-6 and TNF-α in addition to significant increase in the total sleep duration, sleep efficiency, sleep onset latency and IL-10 after 6 months of in group(A) as a result of weight loss program ; while tSleep loss alters molecular processes that induce low grade of systemic inflammationConcerning sleep quality parameter, the present study found significant improvement in all sleep parameters as a result of weight loss in group(A), these results are in line with Tan et al. reported that modest energy restriction as a result of six-month individualized diet intervention shorten sleep onset latency in overweight and obese men with insomnia symptoms To our knowledge, this is limited number of researches that measure inflammatory cytokines response to intentional weight loss in subjects with chronic primary insomnia. Our results indicate that intentional weight loss modulated systemic inflammation markers. These results are in line with several previous studies reported that CRP and TNF-α are known to decrease by weight lossLife style intervention modulates systemic inflammation and sleep parameters among obese subjects with Chronic Primary Insomnia.


In older people, the differentiation of HA-specific cells into cTfh cells was impaired. This age-dependent defect in cTfh cell formation was not due to a contraction of the TCR repertoire, but rather was linked with an increased inflammatory gene signature in cTfh cells. Together, this suggests that strategies that temporarily dampen inflammation at the time of vaccination may be a viable strategy to boost optimal antibody generation upon immunisation of older people.Antibody production following vaccination can provide protective immunity to subsequent infection by pathogens such as influenza viruses. However, circumstances where antibody formation is impaired after vaccination, such as in older people, require us to better understand the cellular and molecular mechanisms that underpin successful vaccination in order to improve vaccine design for at-risk groups. Here, by studying the breadth of anti-haemagglutinin (HA) IgG, serum cytokines, and B and T cell responses by flow cytometry before and after influenza vaccination, we show that formation of circulating T follicular helper (cTfh) cells was associated with high-titre antibody responses. Using Major Histocompatability Complex (MHC) class II tetramers, we demonstrate that HA-specific cTfh cells can derive from pre-existing memory CD4 Vaccination is an excellent intervention to limit the morbidity and mortality caused by infectious disease. Yet, despite their success, most vaccines are not completely effective, and efficacy varies significantly between different vaccines. The seasonal influenza vaccine needs to be administered each year in order to provide protection against the most prevalent circulating influenza strains, but its efficacy typically ranges from 40 to 80% even when the vaccine is antigenically matched to circulating viruses. This inefficacy contributes to millions of severe influenza cases and hundreds of thousands of deaths globally , which cThe reasons that the seasonal influenza vaccine provides protection in some individuals, but not others, have yet to be fully established. Antibodies against the influenza surface glycoprotein haemagglutinin (HA) are capable of limiting infection, and anti-HA antibody titres and inhibitory activity are the most commonly used correlate of protection . The hum+ helper T cells  class II tetramers  diversity of the responding T cells, as is commonly proposed as the cause of poor T cell responses in older people , 7 days (d7), and 42 (d42) days after seasonal trivalent influenza vaccination (TIV). A total of 53 distinct variables were measured at two or more time points  was increased on d7  and 2016/2017  carrying either ed on d7 , a populed on d7 . Furthered on d7 .+ T cell responses, we focussed our analysis on the A.Cali09 strain as this was included in both seasons’ vaccine formulations, and used MHC class II tetramers of HLADR*0701 and HLADR*1101 loaded with unique A.Cali09 HA peptides to identify antigen-specific T cells (+CD45RA- (Tet+) T cells showed the largest fold change increase after vaccination of any parameter measured  from d0 and Tet+ cTfh cells from d7  cells and to what extent these cTfh Tet+ cells acquired the transcriptional signature of bona fide human lymph node germinal centre Tfh cells. HA-specific cTfh cells that form 7 days after vaccination clustered distinctly from their d0 Tet+ precursors cells by principal component analysis (PCA) for both cohorts  to non-Tfh antigen-experienced CD4+ T cells from human inguinal lymph nodes  of which were also enriched in lymph node germinal centre Tfh cells. These ‘Tfh gene sets’ included the downregulation of several cytokine pathways including IL-2 signalling, a cytokine known to inhibit Tfh differentiation  are suggestive of heightened pro-inflammatory responses in cTfh cells, but not in all Tet+ CD4+ T cells, from older individuals, and included enrichment for IL6, CCL2, and CCL5 , 7 and 42 days after vaccination. PBMCs were isolated by density gradient separation using Histopaque-1077 (Sigma), frozen in foetal bovine serum supplemented with 10% dimethyl sulfoxide (Sigma), and kept in liquid nitrogen prior to analysis by flow cytometry or flow sorting for RNA sequencing. Lymph node samples were taken from patients recruited from the renal transplant live donor program at Cambridge University Hospitals NHS Foundation Trust, as part of the routine operative procedure, as described previously  assay using the standard WHO protocol, as previously described . Sera we+ T cells by using magnetic separation , and subsequently stained with the antibody panel outlined in + T cell subsets were analysed post acquisition as per the gating strategy outlined in Cryopreserved mononuclear cells were thawed and rested for 1 hr at 37℃. Fc receptors were blocked using anti-human CD32 antibody . 4 million PBMCs were stained with a panel of antibodies to measure B cells , acquire+CD45RA− T cell populations was isolated from 1000 cells sorted into lysis buffer from six individuals as previously described . Sequencing libraries were subsequently generated using the Nextera XT DNA Library Prep Kit (Illumina), followed by sequencing on the Illumina HiSeq 2000 with ∼50 million 100 bp single-end reads per sample. mRNA from lymph node CD4escribed . Sequencescribed  and quanescribed . Samplesescribed  for each package . SignifiSignature genes were determined from the fgsea-defined leading-edge gene lists for TNF signalling pathway, IL-2 STAT5 signalling pathway, and inflammation pathways . Genes present in the leading edge for both cohorts were included, and where a gene was present across multiple pathways, it was assigned to the pathway where it was ranked highest, ensuring each gene signature was non-overlapping. VSN-normalised gene expression values for selected signatures were then converted to z-scores with all time points combined, and then z-scores were summed for each sample . MicroarTCRA or TCRB clonotypes at the amino acid level and requiring more than five reads to identify a clonotype. Clonotype diversity was determined using vdjtools  VP diet  ad libitum and received seeds  at the time of cage-cleaning as part of their environmental enrichment. All mouse experimentation was approved by the Babraham Institute Animal Welfare and Ethical Review Body. Animal husbandry and experimentation complied with existing European Union and United Kingdom Home Office legislation and local standards (PPL: PP3981824). Influenza infections were done as previously described (Denton JEM 2019), and mice were administered 104 plaque-forming units of influenza A/Hong Kong/1/1968/x31 virus intranasally under inhalation anaesthesia.Il2cre  and RosaTissue processing and staining was performed as previously described . Single-All statistical tests for cell-type frequencies assumed non-parametric data. Fold change for immune parameters was calculated by dividing d7 or d42 frequency by d0, and post-vaccination increases relative to baseline were determined by subtracting d0 from d7 or d42 data. Two-group comparisons were made using either two-tailed Mann–Whitney tests or Wilcoxon tests for paired data from the same individual at different time points. Multiple group comparisons p-values were calculated using Dunn’s post hoc test. Correlation analyses used Spearman correlation, with the exception of log-normalised TCRB clone frequency analysis, which used Pearson’s correlation. As necessary, false discovery rate (FDR)(Benjamini–Hochberg) adjustment was used to adjust for multiple testing on two-group comparisons. Heatmaps of manual gated cell subsets that were altered by vaccination or age group were generated using Pheatmap package . public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Acceptance summary:+ T cell responses to influenza vaccination in younger and older adults, in an effort to understand why older individuals tend to have reduced antibody responses to immunization.This paper will be of significant interest to immunologists interested in understanding the determinants of antibody responses to vaccination. It uses tetramers to identify and track CD4Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Betty Diamond as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Impaired HA-specific T follicular helper cell and antibody responses to influenza vaccination are linked to inflammation in humans" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:The reviewers and editor were in consensus about the necessary revisions, which are listed separately under each review below. Many involve changes to the framing or interpretation of results, but some additional data (likely easily obtainable) is also requested.Reviewer #2 (Recommendations for the authors):Overall, I am very enthusiastic about this manuscript, but feel the data analysis and discussion could be strengthened in several key areas:1) Comparison of the d7 tet+ cTFH vs d0 CD4 tet+ populations  provides useful information regarding genes involved in cTFH differentiation, but it would be equally informative to assess the differences in d7 tet+ cTFH and d7 tet+ CXCR5- cells, to further understand how recall of responses into the cTFH pool differs from responses lacking a cTFH phenotype. At