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Selenium is an important micronutrient for foetal development. MicroRNAs play an important role in the function of the placenta, in communication between the placenta and maternal systems, and their expression can be altered through environmental and nutritional cues. To investigate the associations between placental selenium concentration and microRNA expression in the placenta, our observational study included 393 mother-child pairs from the New Hampshire Birth Cohort Study (NHBCS) and the Rhode Island Child Health Study (RICHS). Placental selenium concentrations were quantified using inductively coupled plasma mass spectrometry, and microRNA transcripts were measured using RNA-seq. We fit negative binomial additive models for assessing the association between selenium and microRNAs. We used the microRNA Data Integration Portal (mirDIP) to predict the target mRNAs of the differentially expressed microRNAs and verified the relationships between miRNA and mRNA targets in a subset of samples using existing whole transcriptome data (N = 199). We identified a non-monotonic association between selenium concentration and the expression of miR-216a-5p/miR-217-5p cluster (effective degrees of freedom, EDF = 2.44 and 2.08; FDR = 3.08 × 10−5) in placenta. Thirty putative target mRNAs of miR-216a-5p and/or miR-217-5p were identified computationally and empirically and were enriched in selenium metabolic pathways (driven by selenoprotein coding genes, TXNRD2 and SELENON). Our findings suggest that selenium influences placental microRNA expression. Further, miR-216a-5p and its putative target mRNAs could be the potential mechanistic targets of the health effect of selenium.

BackgroundTo reduce lung cancer burden in the US, a better understanding of biological mechanisms in early disease development could provide new opportunities for risk stratification.MethodsIn a nested case-control study, we measured blood leukocyte DNA methylation levels in pre-diagnostic samples collected from 430 men and women in the 1989 CLUE II cohort. Median time from blood drawn to diagnosis was 14 years for all participants. We compared DNA methylation levels by case/control status to identify novel genomic regions, both single CpG sites and differentially methylated regions (DMRs), while controlling for known DNA methylation changes associated with smoking using a previously described pack-years based smoking methylation score. Stratification analyses were conducted by time from blood draw to diagnosis, histology, and smoking status.ResultsWe identified sixteen single CpG sites and forty DMRs significantly associated with lung cancer risk (q < 0.05). The identified genomic regions were associated with genes including H19, HOXA4, RUNX3, BRICD5, PLXNB2, and RP13. For the single CpG sites, the strongest association was noted for cg09736286 in the DIABLO gene (OR [for 1 SD] = 2.99, 95% CI: 1.95-4.59, P-value = 4.81 × 10−7). For the DMRs, we found that CpG sites in the HOXA4 region were hypermethylated in cases compared to controls.ConclusionThe single CpG sites and DMRs that we identified represented significant measurable differences in lung cancer risk, providing new insights into the biological processes of early lung cancer development and potential biomarkers for lung cancer risk stratification.

The mammalian kidney has extensive repair capacity; however, identifying adult renal stem cells has proven elusive. We applied an epigenetic marker of foetal cell origin (FCO) in diverse human tissues as a probe for developmental cell persistence, finding a 5.4-fold greater FCO proportion in kidney. Normal kidney FCO proportions averaged 49% with extensive interindividual variation. FCO proportions were significantly negatively correlated with immune-related gene expression and positively correlated with genes expressed in the renal medulla, including those involved in renal organogenesis (e.g., FGF2, PAX8, and HOXB7). FCO associated genes also mapped to medullary nephron segments in mouse and rat, suggesting evolutionary conservation of this cellular compartment. Renal cancer patients whose tumours contained non-zero FCO scores survived longer. The kidney appears unique in possessing substantial foetal epigenetic features. Further study of FCO-related gene methylation may elucidate regenerative regulatory programmes in tissues without apparent discrete stem cell compartments.

Structured AbstractBackgroundPancreatic cancer is projected to become the second leading cause of cancer-related death by 2030 in the United States. DNA methylation (DNAm) age may reflect age-related variations in the biological changes and abnormalities related to cancer development.MethodWe conducted a pooled analysis using prediagnostic blood samples of pancreatic cancer cases and matched controls selected from the Nurses’ Health Study (NHS), the Physician’s Health Study (PHS), and the Health Professionals Follow-up Study (HPFS). We used three DNAm aging clocks (Hannum, Horvath, and PhenoAge) to estimate subjects’ DNAm age, epigenetic age acceleration (AA) and intrinsic epigenetic age acceleration (IEAA) metrics. We performed conditional logistic regression and multivariable Cox proportional hazard regression to examine associations between six AA and IEAA metrics and risk of pancreatic cancer and survival, respectively.ResultsA total of 393 incidence pancreatic cancer cases and 431 matched controls from the NHS, PHS, and HPFS cohorts were included in this analysis. The medians of all three epigenetic AA and three IEAA metrics were consistently above zero (indicating accelerated age) among cases, while they were below zero (indicating decelerated age) among the matched controls. Comparing participants in the highest quartile of age acceleration metrics, the pancreatic cancer risks were significantly increased by 67% to 83% for Hannum and PhenoAge AA or IEAA metrics with minimal of 7- to 9-years accelerated ages. Except for Hovarth AA and IEAA metrics, there were significant dose-response trends, such that higher age accelerations were associated with higher pancreatic cancer risk, but the relationships were nonlinear. Stratified analyses showed heterogeneous associations, varying by participants’ characteristics and by epigenetic AA or IEAA metrics. As time to diagnosis increased, the ORs of pancreatic cancer for the Hannum AA and Horvath AA or IEAA metrics trended upwards, while the ORs for the PhenoAge AA or IEAA and Hannum IEAA metrics trended downward. Overall, we observed no significant association between pancreatic cancer survival and any of the prediagnostic epigenetic AA or IEAA metrics.ConclusionOur results indicate DNAm age acceleration is associated with an increased risk of pancreatic cancer in a nonlinear, dose-response manner. Epigenetic IEAA metrics may be a useful addition to current methods for pancreatic cancer risk prediction.

MicroRNAs are non-coding RNAs that regulate gene expression post-transcriptionally. In the placenta, the master regulator of fetal growth and development, microRNAs shape the basic processes of trophoblast biology and specific microRNA have been associated with fetal growth. To comprehensively assess the role of microRNAs in placental function and fetal development, we have performed small RNA sequencing to profile placental microRNAs from two independent mother-infant cohorts: the Rhode Island Child Health Study (n=225) and the New Hampshire Birth Cohort Study (n=317). We modeled microRNA counts on infant birthweight percentile (BWP) in each cohort, while accounting for race, sex, parity and technical factors, using negative binomial generalized linear models. We identified microRNAs that were differentially expressed (DEmiRs) with BWP at false discovery rate (FDR) less than 0.05 in both cohorts. hsa-miR-532-5p (miR-532) was positively associated with BWP in both cohorts. By integrating parallel whole transcriptome and small RNA sequencing in the RICHS cohort, we identified putative targets of miR-532. These targets are enriched for pathways involved in adipogenesis, adipocytokine signaling, energy metabolism and hypoxia response, and included Leptin, which we further demonstrated to have decreasing expression with increasing BWP, particularly in male infants. Overall, we have shown a robust and reproducible association of miR-532 with BWP, which could influence BWP through regulation of adipocytokines Leptin and Adiponectin.

Objective The aim of this study was to test whether increased energy expenditure (EE), independent of physical activity, reduces acute diet-induced weight gain through tighter coupling of energy intake to energy demand and enhanced metabolic adaptations. Methods Indirect calorimetry and quantitative magnetic resonance imaging were used to assess energy metabolism and body composition during 7-day high-fat/high-sucrose (HFHS) feeding in male and female mice housed at divergent temperatures (20°C vs. 30°C). Results As previously observed, 30°C housing resulted in lower total EE and energy intake compared with 20°C mice regardless of sex. Interestingly, housing temperature did not impact HFHS-induced weight gain in females, whereas 30°C male mice gained more weight than 20°C males. Energy intake coupling to EE during HFHS feeding was greater in 20°C versus 30°C housing, with females greater at both temperatures. Fat mass gain was greater in 30°C mice compared with 20°C mice, whereas females gained less fat mass than males. Strikingly, female 20°C mice gained considerably more fat-free mass than 30°C mice. Reduced fat mass gain was associated with greater metabolic flexibility to HFHS, whereas fat-free mass gain was associated with diet-induced adaptive thermogenesis. Conclusions These data reveal that EE and sex interact to impact energy homeostasis and metabolic adaptation to acute HFHS feeding, altering weight gain and body composition change.

Mitochondria contribute to tumor growth through multiple metabolic pathways, regulation of extracellular pH, calcium signaling, and apoptosis. Using the Mitochondrial Nuclear Exchange (MNX) mouse models, which pair nuclear genomes with different mitochondrial genomes, we previously showed that mitochondrial SNPs regulate mammary carcinoma tumorigenicity and metastatic potential in genetic crosses. Here, we tested the hypothesis that polymorphisms in stroma significantly affect tumorigenicity and experimental lung metastasis. Using syngeneic cancer cells (EO771 mammary carcinoma and B16-F10 melanoma cells) injected into wild-type and MNX mice (i.e., same nuclear DNA but different mitochondrial DNA), we showed mt-SNP–dependent increases (C3H/HeN) or decreases (C57BL/6J) in experimental metastasis. Superoxide scavenging reduced experimental metastasis. In addition, expression of lung nuclear-encoded genes changed specifically with mt-SNP. Thus, mitochondrial–nuclear cross-talk alters nuclear-encoded signaling pathways that mediate metastasis via both intrinsic and extrinsic mechanisms.Significance:Stromal mitochondrial polymorphisms affect metastatic colonization through reactive oxygen species and mitochondrial–nuclear cross-talk.

DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, naïve and memory B cells, naïve and memory CD4 + and CD8 + T cells, natural killer, and T regulatory cells). Including derived variables, our method provides 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data for current and previous platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of immune profiles in human health and disease.

Background Exposure to the herbicide Agent Orange during the Vietnam War was widespread and is associated with numerous adverse health outcomes. A continuing concern of veterans is the possibility that exposure to the dioxin-containing herbicide might induce adverse reproductive outcomes. We sought to assess whether exposure to Agent Orange in Vietnam was associated with changes in DNA methylation in sperm in a subset of Vietnam veterans who participated in the Air Force Health Study (AFHS). Methods We studied 37 members of the AFHS chosen to have no, low, medium or high exposure to Agent Orange, based upon serum dioxin levels obtained during a series of examinations. DNA from stored semen was extracted and DNA methylation assessed on the Illumina 450 K platform. Results Initial epigenome-wide analysis returned no loci that survived control for false discovery. However, the TEAD3 gene had four different CpG sites that showed loss of DNA methylation associated with dioxin exposure. Analysis assessing regional DNA methylation changes revealed 36 gene regions, including the region of the imprinted gene H19 to have altered DNA methylation associated with high exposure compared to the low exposure group. Additional comparison of our data with sperm DNA methylation data from Russian boys exposed to dioxin found an additional 5 loci that were altered in both studies and exhibited a consistent direction of association. Conclusions Studying a small number of sperm samples from veterans enrolled in the AFHS, we did not find evidence of significant epigenome-wide alterations associated with exposure to Agent Orange. However, additional analysis showed that the H19 gene region is altered in the sperm of Agent Orange-exposed Ranch Hand veterans. Our study also replicated several findings of a prior study of dioxin-exposed Russian boys. These results provide additional candidate loci for further investigation and may have implications for the reproductive health of dioxin-exposed individuals.

The impact of sexual dimorphism and mitophagy on hepatic mitochondrial adaptations during the treatment of steatosis with physical activity are largely unknown. Here, we tested if deficiencies in liver-specific peroxisome proliferative activated-receptor-γ coactivator-1α (PGC-1α), a transcriptional coactivator of biogenesis, and BCL-2/ADENOVIRUS EIB 19-kDa interacting protein (BNIP3), a mitophagy regulator, would impact hepatic mitochondrial adaptations (respiratory capacity, H2O2production, mitophagy) to a high-fat diet (HFD) and HFD plus physical activity via voluntary wheel running (VWR) in both sexes. Male and female wild-type (WT), liver-specific PGC-1α heterozygote (LPGC-1α), and BNIP3 null mice were thermoneutral housed (29–31°C) and divided into three groups: sedentary-low-fat diet (LFD), 16 wk of (HFD), or 16 wk of HFD with VWR for the final 8 wk (HFD + VWR) ( n = 5–7/sex/group). HFD did not impair mitochondrial respiratory capacity or coupling in any group; however, HFD + VWR significantly increased maximal respiratory capacity only in WT and PGC-1α females. Males required VWR to elicit mitochondrial adaptations that were inherently present in sedentary females including greater mitochondrial coupling control and reduced H2O2production. Females had overall reduced markers of mitophagy, steatosis, and liver damage. Steatosis and markers of liver injury were present in sedentary male mice on the HFD and were effectively reduced with VWR despite no resolution of steatosis. Overall, reductions in PGC-1α and loss of BNIP3 only modestly impacted mitochondrial adaptations to HFD and HFD + VWR with the biggest effect seen in BNIP3 females. In conclusion, hepatic mitochondrial adaptations to HFD and treatment of HFD-induced steatosis with VWR are more dependent on sex than PGC-1α or BNIP3.

In the United States, cardiovascular disease is the leading cause of death, and the rate of maternal mortality remains among the highest of any industrialized nation. Maternal cardiometabolic health throughout gestation and postpartum is representative of placental health and physiology. Both proper placental functionality and placental microRNA expression are essential to successful pregnancy outcomes, and both are highly sensitive to genetic and environmental sources of variation. While placental pathologies, such as preeclampsia, are associated with maternal cardiovascular health and may contribute to the developmental programming of cardiovascular disease, the role of more subtle alterations to placental function and microRNA expression in this relationship remains poorly understood. To develop a more comprehensive understanding of how cardiometabolic health influences placental microRNA expression, and how this shapes placental functionality, we performed small RNA sequencing to investigate microRNA in the placentae from the Rhode Island Child Health Study (n=230). We modeled microRNA counts on maternal family history of cardiovascular disease using negative binomial generalized linear models, and identified microRNAs that were differential expressed (DEmiRs) at a false discovery rate (FDR) less than 0.10. Utilizing parallel mRNA sequencing data and bioinformatic target prediction software, we identified potential mRNA targets of these DEmiRs. We identified 9 DEmiRs, with predicted targets of those miRNA enriched overwhelmingly in the TGFβ signaling pathway but also in pathways involving cellular metabolism and immunomodulation. Overall, we identified a robust association existing between familial cardiovascular disease and placental microRNA expression which may be implicated in both placental insufficiencies and the developmental programming of cardiovascular disease.

Ketogenic diets (KDs) alter brain metabolism. Multiple mechanisms may account for their effects, and different brain regions may variably respond. Here, we considered how a KD affects brain neuron and astrocyte transcription. We placed male C57Bl6/N mice on either a 3-month KD or chow diet, generated enriched neuron and astrocyte fractions, and used RNA-Seq to assess transcription. Neurons from KD-treated mice generally showed transcriptional pathway activation while their astrocytes showed a mix of transcriptional pathway suppression and activation. The KD especially affected pathways implicated in mitochondrial and endoplasmic reticulum function, insulin signaling, and inflammation. An unbiased analysis of KD-associated expression changes strongly implicated transcriptional pathways altered in AD, which prompted us to explore in more detail the potential molecular relevance of a KD to AD. Our results indicate a KD differently affects neurons and astrocytes, and provide unbiased evidence that KD-induced brain effects are potentially relevant to neurodegenerative diseases such as AD.

Statistical methods that allow for cell type specific DNA methylation (DNAm) analyses based on bulk-tissue methylation data have great potential to improve our understanding of human disease and have created unprecedented opportunities for new insights using the wealth of publicly available bulk-tissue methylation data. These methodologies involve incorporating interaction terms formed between the phenotypes/exposures of interest and proportions of the cell types underlying the bulk-tissue sample used for DNAm profiling. Despite growing interest in such “interaction-based” methods, there has been no comprehensive assessment how variability in the cellular landscape across study samples affects their performance. To answer this question, we used numerous publicly available whole-blood DNAm data sets along with extensive simulation studies and evaluated the performance of interaction-based approaches in detecting cell-specific methylation effects. Our results show that low cell proportion variability results in large estimation error and low statistical power for detecting cell-specific effects of DNAm. Further, we identified that many studies targeting methylation profiling in whole-blood may be at risk to be underpowered due to low variability in the cellular landscape across study samples. Finally, we discuss guidelines for researchers seeking to conduct studies utilizing interaction-based approaches to help ensure that their studies are adequately powered.

Background: Oral microbiota is believed to play important roles in systemic diseases, including cancer. Methods: We collected oral samples (tongue, buccal, supragingival, and saliva) and pancreatic tissue or intestinal samples from 52 subjects, and characterized 16S rRNA genes using high-throughput DNA sequencing. Results: Bray–Curtis plot showed clear separations between bacterial communities in the oral cavity and those in intestinal and pancreatic tissue samples. PERMANOVA tests indicated that bacterial communities from buccal samples were similar to supragingival and saliva samples, and pancreatic duct samples were similar to pancreatic tumor samples, but all other samples were significantly different from each other. A total of 73 unique Amplicon Sequence Variants (ASVs) were shared between oral and pancreatic or intestinal samples. Only four ASVs showed significant concordance, and two specific bacterial species (Gemella morbillorum and Fusobacterium nucleatum subsp. vincentii) showed consistent presence or absence patterns between oral and intestinal or pancreatic samples, after adjusting for within-subject correlation and disease status. Lastly, microbial co-abundance analyses showed distinct strain-level cluster patterns among microbiome members in buccal, saliva, duodenum, jejunum, and pancreatic tumor samples. Conclusions: Our findings indicate that oral, intestinal, and pancreatic bacterial microbiomes overlap but exhibit distinct co-abundance patterns in patients with pancreatic cancer and other gastrointestinal diseases.

Breast Cancer Metastasis Suppressor 1 (BRMS1) expression is associated with longer patient survival in multiple cancer types. Understanding BRMS1 functionality will provide insights into both mechanism of action and will enhance potential therapeutic development. In this study, we confirmed that the C-terminus of BRMS1 is critical for metastasis suppression and hypothesized that critical protein interactions in this region would explain its function. Phosphorylation status at S237 regulates BRMS1 protein interactions related to a variety of biological processes, phenotypes [cell cycle (e.g., CDKN2A), DNA repair (e.g., BRCA1)], and metastasis [(e.g., TCF2 and POLE2)]. Presence of S237 also directly decreased MDA-MB-231 breast carcinoma migrationin vitroand metastasesin vivo. The results add significantly to our understanding of how BRMS1 interactions with Sin3/HDAC complexes regulate metastasis and expand insights into BRMS1’s molecular role, as they demonstrate BRMS1 C-terminus involvement in distinct protein-protein interactions.

Coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the pressing contemporary public health challenges. Investigations into the genomic structure of SARS-CoV-2 may inform ongoing vaccine development efforts and/or provide insights into vaccine efficacy to fight against COVID-19. Evolutionary analysis of 540 genomes spanning 20 different countries/territories was conducted and revealed an increase in the genomic divergence across successive generations. The ancestor of the phylogeny was found to be the isolate from the 2019/2020 Wuhan outbreak. