Psychosocial experiences are associated with human brain mitochondrial biology.

Psychosocial experiences affect brain health and aging trajectories, but the molecular pathways underlying these associations remain unclear. Normal brain function relies on energy transformation by mitochondria oxidative phosphorylation (OxPhos). Two main lines of evidence position mitochondria both as targets and drivers of psychosocial experiences. On the one hand, chronic stress exposure and mood states may alter multiple aspects of mitochondrial biology; on the other hand, functional variations in mitochondrial OxPhos capacity may alter social behavior, stress reactivity, and mood. But are psychosocial exposures and subjective experiences linked to mitochondrial biology in the human brain? By combining longitudinal antemortem assessments of psychosocial factors with postmortem brain (dorsolateral prefrontal cortex) proteomics in older adults, we find that higher well-being is linked to greater abundance of the mitochondrial OxPhos machinery, whereas higher negative mood is linked to lower OxPhos protein content. Combined, positive and negative psychosocial factors explained 18 to 25% of the variance in the abundance of OxPhos complex I, the primary biochemical entry point that energizes brain mitochondria. Moreover, interrogating mitochondrial psychobiological associations in specific neuronal and nonneuronal brain cells with single-nucleus RNA sequencing (RNA-seq) revealed strong cell-type-specific associations for positive psychosocial experiences and mitochondria in glia but opposite associations in neurons. As a result, these "mind-mitochondria" associations were masked in bulk RNA-seq, highlighting the likely underestimation of true psychobiological effect sizes in bulk brain tissues. Thus, self-reported psychosocial experiences are linked to human brain mitochondrial phenotypes.

ZCCHC17 Modulates Neuronal RNA Splicing and Supports Cognitive Resilience in Alzheimer's Disease.

ZCCHC17 is a putative master regulator of synaptic gene dysfunction in Alzheimer's disease (AD), and ZCCHC17 protein declines early in AD brain tissue, before significant gliosis or neuronal loss. Here, we investigate the function of ZCCHC17 and its role in AD pathogenesis using data from human autopsy tissue (consisting of males and females) and female human cell lines. Co-immunoprecipitation (co-IP) of ZCCHC17 followed by mass spectrometry analysis in human iPSC-derived neurons reveals that ZCCHC17's binding partners are enriched for RNA-splicing proteins. ZCCHC17 knockdown results in widespread RNA-splicing changes that significantly overlap with splicing changes found in AD brain tissue, with synaptic genes commonly affected. ZCCHC17 expression correlates with cognitive resilience in AD patients, and we uncover an APOE4-dependent negative correlation of ZCCHC17 expression with tangle burden. Furthermore, a majority of ZCCHC17 interactors also co-IP with known tau interactors, and we find a significant overlap between alternatively spliced genes in ZCCHC17 knockdown and tau overexpression neurons. These results demonstrate ZCCHC17's role in neuronal RNA processing and its interaction with pathology and cognitive resilience in AD, and suggest that the maintenance of ZCCHC17 function may be a therapeutic strategy for preserving cognitive function in the setting of AD pathology.

demuxmix: demultiplexing oligonucleotide-barcoded single-cell RNA sequencing data with regression mixture models.

MOTIVATION: Droplet-based single-cell RNA sequencing (scRNA-seq) is widely used in biomedical research for interrogating the transcriptomes of single cells on a large scale. Pooling and processing cells from different samples together can reduce costs and batch effects. To pool cells, they are often first labeled with hashtag oligonucleotides (HTOs). These HTOs are sequenced alongside the cells' RNA in the droplets and subsequently used to computationally assign each droplet to its sample of origin, a process referred to as demultiplexing. Accurate demultiplexing is crucial but can be challenging due to background HTOs, low-quality cells/cell debris, and multiplets. RESULTS: A new demultiplexing method based on negative binomial regression mixture models is introduced. The method, called demuxmix, implements two significant improvements. First, demuxmix's probabilistic classification framework provides error probabilities for droplet assignments that can be used to discard uncertain droplets and inform about the quality of the HTO data and the success of the demultiplexing process. Second, demuxmix utilizes the positive association between detected genes in the RNA library and HTO counts to explain parts of the variance in the HTO data resulting in improved droplet assignments. The improved performance of demuxmix compared with existing demultiplexing methods is assessed using real and simulated data. Finally, the feasibility of accurately demultiplexing experimental designs where non-labeled cells are pooled with labeled cells is demonstrated. AVAILABILITY AND IMPLEMENTATION: R/Bioconductor package demuxmix (https://doi.org/doi:10.18129/B9.bioc.demuxmix).

Integration of GWAS and brain transcriptomic analyses in a multiethnic sample of 35,245 older adults identifies DCDC2 gene as predictor of episodic memory maintenance.

Identifying genes underlying memory function will help characterize cognitively resilient and high-risk declining subpopulations contributing to precision medicine strategies. We estimated episodic memory trajectories in 35,245 ethnically diverse older adults representing eight independent cohorts. We conducted apolipoprotein E (APOE)-stratified genome-wide association study (GWAS) analyses and combined individual cohorts' results via meta-analysis. Three independent transcriptomics datasets were used to further interpret GWAS signals. We identified DCDC2 gene significantly associated with episodic memory (Pmeta = 3.3 x 10-8 ) among non-carriers of APOE ε4 (N = 24,941). Brain transcriptomics revealed an association between episodic memory maintenance and (1) increased dorsolateral prefrontal cortex DCDC2 expression (P = 3.8 x 10-4 ) and (2) lower burden of pathological Alzheimer's disease (AD) hallmarks (paired helical fragment tau P = .003, and amyloid beta load P = .008). Additional transcriptomics results comparing AD and cognitively healthy brain samples showed a downregulation of DCDC2 levels in superior temporal gyrus (P = .007) and inferior frontal gyrus (P = .013). Our work identified DCDC2 gene as a novel predictor of memory maintenance.

Epigenomic features related to microglia are associated with attenuated effect of APOE ε4 on Alzheimer's disease risk in humans.

Not all apolipoprotein E (APOE) ε4 carriers who survive to advanced age develop Alzheimer's disease (AD); factors attenuating the risk of ε4 on AD may exist. Guided by the top ε4-attenuating signals from methylome-wide association analyses (N = 572, ε4+ and ε4-) of neurofibrillary tangles and neuritic plaques, we conducted a meta-analysis for pathological AD within the ε4+ subgroups (N = 235) across four independent collections of brains. Cortical RNA-seq and microglial morphology measurements were used in functional analyses. Three out of the four significant CpG dinucleotides were captured by one principal component (PC1), which interacts with ε4 on AD, and is associated with expression of innate immune genes and activated microglia. In ε4 carriers, reduction in each unit of PC1 attenuated the odds of AD by 58% (odds ratio = 2.39, 95% confidence interval = [1.64,3.46], P = 7.08 × 10-6 ). An epigenomic factor associated with a reduced proportion of activated microglia (epigenomic factor of activated microglia, EFAM) appears to attenuate the risk of ε4 on AD.

The association of epigenetic clocks in brain tissue with brain pathologies and common aging phenotypes.