present, it is difficult to know whether the downregulation of inflammatory signalling pathways is specific to the cTFH compartment, or is shared by all recalled tet+ cells.2) Relating to point #1 raised in the public review, Figure 5H does indicate that in both the overall data set and in cohort 1, there is a significant reduction in total tet+ cells (as % of CD4) in the older cohort compared to the younger group. The authors generally seem to minimise this point in order to focus on the cTFH population, but it is unclear to what extent there is a general defect in HA-specific T cell recall/expansion in older individuals.3) My major concern is around the discussion of the implications of the inflammation-related gene sets that are differentially expressed among cTFH in older individuals. Without additional data, it seems difficult to me to conclude that vaccine-induced inflammation is driving poor cTFH differentiation, as the authors have not shown any such causative relationship. It seems equally plausible that a defect in antigen presentation, DC function, etc that impacts on initial CD4 Tmem activation and subsequent differentiation could cause the observed cTFH defect, which is simply then reflected in transcriptional pathways that are associated with the cTFH phenotype. Essential revisions:The reviewers and editor were in consensus about the necessary revisions, which are listed separately under each review below. Many involve changes to the framing or interpretation of results, but some additional data (likely easily obtainable) is also requested.Reviewer #2 (Recommendations for the authors):Overall, I am very enthusiastic about this manuscript, but feel the data analysis and discussion could be strengthened in several key areas:Thank you for the positive evaluation of our manuscript, and the opportunity to improve the paper in response to these comments.1) Comparison of the d7 tet+ cTFH vs d0 CD4 tet+ populations  provides useful information regarding genes involved in cTFH differentiation, but it would be equally informative to assess the differences in d7 tet+ cTFH and d7 tet+ CXCR5- cells, to further understand how recall of responses into the cTFH pool differs from responses lacking a cTFH phenotype. At present, it is difficult to know whether the downregulation of inflammatory signalling pathways is specific to the cTFH compartment, or is shared by all recalled tet+ cells.To determine whether the failure to downregulate of the inflammatory signalling pathways in ageing is specific to the cTfh cell compartment, or is shared by all recalled Tet+ cells at day 7 we have looked at these gene signatures in all Tet+ cells that were sequenced in the second cohort of vaccinees. This data indicates that the persistence of the TNFA signature in older people is specific to cTfh cells, but that the enhanced IL-2 signature is present in all Tet+ cells. These data have been added to Figure 6H and have been included in the results text on pages 10-11.Results, pages 10-11:“To further resolve these age-related transcriptional differences, we performed enrichment analysis with the Hallmarks genesets and observed 7 gene sets that were consistently positively enriched in d7 HA-specific Tet+cTfh cells from over 65 year old compared to 18-36 year old individuals . Four of these elevated genesets  we previously identified as negatively enriched in Tet+cTfh from 18-36 year old individuals compared to d0 Tet+ cells. Of these enriched gene sets, all except TNFA signalling via NFKB were also enriched in all Tet+ cells in older persons in addition to cTfh cells, suggesting that there are both common and unique pathways for how vaccine-reactive CD4+ cells and cTfh cells sense their environment in ageing. This indicates that these genes are normally downregulated as HA-specific CD4+ T cells differentiate into Tfh cells in response to vaccination, and that this process appears dysregulated in older individuals. The remaining three enriched genesets  are suggestive of heightened pro-inflammatory responses in cTfh cells, but not in all Tet+ CD4 T cells, from older individuals, and included enrichment for IL6, CCL2 and CCL5.”2) Relating to point #1 raised in the public review, Figure 5H does indicate that in both the overall data set and in cohort 1, there is a significant reduction in total tet+ cells (as % of CD4) in the older cohort compared to the younger group. The authors generally seem to minimise this point in order to focus on the cTFH population, but it is unclear to what extent there is a general defect in HA-specific T cell recall/expansion in older individuals.In the first cohort we observed a statistically significant reduction in the frequency of Tet+ cells in older persons compared to younger individuals. However, as this did not replicate in the second cohort we did not feel able to draw strong conclusions about whether the total Tet+ population is diminished by ageing, which is why it was not a focus of the paper. But, it is important to establish whether there is a relationship between the degree of Tet+ CD4+ T cell expansion and cTfh cell differentiation with age, to do this we performed correlation analyses. There is no correlation between the expansion of Tet+ cells and the frequency of cTfh cells formed seven days after immunisation in either age group. This suggests that the impaired cTfh cell differentiation in older persons is most likely caused by factors other than the capacity of CD4+ T cells to expand after vaccination. These data have been added as Figure 5—figure supplement 1D, and included in the results text on page 8.Text changes, Page 8:“There was no consistent difference in the total d7 Tet+ HA-specific T cell population with age for both cohorts  and we observed no age-related correlation between the ability of an individual to differentiate Tet+ cells into a cTfh cell and the overall expansion of Tet+ HA-specific T cell population . Thus, our data suggests that the poor vaccine antibody responses in older individuals is impacted by impaired cTfh cell differentiation  rather than size of the vaccine-specific CD4+ T cell pool.”3) My major concern is around the discussion of the implications of the inflammation-related gene sets that are differentially expressed among cTFH in older individuals. Without additional data, it seems difficult to me to conclude that vaccine-induced inflammation is driving poor cTFH differentiation, as the authors have not shown any such causative relationship. It seems equally plausible that a defect in antigen presentation, DC function, etc that impacts on initial CD4 Tmem activation and subsequent differentiation could cause the observed cTFH defect, which is simply then reflected in transcriptional pathways that are associated with the cTFH phenotype.We agree with the reviewer that the data presented in figure 7 are correlative, rather than causative. In order to test the hypothesis that increased inflammatory cytokine production in lymphoid organs limits Tfh cell differentiation we have used Il2cre/+; Rosa26stop-flox-Il2/+ transgenic mice. In this mouse model, IL-2-dependent cre-recombinase activity facilitates the expression of low levels of IL-2 in cells that have previously expressed IL-2. This creates a scenario in which cells that physiologically express IL-2 cannot turn off its expression therefore increasing expression IL-2 after antigenic stimulation . After influenza infection, Il2cre/+; Rosa26stop-flox-Il2/+ transgenic mice have fewer Tfh cells in the draining mediastinal lymph node and in the spleen , this is accompanied by a reduction in the magnitude of the GC B cell response . These data provide a proof of concept that increased inflammatory signals in lymphoid organs limit the formation of Tfh cells, consistent with the negative correlation of IL-2 signalling and cTfh cell formation in humans . This new data supports the conclusion that excess IL-2 signalling can limit the Tfh cell response. These data are presented in Figure 8, and are discussed on page 12 in the results, and pages 12-13 in the discussion.We also agree with the reviewer that ageing is complex, and it is likely that there are multiple contributing factors to the poor immune response upon vaccination in older people. Here, we identify IL-2 signalling as one such factor, but have revised our discussion on pages 14-15 to clearly communicate that other pathways/cell types have also been implicated in ageing, and the causes are likely multi-factorial.Results text, page 12.“Sustained IL-2 production inhibits Tfh cell frequency and the germinal centre response. To test the hypothesis that cytokine signalling needs to be curtailed to facilitate Tfh cell differentiation turned to a genetically modified mouse model in which cells that have initiated IL-2 production cannot switch it off, Il2cre/+; Rosa26stop-flox-Il2/+ mice (37). Twelve days after influenza infection Il2cre/+; Rosa26stop-flox-Il2/+ mice have fewer Tfh cells in the draining lymph node and spleen , which is associated with a reduced frequency of germinal center B cells . This provides a proof of concept that proinflammatory cytokine production needs to be limited to enable full Tfh cell differentiation in secondary lymphoid organs.”Discussion text, pages 12, 13:These enhanced inflammatory signatures associated with poor antibody titre in an independent cohort of influenza vaccinees. The dampening of Tfh cell formation by enhanced cytokine production was confirmed by the use of genetically modified mice where IL-2 production is restricted to the appropriate anatomical and cellular compartments, but once initiated cannot be inactivated Together, this suggests that formation of antigen-specific Tfh cells is essential for high titre antibody responses, and that excessive inflammatory factors can contribute to poor cTfh cell responses.”Discussion text, pages 14, 15:“We have previously demonstrated in older people and aged mice that type 1 interferon and TLR7 signalling is important for conventional dendritic cells type 2 to support Tfh differentiation after vaccination (9). These studies, together with the data presented here, prompt the hypothesis that while some types inflammation are ‘good’ for promoting long-lived antibody responses in ageing, prolonged exposure to other cytokines such IL-2 are ‘bad’ and negatively impact Tfh cell differentiation.”


RNA sequencing analysis focus on the detection of differential gene expression changes that meet a two-fold minimum change between groups. The variability present in RNA sequencing data may obscure the detection of valuable information when specific genes within certain samples display large expression variability. This paper develops methods that apply variance and dispersion estimates to intra-group data to identify genes with expression values that diverge from the group envelope. STRING database analysis of the identified genes characterize gene affiliations involved in physiological regulatory networks that contribute to biological variability. Individuals with divergent gene groupings within network pathways can thereby be identified and judiciously evaluated prior to standard differential analysis.A three-step process is presented for evaluating biological variability within a group in RNA sequencing data in which gene counts were: (1) scaled to minimize heteroscedasticity; (2) rank-ordered to detect potentially divergent “trendlines” for every gene in the data set; and (3) tested with the STRING database to identify statistically significant pathway associations among the genes displaying marked trendline variability and dispersion. This approach was used to identify the “trendline” profile of every gene in three test data sets. Control data from an in-house data set and two archived samples revealed that 65–70% of the sequenced genes displayed trendlines with minimal variation and dispersion across the sample group after rank-ordering the samples; this is referred to as a linear trendline. Smaller subsets of genes within the three data sets displayed markedly skewed trendlines, wide dispersion and variability. STRING database analysis of these genes identified interferon-mediated response networks in 11–20% of the individuals sampled at the time of blood collection. For example, in the three control data sets, 14 to 26 genes in the defense response to virus pathway were identified in 7 individuals at false discovery rates ≤1.92 E-15.This analysis provides a rationale for identifying and characterizing notable gene expression variability within a study group. The identification of highly variable genes and their network associations within specific individuals empowers more judicious inspection of the sample group prior to differential gene expression analysis.The online version contains supplementary material available at 10.1186/s12864-021-07563-9. A major goal of RNA-seq studies is to improve and extend our understanding of gene expression responses amidst the challenging variability commonly found in sequencing data. Although numerous factors are known to affect sequencing results such as the reference genome, the read processing pipeline, internal references, read fragment size, and the selected data analysis algorithms, among others , thus faConclusions relating to biological variation are usually based on Analysis of Variance (ANOVA) Sums of Squares estimations. Although increasing the level of replication may increase the Between Sums of Square difference and provide a more definitive statistical conclusion about an identified biological response , an increase in the Sums of Squares does not identify the factor(s) contributing to the variability. More broadly untangling the impact of variability on each step of the RNA-seq pipeline is difficult. One must identify specific sources of biological variability in the data set and consider how the normalization process impacts the overall results. This problem becomes increasingly difficult to resolve in samples in which cell number and cell type fluctuate significantly. Identifying and quantifying significant variability within RNA sequencing data sets would provide information that would be very useful for evaluating the robustness of computational steps, for example, devising and evaluating methodologies for determining how normalization protocols impact technical and biological variation.Van den Berg et al.  have empBlood from 35 healthy adults was extracted and processed for RNA sequencing , 11. TheWe also demonstrate that the scaling normalization strategy employed in our study reduced gene expression heteroscedasticity within three different control data sets as previously demonstrated by van den Berg et al. . Scalinged and 3rd quartiles (25th–75th percentile). The breadth of the box illustrates the degree of count dispersion across the 35 data points for each gene. The mean for the INTS6 gene is 10.52 ± 1.88 (1 SD) counts and plotting the counts for the 35 samples in ascending rank-order created a linear INTS6 trendline as illustrated in Fig. 2) of 0.9498 further supports the linear profile of the INTS6 trendline. This trendline profile was identical to the pattern obtained when numbers were randomly selected from a normally distributed population within a defined range of values and rank-ordered  gene counts for 35 individuals were processed through our pipeline  and the 2 values of 0.8499 and 0.8418, respectively), rank-ordering the counts revealed more complex trendlines where the slope of the line for samples in quartiles 1 and/or 4 deviated from the slope of the line for samples in quartiles 2 plus 3 .The last two example genes, IFIT3 and EIF1AY, displayed much greater deviation from the linear trendline model  displayed trendlines where the incremental difference in gene expression across the group followed a linear pattern resulting in RFigure After rank-ordering unadjusted and MVA gene counts to create gene trendlines, standard Excel functions were used to perform a variety of statistical calculations . Mean anThe unadjusted and MVA gene counts identified Biological Gene Ontology (GO) pathways associated with cotranslational protein targeting to membrane section 4A) or immune system process pathways (section 4B) when the largest means representing the various statistical calculations were evaluated for the two groups. The unadjusted mean counts identified gene pathway groupings having the largest relative gene expression levels. When the gene counts are scaled by MVA to reflect the sample-to-sample incremental changes of each gene, the resulting trendline means identified immune pathway classifications rather than the highly expressed genes associated with protein synthesis . The 300 genes displaying the largest range/Q3 (FDR = 6.22 E-32), range/median (FDR = 5.33 E-26) and kurtosis values (FDR = 6.85 E-27) detected the greatest trendline variability and had the smallest R2 values ranging from 0.2253 to 0.8754. These three statistical calculations selected trendline “tailing” patterns with the greatest fidelity that were similar to the profile previously depicted by the IFIT3 gene in Fig. Other statistical calculations that measure sample variability and trendline asymmetry such as coefficient of variation, maximum/minimum ratio, range/median, skewness, kurtosis, range/quartile 3, and RThe statistical parameters depicted in file The identification of the top 300 computed trendline values, as outlined above, was also used to evaluate gene groupings that were selected using various combinations of sample size (e.g. 250–450 genes) and statistical parameter groupings (combine 1–3 measures for pathway selection). STRING-db analysis of 250–300 genes based on trendline kurtosis estimates selected identical pathways (data not shown). Samples of 300 genes surveyed at various rank position locations, ranging from 1 to 6000, selected different GO pathways with lower FDR’s following STRING-db analysis. Sampling genes at lower gene rankings identified large pathways involved in cellular metabolism and function. These pathways involve thousands of genes and due to the size of the pathways much lower FDR’s were observed (e.g. FDR > E-15).The application of the MVA scaling reduced heteroscedasticity as previously noted  while pr6 values) to a more discernable number of terms, estimated values for the highest correlation and anticorrelation range was used to provide a count of the number of genes displaying correlation values > or < input values and the number of genes assigned r values ≥ or ≤ the input terms were identified  using the BowTie2 aligner supporting gapped alignments as previously described , 36.Data files containing 10–13 thousand transcripts were imported into an Excel spreadsheet and matched to a list of human protein coding genes identified at the Gene Ontology website –22. To oSingle genes that mapped to multiple genomic locations  were idThe final raw data processing step was the Minimum Value Adjustment scaling procedure described below  scaling normalization strategy to our counts. The smallest count for each gene was identified and used to proportionally adjust the remaining samples. MVA = Gene A (Sample Counts (1 to n) / Gene A Minimum Value Count). This adjustment assigns a value of 1 to the smallest count value for every gene. After MVA, all of the incremental changes for the 8746 protein-coding genes across the 35-sample data set fall within a numeric range of 1–60 relative counts. This adjustment of every gene to its lowest common denominator eliminated large comparative differences in the relative magnitude of the observed counts between genes within and across individuals while maintaining the important incremental changes when the adjusted counts are rank-ordered. In addition, the trendline starting point is identical for every gene in the analysis. The removal of inordinately low (counts < 0.5) and nonsignificant gene counts minimized the possibility of inflating the magnitude of the sample-to-sample incremental changes that can be sensitive to very small outliers during scaling adjustments [Read counts for various genes ranged over a 5 Logustments .Statistical calculations were performed using the resident statistical macros provided with Windows 10 Excel software.For statistical analysis, the Microsoft Windows Excel Platform and its statistical and mathematical functions were used. In order to organize and manage the data, software was developed to streamline and augment our analysis . RNA seqA primary objective of our study was to determine whether rank-order analysis was a useful strategy to identify sources of intra-group biological variability that otherwise remain difficult to detect in RNA-seq data. In order to omit nonprotein coding genes from the sequencing results, each gene was surveyed to determine whether the transcript was a known protein coding gene. We used the Gene Ontology website to provide a list of 19,623 known human protein coding genes –22. Gene2 > 0.9 (linear profile) and R2 < 0.9 (nonlinear) trendline patterns. These mathematical calculations were used to further characterize gene trendlines and identify gene groupings (clusters) that shared similar mathematical features. This analysis identified relational associations among genes that would otherwise be indiscernible using more classical analytical procedures. Supplement file Two markedly different gene trendline expression profiles are presented in supplement 1 to illustrate how linear regression analysis and other calculations can be used to analyze rank-ordered gene count data. Trendlines were constructed for each gene by ranking the adjusted and unadjusted gene counts for the 35 samples in ascending order as described in Additional files 2, etc.). Prominent genes considered for additional analysis fell among the top 300 genes characterized by the largest or smallest measurements for any of the computed parameters. For each computed statistical parameter, the identified genes were imported into the STRING database (db) to determine if that parameter linked genes to functionally related protein associations. For example, 300 genes that displayed the largest computed means were identified and imported into the STRING database version 11 [A variety of statistical calculations were performed on the trendline of each gene. Our results were based on a list of 8746 protein-coding genes with a mean baseline count ≥0.5 counts that were rank-ordered to identify genes with the most prominent trendline property .Additional file 3. The Identification of Gene Trendlines with “Tailing” Profiles Using Quartile Slope Analysis.Additional file 4. STRING db Pathway Identification Based on the Analysis of the Top 300 Genes Identified by the Designated Statistical Measures.Additional file 5. Examination of Intraindividual Gene Rankings to Identify Individuals Displaying Coordinated Gene Regulatory Activity.Additional file 6. STRING db Analysis of Intra-individual Positional Gene Rankings in 35 Control Samples Based On Range/Median, Range/Q3, Kurtosis and Q4/Q(2 + 3) Slope Calculations.Additional file 7. Rank-Ordered Distribution of White Blood Cells Among 35 Control Samples.Additional file 8. STRING db Analysis of Intra-individual Positional Gene Rankings In 9 Archived Controls Based on Range/Median, Range/Q3, Kurtosis and Q4/Q(2 + 3) Slope Calculations.Additional file 9. STRING db Analysis of Intra-individual Positional Gene Rankings In 12 Archived Control Samples Based On Range/Median, Range/Q3, Kurtosis and Q4/Q(2 + 3) Slope Calculations.Additional file 10. Reference Genes with Raw Counts Greater than 5 and R2 Values > 0.9 in three separate RNA sequencing Studies.Additional file 11. Control Reference Data Files.


