It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation  and conduct association analyses with PrCa risk . We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes. Genome wide association studies have identified multiple loci associated with risk of developing prostate cancer but the functional significance of many of these are unknown. Here, after generating models to predict methylation, the authors identify CpG methylation sites associated with prostate cancer. Its survival rate is relatively high for localized stage disease, but decreases substantially for metastatic disease2. Effective strategies are critical for risk assessment, screening, and early detection of PrCa, aimed at decreasing its public health burden. Although prostate-specific antigen (PSA) has demonstrated efficacy for detecting PrCa early4, there lacks a clear cutoff point for PSA with high sensitivity and specificity7. The benefits of PSA screening for reducing PrCa mortality remains controversial10. Furthermore, there are adverse effects, such as overdiagnosis11. Therefore, additional effective biomarkers are needed for risk assessment and early detection of PrCa.Prostate cancer (PrCa) is the second most frequently diagnosed malignancy among men and the fifth leading cause of cancer death worldwide12, research has identified several methylation markers to be potentially associated with PrCa risk, such as methylation at GSTP1, CDKN2A, DNMT3B, SCGB3A1, and HIF3A16. However, most prior studies have assessed only a couple of candidates. Recent emerging studies profiling genome-wide methylation usually included a relatively small number of subjects17, resulting in inadequate power for the identification of associated methylation biomarkers. Besides these limitations, there are a number of biases commonly encountered in conventional epidemiologic studies, including selection bias, uncontrolled confounding, and reverse causation, that make it difficult to determine whether the identified associated markers are causally associated with PrCa.Aligned with findings of a crucial role for DNA methylation in PrCa development19. Genome-wide association studies (GWAS) have also identified a large number of genetic loci associated with DNA methylation levels21. Many of these genetic variants could potentially serve as strong instrumental variables for evaluating associations between DNA methylation and PrCa risk in an adequately powered study.One strategy to reduce some of these biases is to use genetic variants to develop an instrument to assess the association between DNA methylation and PrCa. Such an approach is based on the principle of the random assortment of alleles from parents to offspring during gamete formation, and thus a genetically determined proportion of DNA methylation levels should be less susceptible to selection bias and reverse causation in principal. Research has shown that a large portion of CpG sites have high heritability23. To date, GWAS have identified ~150 genetic loci for PrCa26. However, together these variants explain <30% of the familial relative risk, and the underlying biological mechanisms for a majority of the identified loci remain unclear24. Recently, we performed a large transcriptome-wide association study (TWAS) of PrCa, in which we identified multiple associations between genetically predicted gene expression and PrCa risk27. Interestingly, many of the associated genes were identified to be candidate target genes of GWAS-identified risk SNPs27. Aligned with the recognized role of DNA methylation in regulating gene expression, we hypothesize that some GWAS-identified risk SNPs may regulate expression of their target genes through influencing DNA methylation levels. In this study, we perform a large integrative multi-omics analysis involving data of genomics, methylomics, and transcriptomics aiming to uncover novel CpG sites and genes that may contribute to PrCa development.Besides a potential utility in improving PrCa risk assessment, the identification of promising DNA methylation markers using a design of genetic instruments may also contribute to understanding of the genetics and etiology of PrCa. Epidemiological research provides strong support for a genetic predisposition to PrCaR2) of at least 0.01 (≥10% correlation between predicted and measured DNA methylation levels). For 77,243 of those CpG sites, there were no SNPs within the binding site. Interestingly, there tended to be positive weak correlations between methylation prediction model performance and number of input variants within the 2-MB window of each CpG site . We further applied these 77,243 models to the genetic data in WHI and evaluated their performance by comparing predicted methylation levels with measured levels. Overall, DNA methylation that could be predicted well in FHS also tended to be predicted well in WHI   (Supplementary Table P = 1.13 × 10−9) .Through an annotation of the 759 PrCa-associated CpG sites using eFORGE v1.2, there tends to be an overlap of their positions with regions containing lysine 4 mono-methylated H3 histone (H3K4me1) markers across 38 of 39 cell types included in the consolidated Roadmap Epigenomics Project, including blood tissues  also showed an association at P < 0.05 (Supplementary Table n = 34), we observed that 26 of the 37 associations that could be assessed showed the same direction of effect compared with that in the blood tissue  < 0.05 were observed for methylation levels of 42 CpG sites with expression of 28 neighbor genes in blood tissue (Supplementary Table R2) of at least 0.01 (≥10% correlation). Using the S-PrediXcan method, we evaluated associations between the genetically predicted expression of these genes and PrCa risk. Of the 22 genes with blood tissue prediction models built, 18 demonstrated an association at FDR < 0.05 , and C11orf21 did not show a significant association (P = 0.21).Of the 28 potential target genes of the identified CpG sites based on blood tissue analyses, blood tissue gene expression prediction models were built for 22 genes, and prostate tissue prediction models were built for 14 genes with a prediction performance . There was an inverse association between genetically predicted expression of MDM4 and PrCa risk . There was also evidence supporting the genetically predicted DNA methylation of cg20240347 to be associated with a decreased PrCa risk . Interestingly, MDM4 has been previously implicated as a potential target gene that is responsible for the identified association signal of index SNP rs4245739 in GWAS25, and in our recent TWAS study27. Our results highlight a possible role of the CpG site cg20240347 in the underlying biological mechanism of the link between MDM4 and PrCa. Whether the DNA methylation of these CpG sites at the corresponding loci of the genes in Table 28 suggested potential enrichment of cancer-related functions for the 14 implicated genes (Supplementary Table P = 0.033) and cancer drug resistance (P = 0.039). It is worth noting that based on the predicted DNA methylation–PrCa risk, DNA methylation–gene expression, and predicted gene expression–PrCa risk results, we also observed six CpG sites and four genes  with inconsistent directions of association for the DNA methylation–gene expression–PrCa pathway  demonstrated an association with PrCa risk in recent TWAS studies30. Furthermore, MDM4 and NUCKS1 have been previously implicated as potential target genes at GWAS-identified PrCa risk loci32. Our results incorporating DNA methylation provide additional insight into the potential mechanism for the link between these genes and PrCa development. Interestingly, in vitro experiments of silencing PDK1 could decrease cell proliferation and inhibit the invasion and migration capability of PrCa cells33. Further functional studies are needed to better characterize whether there are potential regulatory effects of the identified 25 CpG sites on the expression of the 14 adjacent genes for PrCa development. Importantly, our design of integrating genome, methylome, and transcriptome data provides some evidence that 25 CpG sites may regulate expression of 14 candidate target genes, which further influences PrCa risk. Through the innovative integrative analyses harnessing large-scale human subject data, our study not only identifies several associations consistent with prior findings but it also uncovers potentially important roles of novel CpG sites and putative target genes  in prostate tumorigenesis.For processing DNA methylation data for genetic model building, we performed quartile normalization for subjects followed by rank normalization for methylation levels, a standard approach widely used in the community for DNA methylation analyses34. Furthermore, it is challenging to obtain prostate tissues from a large number of healthy individuals. Although prostate tumor-adjacent normal tissue methylation data are available in TCGA, tumor-adjacent normal tissue samples from PrCa patients may contain cancer cells; therefore, the methylation profile of these samples could be different from that of normal prostate tissue samples from healthy men. The statistical power for the model building using TCGA data could also be low due to the relatively small sample size available. In this study, for assessing DNA methylation–gene expression associations to determine potential target genes of identified CpG sites, besides using data from blood tissue  in the reference FHS dataset was used for the DNA methylation prediction model building. Aligned with the huge sample size for our main association analyses for PrCa risk , our study provides an unparalleled opportunity to detect the DNA methylation–PrCa associations. The use of genetic instruments rendered our study as potentially less susceptible to several limitations commonly encountered in conventional epidemiological studies, such as selection bias and reverse causation. On the other hand, it is worth noting that similar to TWAS, the associations observed in our analyses focusing on CpG sites are also vulnerable to confounding due to pleiotropy and co-localization of genetic signals. For instance, it would be difficult to distinguish a situation in which one causal methylation quantitative trait locus (mQTL) regulates the methylation of two CpG sites from a scenario in which two CpG sites have two causal mQTLs that are in linkage disequilibrium (LD) with each other. Correlated total methylation levels across CpG sites, correlated predicted DNA methylation across CpG sites, as well as shared genetic variants between DNA methylation genetic prediction models and gene expression prediction models, could all lead to spurious associations in our analyses35. When faced with two correlated predictors, regularized regression models like elastic net will randomly down weight one of them, which may be the true causal variant. Despite these potential limitations, our study generated a list of promising PrCa-associated CpG sites that warrant further investigation. By integrating the relationship between DNA methylation, gene expression, and PrCa risk using multi-omics data from different sources, we were able to identify consistent associations of the DNA methylation–gene expression–PrCa risk pathway. This supports a very interesting hypothesis that methylation at selected CpG sites could influence PrCa risk through the regulation of expression of adjacent target genes, which warrants further investigation. The current work generates a list of promising CpG sites showing an association with PrCa, which can be investigated further in future studies that directly measure levels of these CpG sites. Identification of circulating DNA methylation biomarkers could be useful for PrCa risk assessment.In the current work, a large number of subjects , to identify potential target genes of these CpG sites. For the suggested candidate target genes, we further assessed associations of their genetically predicted expression with PrCa risk.The overall study design is shown in Fig. 36. In brief, DNA was genotyped using the Affymetrix 500 K array, and DNA methylation was profiled using the Illumina HumanMethylation450 BeadChip. The genotype data were imputed to the Haplotype Reference Consortium reference panel37. SNPs with high imputation quality (R2 ≥ 0.8), minor allele frequency ≥0.05, included in the HapMap Phase 2 version, and those that were not strand ambiguous were used to build DNA methylation prediction models. For DNA methylation data, the “minfi” package38 was used to filter out low-quality samples, exclude low-quality methylation probes, estimate cell-type composition, and calculate methylation beta values. We performed quantile normalization to bring the methylation profile of each sample to the same scale, and rank normalization for each CpG site to map each set of DNA methylation values to a standard normal. We adjusted for age, sex, six cell-type composition variables, and the top ten principal components (PCs) derived from genotype data. Genetic and DNA methylation data from 1595 genetically unrelated subjects of European descent were used to build DNA methylation prediction models for this study.We obtained the individual level genome-wide genotyping and white blood cell DNA methylation data from the FHS Offspring Cohort (dbGaP accession numbers: phs000342 and phs000724). The details of the FHS Offspring Cohort have been described elsewhereα = 0.541  . Genotype data and white blood cell DNA methylation data were processed using a similar approach, as described above. The predicted DNA methylation for each CpG site was calculated using the models that were established using FHS data, and then compared with the measured level using Spearman’s correlation.R2) in FHS and WHI, we included DNA methylation prediction models (1) with a R2 ≥ 0.01 (≥10% correlation between predicted and measured methylation levels) in FHS, a standard criterion used in TWAS for gene expression44, heritability of which tends to be similar to that of DNA methylation in blood45, and (2) for probes with no SNPs within the probe-binding site, considering that the measurement of DNA methylation levels for such probes tends to be unbiased46. Overall, we evaluated associations between genetically predicted methylation levels of 77,243 CpG sites with PrCa risk.Considering that our model external validation dataset WHI included females only, and that there is a high concordance of the model performance . Also included were data from several previous PrCa GWAS of European ancestry: UK stage 1 and stage 2, CaPS 1 and CaPS 2, BPC3, NCI PEGASUS, and iCOGS. These genotype data were imputed using the June 2014 release of the 1000 Genomes Project data as reference. Logistic regression summary statistics were then meta-analyzed using an inverse variance fixed effect approach.We estimated the association between genetically predicted DNA methylation levels and PrCa risk using S-PrediXcan, which has been described elsewhereP < 6.47 × 10−7  was used to determine a statistically significant association. For CpG sites showing a significant association between genetically predicted methylation levels with PrCa risk, we further evaluated whether the observed associations were independent of nearby PrCa risk variants identified in GWAS or fine-mapping studies, by performing GCTA-COJO analysis49. For this analysis, the risk SNP showing the most significant association with PrCa risk in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia was adjusted for calculating association betas and standard errors of DNA methylation predicting SNPs with PrCa risk. These association statistics were then used for re-running the S-PrediXcan analyses.A Bonferroni-corrected threshold of 50 to evaluate the proportion of familial relative risk of PrCa that could be explained by predicted methylation levels of these CpG sites. In brief, we firstly applied the prediction models of these CpGs to the genetic data of male controls included in the pancreatic cancer GWAS data (N = 3655) to generate the predicted methylation of these CpGs for each of the participants. Detailed information for this dataset, quality control, and imputation has been described elsewhere51. We further used the formula Z2 = 1 + (NTl/M)/2 to estimate the heritability explained by these CpG sites. Here for each CpG, Z represents the Z score of the association between the predicted methylation and PrCa risk; NT represents the number of individuals included in the GWAS of the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia, namely, 140,306; l represents the LD score of the CpG of interest; M represents the number of CpG sites that were significantly associated with PrCa risk; and 2 is the estimated heritability of PrCa risk that could be explained by the predicted methylation of the CpG sites of interest. The LD score for each CpG was estimated by adding up the squared Pearson correlation coefficient (R2) of the CpG of interest with all the other CpG sites. Finally, after fitting a linear regression model using data of all these CpGs, the estimated heritability of PrCa risk that could be explained by the predicted methylation of the CpGs of interest, along with the standard error and P value, were estimated. Given that the heritability of PrCa was estimated to be 57%52, the familial relative risk of PrCa that could be explained by predicted methylation levels of these CpGs was calculated as ℎ2/0.57.For PrCa-associated CpG sites that were located at novel loci or independent from known PrCa risk variants, we used the linkage disequilibrium (LD) score regression method53. PrCa cases were determined by combining Hospital Episode Statistics (HES) data and self-reported data. Specifically, cases were defined as hospital admission, type of cancer, or cause of death due to ICD-9 185.9 or ICD-10 C61 or a self-reported cancer code. We calculated associations of genetically predicted DNA methylation of the identified CpG sites with PrCa risk, adjusting for age, age2, and top 20 PCs provided by the UK Biobank. As the number of cases in the UK Biobank is substantially smaller than that in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia, we used results from the UK Biobank to confirm the validity of the CpG sites identified in analyses of the consortia data, instead of using their results to filter out CpG sites.Individual level data of the UK Biobank were used to validate the identified associated CpG sites. The UK Biobank released GWAS data on ~500,000 individuals54. The CpG sites were annotated into one of 13 functional categories, including exonic, intronic, intergenic, upstream, 3′-UTR, 5′-UTR, ncRNA intronic, ncRNA exonic, splicing, downstream, upstream/downstream, 5′-UTR/3′-UTR, and exonic/splicing. We used eFORGE55 v1.2 to assess whether the identified CpG sites were enriched in DNase I hypersensitive sites (DHSs) and loci overlapping with various histone modification types, such as H3K27me3, H3K36me3, H3K4me3, H3K9me3, and H3K4me1, across different tissues and cell lines available in the Roadmap Epigenomics Project56, the Encyclopedia of DNA Elements (ENCODE)57 and the BLUPRINT Epigenome58. For each CpG site set of interest, eFORGE performs an overlap analysis against the functional elements for each tissue or cell line separately, and then counts the number of overlaps. A background distribution of the expected overlap counts for the CpG site set of interest is obtained by picking sets of CpG sites with the same number as the test set, matched for gene relationship and CpG island relationship annotation. The matched background sets are then overlapped with the functional elements and the background distribution of overlaps are determined. 1000 matched sets are used. The enrichment value for the test set is expressed as the -log10. Enrichments outside the nominal 95th and 99th percentile of the binomial distribution (after Benjamini–Yekutieli multiple testing correction) are considered significant. We also evaluated whether the associated CpG sites were enriched in loci of genes encoding transcription factors59.We annotated the position and genomic region information of the identified PrCa-associated CpG sites through ANNOVAR61. Overall, DNA methylation and gene expression data were available for 1367 unrelated individuals. For the CpG sites showing a significant association with PrCa risk, associations between the normalized methylation levels in beta values and normalized expression levels of genes flanking the CpG sites were estimated, after adjusting for age, sex, top PCs, and estimated cell-type compositions based on methylation data. We further assessed significant methylation–gene expression associations identified in blood tissue analyses in adjacent normal prostate tissue of PrCa patients in the TCGA (N = 34). The processing of DNA methylation and gene expression data has been described elsewhere63.For CpG sites with genetically predicted DNA methylation levels significantly associated with PrCa risk, we evaluated associations between methylation and expression levels of genes flanking their loci by using data from the FHS Offspring Cohort (dbGaP accession numbers: phs000363 and phs000724) and The Cancer Genome Atlas (TCGA). Details of the FHS Offspring Cohort, DNA methylation, and gene expression data have been described elsewhere65. We used prediction models developed using the PrediXcan method (Elastic Net) and leveraging data from the v8 version of the Genotype-Tissue Expression dataset (GTEx) project (http://predictdb.org/). Details of the methods of building gene expression prediction models using SNPs have been described elsewhere66. The prediction models were used to estimate the associations between genetically predicted gene expression levels and PrCa risk in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia using S-PrediXcan47.For genes whose expression levels were associated with DNA methylation levels, we assessed whether the genetically predicted expression levels of these genes in blood and prostate tissue were also associated with PrCa riskWe assessed the associations between genetically predicted DNA methylation levels and PrCa risk, associations between DNA methylation and gene expression levels, and the associations between genetically predicted gene expression and PrCa risk to assess associations showing consistent direction of effect for the DNA methylation–gene expression–PrCa risk pathway. This could indicate the possibility that genetically predicted DNA methylation might putatively influence PrCa risk through the regulation of expression of flanking target genes.28.We performed functional enrichment analysis for the identified genes consistent with the DNA methylation–gene expression–PrCa risk pathway. Canonical pathways, top associated diseases and biofunctions, and top networks associated with these genes were estimated using IPA softwareFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Software 1Reporting Summary