Epigenetic clocks are calculated by combining DNA methylation states across select CpG sites to estimate biologic age, and have been noted as the most successful markers of biologic aging to date. Yet, limited research has considered epigenetic clocks calculated in brain tissue. We used DNA methylation states in dorsolateral prefrontal cortex specimens from 721 older participants of the Religious Orders Study and Rush Memory and Aging Project, to calculate DNA methylation age using four established epigenetic clocks: Hannum, Horvath, PhenoAge, GrimAge, and a new Cortical clock. The four established clocks were trained in blood samples (Hannum, PhenoAge, GrimAge) or using 51 human tissue and cell types (Horvath); the recent Cortical clock is the first trained in postmortem cortical tissue. Participants were recruited beginning in 1994 (Religious Orders Study) and 1997 (Memory and Aging Project), and followed annually with questionnaires and clinical evaluations; brain specimens were obtained for 80-90% of participants. Mean age at death was 88.0 (SD 6.7) years. We used linear regression, logistic regression, and linear mixed models, to examine relations of epigenetic clock ages to neuropathologic and clinical aging phenotypes, controlling for chronologic age, sex, education, and depressive symptomatology. Hannum, Horvath, PhenoAge and Cortical clock ages were related to pathologic diagnosis of Alzheimer's disease (AD), as well as to Aß load (a hallmark pathology of Alzheimer's disease). However, associations were substantially stronger for the Cortical than other clocks; for example, each standard deviation (SD) increase in Hannum, Horvath, and PhenoAge clock age was related to approximately 30% greater likelihood of pathologic AD (all p < 0.05), while each SD increase in Cortical age was related to 90% greater likelihood of pathologic AD (odds ratio = 1.91, 95% confidence interval 1.38, 2.62). Moreover, Cortical age was significantly related to other AD pathology (eg, mean tau tangle density, p = 0.003), and to odds of neocortical Lewy body pathology (for each SD increase in Cortical age, odds ratio = 2.00, 95% confidence 1.27, 3.17), although no clocks were related to cerebrovascular neuropathology. Cortical age was the only epigenetic clock significantly associated with the clinical phenotypes examined, from dementia to cognitive decline (5 specific cognitive systems, and a global cognitive measure averaging 17 tasks) to Parkinsonian signs. Overall, our findings provide evidence of the critical necessity for bespoke clocks of brain aging for advancing research to understand, and eventually prevent, neurodegenerative diseases of aging.

Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks.

Biomedical research studies have generated large multi-omic datasets to study complex diseases like Alzheimer's disease (AD). An important aim of these studies is the identification of candidate genes that demonstrate congruent disease-related alterations across the different data types measured by the study. We developed a new method to detect such candidate genes in large multi-omic case-control studies that measure multiple data types in the same set of samples. The method is based on a gene-centric integrative coefficient quantifying to what degree consistent differences are observed in the different data types. For statistical inference, a Bayesian hierarchical model is used to study the distribution of the integrative coefficient. The model employs a conditional autoregressive prior to integrate a functional gene network and to share information between genes known to be functionally related. We applied the method to an AD dataset consisting of histone acetylation, DNA methylation, and RNA transcription data from human cortical tissue samples of 233 subjects, and we detected 816 genes with consistent differences between persons with AD and controls. The findings were validated in protein data and in RNA transcription data from two independent AD studies. Finally, we found three subnetworks of jointly dysregulated genes within the functional gene network which capture three distinct biological processes: myeloid cell differentiation, protein phosphorylation and synaptic signaling. Further investigation of the myeloid network indicated an upregulation of this network in early stages of AD prior to accumulation of hyperphosphorylated tau and suggested that increased CSF1 transcription in astrocytes may contribute to microglial activation in AD. Thus, we developed a method that integrates multiple data types and external knowledge of gene function to detect candidate genes, applied the method to an AD dataset, and identified several disease-related genes and processes demonstrating the usefulness of the integrative approach.

EPIGENOMIC FEATURES RELATED TO MICROGLIA ARE ASSOCIATED WITH ATTENUATED EFFECT OF APOE ε4 ON ALZHEIMER'S DISEASE RISK IN HUMANS.

Not all APOE ε4 carriers who survive to advanced age develop Alzheimer's disease (AD); factors attenuating the risk of ε4 on AD may exist. Guided by the top ε4-attenuating signals from methylome-wide association analyses (N=572, ε4+ and ε4-) of neurofibrillary tangles and neuritic plaques, we conducted a meta-analysis for pathological AD within the ε4+ subgroups (N=235) across four independent collections of brains. Cortical RNA-seq and microglial morphology measurements were used in functional analyses. Three out of the four significant CpG dinucleotides were captured by one principle component (PC1), which interacts with ε4 on AD, and is associated with expression of innate immune genes and activated microglia. In ε4 carriers, reduction in each unit of PC1 attenuated the odds of AD by 58% (OR=2.39, 95%CI=[1.64,3.46], P=7.08x10-6). An epigenomic factor associated with a reduced proportion of activated microglia (microglial epigenomic factor 1) appears to attenuate the risk of ε4 on AD.

Rheumatoid arthritis-associated RBPJ polymorphism alters memory CD4+ T cells.