Reactive oxygen species (ROS) derived from the phagocyte NADPH oxidase (NOX2) are essential for host defence and immunoregulation. Their levels must be tightly controlled. ROS are required to prevent infection and are used in signalling to regulate several processes that are essential for normal immunity. A lack of ROS then leads to immunodeficiency and autoinflammation. However, excess ROS are also deleterious, damaging tissues by causing oxidative stress. In this review, we focus on two particular aspects of ROS biology: (i) the emerging understanding that NOX2-derived ROS play a pivotal role in the development and maintenance of adaptive immunity and (ii) the effects of excess ROS in systemic disease and how limiting ROS might represent a therapeutic avenue in limiting excess inflammation. H2O2 can diffuse across membranes to oxidise specific targets, or can be converted to O2 and H2O (Reactive Oxygen Species (ROS) are small molecules that are derived from molecular oxygen. They can either be classed as radicals or non-radicals, depending on whether they have an unpaired electron . Superox2O2 is a very useful signalling molecule because it can be rapidly generated and rapidly removed via specific enzymes such as catalase, superoxide dismutase and peroxiredoxin enzymes. It can also be quenched by non-enzymatic means such as glutathione (GSH)  . As suchROS are produced during metabolic reactions within many cellular compartments, including the mitochondria, peroxisome and endoplasmic reticulum , 5. This2O2 production succeeded a large and rapid increase in oxygen consumption  the finding that NADPH is the dominant physiological electron donor  that allows the production of ROS –13 and .These findings show that neutrophils possess an enzyme that facilitates the donation of electrons to molecular oxygen. The identification of cytochrome b558, which we refer to as NOX2, as the relevant enzyme resulted from insightful biochemistry and the study of the monogenic immunodeficiency X-linked chronic granulomatous disease (X-CGD). This “fatal granulomatous disease of childhood” was first described in the 1950s. It described boys whose neutrophils were unable to kill certain bacteria and did not increase oxygen consumption or produce ROS .In a landmark study for the field, Segal and colleagues showed that neutrophils from patients with CGD lacked both NADPH oxidase activity and a particular unusual b type cytochrome that localised to the plasma membrane , 17. Thephox and 22kDa p22phox, which together form cytochrome b558  is a multi-subunit protein complex that, upon interaction, can form an active enzyme complex capable of producing superoxide. It is comprised of two integral membrane bound components; the 91kDa gp91ome b558 , 21. p22 p40phox , p47phoxox . In the presence of H2O2, MPO catalyses the production of Hypochlorous acid (HOCL), a very potent anti-microbial agent. HOCL, in turn, can modify multiple proteins (both host and microbe) in the phagosome to generate chloramines and aldehydes.The role of NOX2-derived ROS was first identified in killing microbes. This is well illustrated by the susceptibility of patients with CGD to an extensive, but nonetheless demarcated, range of pathogens. For example, neutrophils from patients with CGD can kill While we do not cover these processes in detail, we would direct readers interested in this fascinating area to excellent reviews by Nauseef  and also(i) That there is synergy between ROS and other granule constituents, including HOCL and non-oxidative killing mechanisms such as cathelicidins, serine proteases and lactoferrin.(ii) That microbes employ a variety of strategies to evade phagosomal killing.(iii) That though the phagocyte NADPH oxidase is present in both neutrophils and macrophages, the intra-phagosomal environment in these cells differs greatly with respect to parameters such as pH and other anti-microbial components (such as MPO).Salmonella Typhimurium and Listeria monocytogenes. It transpired that they failed to make ROS, owing to almost complete deficiency of gp91phox and p22phox. bc017643 encodes an ER-resident transmembrane protein that co-immunoprecipitates with gp91phox. We also observed that P2X7 receptor expression was downregulated in EROS deficient cells, which was later independently verified by another group . Recent work in our laboratory suggests that EROS is a highly selective placeholder chaperone, binding to an “unsatisfied surface” on the gp91phox precursor and stabilising it until p22phox can bind . The lack of ROS and NOX2 expression in EROS-deficient mice, akin to that of gp91phox-/- mice, suggested that mutations in the human orthologue C17ORF62 might lead to CGD. In 2018, we and another group reported separate homozygous mutations in C17ORF62, leading to EROS-deficiency, as a novel cause of chronic granulomatous disease (CGD5) (C17ORF62 has been re-named CYBC1 (CYtochrome B Chaperone 1).An important recent addition to the biology of the phagocyte NADPH oxidase and CGD is the discovery of EROS (gene symbol CYBC1) . In 2017er group , demonste (CGD5) , 43. C17phox. Although they have not yet been implicated in CGD, several other proteins are capable of regulating gp91phox abundance. Negative Regulator of Reactive Oxygen Species (NRROS) was described in 2014 as an ER-resident protein that binds gp91phox and facilitates its degradation  is neutral, tending towards slightly alkaline, but that NOX2-deficient DCs have acidic phagosomes which leads to enhanced antigen degradation and impaired cross presentation to CD8+ T cells  Class I . The sam Class I  and that Class I . The sma Class I . Howeverenerated , 56.A recent publication by Reis e Sousa and colleagues provided an intriguing new insight into the role of NOX2-derived ROS in antigen presentation . The DNGThis group demonstrated that DNGR1 ligation facilitates Syk kinase activation and this, in turn, leads to NOX2 activation within phagosomes containing internalised antigen. The oxidative stress caused by the resulting free radicals damages the phagosome, causing membrane rupture, thus allowing leak of antigen into the cytosol and its translocation into the MHC class I presentation pathway. Cross presentation is markedly impaired in gp91phox-deficient DCs. How exactly Syk drives NOX2 activation in this context is unknown but the NOX2 activators Vav1 and Rac have previously been shown to be necessary for efficient cross-presentation and they are likely to be involved to some extent.phox or gp91phox deficient mice were partially protected from the central nervous system injury and inflammation that characterises EAE. MHC class II presentation is also impaired in human B cells deficient in p40phox .There is also evidence that NOX2 can influence MHC class II peptide processing and presentation. Extending their previous work on cysteine cathepsins Yates and colleagues demonstrated that NOX2 not only affects the amount of proteolytic processing but affects it qualitatively too . They us p40phox . This waAlthough most commonly associated with innate immunity, NOX2 also has a variety of signalling roles in T and B cell responses. Some of these include; modulating T helper differentiation, proliferation of B cells and inducing apoptosis.-/- CD4+ T cells . One study found fewer peripheral CD4+CD25+ Tregs and decreased FOXP3 expression in NOX2of Tregs . Howeverficiency . The autsubunits , 67.phox-/- Tregs. Additionally, gp91phox-/- Tregs have increased NF-kB activation and greater FOXP3 expression. Subsequently, Tregs deficient in gp91phox have greater suppressive activity than wildtype control Tregs  infection. The authors state this improved CD8+ T cell viral response may be due to less immunopathology that occurs in the absence of p47phox  stimulation, NOX2 is responsible for generating the rapid initial production of ROS, whereas the later stages of ROS production are not NOX2 dependant. NOX2e normal , 79. Howmulation . This su-/- B cells have increased expression of the Toll-like receptors (TLR) TLR7 and TLR9, and subsequently have greater responsiveness to TLR7/9 stimulation  and promote prolonged neuroinflammation . NOX proAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by the progressive loss of motor neurons in the brain, brain stem and spinal cord. Disease progression is rapid, with a prognosis of only 2-5 years after diagnosis for most individuals .G93A transgenic mouse, a common mouse model of ALS. NOX2 expression and activation was shown to be significantly upregulated in microglia in the spinal cord of SOD1G93A mice compared to controls  is a common neurodegenerative disorder characterised by inflammation and demyelination in the CNS. Microglial activation is hypothesised to play an important role in the pathogenesis of MS. NOX2 has also been shown to be upregulated in microglia in active lesions in MS patients .