Due to the combined effect of the materials formulations and sensing mechanisms, changing the anion from [DCA]− to the much more hygroscopic [Cl]−, leads to stronger electrical responses and much weaker optical responses to humidity. Thus, either humidity sensors or humidity-tolerant VOC sensors that do not require sample preconditioning or signal processing to correct humidity impact are obtained. With the wide spread of 3D- and 4D-printing and intelligent devices, the monitoring and tuning of humidity in sustainable biobased materials offers excellent opportunities in e-nose sensing arrays and wearable devices compatible with operation at room conditions.Relative humidity is simultaneously a sensing target and a contaminant in gas and volatile organic compound (VOC) sensing systems, where strategies to control humidity interference are required. An unmet challenge is the creation of gas-sensitive materials where the response to humidity is controlled by the material itself. Here, humidity effects are controlled through the design of gelatin formulations in ionic liquids without and with liquid crystals as electrical and optical sensors, respectively. In this design, the anions [DCA] In these examples, humidity is a useful bio-marker that needs monitoring at ambient and physiological conditions. However, humidity is often seen as an undesirable confounding factor in sensing of gases and volatile organic compounds (VOCs). Diseases as diabetes and cancer are associated with the presence of specific VOC bio-markers in exhaled air.,4 As such, one of the most challenging goals in gas sensing and artificial olfaction is the detection and measurement of VOCs in ambient conditions where humidity is ubiquitous. Metal oxide semiconductors (MOS) and conducting polymers, the most standardized and used gas sensing materials, are greatly affected by humidity as it causes signal variations due to water cross-reactivity.–8 To avoid humidity interference, gas sensors and electronic noses (e-nose systems) employ several mitigation measures, namely the use of nitrogen or dry air as a sample carrier gas,,10 sample preconditioning chambers to control humidity content of the sample before sensor delivery, coating sensing materials with hydrophobic layers, or the employment of algorithms to correct the sensor’s signals based on humidity conditions.,13 Another option lies in the search for alternative gas sensing materials where humidity monitoring and interference can be easily tailored and controlled.Humidity is ubiquitous, as water is part of all systems on Earth, from soils and air to animals and humans. Monitoring relative humidity (RH) is relevant in a wide variety of fields, including industrial processing and manufacturing, environment and agriculture, and biometric and medical applications. In gas sensing, ionogels are also considered ideal sensors due to their stability in ambient conditions and tunable selectivity conferred by the ionic liquid.,16 Ionic liquids play a key role in these properties due to their intrinsic high chemical diversity, low melting points, high chemical and thermal stability, very low volatility, intrinsic ionic conductivity, wide electrochemical window, high viscosity, high polarity, and good solvation properties.Ionogels and derived materials represent a versatile class of transparent, flexible, mechanically stable, and durable sensors being considered excellent ionic skins as central components in wearable electronics, human-machine systems, implantable devices and soft robotics.− plays a dominant role on the interactions with water.,19 Thus, we hypothesized that the effect of humidity in gas sensing materials containing ionic liquids with the 1-butyl-3-methylimidazolium ([BMIM]+) cation could be tailored by the choice of the ionic liquid anion. The anions dicyanamide ([DCA]−) and chloride ([Cl]−) were chosen due to their wide availability and different hydrophilicity and hydrogen bonding acceptor properties.–21 To show the effect of the anion, in this work, we used gelatin formulations in ionic liquid, named as ionomaterials, and gelatin formulations in ionic liquid-incorporating liquid crystal  compartments, named as hybrid materials ; water-mediated H-bonds (with and without the contribution of Hyp hydroxy groups); and less conventional Cα-H⋯O═C between neighbour strands. Water is also considered an intrinsic component of collagen by creating ordered water networks between neighbouring triple helices.Gelatin is a well-known gelling biopolymer with a typical repetitive sequence Ala-Gly-Pro-Arg-Gly-Glu-4Hyp-Gly-Pro, where Hyp is hydroxyproline. In this work, the imidazolium-based ionic liquids [BMIM][DCA] and [BMIM][Cl] were studied to yield gelatin-ionomaterials. When cooled down, the gelatin/[BMIM][DCA]/water solution gave rise to self-supporting gels while gelatin/[BMIM][Cl]/water formulation remained a viscous fluid , representing C═O stretching of imide residues hydrogen bonded with water, is shifted to higher wavenumbers (1645–1647 cm−1), close to the vibration frequency of C═O groups in random coils  and weak interactions with Pro. The ionic liquid anions can also establish ionic and electrostatic interactions with positively charged amino acid residues as Arg. However, these interactions alone cannot justify the difference between [BMIM][DCA] (gels) and [BMIM][Cl] (viscous fluids) ionomaterials, as these anions differ in volume, hydrogen bonding ability, and affinity to water. Several authors have shown that ionic liquid anions in particular have great impact on gelatin gelation.,26,31 It is known that the [Cl]− anion is hydrophilic and establishes very strong hydrogen bonds with water, acquiring a dense hydration layer of around 6 water molecules retained by strong O–H⋯Cl− hydrogen bonds. The [Cl]− anion is a stronger hydrogen bond acceptor than [DCA]−: according to the empirical polarity Kamlet-Taff parameters, the hydrogen bond acceptor ability (β) of [Cl]− is 0.95 while that of [DCA]− is 0.64, when associated with the [BMIM]+ cation. With this evidence, and in line with previous reports, the [Cl]− anion will sequester water molecules contributing to the formation of water-rich and water-depleted compartments in the material, hence further reducing the amount of water molecules to mediate hydrogen bonds between polypeptide chains. In addition, as a strong H-bond acceptor, the [Cl]− anion is known to break interstrand hydrogen bonds between neighbor backbone polypeptide chains. As a result, the lack of intermolecular bonding and the presence of water-depleted regions in the matrix limits the cohesion of the matrix, yielding the observable viscous iono-materials. Regarding the dicyanamide anion [DCA]−, it is considered less hydrophilic than [Cl]−, therefore establishing less interactions with water which becomes more available to interact with polypeptide chains. [DCA]− has a boomerang-like structure, with a permanent dipole and varied resonance structures, being often used as a bridge ligand to form cross-linked and organized structures.,36 So, physical crosslinking in [DCA]− ionomaterials likely results from a combination of ionic and electrostatic interactions, dipole–dipole (for example with Gly) and hydrogen bonds with hydrogen bond donor groups in gelatin. Taken together, the properties of dicyanamide anion [DCA]− promote physical cross-linking between gelatin chains and yield mechanically robust ionogels.The [BMIM][DCA] and [BMIM][Cl] ionomaterials have the reduction of water content in common, when comparing with the control hydrogel. It has been proposed by several authors that physical crosslinking in gelatin ionomaterials results from hydrogen bonds, ionic and electrostatic interactions between gelatin polypeptide chains and ionic liquid ions. Due to the ionic conductivity of the ionomaterials . The films were stored under controlled humidity (around 50-60% RH) during a maximum period of 1 week before being introduced in the sensors’ chamber and subjected to the cycles of RH variation. Prior to the first cycle, the sensors’ electrical signal was equilibrated during 15 minutes at 0% RH. Controlled levels of RH were generated using a nitrogen stream and a bubbler system with different saturated salt solutions  mass gain upon environment humidification (0% to 70% RH) and a 26% (±13%) mass loss upon subsequent drying to 0% RH. [BMIM][DCA] ionomaterials practically did not suffer mass changes detectable with the methodology used (in the order of 5%)  of both sensor films  linearly increases with the increase of RH between 30–70% RH values , which has no implications in humidity sensing, as the signal-RH conversion function can be corrected to have a zero value to 0% RH.The relative electrical response  and humidified (50% RH) conditions was further studied by exposing the ionomaterial thin films to vapors of four distinct model solvents  between the relative electrical response of [BMIM][DCA]- and [BMIM][Cl]-based ionomaterials and ethanol concentration (0.3–4.4%(v/v)) for both 0% and 50% RH ), the relative responses were approximately 3 times higher than those of [BMIM][Cl]-based ionomaterials (R2 = 0.94 to 2R = 0.99) within the tested VOCs concentrations (0.3–6.4%(v/v)) for the two studied ionomaterial formulations, for [BMIM][DCA] ionomaterials saturation was observed at 5.4%(v/v). Additionally, acetone detection limit was determined for [BMIM][DCA] ionomaterial thin film at 50% RH, 0.6%(v/v) , different results were obtained. Hexane did not change the relative electrical response of both ionomaterials, as there were no differences between the blank and hexane signals  in the range of tested concentrations (0.1–1.8%(v/v)). On the other hand, the relative response of [BMIM][Cl] ionomaterial thin films stabilized, showing saturation for 0.1%(v/v) . This is a very fast response compared to that reported recently metal oxide gas sensors, that can range between 30 and 80 s or even take a few minutes to respond to ethanol.The response times of our electrical system to VOCs are in the order of 0.02–5 s, which are comparable to other works performed with different gelatin-based ionomaterials,2.3 Hybrid materials have mechanical properties identical to the corresponding ionomaterials: the [BMIM][DCA] hybrid material yields a self-supporting material whereas the [BMIM][Cl]-based formulation yields viscous materials  embedded in the ionomaterial matrix .l matrix . When thl matrix , which aguration , we can − and [DCA]− when interacting with the [BMIM]+ imidazolium headgroup. The nitrile and chloride anions have similar ionic radius . As [DCA]− contains two nitrile groups, it may cause steric hindrance at the liquid crystal droplet interface, thus promoting the assembly of larger droplets (33 ± 8 μm) than those formed with [Cl]− (13 ± 4 μm). Due to these constraints, the hybrid materials made with [BMIM][DCA] usually exhibit a smaller droplet population (2 droplets/100 μm2) than those made with [BMIM][Cl] (19 droplets/100 μm2).The distinct composition of the droplet interface in [BMIM] [DCA] and [BMIM][Cl] hybrid materials had an impact on the droplets’ diameter, likely due to the different physical constraints created by [Cl],50,51 and in SI  is shown in R2 = 0.996) (R2 = 0.997), reaching 40 s for the 0–80% RH change   while thTo understand the differences observed between the two hybrid materials, the changes in liquid crystal order and in the gelatin ionogel matrix during a humidification and drying cycle were monitored by POM . The int−,,52 water molecules associated with [DCA]− anions at the droplets interfaces easily desorb during drying. We hypothesize that, in the absence of water, most [BMIM][DCA] molecules become miscible with 5CB and consequently enter the droplets as an impurity thus lowering the clearing temperature of the liquid crystal and driving 5CB to isotropization. It is also plausible that some [BMIM][DCA] molecules migrate to the gelatin matrix. In both scenarios, the ionic liquid interface is disrupted and a partial leakage of isotropic liquid crystal from the droplets to the matrix (2) ((iv) in Our POM investigations showed that humidification of [BMIM][DCA] hybrid material films above room conditions (ranging from roughly 60% RH to 80% RH) has a minimal impact on the liquid crystal droplets morphology , leadinge matrix , the ion,53 and the [Cl]− anion is a strong hydrogen bond acceptor, the droplet interface facing the matrix is probably stabilized by a hydration shell strongly bound to [Cl]− anions. Thus, the optical signal collected from [BMIM][Cl] hybrid films in the signal acquisition device  and drying (20% RH) periods, the liquid crystal droplets preserve the radial configuration . Despiten device , is pracSimilar to the electrical signals , the optAs a conclusion, the optical changes in [BMIM][DCA] hybrid films to humidity are predominantly driven by the liquid crystal ordering and phase transitions due to droplet interface instability. In [BMIM][Cl] hybrid films the existence of a stable hydration layer at the droplets surface results in a response profile to varying RH limited by the kinetics of water sorption-desorption and therefore is less intense and much slower. Matrix swelling and contraction occur in both hybrid films  for both [BMIM][DCA] (R2 = 0.992) and [BMIM][Cl] (R2 = 0.999) hybrid materials at 50% RH.  with the alkyl chains of either [BMIM]+ or 5CB, which might require higher concentration of VOC.Acetone, toluene, and hexane have distinct polarity characters. Due to the "domino effect" that characterizes the propagation of ordering perturbations through liquid crystal molecules, these VOCs cause a pronounced increase in the relative response of both sensor formulations after a certain triggering concentration, likely at which the number of VOC molecules becomes sufficient to reach the droplets interface and trigger the phase transition of more and more liquid crystal droplets (The lowest detection limit was observed for toluene (0.53% (v/v)) with [BMIM][Cl] hybrid materials at 50% RH. The lower saturation limit was also observed for toluene, between 1.4 and 1.8% (v/v) for both hybrid material formulations. Hexane was the VOC detected up to larger concentrations (7–8% (v/v)) with the two hybrid material formulations.  compositions with the selective reflection band in a visible spectral range for optical detection of various volatiles. It is a multi-element array and each element of the array is a droplet of CLC with a different composition. In the case of toluene vapours isotropization occurs after 3–4 min of exposure and in the case of ethanol isotropization starts only after 40 min of exposure.The response times of our optical systems to VOCs are in the order of 3–5 s, which in fact are faster than most reported examples using liquid crystals.3− and [Cl]− of room temperature ionic liquids from the 1-butyl-3-methylimidazolium family, tailor the effect of humidity in the materials properties. We obtain either humidity-sensitive or humidity-tolerant VOC sensors that do not require sample preconditioning or signal processing for correction of humidity impact.An unmet challenge in artificial olfaction is the ability to develop gas sensors where the response to humidity is tuned and controlled by the sensing material itself. Here, this challenge is tackled with a simple design approach. Humidity sensing and humidity interference in VOC sensing is controlled through the design of gelatin-based ionomaterials and hybrid materials as electrical and optical sensors. In this design, the anions [DCA]− anion and water molecules. For this same reason, higher VOC sensing responses were obtained under dried environments (0% RH) as VOC interaction with ionic liquid cation or anion were weaker than those established with water. Regardless of the ionic liquid, the ionomaterial thin films’ sensing response increases with the increase of VOC molecules polarity.The electrical signal of gelatin ionomaterials thin films to the presence of gaseous analytes is associated with changes in ionic conductivity. Both [BMIM][DCA] and [BMIM][Cl] ionomaterial thin films are linearly responsive to increasing variations of RH (between 30–70% RH) due to the intrinsic hydrophilicity of the matrix as water increases the mobility of charge carriers. However, [BMIM][Cl] ionomaterials yield more intense responses (2 order of magnitude higher) that did not stabilize in the time course of the experiments due to strong hydrogen bond interactions between the [Cl]− anion increases sensitivity to humidity for the electrical response, for the optical signal, the reverse is observed. The [Cl]− anion stabilizes the droplet interface and prevents water sorption and desorption from disturbing the ordered liquid crystal molecules, thus blocking humidity sensing properties. Thus, [BMIM][Cl] hybrid material thin films can detect and quantify all four tested VOCs in dry or room (50% RH) conditions, which represents a competitive advantage regarding most gas-sensor types, that require signal correction for cross-reactivity in humid environments. The less hydrophilic [DCA]− anion, on the other hand, creates a more unstable droplet interface. When the materials are dry, the liquid crystal becomes isotropic, which hampers optical VOC sensing in dry environments with [BMIM][DCA] hybrid films.The optical signal of the hybrid materials depends on the ordering and phase transitions of the liquid crystal molecules encapsulated by the ionic liquid in droplets. While the presence of the highly hydrophilic [Cl]The performance of hybrid materials as optical VOC sensors complements the performance of the corresponding ionomaterials electrical VOC sensors, as for example the ionomaterials could not detect hexane while the hybrid materials did. and there is in fact a lack of sensing solutions for environments with higher VOC concentrations. The presented ionomaterials and hybrid materials could fill this gap, as they operate in the high ppm to percent VOC concentration ranges. These environments are found in headspace samples from fermentation processes and quality control of alcoholic beverages or hand sanitizers, that could benefit from the use of non-invasive rapid or on-line sensing solutions.In the future, the response function of both electrical and optical sensors for different VOCs at distinct RH values can be determined for well-defined applications and respective target VOCs. Typically, gas sensors operate in a wide range from ppb (or even ppt) to ppm VOC concentrations. For applications where VOC concentrations are higher, most current gas sensors require a sample dilution step,,45 or liquid crystals.Moreover, our electrical and optical systems presented lower response times to VOCs when compared with other reported sensors such as metal oxide sensorsThis work has established the design rules for the development of synergetic dual electro-optical transduction for humidity and VOC sensing. The simple variation of the ionic liquid anion in the formulation of the materials directly reflects in the performance of electrical and optical sensing of dry or humid samples, as well as in changes of VOC selectivity. There are innumerable ionic liquid compositions commercially available, and such a diversity extends the impact of the concept presented in this work, as tuning the anion and cation can give further improvements for sensing applications. The dual-mode opto-electrical sensing properties of the materials can be further explored regarding the complementarity of the signals and versatility to adapt to the sample conditions, offering potential to design sensor devices compatible with operation at room conditions, which are promising for innovative applications in gas sensing and artificial olfaction, namely when envisaging wearable and bionic devices.Supporting InformationSupporting Information is available from the Wiley Online Library or from the author.