Notch signaling has recently emerged as an important regulator of immune responses in autoimmune diseases. The recombination signal-binding protein for immunoglobulin kappa J region (RBPJ) is a transcriptional repressor, but converts into a transcriptional activator upon activation of the canonical Notch pathway. Genome-wide association studies of rheumatoid arthritis (RA) identified a susceptibility locus, rs874040(CC), which implicated the RBPJ gene. Here, chromatin state mapping generated using the chromHMM algorithm reveals strong enhancer regions containing DNase I hypersensitive sites overlapping the rs874040 linkage disequilibrium block in human memory, but not in naïve CD4(+) T cells. The rs874040 overlapping this chromatin state was associated with increased RBPJ expression in stimulated memory CD4(+) T cells from healthy subjects homozygous for the risk allele (CC) compared with memory CD4(+) T cells bearing the protective allele (GG). Transcriptomic analysis of rs874040(CC) memory T cells showed a repression of canonical Notch target genes IL (interleukin)-9, IL-17 and interferon (IFN)γ in the basal state. Interestingly, activation of the Notch pathway using soluble Notch ligand, Jagged2-Fc, induced IL-9 and IL-17A while delta-like 4Fc, another Notch ligand, induced higher IFNγ expression in the rs874040(CC) memory CD4(+) T cells compared with their rs874040(GG) counterparts. In RA, RBPJ expression is elevated in memory T cells from RA patients compared with control subjects, and this was associated with induced inflammatory cytokines IL-9, IL-17A and IFNγ in response to Notch ligation in vitro. These findings demonstrate that the rs874040(CC) allele skews memory T cells toward a pro-inflammatory phenotype involving Notch signaling, thus increasing the susceptibility to develop RA.

An evaluation of methods to test predefined genomic regions for differential methylation in bisulfite sequencing data.

In the biology of tissue development and diseases, DNA methylation plays an important role. For a deeper understanding, it is crucial to accurately compare DNA methylation patterns between groups of samples representing different conditions. A widely used method to investigate DNA methylation in the CpG context is bisulfite sequencing, which produces data on the single-nucleotide scale. While there are benefits to analyzing CpG sites on a basepair level, there are both biological and statistical reasons to test entire genomic regions for differential methylation. However, the analysis of DNA methylation is hampered by the lack of best practice standards. Here, we compared multiple approaches for testing predefined genomic regions for differential DNA methylation in bisulfite sequencing data. Nine methods were evaluated: BiSeq, COHCAP, Goeman's Global Test, Limma, methylKit/eDMR, RADMeth and three log-linear regression approaches with different distribution assumptions. We applied these methods to simulated data and determined their sensitivity and specificity. This revealed performance differences, which were also seen when applied to real data. Methods that first test single CpG sites and then test regions based on transformed CpG-wise P-values performed better than methods that summarize methylation levels or raw reads. Interestingly, smoothing of methylation levels had a negligible impact. In particular, Global Test, BiSeq and RADMeth/z-test outperformed the other methods we evaluated, providing valuable guidance for more accurate analysis of DNA methylation.

Integrative analysis of multiple genomic variables using a hierarchical Bayesian model.

MOTIVATION: Genes showing congruent differences in several genomic variables between two biological conditions are crucial to unravel causalities behind phenotypes of interest. Detecting such genes is important in biomedical research, e.g. when identifying genes responsible for cancer development. Small sample sizes common in next-generation sequencing studies are a key challenge, and there are still only very few statistical methods to analyze more than two genomic variables in an integrative, model-based way. Here, we present a novel bioinformatics approach to detect congruent differences between two biological conditions in a larger number of different measurements such as various epigenetic marks or mRNA transcript levels. RESULTS: We propose a coefficient quantifying the degree to which genes present consistent alterations in multiple (more than two) genomic variables when comparing samples presenting a condition of interest (e.g. cancer) to a reference group. A hierarchical Bayesian model is employed to assess uncertainty on a gene level, incorporating information on functional relationships between genes. We demonstrate the approach on different data sets containing RNA-seq gene transcripton and up to four ChIP-seq histone modification measurements. Both the coefficient-based ranking and the inference based on the model lead to a plausible prioritizing of candidate genes when analyzing multiple genomic variables. AVAILABILITY AND IMPLEMENTATION: BUGS code in the Supplement. CONTACT: m.schaefer@uni-duesseldorf.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of genes associated with dissociation of cognitive performance and neuropathological burden: Multistep analysis of genetic, epigenetic, and transcriptional data.

INTRODUCTION: The molecular underpinnings of the dissociation of cognitive performance and neuropathological burden are poorly understood, and there are currently no known genetic or epigenetic determinants of the dissociation. METHODS AND FINDINGS: "Residual cognition" was quantified by regressing out the effects of cerebral pathologies and demographic characteristics on global cognitive performance proximate to death. To identify genes influencing residual cognition, we leveraged neuropathological, genetic, epigenetic, and transcriptional data available for deceased participants of the Religious Orders Study (n = 492) and the Rush Memory and Aging Project (n = 487). Given that our sample size was underpowered to detect genome-wide significance, we applied a multistep approach to identify genes influencing residual cognition, based on our prior observation that independent genetic and epigenetic risk factors can converge on the same locus. In the first step (n = 979), we performed a genome-wide association study with a predefined suggestive p < 10-5, and nine independent loci met this threshold in eight distinct chromosomal regions. Three of the six genes within 100 kb of the lead SNP are expressed in the dorsolateral prefrontal cortex (DLPFC): UNC5C, ENC1, and TMEM106B. In the second step, in the subset of participants with DLPFC DNA methylation data (n = 648), we found that residual cognition was related to differential DNA methylation of UNC5C and ENC1 (false discovery rate < 0.05). In the third step, in the subset of participants with DLPFC RNA sequencing data (n = 469), brain transcription levels of UNC5C and ENC1 were evaluated for their association with residual cognition: RNA levels of both UNC5C (estimated effect = -0.40, 95% CI -0.69 to -0.10, p = 0.0089) and ENC1 (estimated effect = 0.0064, 95% CI 0.0033 to 0.0096, p = 5.7 × 10-5) were associated with residual cognition. In secondary analyses, we explored the mechanism of these associations and found that ENC1 may be related to the previously documented effect of depression on cognitive decline, while UNC5C may alter the composition of presynaptic terminals. Of note, the TMEM106B allele identified in the first step as being associated with better residual cognition is in strong linkage disequilibrium with rs1990622A (r2 = 0.66), a previously identified protective allele for TDP-43 proteinopathy. Limitations include the small sample size for the genetic analysis, which was underpowered to detect genome-wide significance, the evaluation being limited to a single cortical region for epigenetic and transcriptomic data, and the use of categorical measures for certain non-amyloid-plaque, non-neurofibrillary-tangle neuropathologies. CONCLUSIONS: Through a multistep analysis of cognitive, neuropathological, genomic, epigenomic, and transcriptomic data, we identified ENC1 and UNC5C as genes with convergent genetic, epigenetic, and transcriptomic evidence supporting a potential role in the dissociation of cognition and neuropathology in an aging population, and we expanded our understanding of the TMEM106B haplotype that is protective against TDP-43 proteinopathy.

The epigenome in Alzheimer's disease: current state and approaches for a new path to gene discovery and understanding disease mechanism.

The advent of new technologies and analytic approaches is beginning to provide an unprecedented look at features of the human genome that affect RNA expression. These "epigenomic" features are found in a number of different forms: they include DNA methylation, covalent modifications of histone proteins and non-coding RNAs. Some of these features have now been implicated in Alzheimer's disease (AD). Here, we focus on recent studies that have identified robust observations relating to DNA methylation and chromatin in human brain tissue; these findings will ground the next generation of studies and provide a model for the design of such studies. Stemming from observations that compounds with histone deacetylase activity may be beneficial in AD, epigenome-wide studies in cortical samples from large numbers of human subjects have now shown that AD-associated epigenomic changes are reproducible, are not driven by genetic risk factors, and are widespread at specific locations in the genome. A fundamental question of whether such changes are causal remains to be demonstrated, but it is already clear that well-powered investigations of the human epigenome in the target organ of a neurodegenerative disease are feasible, are implicating new areas of the genome in the disease, and will be an important tool for future studies. We are now at an inflection point: as genome-wide association studies of genetic variants come to an end, a new generation of studies exploring the epigenome will provide an important new layer of information with which to enrich our understanding of AD pathogenesis and to possibly guide development of new therapeutic targets.

Epigenetic dysregulation of KCa 3.1 channels induces poor prognosis in lung cancer.

Epigenomic changes are an important feature of malignant tumors. How tumor aggressiveness is affected by DNA methylation of specific loci is largely unexplored. In genome-wide DNA methylation analyses, we identified the KCa 3.1 channel gene (KCNN4) promoter to be hypomethylated in an aggressive non-small-cell lung carcinoma (NSCLC) cell line and in patient samples. Accordingly, KCa 3.1 expression was increased in more aggressive NSCLC cells. Both findings were strong predictors for poor prognosis in lung adenocarcinoma. Increased KCa 3.1 expression was associated with aggressive features of NSCLC cells. Proliferation and migration of pro-metastatic NSCLC cells depended on KCa 3.1 activity. Mechanistically, elevated KCa 3.1 expression hyperpolarized the membrane potential, thereby augmenting the driving force for Ca(2+) influx. KCa 3.1 blockade strongly reduced the growth of xenografted NSCLC cells in mice as measured by positron emission tomography-computed tomography. Thus, loss of DNA methylation of the KCNN4 promoter and increased KCa 3.1 channel expression and function are mechanistically linked to poor survival of NSCLC patients.

Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review.

A variety of genome-wide profiling techniques are available to investigate complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we highlight common approaches to genomic data integration and provide a transparent benchmarking procedure to quantitatively compare method performances in cancer gene prioritization. Algorithms, data sets and benchmarking results are available at http://intcomp.r-forge.r-project.org.

Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models.

MOTIVATION: Histone modifications are a key epigenetic mechanism to activate or repress the transcription of genes. Datasets of matched transcription data and histone modification data obtained by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatics approach to detect genes that show different transcript abundances between two conditions putatively caused by alterations in histone modification. RESULTS: We introduce a correlation measure for integrative analysis of ChIP-seq and gene transcription data measured by RNA sequencing or microarrays and demonstrate that a proper normalization of ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different types of distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene transcription and histone modification. The method is applied to different datasets, and its superiority to a naive separate analysis of both data types is demonstrated. AVAILABILITY AND IMPLEMENTATION: R/Bioconductor package epigenomix. CONTACT: h.klein@uni-muenster.de Supplementary information: Supplementary data are available at Bioinformatics online.

Genomic instability may originate from imatinib-refractory chronic myeloid leukemia stem cells.

Genomic instability is a hallmark of chronic myeloid leukemia in chronic phase (CML-CP) resulting in BCR-ABL1 mutations encoding resistance to tyrosine kinase inhibitors (TKIs) and/or additional chromosomal aberrations leading to disease relapse and/or malignant progression. TKI-naive and TKI-treated leukemia stem cells (LSCs) and leukemia progenitor cells (LPCs) accumulate high levels of reactive oxygen species (ROS) and oxidative DNA damage. To determine the role of TKI-refractory LSCs in genomic instability, we used a murine model of CML-CP where ROS-induced oxidative DNA damage was elevated in LSCs, including quiescent LSCs, but not in LPCs. ROS-induced oxidative DNA damage in LSCs caused clinically relevant genomic instability in CML-CP-like mice, such as TKI-resistant BCR-ABL1 mutations (E255K, T315I, H396P), deletions in Ikzf1 and Trp53, and additions in Zfp423 and Idh1. Despite inhibition of BCR-ABL1 kinase, imatinib did not downregulate ROS and oxidative DNA damage in TKI-refractory LSCs to the levels detected in normal cells, and CML-CP-like mice treated with imatinib continued to accumulate clinically relevant genetic aberrations. Inhibition of class I p21-activated protein kinases by IPA3 downregulated ROS in TKI-naive and TKI-treated LSCs. Altogether, we postulate that genomic instability may originate in the most primitive TKI-refractory LSCs in TKI-naive and TKI-treated patients.

DNA methylation changes are a late event in acute promyelocytic leukemia and coincide with loss of transcription factor binding.

The origin of aberrant DNA methylation in cancer remains largely unknown. In the present study, we elucidated the DNA methylome in primary acute promyelocytic leukemia (APL) and the role of promyelocytic leukemia-retinoic acid receptor α (PML-RARα) in establishing these patterns. Cells from APL patients showed increased genome-wide DNA methylation with higher variability than healthy CD34(+) cells, promyelocytes, and remission BM cells. A core set of differentially methylated regions in APL was identified. Age at diagnosis, Sanz score, and Flt3-mutation status characterized methylation subtypes. Transcription factor-binding sites (eg, the c-myc-binding sites) were associated with low methylation. However, SUZ12- and REST-binding sites identified in embryonic stem cells were preferentially DNA hypermethylated in APL cells. Unexpectedly, PML-RARα-binding sites were also protected from aberrant DNA methylation in APL cells. Consistent with this, myeloid cells from preleukemic PML-RARα knock-in mice did not show altered DNA methylation and the expression of PML-RARα in hematopoietic progenitor cells prevented differentiation without affecting DNA methylation. Treatment of APL blasts with all-trans retinoic acid also did not result in immediate DNA methylation changes. The results of the present study suggest that aberrant DNA methylation is associated with leukemia phenotype but is not required for PML-RARα-mediated initiation of leukemogenesis.

BRCC3 mutations in myeloid neoplasms.

Next generation sequencing technologies have provided insights into the molecular heterogeneity of various myeloid neoplasms, revealing previously unknown somatic genetic events. In our cohort of 1444 cases analyzed by next generation sequencing, somatic mutations in the gene BRCA1-BRCA2-containing complex 3 (BRCC3) were identified in 28 cases (1.9%). BRCC3 is a member of the JAMM/MPN+ family of zinc metalloproteases capable of cleaving Lys-63 linked polyubiquitin chains, and is implicated in DNA repair. The mutations were located throughout its coding region. The average variant allelic frequency of BRCC3 mutations was 30.1%, and by a serial sample analysis at two different time points a BRCC3 mutation was already identified in the initial stage of a myelodysplastic syndrome. BRCC3 mutations commonly occurred in nonsense (n=12), frameshift (n=4), and splice site (n=5) configurations. Due to the marginal male dominance (odds ratio; 2.00, 0.84-4.73) of BRCC3 mutations, the majority of mutations (n=23; 82%) were hemizygous. Phenotypically, BRCC3 mutations were frequently observed in myelodysplastic syndromes and myelodysplastic/myeloproliferative neoplasms and associated with -Y abnormality (odds ratio; 3.70, 1.25-11.0). Clinically, BRCC3 mutations were also related to higher age (P=0.01), although prognosis was not affected. Knockdown of Brcc3 gene expression in murine bone marrow lineage negative, Sca1 positive, c-kit positive cells resulted in 2-fold more colony formation and modest differentiation defect. Thus, BRCC3 likely plays a role as tumor-associated gene in myelodysplastic syndromes and myelodysplastic/myeloproliferative neoplasms.

A new workflow for whole-genome sequencing of single human cells.

Unbiased amplification of the whole-genome amplification (WGA) of single cells is crucial to study cancer evolution and genetic heterogeneity, but is challenging due to the high complexity of the human genome. Here, we present a new workflow combining an efficient adapter-linker PCR-based WGA method with second-generation sequencing. This approach allows comparison of single cells at base pair resolution. Amplification recovered up to 74% of the human genome. Copy-number variants and loss of heterozygosity detected in single cell genomes showed concordance of up to 99% to pooled genomic DNA. Allele frequencies of mutations could be determined accurately due to an allele dropout rate of only 2%, clearly demonstrating the low bias of our PCR-based WGA approach. Sequencing with paired-end reads allowed genome-wide analysis of structural variants. By direct comparison to other WGA methods, we further endorse its suitability to analyze genetic heterogeneity.