-/- mice after EAE induction. Lymphocyte and microglial infiltration in the CNS was also significantly decreased compared to heterozygous and wild-type controls. Interestingly, the authors showed that only immune infiltration in the CNS is reduced, as immune cell populations in peripheral tissues such as the spleen and cervical lymph node are similar in NOX2-/- and wild-type mice post EAE induction  model is commonly used as a model to study MS. Recent evidence has demonstrated that disease severity is reduced in NOX2nduction . MicroglAnother recent paper demonstrated that deletion of NOX2 in conventional DCs (cDCs) reduced disease severity and demyelination in an adoptive transfer model of EAE . InteresOxidative stress and damage have also been hypothesised to play a role in Alzheimer’s disease, although the role of NADPH oxidases remains unclear. As mentioned previously, microglia play a vital immune role in CNS homeostasis through clearance of dead cells and debris. However, dysregulation of microglia can lead to prolonged neuroinflammation and the development of neurodegenerative disorders. Microglia have been shown to associate with and promote clearance of amyloid-β (Aβ) deposits in the early stages of Alzheimer’s. However, in aging mice microglia appear to have a reduced ability to clear Aβ deposits and drive inflammation within the CNS .-/- aged mice had significantly less Aβ deposition and plaque formation compared to aged wild-type controls. ROS production was also much lower in NOX2-/- mice than in wild-type mice, indicating that ROS production in the aged mice was NOX2-dependent. The authors also investigated ROS production in human brain tissue, and found older individuals had higher levels of ROS production when compared to young controls. Stimulation of the BV2 microglial cell line with Aβ42 peptide also resulted in significantly increased NOX2-depdendent ROS production, which could be inhibited using NOX2 inhibitors such as apocynin or Nox2tat. These results indicate that NOX2 may play an important role in the regulation of microglia and the microglial response to Aβ plaques and therefore it may be an important driver of the pathogenesis of Alzheimer’s disease.NOX2 expression in microglia has also been hypothesised to play an important role in the pathogenesis of Alzheimer’s disease. A recent study demonstrated high NOX2 expression in microglia and increased microglial infiltration in aged wild-type brains, compared to young mice . InteresWhilst it is clear that oxidative stress is involved in aging and the development of neurodegenerative diseases, the precise mechanisms defining how aberrant NOX2-depdendent ROS production drives neuroinflammation require further investigation. In addition, it remains to be investigated whether targeting of NOX2 through the use of inhibitors would provide therapeutic benefit in neurodegenerative disorders.ROS production by phagocytes plays a vital role in the innate immune response and the clearance of pathogens during infection. However, it is essential that the mechanisms which regulate ROS generation are tightly controlled. Failure to regulate the innate immune response results in excessive ROS production, or oxidative stress, which promotes inflammation. Oxidative stress and the resultant sustained inflammation can result in tissue damage, particularly in barrier sites such as the lung . Recent -/- mice exhibited enhanced viral clearance, increased lung function and reduced lung damage when compared to wild-type mice. Increased macrophage and neutrophil infiltration into the airway epithelia was also observed. Another study by Vahlos et al. in 2011 also demonstrated that deletion of NOX2 resulted in reduced viral titres in mice infected with Influenza A virus  was significantly decreased in NOX2-/- mice. However, the authors hypothesise that this could be due to sex differences between the mouse models used in the studies. Treatment of wild-type mice with the ROS inhibitor apocynin after Influenza A infection also significantly reduced macrophage and neutrophil infiltration and viral titres were reduced by 50%. These results indicate that NOX2 is driving inflammation and acute lung injury in response to Influenza A infection. Therefore, modulation of NOX2-dependent ROS production may provide therapeutic benefit and reduce lung damage in patients suffering from acute lung injury during infection.In 2006, Snelgrove et al. demonstrated that deletion of NOX2 in mice resulted in reduced viral load after infection with Influenza virus . NOX2-/- A virus . Apoptos-/- macrophages transfected with a TLR7C98A mutant could not restore TLR7-dependent cytokine secretion. The authors hypothesise that ROS production by NOX2 may modify the Cys98 residue, resulting in reduced cytokine secretion and a dampened antiviral response  production in response to bacterial infections. NOX2ial load . Deletioin vitro or animal models of COVID-19 as it is likely that the virus will continue to mutate and become endemic, possibly escaping vaccine-mediated control in some instances.The balanced generation of ROS is essential to induce appropriate immune responses and avoid tissue damage by oxidative stress  or IL-6 in silico technology such as alpha-fold will also better inform our efforts to inhibit the action of this and other NADPH oxidases  and Imperial College London. Research in DT lab is supported by a generous donation from the family of Sidharth Burman.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.


This case-control study assesses the clinical significance of germline sequencing in children with cancer.  We set out to quantify the etiological association between hmGPVs in associated and nonassociated pediatric and adult-onset cancers.The increasing uptake of multigene sequencing of germline DNA from children with cancer has led to an increase in the discovery of high- and moderate-penetrance germline pathogenic or likely pathogenic (P/LP) variants  in persons with cancer.2 and Mi-ONCOSEQ in 1015 adult metastatic tumors (n = 129 hmGPVs).3 Zygosity of hmGPVs was determined using loss of heterozygosity (LOH) or a second coding sequence somatic hit. We determined if tumors were associated or nonassociated .This case-control study used publicly available deidentified human data and does not require institutional review board approval per the Tri-Council Policy Statement “Ethical Conduct for Research Involving Humans” Article 2.2b  and the Icahn School of Medicine at Mount Sinai (ISMMS) Human Subjects Research (HSR) Determination Guidance. We used tumor zygosity of hmGPVs in tumor suppressor genes (TSG) to ascertain whether hmGPVs are associated with tumorigenesis. We used germline and zygosity data from 2 recent prospectively matched tumor-normal panel-based germline testing studies: IMPACT in 751 pediatric patients with solid tumors (n = 99 hmGPVs)P < .001) , and 129 adult patients  with hmGPVs were included the analysis. Demographic data are shown in the  < .001) .2RB1 GPV that had available zygosity information, 20 had second hit. In contrast, none of 3 cases in non–RB1-associated tumor types had second hits . In contrast, 5 patients with BRCA1, BRCA2, and PALB2—all found in patients with tumor types not known to be associated with GPVs in these genes—did not have second hit and are unlikely to respond to PARP inhibitors.4Of 22 RB1-associated tumors with an P < .001)  nonassociated tumors with an hmGPV harbored second hits compared with 40 of 55 (73%) in patients with associated tumors  .35 These results demonstrate how companion tumor sequencing can be informative in determining the etiologic role of germline variants,6 especially during broad germline testing beyond associated cancer types for both adult and pediatric cases. This study has several limitations, such as a small sample size, especially regarding specific pairs of tumors and genes. In addition, functional assays to determine the role of hmGPVs are needed in addition to information on zygosity.The high frequency of biallelic hits observed for TSGs that are known to be associated with the cancer in which the hmGPVs occurred indicate that in pediatric patients with cancer, when hmGPVs are detected in associated tumors, these variants are almost always associated with tumor development. In contrast, when the association between hmGPV and the cancer is not established, a hmGPV in a child with this class of cancer is rarely associated with the tested tumor. In adult carriers of hmGPVs, cancers can arise from multiple mechanisms, whereas in children, exposures are limited.


UBE3A in neurons. There are several genetic mechanisms that impair UBE3A expression, but they differ in how neighboring genes on chromosome 15 at 15q11–q13 are affected. There is evidence that different genetic subtypes present with different clinical severity, but a systematic quantitative investigation is lacking. Here we analyze natural history data on a large sample of individuals with AS , including clinical scales that quantify development of motor, cognitive, and language skills , adaptive behavior , and AS-specific symptoms . We found that clinical severity, as captured by these scales, differs between genetic subtypes: individuals with UBE3A pathogenic variants and imprinting defects (IPD) are less affected than individuals with uniparental paternal disomy (UPD); of those with UBE3A pathogenic variants, individuals with truncating mutations are more impaired than those with missense mutations. Individuals with a deletion that encompasses UBE3A and other genes are most impaired, but in contrast to previous work, we found little evidence for an influence of deletion length (class I vs. II) on severity of manifestations. The results of this systematic analysis highlight the relevance of genomic regions beyond UBE3A as contributing factors in the AS phenotype, and provide important information for the development of new therapies for AS. More generally, this work exemplifies how increasing genetic irregularities are reflected in clinical severity.Angelman Syndrome (AS) is a severe neurodevelopmental disorder due to impaired expression of  Angelman syndrome (AS) is a rare genetic neurodevelopmental disorder with a prevalence of 1 in 10,000–24,000 births –6.UBE3A in the chromosome 15q11–13 region [UBE3A is silenced in neurons by genomic imprinting [UBE3A expression is impaired either through deletions including the maternal copy of UBE3A or through one of several other mechanisms: pathogenic variants of the maternal copy of UBE3A (Mut), imprinting defects (IPD), and paternal uniparental disomy (UPD) of chromosome 15 [UBE3A pathogenic variants, IPD, and UPD for ~10% each [AS is due to the lack of expression of the maternal copy of 3 region , 7. In hprinting . In AS, osome 15 . Deletio10% each . Some pa10% each , 10 and Among individuals with a deletion, the length of the chromosomal deletion varies. Deletions of 15q11–q13 commonly occur at recurring breakpoints, resulting in two typical deletion sizes: class 1  and class 2 . Atypical deletions  can span chromosomal segments longer than Del1 or shorter than Del2 [UBE3A, resulting in a near-complete lack of expression in neurons. Furthermore, genes and noncoding sequences in this region that are imprinted and paternally expressed are likely overexpressed as there are two active copies in UPD patients [Individuals with UPD have two paternal copies of the chromosome 15q11–q13 segment and therefore two silenced copies of Imprinting center defects (IPDs) can result from epigenetic events (~85%) or deletions within the AS imprinting center (~15%) and effectively cause the maternal chromosome 15q11q13 region to “behave” like the paternal copy. Therefore, IPD can be expected to be effectively like UPD –16. HoweUBE3A pathogenic variants lead to a selective impairment of expression of functional UBE3A protein, leaving expression of other genes presumably intact [UBE3A pathogenic variants can be further grouped into missense mutations (MutM) and truncating mutations (MutT). Whereas truncating mutations highly likely lead to a complete lack of UBE3A expression, missense mutations may lead to production of a modified UBE3A protein that retains residual functionality [y intact . Many ofy intact . UBE3A pionality , 20.To our knowledge, nine previous studies have characterized the developmental and clinical differences between AS genotypes  , a longitudinal multicenter study of AS. A subset of these data have been analyzed previously . ConsentPer study protocol, participants were seen approximately annually over 8 years (mean number visits: 2.9). Data reported here are from 250 participants  that fall into one of six genetic subgroups , and the Clinical Severity Scale (CSS), a scale developed for the ASNHS. Trained personnel (physicians and licensed psychologists) carried out all assessments  , the VinData were analyzed using linear mixed-effects models (LMM). We fit a LMM to the raw scores of each subscale and the CSS sum score. We modeled random intercepts per participant (to account for repeated measurements) and per study site . As fixed effects, we specified a third-order mean-centered orthogonal polylogarithmic function of age. We chose this parameterization to capture nonlinear developmental trajectories apparent from visual inspection of the data see Fig.  and 3.FiFirst, we tested for differences between participants with  and without  deletions. For each scale, we compared a model using only age but no genotype information to a model with additional information about the presence or absence of a deletion and the interaction of the presence or absence of a deletion with age. We then separated the dataset into deletion and non-deletion participants and further compared subgroups within them. We tested whether introducing diagnostic information concerning the class of deletion  and subtype of non-deletion  would significantly improve the models using likelihood-ratio tests (LRT).p values obtained in these post-hoc comparisons using the Benjamini–Hochberg method [When the best model contained the full diagnostic information for the non-deletion group, we performed pair-wise post-hoc comparisons between genotypes. We adjusted the g method .We used the coefficients of the “best model” for each scale . Visual inspection of the BSID-III scales Fig.  suggest χ2 > 100, p < 0.001 for all scales, see Supplementary Table First, we compared the scores on the BSID-III scales between deletion and non-deletion AS. A model differentiating the deletion and non-deletion genotypes fit the data significantly better (compared with a simple model without differentiation), with higher scores for individuals with non-deletion AS for all five domains  showed significantly higher scores for non-deletion compared with deletion AS  fit the data significantly better compared with a simple model without differentiation for 15 out of 19 scales tested  and MutM tended to score significantly higher , see Table In line with these qualitative observations, post-hoc statistical comparisons (for the 15 scores found significant above) revealed that for many scales, UPD indeed scored significantly lower compared with all other non-deletion genotypes  for 17/19 scales  that are single-copy (haploid) for the deletion genotypes and intact (diploid) for all non-deletion genotypes. Indeed, loss of function variants in these GABAA subunit genes have been linked to epilepsy and developmental delay [We corroborated previous findings that individuals with deletion AS are on average clinically and developmentally more severely impaired than individuals with non-deletion AS. This is genetically plausible because deletions include additional genes that likely have an impact on development and brain function. Genes that may drive the difference include three GABAal delay . In lineal delay .There was prior evidence that individuals with class 1 (larger) deletions might be more impaired than individuals with class 2 deletions in the domains of language, cognition and motor , 29. FurNon-deletion AS genotypes have a low prevalence  such that even specialized clinicians see only a few patients from each non-deletion subtype in their professional lives. The order of clinical severity within the non-deletion AS population had previously not been systematically examined. The current study addressed this question by using a large dataset, collected across six expert centers over ~8 years allowing investigation of differences in clinical features across non-deletion AS subgroups.UBE3A is ‘leaky’, i.e., not leading to a 100% silencing, such that UBE3A in UPD could have additional residual expression from two incompletely silenced copies compared with the residual expression from one incompletely silenced copy in MutT, which may be of functional relevance . If the ‘leaking hypothesis’ were true, UPD should be less affected than MutT , but we found the opposite. Our results suggest that, if existent, a residual expression of silenced UBE3A  has less relevance for the overall severity of the phenotype than overexpression of maternally silenced genes in UPD or other genetic factors in the paternally duplicated region. These findings suggest investigating UPD-specific pathophysiology in future studies.The results revealed that individuals with UPD have lower scores on the investigated clinical scales compared with individuals with other non-deletion genotypes . In particular, individuals with UPD exhibit lower scores compared with individuals with truncating variants (MutT), the genetic subgroup that highly likely leads to a specific and complete UBE3A disruption in neurons. Mouse models of AS suggest no relevant quantity of Ube3a postnatally . HoweverIn theory, IPD should present phenotypically like UPD , which is presumably critical for resulting phenotype [n = 14), a further investigation of these relevant question is beyond the scope of this publication.We found that individuals with missense variants (MutM) have generally higher scores than individuals with truncating variants (MutT). In line with in vitro work , this suhenotype , 33. GivIn summary, the analysis of non-deletion AS revealed a complex picture suggesting different degrees of clinical severity that can be plausibly related to differences in the genetic irregularities.written communication scale, show clear flooring effects  and immutable  groups for potential use as a response measure for treatments.Our findings highlight the importance of taking genotype information into account in the clinical care and in clinical studies in patients with AS. Furthermore, we find that BSID-III , PLS-4 (communication domains) and some domains of the VABS-2  that capture clinical features reasonably well, show differentiation between genetic subgroups with different levels of impairment, and therefore may be useful as endpoints in this population (Supplementary Table Despite the overall large sample size, the number of individuals in non-deletion AS subgroups was limited. The scales investigated cover a limited scope of symptoms that may not address the specific aspects of each AS subpopulation. For example, specific symptom domains such as food seeking which may be relevant for UPD have not been assessed. Furthermore, a detailed consideration of epilepsy is beyond the scope of this publication and will be presented elsewhere.Supplemental Material


Despite a modest association between tobacco smoking and breast cancer risk reported by recent epidemiological studies, it is still equivocal whether smoking is causally related to breast cancer risk.We applied Mendelian randomisation (MR) to evaluate a potential causal effect of cigarette smoking on breast cancer risk. Both individual-level data as well as summary statistics for 164 single-nucleotide polymorphisms (SNPs) reported in genome-wide association studies of lifetime smoking index (LSI) or cigarette per day (CPD) were used to obtain MR effect estimates. Data from 108,420 invasive breast cancer cases and 87,681 controls were used for the LSI analysis and for the CPD analysis conducted among ever-smokers from 26,147 cancer cases and 26,072 controls. Sensitivity analyses were conducted to address pleiotropy.P = 0.11 × 10–2), but there was no evidence of association for genetically predicted CPD . The sensitivity analyses yielded similar results and showed no strong evidence of pleiotropic effect.Genetically predicted LSI was associated with increased breast cancer risk (OR 1.18 per SD, 95% CI: 1.07–1.30, Our MR study provides supportive evidence for a potential causal association with breast cancer risk for lifetime smoking exposure but not cigarettes per day among smokers. It is however difficult to establish causation based on these observational studies.Based on the experimental and epidemiologic findings, evidence is insufficient to establish a causal relationship between tobacco smoking and breast cancer risk.14 MR uses genetic variants such as single-nucleotide polymorphisms (SNPs) associated with an exposure of interest as an instrumental variable (IV), which is robust if three assumptions are met: (1) the genetic variants are casually associated with the exposure, (2) the variants are not associated with known or potential confounders for the exposure–outcome relationships and (3) the variants are associated with outcome only via the exposure of interest and not through other pathways.16 Given the unclear causal nature of the findings in observational studies, we conducted a MR analysis to investigate the association between smoking traits and breast cancer risk.Mendelian randomisation (MR) has been increasingly used to strengthen causal inference in observational studies and under certain assumptions is less vulnerable to residual confounding, reverse causation and selection bias.https://epi.grants.cancer.gov/oncoarray/)17 and iCOGS arrays in 40,178 cases and 35,314 controls (http://ccge.medschl.cam.ac.uk/research/consortia/icogs/).18 Genotype data were imputed based on the 1000 Genomes project Phase 3 as the reference panel using the programme IMPUTE2.19 SNPs with high imputation quality (imputation r2 > 0.5) were included. Overlapping participants between datasets were excluded from the iCOGS dataset as the OncoArray provides a better genomic coverage than the iCOGS array. Demographic and epidemiologic data were harmonised across BCAC sites based on a standardised protocol and derived with respect to a reference date, which was date at diagnosis for cases and date at interview for controls. For controls and cases from the nested case-control studies, data from the baseline interview were considered, or if available, follow-up information. Chosen characteristics of the two datasets, including self-reported smoking behaviours  are shown in Supplementary Table We used data from 81 studies participating in the Breast Cancer Association Consortium (BCAC), including 108,420 cases and 87,681 controls of European ancestry. Genotyping was performed using two custom-made genotyping arrays: OncoArray in 68,242 invasive breast cancer cases and 52,367 controls  of European ancestry from the UK biobank.20 The genetic scores of the two smoking behaviour-related traits were reported to be associated with significantly higher risks of lung cancer.22Exposure variables were selected based on the availability of associated genetic variants to reflect smoking exposure. We included two quantitative smoking behaviour-related traits, cigarettes per day  and lifetime smoking index .P ≤ 5 × 10−8) and had a minor allele frequency (MAF) above or equal 1%. For each behaviour phenotype, we filtered the list of behaviour-associated SNPs so that the remaining SNPs were not in linkage disequilibrium (LD) (r2 > 0.1). For CPD, the SNP with lowest P value (rs10519203) of the correlated SNPs  was retained. One SNP (rs4886550) was not available in our data and without any proxy SNPs . Given that alcohol consumption is a recognised confounder of the association between smoking and breast cancer risk,25 we excluded nine SNPs  correlated with any alcohol consumption-associated SNPs (r2 > 0.1) from recent large-scale GWAS .21 After exclusions, we included a total of 164 variants associated with one of the smoking traits; 44 and 120 variants, respectively, for CPD and LSI  r > 0.1. FR2 of 0.0109 for CPD and R2 of 0.0036 for LSI, which correspond to the variance in each smoking behaviour explained by the SNPs used for this analysis. Power calculations were performed using an online tool available at http://cnsgenomics.com/shiny/mRnd/.26 Detailed power calculations for all outcomes  to detect different odd ratios are shown in Supplementary Table Power calculations were conducted to estimate the magnitude of effects detectable with our study size assuming 5% α level and an i) (ranging from 0 to 2) for each SNP weighted by the β-coefficient βgx for the effect of the genetic variant (g) related to the smoking quality or/and duration  (x) (Supplementary Table 21 The wPGSs of LSI was weakly correlated with wPGSs of CPD (0.17 in iCOGS and 0.03 in OncoArray) in ever-smokers. Analysis of LSI was conducted using all participants (including ever- and never-smokers), whereas that of CPD was performed solely in ever-smokers, and based on 26,147 cases  and 26,072 controls . These inclusion criteria correspond to those used in the GWAS studies that identified the SNPs for the smoking traits.21For our primary analysis, we generated weighted polygenic scores (wPGSs) using individual-level data of BCAC participants as follow: wPGS =entclass1pt{minima27 In order to assess if the genetic instrument is independent of established risk factors for breast cancer, associations of wPGSs with selected breast cancer risk factors were assessed by linear regression for continuous variables and logistic regression for categorical variables  was adjusted for age (continuous), principal components (PCs) of genetic ancestry  and study site, as previously described.Stratified analysis was performed to assess potential differences in associations with breast cancer risk by menopausal status  adjusting for age, study site and top ten PCs. Heterogeneity was tested employing the likelihood ratio test (LRT) for evaluating the multiplicative interaction terms in nested models. Polytomous regression was used to estimate the association according to oestrogen receptor (ER) status.Scaling was applied to convert the wPGSs for CPD into meaningful units through dividing them by linear regression coefficients of self-reported CPD (0.35 per pack of cigarettes per day). The regression coefficient of CPD was derived from a meta-analysis of iCOGS and OncoArray data on smoking behaviours among 26,072 among ever-smoker controls .28 MR Egger,29 weighted median,30 weighted mode,31 and robust adjusted profile score (RAPS).32 Each of these methods makes slightly different assumptions about the nature of pleiotropy and therefore a roughly consistent point estimate across the multiple methods provides the strongest evidence of causal inference.28 The IVW method was implemented since the instruments consisted of multiple SNPs.33 Multivariable MR methods34 were conducted also using summary association data from GWASs of alcohol consumption (drinks per week),21 body mass index (BMI) among females35 and education attainment.36 To produce valid results, the IVW method requires that all instruments are associated with the exposure of interest (relevance assumption), but neither directly with the outcome of interest  nor any confounders of the relationship between the exposure and the outcome (independence assumption).37 The intercept from MR-Egger regression is a statistical test for horizontal pleiotropy, whereas the slope can be interpreted as the smoking behaviour effect on breast cancer adjusted for horizontal pleiotropy.29 This method assumes however that the pleiotropic effects are independent of the instrument strength (InSIDE assumption). The weighted median estimator provides a valid causal estimate when at least half of the instruments are valid.30 The estimate from the weighted-mode analysis is valid when the largest group of instruments with consistent MR estimates is valid.31 MR-RAPS test extends the basic IVW random-effects approach by making the weight each variant receives in the analysis a function of the causal effect and the precision of the SNP-exposure association.32 The MR pleiotropy residual sum and outlier test (MR-PRESSO) was also implemented to identify outlying genetic variants and analyses were re-run after excluding these variants.38Five different two-sample MR methods using summary association data were applied: inverse-variance weighted (IVW),All two-sample MR analyses using summary association data were performed with respect to three cancer susceptibility phenotypes: overall breast cancer  as well as oestrogen receptor (ER)-positive  and ER-negative tumours . Due to the nature of summary-level data, the analyses of both LSI and CPD were conducted using all samples regardless of smoking status.R version 3.4.3 was used to conduct analyses. R package “Mendelian randomisation”, “mr_raps” and “MR-PRESSO” were used for two-sample MR analysis. All tests were considered at the 0.05 level of significance.P = 0.11 × 10–2) whereas little evidence was found for an association between wPGSs for CPD and invasive breast cancer  after adjustment for age and study (Model 1) (Table P = 0.60 × 10−2) (Table P = 0.39). The association between wPGSs for CPD and invasive breast cancer did not change after adjustments.There was an association of wPGS for LSI with increased invasive breast cancer risk  Table . AdjustmThere was no evidence for effect heterogeneity of the associations of LSI and CPD with breast cancer risk according to ER status or menopausal status  and breast cancers according to ER status  .39 We conducted association analyses of wPGS of smoking initiation and smoking cessation with breast cancer risk (see Supplementary Note) and found no statically significant association for either smoking initiation  or smoking cessation  ,.02,13 a P = 0.84)  status, potential confounding by alcohol consumption. We conducted an association analysis between wPGS of age at smoking initiation and invasive breast cancer (see Supplementary Note). The result showed an inverse association but statistically nonsignificant with low precision . We found no clear evidence of association between genetically predicted alcohol consumption and invasive breast cancer . The association remained unchanged after adjusting for LSI .Despite epidemiological evidence indicating alcohol consumption as an established risk factor for breast cancer risk,13 we cannot exclude that the lack of association observed in the MR analysis particularly regarding CPD may be due to limited power. The unit for LSI is not scaled due to the nature of the phenotype. The reported order of magnitude for associations with LSI can therefore not be directly compared with the relative risk estimates for smoking from observational studies.Despite the relatively large sample size used for our analyses, weak statistical power to detect modest associations should be considered when interpreting the results. The power calculation shows that our study had 68% power to detect an OR of 1.20 per SD change in LSI but only around 9% power for an OR of 1.05 in combined dataset (iCOGS and OncoArray)  to outcome (breast cancer risk). It can make the association between smoking and breast cancer risk appear weaker by removing a part of the association that is attributable to smoking initiation.The genetic instrument for LSI allows for the use of the large entire sample to conduct MR analysis without stratifying on smoking status. The analyses for CPD were restricted to smokers by reason that the CPD-associated SNPs were identified among ever-smokers in the GWAS study,Another limitation is that our analysis was restricted to participants of European ancestry; therefore, our results may not apply to populations of other ethnicities. However, it reduces the potential bias caused by population stratification.In conclusion, this Mendelian randomisation analysis using both individual-level data and summary statistics supports a causal association between lifetime smoking exposure and breast cancer risk. Larger studies for MR analysis are warranted to address additional aspects of smoking behaviour.Supplementary Materials


Prostate cancer (PCa) is the most commonly diagnosed cancer and the second leading cause of cancer-associated deaths in men in the USA. Glyoxalase 1 (GLO1) is an enzyme involved in energy metabolism in various tumor types including PCa. However, GLO1 expression has not been explored in the context of PCa progression with a focus on high-grade prostatic intraepithelial neoplasia (HGPIN), a frequent precursor to invasive cancer. Here, we have evaluated GLO1 expression by immunohistochemistry in tumor samples from a PCa patient cohort. Immunohistochemical analysis indicated that GLO1 is upregulated during tumor progression, observable in HGPIN and PCa as compared to normal prostatic tissue. Remarkably, GLO1 upregulation was identified as a hallmark of HGPIN lesions, displaying the highest staining intensity in all clinical patient specimens. Since current pathological assessment of HGPIN status solely depends on morphological features, GLO1 may serve as a novel diagnostic marker that identifies these precancerous lesions.Glyoxalase 1 (GLO1) is an enzyme involved in the detoxification of methylglyoxal (MG), a reactive oncometabolite formed in the context of energy metabolism as a result of high glycolytic flux. Prior clinical evidence has documented GLO1 upregulation in various tumor types including prostate cancer (PCa). However, GLO1 expression has not been explored in the context of PCa progression with a focus on high-grade prostatic intraepithelial neoplasia (HGPIN), a frequent precursor to invasive cancer. Here, we have evaluated GLO1 expression by immunohistochemistry in archival tumor samples from 187 PCa patients (stage 2 and 3). Immunohistochemical analysis revealed GLO1 upregulation during tumor progression, observable in HGPIN and PCa versus normal prostatic tissue. GLO1 upregulation was identified as a novel hallmark of HGPIN lesions, displaying the highest staining intensity in all clinical patient specimens. GLO1 expression correlated with intermediate–high risk Gleason grade but not with patient age, biochemical recurrence, or pathological stage. Our data identify upregulated GLO1 expression as a molecular hallmark of HGPIN lesions detectable by immunohistochemical analysis. Since current pathological assessment of HGPIN status solely depends on morphological features, GLO1 may serve as a novel diagnostic marker that identifies this precancerous lesion. Prostate cancer (PCa) is the most commonly diagnosed cancer and the second leading cause of cancer-associated deaths in men in the USA. It is estimated that in the year of 2021, a total of 248,530 new cases will be diagnosed and 34,130 of those men will succumb to the disease ; also, hPCa is a disease with a disproportionate burden afflicting the elderly, and risk prediction is crucial. Most patients present with low-risk, relatively indolent tumors; however, 20–30% of patients present with tumor characteristics associated with high-risk PCa, more likely to progress and relapse . In thisStandard diagnostic immunohistochemistry of PCa currently involves detection of a-methylacyl-CoA racemase  together with basal cell markers including p63 and high-molecular-weight cytokeratin 34βE12, HMWCK). However, the diagnostic value and correct interpretation of these markers require a distinct assessment of their morphological context . For exa, HMWCK. D-lactoylgluthatione. The resulting thioester, in the case of MG, is then hydrolyzed by GLO2 to produce D-lactate and reformed glutathione (GSH)  was employed ,36. SectTissue cores were excluded based on the occurrence of staining artifacts . Additional exclusion criteria included tissue cores with folds due to poor adherence to the tissue slide. The final analysis included 797 tissue cores from 170 patient cases . The majp < 0.05; *** p < 0.0005; **** p < 0.0001.GraphPad Prism 9.1.0 software  was used for statistical analysis. Tissue data sets were analyzed employing non-parametric analysis using the Mann–Whitney test or the Kruskal–Wallis test ; * In order to comprehensively profile GLO1 expression during tumorigenic progression, tissue specimens originating from PCa (stage 2 and stage 3) patients were analyzed in TMA format A,B. For Remarkably, out of 564 PCa cores, most displayed weak to moderate levels of GLO1 expression  and almost one-third of tissue cores demonstrated strong staining  . Taken tAmong these observations, pronounced GLO1 upregulation characteristic of HGPIN lesions is of particular interest given the role of HGPIN in PCa tumorigenesis. Morphological features of HGPIN as revealed by H&E staining include features such as a pronounced increase in nuclear size and chromatin as well as prominent nucleoli ,8,38. InNext, we evaluated GLO1 expression in adjacent normal and PCa cores from cases with either strong or weak–moderate GLO1 immunostaining status detectable in HGPIN lesions. We also assessed the frequency of normal and PCa tissue adjacent to HGPIN. Although the number of HGPIN cores with weak–moderate expression of GLO1 was considerably lower to those displaying strong GLO1 staining, the proportion of PCa in both categories remained the same (60% vs. 65%) A. Of notNext, we examined the potential association of GLO1 expression with clinical patient parameters. First, a potential relationship between Gleason grade and GLO1 expression status was examined A. IndeedPCa is a progressive malignant disease with poor treatment outcomes when detected at late stages. Even though highly informative molecular markers exist that allow clinical detection of advanced malignancy (for example racemase- and cytokeratin-based markers), there is an urgent need for improved molecular biomarkers with diagnostic value that will allow detection of early-stage disease. Indeed, the availability of a valid biomarker indicative of precancerous stages would be expected to change current intervention paradigms that interfere with progression of PCa. Here, we show for the first time that pronounced GLO1 immunostaining is a distinct characteristic of HGPIN lesions analyzed in representative stage 2 and stage 3 prostate tissue specimens embedded in TMAs . FurtherGLO1 upregulation in HGPIN lesions is of particular interest given the clinical role of HGPIN, widely considered as a premalignant state preceding PCa characterized by morphological features such as marked increase in nuclear size and chromatin, prominent nucleoli, and genomic instability ,8,38. CuReprogramming of energy metabolism has now been identified as a hallmark of tumorigenic progression, and changes in GLO1 expression observable in precancerous and cancerous lesions of PCa patients must be interpreted in this context ,43,44,45Additionally, it has been shown that even though normal prostatic epithelial cells rely on aerobic glycolysis (‘Warburg effect’) for energy production, reversion to mitochondrial OXPHOS-based energy metabolism during late stages of PCa progression has been documented . MoreoveIn summary, these data suggest that GLO1 may serve as a novel diagnostic biomarker of early PCa progression. Future research with inclusion of additional molecular parameters will substantiate the validity of GLO1 expression that selectively identifies HGPIN lesions in clinical samples and will also explore the mechanistic role of GLO1 as a causative effector in PCa progression that might also serve as a novel target for chemotherapeutic intervention at the early stage of the disease. In conclusion, we have identified GLO1 as a distinct molecular marker characteristic of early PCa development, as substantiated by high expression levels observable in HGPIN. Given its crucial role in regulation of glycolytic energy metabolism, GLO1 association with low to intermediate Gleason grade may be indicative of an early oncometabolic adaptation in PCa. Taken together, our data suggest that GLO1 may serve as a novel diagnostic marker for HGPIN detection during early PCa progression. Our ongoing research aims at substantiating the biomarker role of GLO1 that might also serve as a novel molecular target in PCa tumorigenesis.