Copy number alterations (CNAs) can promote tumor progression by altering gene expression levels. Due to transcriptional adaptive mechanisms, however, CNAs do not always translate proportionally into altered expression levels. By reanalyzing >34,000 gene expression profiles, we reveal the degree of transcriptional adaptation to CNAs in a genome-wide fashion, which strongly associate with distinct biological processes. We then develop a platform-independent method—transcriptional adaptation to CNA profiling (TACNA profiling)—that extracts the transcriptional effects of CNAs from gene expression profiles without requiring paired CNA profiles. By applying TACNA profiling to >28,000 patient-derived tumor samples we define the landscape of transcriptional effects of CNAs. The utility of this landscape is demonstrated by the identification of four genes that are predicted to be involved in tumor immune evasion when transcriptionally affected by CNAs. In conclusion, we provide a novel tool to gain insight into how CNAs drive tumor behavior via altered expression levels. Copy number alterations (CNAs) can drive tumor progression in cancer by altering gene expression levels, but transcriptional adaption can skew CNA impact. Here, the authors present transcriptional adaptation to CNA (TACNA) profiling; a tool to extract the transcriptional effect of CNAs from expression data without requiring paired CNA profiles. Genomic instability can result in the accumulation of structural chromosome aberrancies, including copy-number alterations (CNAs). CNAs are important genomic events in the progressive molecular rewiring that takes place during tumor cell evolution2.The genomic composition of a tumor results from defects in genome maintenance, leading to the genomic instability that characterizes many cancers5. Due to transcriptional adaptive mechanisms, however, changes in gene copy number at the genomic level do not always translate proportionally into altered gene expression levels . TACNA profiling extracts the effects of CNAs on gene expression levels from a single gene expression profile—generated from a tumor biopsy—without the need for a paired genomic CNA profile. Using this method, we define the landscape of transcriptional effects of CNAs in >34,000 expression profiles. To demonstrate this landscape’s utility, we determine which genes and biological pathways affected by CNAs might be associated with tumor-infiltrating immune cell composition. These insights could ultimately contribute to the development of new therapeutic strategies.n = 21,592, generated with Affymetrix HG-U133 Plus 2.0), The Cancer Genome Atlas , Cancer Cell Line Encyclopedia , and Genomics of Drug Sensitivity in Cancer   Fig. . In tota9 Fig. . 12 on each of the four datasets separately , and non-genetic factors. To identify the effects of these factors on gene expression levels, we first performed consensus-independent component analysis (consensus-ICA)In all four datasets, many CESs showed a consistent pattern in which only genes mapping to a specific contiguous genomic region had a high absolute weight Fig. . We hypoTo continue testing the above hypothesis, we developed a method called transcriptional adaptation to CNA profiling , CCLE dataset (n = 1011) and the GDSC dataset (n = 962). Examples of TACNA profiles showed that the observed patterns clearly matched those in their paired, independently generated CNA profiles  we also observed clearly matching patterns between TACNA profiles and the expected karyotype in these samples  and an isogenic model with chromosome 5 tetrasomy (n = 3) . We have previously shown that the effects of CNAs on gene expression levels are often overshadowed by other (non-genetic) effects that are not associated with specific single CNAs9. For example, Stingele et al. showed that aneuploidy invokes a general cellular mRNA response which is not the result of any specific CNAs but only the presence of aneuploidy14. In addition, tumor cells in vivo may develop transcriptional adaptive mechanisms to survive under selective pressure, which may be different from the adaptive mechanisms observed in vitro.We obtained gene expression profiles of an experimental model of aneuploidy introduced in HCT116 cellsn = 10,620). In general, we observed a stronger correlation between TACNA profiles and CNA profiles compared with the correlation between mRNA expression profiles and CNA profiles on the gene level  analyses using the mRNA expression profiles, the TACNA profiles and the CNA profiles (derived from SNP arrays) from the TCGA dataset  CNA-CESs in the GEO dataset and 141/447 (32%) CNA-CESs in the TCGA dataset colocalized with a stringent set of common fragile sites on the human genome . Moreover, we performed TACNA profiling using 570 samples from the TCGA that were subjected to both RNA sequencing and microarray profiling (Affymetrix HG-U133A). Paired TACNA profiles generated with RNA-seq and microarray data were highly correlated . In addition, TACNA profiling was robust to both cross-validation within one platform  and cross-platform cross-validation  and one corresponds to the lowest absolute weight . Subsequently, all genes mapping to marked genomic regions in the CNAs-CESs were pooled and ranked according to their metric. We then performed gene set enrichment analysis (GSEA) on this ranked gene list using 12 gene set databases from the Molecular Signatures Database (MSigDB)18. When we explored available DNA methylation data for a subset of samples from the TCGA dataset (n = 9317), we observed that for a subset of genes their TACNA expression levels correlated with their mean methylation levels  that patient-derived cancer cells might coordinately adjust the mRNA expression levels of some protein complex subunits in response to the occurrence of CNAs  highly correlated between the GEO and TCGA dataset . Of these 7641 genes, 786 (10.2%) had low adaptation (metric < 0.25), 3898 (51.0%) moderate adaptation (metric 0.25–0.75), and 2957 genes (38.7%) high adaptation , most showed a relatively high degree of transcriptional adaptation to CNAs . This suggests that also transcription of many oncogenes is under the strong control of non-genetic regulatory factors that buffer the effects of CNAs.Amplification of oncogenes and the resulting enhanced gene expression levels have been implicated in the progression of many cancerssus Fig. 21. Wheren = 13,180) and TCGA dataset (n = 8150) to define the landscape of transcriptional effects of CNAs. This showed a high correlation between the average pan-cancer TACNA profiles in the GEO and TCGA dataset , colorectal adenocarcinoma (r = 0.74), and ovarian carcinoma (r = 0.73) were observed , which confirmed the negative association between CNA burden and the expression-based metric describing CD8+ T cell activity in both the GEO and TCGA datasets Fig. . We obser > 0.8, Fig. To identify the genes in these enriched genomic regions that may be linked to CD8+ T cell abundance, we constructed a co-functionality network for the top 350 genes with highest inverse correlation between their TACNA expression levels and CD8+ T cell abundance. This co-functionality network was generated with an integrative tool that predicts gene functions based on a guilt-by-association (GBA) strategy utilizing >106,000 expression profiles (see Methods). We identified four big clusters in which genes have strong predicted co-functionality  would indeed result in decreased CD8+/αβ T cell numbers  could thus result in more extreme tumor phenotypes. These genes could therefore be potential therapeutic targets. In addition, our results facilitate future research into the underlying mechanisms of transcriptional adaptation of genes to CNAs, which are currently poorly understood30.We observed that most oncogenes in our set had a high degree of transcriptional adaptation to CNAs. This suggests that elevation of expression levels of many oncogenes is only beneficial for tumor progression up to a certain level; excessive expression of several oncogenes was previously linked to cell senescence and apoptosis29.TACNA profiling was applied to gene expression profiles in two patient-derived datasets and two cell line compendia. These expression profiles were generated with three platforms using either RNA-seq or microarray technology. TACNA profiling proved robust, as shown by the highly reproducible average pan-cancer and tumor-type-specific TACNA profiles in the GEO and TCGA datasets. This enables reanalysis of the tens of thousands publicly available gene expression profiles generated from various platforms that are lacking a paired CNA profile. This approach provides a novel tool to determine how CNAs drive the progressive acquisition of tumor phenotypes, such as the hallmarks of cancer33. Targeting these potentially causal genes might increase CD8+ T cell infiltration. In combination with immune checkpoint inhibitors, this might induce or enhance an anti-cancer immune response, and thereby improve disease outcome.After generating the landscape of transcriptional effects of CNAs, we identified four genes which are associated with reduced CD8+ T cell abundance in the tumor microenvironment. This is especially of interest as a high number of CD8+ T cells is associated with higher response rates to immune checkpoint inhibitorsIn conclusion, our study provides a new tool to explore the transcriptional effects of CNAs which can lead to novel biological insights into how CNAs drive tumor behavior, and guide the development of new therapeutic strategies.http://www.genomicinstability.org/.The degree of transcriptional adaptation, source code of TACNA profiling, landscapes of transcriptional effects of CNAs per dataset, and additional datasets and results are available at GPL570 (generated with Affymetrix HG-U133 Plus 2.0)34, CCLE (generated with Affymetrix HG-U133 Plus 2.0)35, and GDSC (generated with Affymetrix HG-U219)36. Preprocessing and aggregation of raw expression data was performed using the robust multi-array average algorithm with RMAExpress (version 1.1.0)37. Quality control of the processed expression data is described in the Supplementary Note Microarray expression data was collected from three public data repositories: GEO platform 12. For each dataset containing mRNA expression profiles of p genes from n samples, ICA resulted in (i) the extraction of i independent components , (ii) an ES matrix of dimension i × p, where the weight of each ES represents the direction and magnitude of its effect on the expression level of each gene, and (iii) a mixing matrix (MM) of dimension i × n which contains the coefficients  of ESs in each sample. Within each dataset, principal component analysis was performed on the covariance matrix between samples, after which i was chosen as the minimum number of top principal components which captured at least 85% of the total variance in the dataset. The inner product between the vector of coefficients in the MM and the vector of ES weights per individual gene results in the original mRNA expression level.ICA was used to identify a regulatory model for the mRNA transcriptomer > 0.9 were clustered, where the number of ESs in each cluster could be at most the total number of ICA runs. CESs were computed by obtaining the average of ESs inside each cluster. Next, we calculated a credibility index for each CES by determining the ratio between the number of ESs in its cluster and the total number of ICA runs . CESs with a credibility index of ≥50% were used for obtaining the CES matrix and the consensus MM. We hypothesized that each CES describes the effect of a latent transcriptional regulatory factor on gene expression levels.In ICA, an initial random weight vector with a variance of 1 has to be chosen in order to obtain statistically independent ESs. Hence, varying initial random weight vectors could result in different sets of ESs. To retrieve a set of consensus ESs (or CESs), we performed 25 ICA runs, each with a different random initialization weight vector. After all ICA runs were completed, ESs with an absolute Pearson A subset of CESs harbored a pattern in which genes having high absolute weights mapped to a specific contiguous genomic region . To identify these CNA-CESs, we developed and used a detection algorithm, which is described in detail in the Supplementary Note For each dataset, weights of genes mapping to a contiguous genomic region in CNA-CESs marked by the detection algorithm were retained in the CES matrix. Weights of genes that mapped outside marked genomic regions were instead set to zero. Next, TACNA profiles were calculated as the product between this transformed CES matrix and the consensus MM. TACNA profiles in the GEO dataset and TCGA dataset were adjusted by subtracting the Hodges Lehmann estimate  of the TACNA expression of genes observed in normal tissue samples in each dataset. This ensured that a TACNA expression value of zero corresponded to a situation where a gene has exactly two copies on the genome.15. Genomic coordinates were converted to human genome reference GRCh38 using the Batch Coordinate Conversion tool from USCS Genome Browser38. Borders of marked genomic regions in CNA-CESs were assumed to colocalize with common fragile sites when one or both borders were located inside a common fragile site.Previously described genomic locations of aphidicolin-induced fragile sites identified through cytogenic analyses were used to assess the colocalization of borders of marked genomic regions in CNA-CESs with common fragile sitest-test for unequal variance. The proportion of false discoveries was controlled using a multivariate permutation testing framework with 100 permutations, a 1% false discovery rate and confidence level of 80%.Weights of genes in a marked genomic region of a CNA-CES were transformed to metrics ranging from zero to one, where zero would correspond to the highest absolute weight  and one would correspond to the lowest absolute weight . When genes occurred in marked regions of multiple CNA-CESs, the lowest metric was used. Enrichment of gene sets was tested according to the two-sample Welch’s For each sample in the GEO dataset and TCGA dataset, CNA load was estimated as the sum of the coefficients  of all CNA-CESs in the MM with at least 50 genes in their marked genomic regions. CNA loads were normalized to a 0–1 range across samples of the same tumor type in each dataset separately.CD2, CD3E, CD247, GZMK, NKG7, and PRF1) was used to calculate immune metric scores27. Per sample, the rank position of the mRNA expression levels of each of these genes was calculated. Scores for each sample were determined by calculating the mean rank position of the seven genes. Immune metric scores were normalized to a 0–1 range across samples of the same tumor type in each dataset separately.A previously defined set of genes describing CD8+ T cell and natural killer cell activity  on the reconstructed profiles.Inference of the abundance of 22 immune cell types was performed using CIBERSORThttp://www.genetica-network.com.We used a GBA approach to predict likely functions for genes based on gene co-regulation. For this, we conducted a consensus-ICA on an unprecedented scale (manuscript in preparation). In short, a covariance matrix was calculated between 19,635 genes using the expression patterns of 106,462 gene expression profiles generated with Affymetrix HG-U133 Plus 2.0 representing the many disease states, cellular states, and genetic and chemical perturbations that were obtained. Consensus-ICA was performed on the covariance matrix, which resulted in the identification of a large set of CESs and a MM reflecting the activity of each source in the expression pattern of each gene across the samples. Next, a GBA approach was used to predict the functionality of individual genes. First, we retrieved 16 public gene set collections describing a large range of biological processes and phenotypes. For each gene set, we calculated its bar code by averaging the MM weights of its member genes per CES. Next, for each gene in the MM, the distance correlation was determined between its MM weights and the gene set bar code. A high correlation between a gene’s MM weight and a gene set bar code indicated that the gene under investigation shared a functionality with the genes of the specific gene set under investigation. Significance levels were obtained with permutated data (1000 permutations). This strategy was used on 23,372 well-described functional gene sets, which enabled us to create a comprehensive network of predicted functionalities of individual genes. This framework is available at A more detailed description of the methods used in this study is provided in the Supplementary Note Further information on research design is available in the Peer 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 15Supplementary Data 16Supplementary Data 17Supplementary InformationReporting Summary


Single-cell genomics has become the method of choice for the study of heterogeneous cell populations and represents an elective application in defining the architecture and clonal evolution in hematological neoplasms. Reconstructing the clonal evolution of a neoplastic population therefore represents the main way to understand more deeply the pathogenesis of the neoplasm, but it is also a potential tool to understand the evolution of the tumor population with respect to its response to therapy. Pre-analytical phase for single-cell genomics analysis is crucial to obtain a cell population suitable for single-cell sorting, and whole genome amplification is required to obtain the necessary amount of DNA from a single cell in order to proceed with sequencing. Here, we evaluated the impact of different methods of cellular immunostaining, fixation and whole genome amplification on the efficiency and yield of single-cell sequencing. The neoplastic evolution results from the accumulation of mutations and from the competition among subclones carrying different combinations of genetic lesions. It has been shown that the temporal order in which mutations accumulate in hematological neoplasms and in many solid human tumors, such as breast , kidney The extraordinary perspectives offered by this technique are however counterbalanced by technical difficulties due to single-cell manipulation. Single-cell genomics approaches require two main critical steps that precede the downstream single-cell sequencing: single-cell isolation and single-cell whole genome amplification (scWGA). Several strategies can be used for the isolation of single cells, the most used of which is Fluorescence-activated cell sorting (FACS). This certainly has the advantage of not needing a cell fixation step. Nevertheless, it has two main drawbacks: the first is represented by the fact that the maximization of cellular recovery generally causes a decrease in cell purity. The DEPArray technology allows to couple high cell recovery while maintaining high levels of cell purity (purity = 100%). Furthermore, another advantage of the DEPArray compared to FACS sorting is that it automatically checks that only a single viable cell has been sorted, saving the researcher from manually checking for wells effectively containing only single cells.Therefore, we adopted the DEPArray sorting even if it requires a cell fixation step. Cell fixation can introduce some bias during scWGA because of the effects exerted by fixative agents upon chromatin accessibility , resultiSeveral scWGA strategies have been developed in order to get a uniform and abundant amplification of single-cell genomic DNA. The main amplification methods available to date are based on the Multiple Displacement Amplification (MDA) , DegenerMyeloproliferative Neoplasms (MPNs) are a complex disease model due to their heterogeneous genetic landscape. The different mutational assets harbored by the patients determine their pathogenic evolution and response to treatment . In hemaThese protocols were employed in order to assess which of these was compatible with cell fixation in terms of sequence quality, yield and ADO rate.In order to compare different cell fixation methods, ascertain their impact on WGA efficiency and determine which WGA strategy would ensure the best genotyping results by Sanger sequencing, primary CD34+CD38-, HEL or K562 cells were fixed using either Paraformaldehyde (PFA) 2 or 0.5% in PBS 1Xat 4 °C for 15 min 45 min, 2 h or overnight or Methanol (MetOH) 100% 2 h at 4 °C. JAK2 and ASXL1).Cells were immunostained and then isolated by means of DEPArray. After DNA isolation from each single cell, five commercially available WGA kits were tested, as detailed in materials and methods section. Amplified DNA was tested for size distribution and for its suitability to be genotyped by Sanger sequencing of the genes most frequently mutated in MPNs  compatible with both immunostaining and WGA. First, we performed 100% MetOH fixation for 2 h at 4 °C upon immunostained CD34+CD38+ cells. In addition to previously described WGA methods, we also employed MALBAC and DOP-PCR protocols, i.e., respectively MALBAC and Genomeplex kits. Our results show that, unlike PFA, MetOH fixation is not able to preserve PE staining, therefore proving to be unsuitable for downstream DEPArray or FACS separation B. An altFor this reason, we tested PFA at lower concentrations and for shorter incubation times in order to limit its cross-linking activity upon DNA and histone residues that could hamper optimal genomic amplification. In particular, we tried out 0.5% PFA incubation for 15 min, 45 min or 2 h. In all three conditions, CD34+CD38+ cells immunostaining with APC and PE fluorochromes was preserved, but WGA performed with either RepliG or Genomeplex kit was inefficient (data not shown). On the contrary, low quality WGAs were obtained through PicoPLEX and MALBAC kits in 45 min- and 2 h-fixed cells (data not shown), but showed good efficiency for downstream Sanger sequencing after 15 min of 0.5% PFA fixation D. For thAfter the optimization of the fixation method, we tested the performance of the different WGA kits in terms of yield, efficiency for downstream Sanger sequencing and ADO rate. To this aim, K562 cells were fixed in PFA 0.5% for 15 min and WGA with the five different kits  after single-cell sorting through DEPArray was performed.Bioanalyzer run performed on different WGA products showed that PicoPLEX and MALBAC amplification products were characterized by highest yield and the larger size distribution. On the contrary, Ampli1 and Genomeplex had a markedly lower yield. Moreover, Ampli1 fragments were shorter than the ones produced by the other kits, highlighting once more the limitations of this WGA protocol. RepliG fragments were longer than the ones generated by other kits, due to the high processivity of φ29 DNA polymerase A. A statASXL1 exon 13 in K562 WGAs coming from the different kits. In WGA samples performed with Ampli1 kit, the region of interest results unamplified due to Mse1 digestion  carried by K562 cells. Our results show that both RepliG and PicoPLEX had a low ADO rate; on the contrary, MALBAC kit produced a higher ADO rate since it detected either wild-type or homozygous cells as shown by histograms and representative electropherograms in To evaluate the ADO rate resulting from the kits allowing a good sequencing efficiency , we performed Sanger sequencing of a heterozygous variant of As a whole, these analyses showed that PicoPLEX is the only kit among those tested featuring the highest amplification yield, the highest suitability for downstream sequencing and the lowest ADO rate .Single-cell WGA is an essential step in analyzing the mutational asset of single cells due to the low amount of genomic material (<10 pg) . In receFor this reason, single-cell analysis has proved to be very useful for the study of cellular subpopulation in many solid human tumors, such as breast , kidney In this work, we aimed at optimizing a workflow for cell immunostaining, fixation and WGA dedicated to the single-cell genomic analysis of the hematopoietic CD34+CD38- stem compartment, in which originate hematological malignancies such as Myeloproliferative Neoplasms.In our experimental design, cell fixation was performed in order to preserve cell immunostaining and allow an efficient single cell sorting through DEPArray technology .However, the use