Donor whole blood DNA methylation is not a strong predictor of acute graft versus host disease in unrelated donor allogeneic haematopoietic cell transplantation.

Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies, however it can also result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n = 288) selected using the same criteria as the discovery cohort. Attempts to validate the classifier failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use.

Sex differences in epigenetic age in Mediterranean high longevity regions.

Sex differences in aging manifest in disparities in disease prevalence, physical health, and lifespan, where women tend to have greater longevity relative to men. However, in the Mediterranean Blue Zones of Sardinia (Italy) and Ikaria (Greece) are regions of centenarian abundance, male-female centenarian ratios are approximately one, diverging from the typical trend and making these useful regions in which to study sex differences of the oldest old. Additionally, these regions can be investigated as examples of healthy aging relative to other populations. DNA methylation (DNAm)-based predictors have been developed to assess various health biomarkers, including biological age, Pace of Aging, serum interleukin-6 (IL-6), and telomere length. Epigenetic clocks are biological age predictors whose deviation from chronological age has been indicative of relative health differences between individuals, making these useful tools for interrogating these differences in aging. We assessed sex differences between the Horvath, Hannum, GrimAge, PhenoAge, Skin and Blood, and Pace of Aging predictors from individuals in two Mediterranean Blue Zones and found that men displayed positive epigenetic age acceleration (EAA) compared to women according to all clocks, with significantly greater rates according to GrimAge (ß = 3.55; p = 1.22 × 10-12), Horvath (ß = 1.07; p = 0.00378) and the Pace of Aging (ß = 0.0344; p = 1.77 × 10-08). Other DNAm-based biomarkers findings indicated that men had lower DNAm-predicted serum IL-6 scores (ß = -0.00301, p = 2.84 × 10-12), while women displayed higher DNAm-predicted proportions of regulatory T cells than men from the Blue Zone (p = 0.0150, 95% Confidence Interval [0.00131, 0.0117], Cohen's d = 0.517). All clocks showed better correlations with chronological age in women from the Blue Zones than men, but all clocks showed large mean absolute errors (MAE >30 years) in both sexes, except for PhenoAge (MAE <5 years). Thus, despite their equal survival to older ages in these Mediterranean Blue Zones, men in these regions remain biologically older by most measured DNAm-derived metrics than women, with the exception of the IL-6 score and proportion of regulatory T cells.

Epigenome-wide association study and epigenetic age acceleration associated with cigarette smoking among Costa Rican adults.

Smoking-associated DNA methylation (DNAm) signatures are reproducible among studies of mostly European descent, with mixed evidence if smoking accelerates epigenetic aging and its relationship to longevity. We evaluated smoking-associated DNAm signatures in the Costa Rican Study on Longevity and Healthy Aging (CRELES), including participants from the high longevity region of Nicoya. We measured genome-wide DNAm in leukocytes, tested Epigenetic Age Acceleration (EAA) from five clocks and estimates of telomere length (DNAmTL), and examined effect modification by the high longevity region. 489 participants had a mean (SD) age of 79.4 (10.8) years, and 18% were from Nicoya. Overall, 7.6% reported currently smoking, 35% were former smokers, and 57.4% never smoked. 46 CpGs and five regions (e.g. AHRR, SCARNA6/SNORD39, SNORA20, and F2RL3) were differentially methylated for current smokers. Former smokers had increased Horvath's EAA (1.69-years; 95% CI 0.72, 2.67), Hannum's EAA (0.77-years; 95% CI 0.01, 1.52), GrimAge (2.34-years; 95% CI1.66, 3.02), extrinsic EAA (1.27-years; 95% CI 0.34, 2.21), intrinsic EAA (1.03-years; 95% CI 0.12, 1.94) and shorter DNAmTL (- 0.04-kb; 95% CI - 0.08, - 0.01) relative to non-smokers. There was no evidence of effect modification among residents of Nicoya. Our findings recapitulate previously reported and novel smoking-associated DNAm changes in a Latino cohort.

Chemotherapy induces canalization of cell state in childhood B-cell precursor acute lymphoblastic leukemia.

Comparison of intratumor genetic heterogeneity in cancer at diagnosis and relapse suggests that chemotherapy induces bottleneck selection of subclonal genotypes. However, evolutionary events subsequent to chemotherapy could also explain changes in clonal dominance seen at relapse. We, therefore, investigated the mechanisms of selection in childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL) during induction chemotherapy where maximal cytoreduction occurs. To distinguish stochastic versus deterministic events, individual leukemias were transplanted into multiple xenografts and chemotherapy administered. Analyses of the immediate post-treatment leukemic residuum at single-cell resolution revealed that chemotherapy has little impact on genetic heterogeneity. Rather, it acts on extensive, previously unappreciated, transcriptional and epigenetic heterogeneity in BCP-ALL, dramatically reducing the spectrum of cell states represented, leaving a genetically polyclonal but phenotypically uniform population with hallmark signatures relating to developmental stage, cell cycle and metabolism. Hence, canalization of cell state accounts for a significant component of bottleneck selection during induction chemotherapy.

Exaggerated IL-17A activity in human in vivo recall responses discriminates active tuberculosis from latent infection and cured disease.

Host immune responses at the site of Mycobacterium tuberculosis infection can mediate pathogenesis of tuberculosis (TB) and onward transmission of infection. We hypothesized that pathological immune responses would be enriched at the site of host-pathogen interactions modeled by a standardized tuberculin skin test (TST) challenge in patients with active TB compared to those without disease, and interrogated immune responses by genome-wide transcriptional profiling. We show exaggerated interleukin-17A (IL-17A) and T helper 17 (TH17) responses among 48 individuals with active TB compared to 191 with latent TB infection, associated with increased neutrophil recruitment and matrix metalloproteinase-1 expression, both involved in TB pathogenesis. Curative antimicrobial treatment reversed these observed changes. Increased IL-1ß and IL-6 responses to mycobacterial stimulation were evident both in circulating monocytes and in molecular changes at the site of TST in individuals with active TB, supporting a model in which monocyte-derived IL-1ß and IL-6 promote TH17 differentiation within tissues. Modulation of these cytokine pathways may provide a rational strategy for host-directed therapy in active TB.

Genetic perturbation of PU.1 binding and chromatin looping at neutrophil enhancers associates with autoimmune disease.

Neutrophils play fundamental roles in innate immune response, shape adaptive immunity, and are a potentially causal cell type underpinning genetic associations with immune system traits and diseases. Here, we profile the binding of myeloid master regulator PU.1 in primary neutrophils across nearly a hundred volunteers. We show that variants associated with differential PU.1 binding underlie genetically-driven differences in cell count and susceptibility to autoimmune and inflammatory diseases. We integrate these results with other multi-individual genomic readouts, revealing coordinated effects of PU.1 binding variants on the local chromatin state, enhancer-promoter contacts and downstream gene expression, and providing a functional interpretation for 27 genes underlying immune traits. Collectively, these results demonstrate the functional role of PU.1 and its target enhancers in neutrophil transcriptional control and immune disease susceptibility.

GenomeChronicler: The Personal Genome Project UK Genomic Report Generator Pipeline.