of fixative agents such as PFA or MetOH can impair the uniform genome amplification due to their cross-linking effect between DNA and histone residues. Our results showed that a strong PFA fixation is compatible with Ampli1 WGA kit, but this WGA strategy is strongly limited by Mse1 restriction enzyme employment. This enzyme is a frequent cutter inside the human genome, as exemplified by the Mse1 restriction sites in two regions of JAK2 and ASXL1 genes . These gOn the other hand, a strong PFA fixation is not compatible with WGA kits that skip the enzymatic fragmentation, such as PicoPlex and RepliG B. The liThe use of MetOH as an alternative cell fixative resulted in efficient WGA and Sanger sequencing with most of the commercially available WGA kits  C. HoweveCD34+CD38- cell fixation with lower PFA concentrations performed for shorter time in order to mitigate its cross-linking effect, resulted in the efficient genome amplification with PicoPlex and MALBAC kits (both based on the MALBAC amplification strategy) D, but waUsing 0.5% PFA for 15’ as the optimal fixation protocol for CD34+CD38- cells, the five WGA kits were tested in these experimental conditions on single K562 cells in order to assess DNA yield, ADO rate and efficiency of downstream Sanger sequencing .WGA yields and fragment size distribution reflected the molecular approaches followed by the different kits. PicoPlex and MALBAC showed the higher yield and the larger fragment size distribution, due to their quasi-random priming strategy and efficient and uniform amplification of the hairpin molecules generated during the pre-amplification step. On the other hand, Ampli1 WGA kit was characterized by a markedly lower yield and shorter fragment size , confirmFinally, we proceeded with ADO rate assessment, which is a fundamental parameter for the evaluation of a correct single-cell genotyping in terms of variant zygosity. Our data demonstrate that RepliG and PicoPlex kits are able to detect heterozygous mutations with a higher accuracy than MALBAC, showing a higher rate of false positives (homozygous mutants) and negatives (wild-type) D.As a whole, our study demonstrated that a mild PFA fixation (0.5% PFA for 15 min) is compatible with CD34+CD38- cell immunostaining, DEPArray sorting and single-cell WGA. In conclusion, the PicoPlex WGA kit, adopting MALBAC strategy, was found to be the better performing kit in terms of WGA yield, fragment size, sequencing efficiency and ADO rate. In order to perform single-cell experiments, it is important to pay attention to robustness and reproducibility of the protocol, since the biological material used in these procedures is often precious or not recoverable. It is important to highlight how the experimental procedure described in this work was aimed at the validation, by means of Sanger sequencing, of genomic variants previously characterized by Next Generation Sequencing (NGS) analysis in bulk. NGS single-cell genomics strategies have recently been developed either following WGA protocols or through the employment of a droplet-based targeted strategy . The teThis workflow could therefore be adopted in order to understand the clonal complexity and evolution of solid and hematological malignancies sustained by multiple clones whose prevalence is modulated during disease progression. Moreover, this novel single-cell genomics approach is a powerful instrument to gain insights into allele distribution in a cell population, understand cells’ zygosity and highlight the presence of rare genetic variants.Noteworthy, in clinical management, the early detection of malignant clones through single-cell genomic analysis could be useful to allow a better prognostic assessment of the patients and the selection of personalized therapies. Human CD34+38- HSCs were purified upon donor’s informed written consent from umbilical Cord Blood samples, collected after normal deliveries, according to the institutional guidelines for discarded material . Cord blood CD34+38- were purified by means of CD34+CD38- Cell Isolation Kit, human . Cell purity was assessed by means of flow cytometry after immunostaining with anti-human CD34-APC (1:50) and Labeling Check Reagent-PE (1:33), Labeling Check Reagent-FITC (1:33) or antibodies (Miltenyi Biotec). The labeling check reagent-PE identify CD38 expression by binding the beads bond to the CD38 antigen. Immunostained cells were then fixed with either Paraformaldehyde (PFA) 2% or 0.5% in PBS1× at 4 °C for 15 min, 45 min, 2 h or overnight or MetOH 100% 2 h at 4 °C and washed with PBS1× before being resuspended in autoMACS Running Buffer (Miltenyi Biotec).Immunostained CD34+38- cells were then subjected to single-cell sorting by means of DEPArray™ System  . The sorIn order to assess the ADO rate of each WGA kit, the evaluation of known Single Nucleotide Variants was performed by means of Sanger sequencing on HEL and K562 cell lines, since they carry known genomic variants either in homozygous or heterozygous state. HEL and K562 cell lines were obtained from the ATCC and cultured in RPMI medium , supplemented with 10% heat-inactivated FBS  and 1 mmol/L l-Glutamine (Euroclone) . HEL cel® Single Cell Whole Genome Amplification Kit (Sigma-Aldrich) following manufacturer’s protocol. For each WGA experiment, a genomic DNA sample and 1uL of Low Tris-EDTA (TE) buffer  were included as positive and negative controls, respectively. WGA product was then purified by means of AMPure XP  immunomagnetic beads and eluted in 20 μL of Low TE buffer. Quality control on WGA yield and amplicons’ size was performed through Bioanalyzer High Sensitivity DNA Analysis .Whole Genome Amplification was performed on 20 single CD34+CD38-, HEL or K562 cells by means of SMARTer PicoPLEX Single Cell WGA kit , REPLI-g Single Cell Kit , Ampli1™ WGA Kit (Menarini Silicon Biosystems), MALBAC Single Cell WGA Kit  or GenomePlex® Direct Cycle Sequencing Kit  following the manufacturer’s protocol. Primers used for PCR reactions  were synthesized with the addition of the M13 tail . Sequencing products were purified by ethanol/EDTA precipitation. Sequencing was performed by capillary electrophoresis on 3130xl Genetic Analyzer (Applied Biosystems®).PCR and sequencing analysis were performed by using BigDye


P < 0.001). Spexin levels negatively correlated with adiposity markers and blood pressure in the whole study population (P < 0.05). Multiple regression analysis revealed blood pressure was the greatest predictive determinant of plasma spexin levels, which significantly increased in response to physical exercise in obese participants without and with T2D (P < 0.05). Spexin levels significantly increased only in responders to exercise  with a concomitant improvement in metabolic profile. In conclusion, plasma spexin levels may be an indicator of response to physical exercise.Spexin is a novel neuropeptide playing an emerging role in metabolic diseases such as obesity and diabetes via involvement in energy homeostasis and food intake. The present study investigated the effects of obesity and type 2 diabetes (T2D) on circulating levels of spexin and its modulation by physical exercise. Normal-weight (n = 50) and obese adults with and without T2D  were enrolled in the study. A subgroup of obese participants (n = 47) underwent a supervised 3-month exercise programme. Plasma spexin levels were measured by ELISA and correlated with various markers. Plasma spexin levels decreased in obese participants with or without T2D compared with those of normal-weight participants (0.43 ± 0.11, 0.44 ± 0.12 and 0.61 ± 0.23 ng/ml, respectively;  The hypothalamus produces neuropeptides that are involved in energy homeostasis. For example, genetic forms of obesity involve neuropeptides such as melanocortin-4-receptor deficiency2. Although these neuropeptides were first discovered in the brain, many are also expressed in various peripheral metabolic organs such as skeletal muscle, the pancreas and adipose tissue3. Molecules that mimic the action of neuropeptides are currently being developed for the treatment of obesity4.Energy homeostasis is regulated by complex communication pathways between the brain and peripheral organs, and disruption of these pathways significantly contributes to chronic metabolic diseases such as obesity and diabetes5. Studies using rodents and goldfish have shown the presence of spexin mRNA and protein in several tissues, including hypothalamus, hippocampus, cerebral cortex, stomach, kidney, adrenal gland and ovaries6. Injection of spexin into goldfish and a diet-induced obesity rodent model decreased food intake, which was linked to downregulation of orexigenic factors and upregulation of anorexigenic factors8. On the other hand, blood spexin levels were decreased in both adults and children with obesity and diabetes6, although another study showed no difference in serum spexin levels between three groups of adolescents 9. Moreover, circulating spexin levels displayed a borderline association with components of metabolic syndrome in adults10. Spexin also contributes to central nervous system-mediated modulation of nociceptive responses11 and, like other neuropeptides, may also play a role in pain control. These neuropeptides were also associated with inherent differences and alterations in pain control between ethnic groups12.Spexin, also known as neuropeptide Q, is a recently identified neuropeptide that is thought to be involved in energy homeostasis. It was primarily discovered using bioinformatics search strategies13. A 1-year longitudinal study conducted in patients following Roux-en-Y gastric bypass (RYGB) in young adults with severe obesity reported increased levels of circulating spexin post-surgery14. Another nonpharmacological strategy widely used to manage obesity and its related complications is regular physical exercise as a component of a healthy lifestyle. Exercise has pleiotropic effects on physiology by reducing food intake and body weight15 as well as altering neuropeptide levels, particularly orexigenic and anorexigenic peptides16, thus improving obesity-related conditions. A recent study reported favourable effects of a 6-month self-monitored lifestyle modification programme that included physical exercise on circulating spexin levels in females with prediabetes17.Spexin gene expression was downregulated in the adipose tissue of obese patients compared with that of normal-weight controls as well as between heavy and lean co-twinsTaken together, these findings suggest that spexin plays a critical role in metabolic homeostasis and is potentially modulated by physical exercise. Since spexin is presumed to play a role in regulating body weight, we compared levels of circulating spexin between normal-weight, obese and obese patients with diabetes in an Arab population and evaluated the association of these levels with demographic and clinical parameters. We also investigated whether physical exercise could influence spexin levels in obese patients. Our findings indicated that levels of spexin were reduced with obesity and diabetes but increased in response to physical exercise.P < 0.001). Both the obese groups (with and without diabetes) displayed significantly higher blood pressure and hsCRP levels but lower VO2 max levels compared with the normal-weight group (P < 0.01). Moreover, the obese with diabetes group showed a more dysregulated lipid profile, as indicated by lower high-density lipoprotein (HDL) (P < 0.001) and higher TG (P < 0.001) levels, compared with the normal-weight and obese without diabetes groups. Both obese groups showed higher levels of glycemic markers (fasting blood glucose (FBG), HbA1c and insulin) (P < 0.05) compared with the normal-weight group  . Not all obese individuals develop metabolic dysregulation and diabetes, and such individuals are classified as metabolically healthy individuals, despite increased body mass index (BMI). Likewise, not all non-obese individuals present a healthy metabolic profile. We divided our obese without diabetes group into metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO) groups on the basis of previously reported criteria18 and found that levels of spexin were significantly lower in the MUO group (P = 0.043)  when we further divided the obese with diabetes group on the basis of HbA1c levels, controlled (HbA1c < 6.5%) and uncontrolled (HbA1c ≥ 6.5%). The clinical and demographic characteristics of these two subcohorts are shown in Tables Plasma spexin levels were significantly lower in both the obese groups P < 0.00  and HbA1c. This correlation differed among obese participants depending on the presence or absence of diabetes. In the obese without diabetes group, spexin levels were negatively correlated with cholesterol and low-density lipoprotein (LDL) but positively correlated with HOMA-IR. On the other hand, spexin levels were positively correlated with HDL but negatively correlated with TG levels in the obese with diabetes group.Spearman correlation analysis of plasma spexin levels with various demographic and clinical parameters was performed for all participants as well as separately in the normal-weight, obese without diabetes and obese with diabetes groups Table . Spexin P < 0.05). However, in the obese without diabetes group, negative independent associations were observed between spexin levels and LDL and HOMA-IR (P < 0.01). In the obese with diabetes group, only HDL was independently associated with circulating spexin levels (P < 0.01).Multivariate stepwise linear regression analysis was performed with spexin as a dependent variable in the whole study population as well as separately in the normal-weight, obese without diabetes and obese with diabetes groups using Spearman correlated variables Table . In the P < 0.05) with concurrent amelioration of clinical and metabolic markers  before and after exercise , with decreased HR and hsCRP levels  and obese with diabetes (n = 27)]. In both obese groups, exercise significantly increased blood spexin levels , DBP, systolic blood pressure (SBP) and lipid markers  but positively correlated with HDL levels. These findings are in line with those of previous reports that examined the role of spexin as a metabolic regulator of body weight and glucose homeostasis in obese adults and children or patients with type 2 diabetes mellitus and demonstrate that spexin could play an important role in obesity and diabetes22. A recent study in healthy adult women reported that circulating spexin levels were negatively correlated with age, BMI, FBG and TG23. A decrease of spexin levels with age suggests a possible role of this peptide in ageing-related functions and disorders. However, despite the significantly older age of the obese with diabetes group in our study, we did not observe differences in spexin levels compared to the obese without diabetes group. This suggests that spexin pattern with age is blunted in diabetes status. Nevertheless, we cannot rule out the potential effects of treatment on spexin levels as study participants with diabetes were treated using antidiabetic as well as antihypertensive drugs, which might have contributed to the lack of difference of spexin between participants with and without diabetes. Very recently, Al-Daghri et al.17 reported increased circulating spexin levels in females with prediabetes but not in males after a 6-month self-monitored lifestyle modification programme. The same group has also reported an inverse association between spexin levels with fasting glucose in adult females with prediabetes, but not in males17. This might be due to a sexual dimorphism in spexin levels in relation to gender differences in glucose metabolism and insulin sensitivity due to sex steroids24. Categorisation of the obese without diabetes group into MHO and MUO groups revealed significantly lower spexin levels in the MUO group, which could not be explained by the increased glycaemic levels in the MUO. Therefore, it is crucial to elucidate the mechanisms of interaction between spexin and glucose/lipid metabolism.These findings suggest that spexin might have different implications in obesity and diabetes depending on age as both of disorders are associated with age25. The present study showed that blood pressure (SBP and DBP) was negatively correlated with spexin and DBP regardless of BMI and was an independent determinant of spexin levels in the whole population and the normal-weight group. This may indicate the involvement of spexin in the early development of cardiovascular disease (CVD) risk in apparently healthy individuals as reflected by decreased spexin levels when CVD risk factors  are increased. In line with this hypothesis, a relationship between circulating spexin levels and markers of CVDs was previously reported in adolescents with obesity26, and spexin was proposed as an emerging marker for intervention and/or monitoring for CVD in children27. However, no significant correlation between spexin levels and blood pressure was found in adolescents9; therefore, further studies into the physiological roles and crosstalk of spexin with blood pressure are required, taking both age and BMI into consideration. It is worth noting that in our study, the negative association between spexin and lipids markers —considered to be CVD risk factors—in the obese groups but not in the normal-weight groups endorses the protective role of spexin in metabolic disorders. In support of this hypothesis, spexin treatment reversed the dramatically increased hepatic levels of TG and total lipids in a diet-induced obesity mouse model without T2D28. The positive correlation and independent association of spexin with HDL further supports the protective role of spexin as HDL decreases with obesity. Furthermore, this relationship was previously suggested in individuals with metabolic syndrome10.Blood pressure increases with BMI31. Interestingly, we showed that spexin levels were increased after exercise in both the obese groups with and without diabetes. We and others have previously reported greater beneficial effects of regular exercise in individuals without diabetes compared with those with diabetes based on other markers33.The present study shows for the first time that exercise increased plasma spexin levels and also improved cardiometabolic markers in obese individuals Fig. . The ple8. Interestingly, in our study the increased levels of spexin observed after exercise were independent from BMI and diet, as only marginal changes in BMI were observed and no diet restrictions were prescribed with the exercise protocol. In line with this, spexin was reported among the BMI-independent changes observed over time in post-RYGB phase in morbidly obese youth, highlighting the fact that the clinical beneficial effects of RYGB may also involve spexin14.The inhibitory effect of spexin on body weight in diet-induced obesity mice after chronic injection of spexin was suggested to be due to a reduced respiratory exchange ratio and increased locomotor activity34. Since these tissues are involved in energy homeostasis and metabolic regulation, it was difficult to determine the source of increased circulating spexin after exercise in our study. Future studies are required to determine the tissue that facilitates spexin release in response to exercise to regulate body weight and food intake in obese patients with metabolic impairment.On the other hand, spexin and its receptor, galanin receptor 2/3 (GALR2/3), are expressed in the central nervous system as well as various peripheral tissues, such as liver, heart, adipose tissue and skeletal muscle36. Several studies have reported a high prevalence of non-responders in diabetes with or without complications, supporting a relationship between diabetes and non-response38. Furthermore, MHO individuals showed higher levels of cardiorespiratory fitness than did MUO individuals, as reflected by our findings, suggesting that a healthier metabolic phenotype is linked, at least in part, to response to lifestyle factors39. This variability could be also associated with other biological factors, including genetics, which account for differences in body weight regulation in response to exercise41. Maximum oxygen consumption (VO2 max or VO2 peak) is the standard parameter of cardiorespiratory fitness and is widely used to assess the effectiveness of training and to estimate the prevalence of non-responders43.Most previous studies using physical exercise intervention have reported the average effect of the exercise on the studied population. However, there is increased evidence that there is interindividual variability in response to exercise, as some individuals are responders who display improved metabolic markers, whereas others are non-responders and exhibit unchanged or worsened markers2 max regardless of other metabolic markers  or had a history of major illness. Eligible participants for our 3-month supervised exercise programme were enrolled at the Fitness and Rehabilitation Centre of the Dasman Diabetes Institute48.The current study recruited 185 adults. Written informed consents were obtained from all participants prior to taking part in the study that was conducted in line with the principles of the Declaration of Helsinki and was approved by our Institutional Review Board. The cohort was divided into three groups: normal-weight without diabetes (n = 50), obese without diabetes (n = 66) and obese with diabetes (n = 69). As we previously reported47, the exercise program involved a combination of both resistance training with treadmill or cycling and moderate intensity aerobic exercise. Each exercise session included 10 min warming up and cooling down steps at 50–60% of max HR with 40 min of the mentioned program at 65–80% of max HR. Under the supervision of qualified fitness professionals at MFC, subjects exercised 3 times per week for a period of 3 months. They were monitored to maintain the recommended heart rates during the training. Anthropometric and whole-body composition measurements and exercise effectiveness was assessed at the end of the 3-month exercise program using similar fitness tests performed at baseline.Briefly, Prior to exercise prescription each subject underwent the Cardiopulmonary exercise test “CPET”  using electromagnetically braked cycle ergometer to measure the maximum heart rate (max HR) and maximum oxygen consumption (VO2 max). The termination criterion of 85% of age-predicted maximal heart rate (HR max) was used to stop the test. HR max was calculated as following: HR max = 220—age (year). Physical fitness was assessed thereafter to determine the muscle strength, endurance and flexibility by performing grip strength (dynamometer), push-ups upper body strength, sit ups and forward bending test (upper and lower body flexibility). As we previously reported30. Plasma biochemical and metabolic markers, including FBG and HbA1c, as well as lipid profile , were measured as previously reported30. hsCRP, insulin and C-peptide levels were measured by hsCRP ELISA , insulin and ultrasensitive C-peptide ELISA, . Plasma spexin levels were measured by ELISA . Plasma samples were diluted 1:10 prior to spexin measurement to fit within the detection range. All measurements were performed according to the manufacturers' instructions. Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) index was calculated using the following formula: HOMA-IR = (glucose × insulin)/22.5.Venous peripheral blood samples were obtained at baseline and after the 3-month exercise programme and processed as previously reported47. All descriptive statistics for variables in the study were reported as mean ± standard deviation. A Chi-square test was used for categorical variables, and a Wilcoxon nonparametric t-test was used for skewed variables. A two-way repeated measures ANOVA test with post hoc Bonferroni analysis was conducted to evaluate the effects of groups. A paired t-test was used to determine the significance of differences in means within each group before and after exercise. Correlations between variables were calculated using Spearman's correlation coefficient. Multivariate linear regression analysis was performed to examine the predictive effect of each factor. Values of P < 0.05 were considered statistically significant.Statistical analyses were performed using SPSS software version 25.0  as we previously reportedSupplementary information