In recent years, there has been a significant increase in whole genome sequencing data of individual genomes produced by research projects as well as direct to consumer service providers. While many of these sources provide their users with an interpretation of the data, there is a lack of free, open tools for generating reports exploring the data in an easy to understand manner. GenomeChronicler was developed as part of the Personal Genome Project UK (PGP-UK) to address this need. PGP-UK provides genomic, transcriptomic, epigenomic and self-reported phenotypic data under an open-access model with full ethical approval. As a result, the reports generated by GenomeChronicler are intended for research purposes only and include information relating to potentially beneficial and potentially harmful variants, but without clinical curation. GenomeChronicler can be used with data from whole genome or whole exome sequencing, producing a genome report containing information on variant statistics, ancestry and known associated phenotypic traits. Example reports are available from the PGP-UK data page (personalgenomes.org.uk/data). The objective of this method is to leverage existing resources to find known phenotypes associated with the genotypes detected in each sample. The provided trait data is based primarily upon information available in SNPedia, but also collates data from ClinVar, GETevidence, and gnomAD to provide additional details on potential health implications, presence of genotype in other PGP participants and population frequency of each genotype. The analysis can be run in a self-contained environment without requiring internet access, making it a good choice for cases where privacy is essential or desired: any third party project can embed GenomeChronicler within their off-line safe-haven environments. GenomeChronicler can be run for one sample at a time, or in parallel making use of the Nextflow workflow manager. The source code is available from GitHub (https://github.com/PGP-UK/GenomeChronicler), container recipes are available for Docker and Singularity, as well as a pre-built container from SingularityHub (https://singularity-hub.org/collections/3664) enabling easy deployment in a variety of settings. Users without access to computational resources to run GenomeChronicler can access the software from the Lifebit CloudOS platform (https://lifebit.ai/cloudos) enabling the production of reports and variant calls from raw sequencing data in a scalable fashion.

Increased DNA methylation variability in rheumatoid arthritis-discordant monozygotic twins.

BACKGROUND: Rheumatoid arthritis is a common autoimmune disorder influenced by both genetic and environmental factors. Epigenome-wide association studies can identify environmentally mediated epigenetic changes such as altered DNA methylation, which may also be influenced by genetic factors. To investigate possible contributions of DNA methylation to the aetiology of rheumatoid arthritis with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs. METHODS: Genome-wide DNA methylation was assessed in 79 monozygotic twin pairs discordant for rheumatoid arthritis using the HumanMethylation450 BeadChip array (Illumina). Discordant twins were tested for both differential DNA methylation and methylation variability between rheumatoid arthritis and healthy twins. The methylation variability signature was then compared with methylation variants from studies of other autoimmune diseases and with an independent healthy population. RESULTS: We have identified a differentially variable DNA methylation signature that suggests multiple stress response pathways may be involved in the aetiology of the disease. This methylation variability signature also highlighted potential epigenetic disruption of multiple RUNX3 transcription factor binding sites as being associated with disease development. Comparison with previously performed epigenome-wide association studies of rheumatoid arthritis and type 1 diabetes identified shared pathways for autoimmune disorders, suggesting that epigenetics plays a role in autoimmunity and offering the possibility of identifying new targets for intervention. CONCLUSIONS: Through genome-wide analysis of DNA methylation in disease-discordant monozygotic twins, we have identified a differentially variable DNA methylation signature, in the absence of differential methylation in rheumatoid arthritis. This finding supports the importance of epigenetic variability as an emerging component in autoimmune disorders.

Epigenetic and Transcriptional Variability Shape Phenotypic Plasticity.

Epigenetic and transcriptional variability contribute to the vast diversity of cellular and organismal phenotypes and are key in human health and disease. In this review, we describe different types, sources, and determinants of epigenetic and transcriptional variability, enabling cells and organisms to adapt and evolve to a changing environment. We highlight the latest research and hypotheses on how chromatin structure and the epigenome influence gene expression variability. Further, we provide an overview of challenges in the analysis of biological variability. An improved understanding of the molecular mechanisms underlying epigenetic and transcriptional variability, at both the intra- and inter-individual level, provides great opportunity for disease prevention, better therapeutic approaches, and personalized medicine.

Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types.

BACKGROUND: A healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability. RESULTS: We apply a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14+CD16- monocytes, CD66b+CD16+ neutrophils, and CD4+CD45RA+ naïve T cells from the same 125 healthy individuals. We discover substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression are found to be implicated in key immune pathways and are associated with cellular properties and environmental exposure. We also observe increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites are enriched at dynamic chromatin regions and active enhancers. CONCLUSIONS: Our data highlight the importance of transcriptional and epigenetic variability for the key role of neutrophils as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability .

Increased DNA methylation variability in type 1 diabetes across three immune effector cell types.

The incidence of type 1 diabetes (T1D) has substantially increased over the past decade, suggesting a role for non-genetic factors such as epigenetic mechanisms in disease development. Here we present an epigenome-wide association study across 406,365 CpGs in 52 monozygotic twin pairs discordant for T1D in three immune effector cell types. We observe a substantial enrichment of differentially variable CpG positions (DVPs) in T1D twins when compared with their healthy co-twins and when compared with healthy, unrelated individuals. These T1D-associated DVPs are found to be temporally stable and enriched at gene regulatory elements. Integration with cell type-specific gene regulatory circuits highlight pathways involved in immune cell metabolism and the cell cycle, including mTOR signalling. Evidence from cord blood of newborns who progress to overt T1D suggests that the DVPs likely emerge after birth. Our findings, based on 772 methylomes, implicate epigenetic changes that could contribute to disease pathogenesis in T1D.

Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells.

Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14+ monocytes, CD16+ neutrophils, and naive CD4+ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.

Whole-genome fingerprint of the DNA methylome during human B cell differentiation.

We analyzed the DNA methylome of ten subpopulations spanning the entire B cell differentiation program by whole-genome bisulfite sequencing and high-density microarrays. We observed that non-CpG methylation disappeared upon B cell commitment, whereas CpG methylation changed extensively during B cell maturation, showing an accumulative pattern and affecting around 30% of all measured CpG sites. Early differentiation stages mainly displayed enhancer demethylation, which was associated with upregulation of key B cell transcription factors and affected multiple genes involved in B cell biology. Late differentiation stages, in contrast, showed extensive demethylation of heterochromatin and methylation gain at Polycomb-repressed areas, and genes with apparent functional impact in B cells were not affected. This signature, which has previously been linked to aging and cancer, was particularly widespread in mature cells with an extended lifespan. Comparing B cell neoplasms with their normal counterparts, we determined that they frequently acquire methylation changes in regions already undergoing dynamic methylation during normal B cell differentiation.

Higher gene expression variability in the more aggressive subtype of chronic lymphocytic leukemia.

BACKGROUND: Chronic lymphocytic leukemia (CLL) presents two subtypes which have drastically different clinical outcomes, IgVH mutated (M-CLL) and IgVH unmutated (U-CLL). So far, these two subtypes are not associated to clear differences in gene expression profiles. Interestingly, recent results have highlighted important roles for heterogeneity, both at the genetic and at the epigenetic level in CLL progression. METHODS: We analyzed gene expression data of two large cohorts of CLL patients and quantified expression variability across individuals to investigate differences between the two subtypes using different measures and statistical tests. Functional significance was explored by pathway enrichment and network analyses. Furthermore, we implemented a random forest approach based on expression variability to classify patients into disease subtypes. RESULTS: We found that U-CLL, the more aggressive type of the disease, shows significantly increased variability of gene expression across patients and that, overall, genes that show higher variability in the aggressive subtype are related to cell cycle, development and inter-cellular communication. These functions indicate a potential relation between gene expression variability and the faster progression of this CLL subtype. Finally, a classifier based on gene expression variability was able to correctly predict the disease subtype of CLL patients. CONCLUSIONS: There are strong relations between gene expression variability and disease subtype linking significantly increased expression variability to phenotypes such as aggressiveness and resistance to therapy in CLL.

The synthetic glucocorticoids prednisolone and dexamethasone regulate the same genes in acute lymphoblastic leukemia cells.

BACKGROUND: Glucocorticoids (GCs) cause apoptosis in malignant cells of lymphoid lineage by transcriptionally regulating a plethora of genes. As a result, GCs are included in almost all treatment protocols for lymphoid malignancies, particularly childhood acute lymphoblastic leukemia (chALL). The most commonly used synthetic GCs in the clinical setting are prednisolone and dexamethasone. While the latter has a higher activity and more effectively reduces the tumor load in patients, it is also accompanied by more serious adverse effects than the former. Whether this difference might be explained by regulation of different genes by the two GCs has never been addressed. RESULTS: Using a recently developed GC bioassay based on a GC-responsive reporter construct in human Jurkat T-ALL cells, we found ~7-fold higher biological activity with dexamethasone than prednisolone. Similarly, 1.0e-7 M dexamethasone and 7.0e-7 M prednisolone triggered similar cell death rates in CCRF-CEM-C7H2 T-chALL cells after 72 hours of treatment. Using microarray-based whole genome expression profiling and a variety of statistical and other approaches, we compared the transcriptional response of chALL cells to 6 hour exposure to both synthetic GCs at the above concentrations. Our experiments did not detect any gene whose regulation by dexamethasone differed significantly from that by prednisolone. CONCLUSIONS: Our findings suggest that the reported differences in treatment efficacy and cytotoxicity of dexamethasone and prednisolone are not caused by inherent differences of the 2 drugs to regulate the expression of certain genes, but rather result either from applying them in biologically in-equivalent concentrations and/or from differences in their pharmacokinetics and - dynamics resulting in different bioactivities in tumor cells and normal tissues.

Transcriptome characterization by RNA sequencing identifies a major molecular and clinical subdivision in chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) has heterogeneous clinical and biological behavior. Whole-genome and -exome sequencing has contributed to the characterization of the mutational spectrum of the disease, but the underlying transcriptional profile is still poorly understood. We have performed deep RNA sequencing in different subpopulations of normal B-lymphocytes and CLL cells from a cohort of 98 patients, and characterized the CLL transcriptional landscape with unprecedented resolution. We detected thousands of transcriptional elements differentially expressed between the CLL and normal B cells, including protein-coding genes, noncoding RNAs, and pseudogenes. Transposable elements are globally derepressed in CLL cells. In addition, two thousand genes-most of which are not differentially expressed-exhibit CLL-specific splicing patterns. Genes involved in metabolic pathways showed higher expression in CLL, while genes related to spliceosome, proteasome, and ribosome were among the most down-regulated in CLL. Clustering of the CLL samples according to RNA-seq derived gene expression levels unveiled two robust molecular subgroups, C1 and C2. C1/C2 subgroups and the mutational status of the immunoglobulin heavy variable (IGHV) region were the only independent variables in predicting time to treatment in a multivariate analysis with main clinico-biological features. This subdivision was validated in an independent cohort of patients monitored through DNA microarrays. Further analysis shows that B-cell receptor (BCR) activation in the microenvironment of the lymph node may be at the origin of the C1/C2 differences.

Characterization of transcriptional changes in ERG rearrangement-positive prostate cancer identifies the regulation of metabolic sensors such as neuropeptide Y.

ERG gene rearrangements are found in about one half of all prostate cancers. Functional analyses do not fully explain the selective pressure causing ERG rearrangement during the development of prostate cancer. To identify transcriptional changes in prostate cancer, including tumors with ERG gene rearrangements, we performed a meta-analysis on published gene expression data followed by validations on mRNA and protein levels as well as first functional investigations. Eight expression studies (n = 561) on human prostate tissues were included in the meta-analysis. Transcriptional changes between prostate cancer and non-cancerous prostate, as well as ERG rearrangement-positive (ERG+) and ERG rearrangement-negative (ERG-) prostate cancer, were analyzed. Detailed results can be accessed through an online database. We validated our meta-analysis using data from our own independent microarray study (n = 57). 84% and 49% (fold-change>2 and >1.5, respectively) of all transcriptional changes between ERG+ and ERG- prostate cancer determined by meta-analysis were verified in the validation study. Selected targets were confirmed by immunohistochemistry: NPY and PLA2G7 (up-regulated in ERG+ cancers), and AZGP1 and TFF3 (down-regulated in ERG+ cancers). First functional investigations for one of the most prominent ERG rearrangement-associated genes - neuropeptide Y (NPY) - revealed increased glucose uptake in vitro indicating the potential role of NPY in regulating cellular metabolism. In summary, we found robust population-independent transcriptional changes in prostate cancer and first signs of ERG rearrangements inducing metabolic changes in cancer cells by activating major metabolic signaling molecules like NPY. Our study indicates that metabolic changes possibly contribute to the selective pressure favoring ERG rearrangements in prostate cancer.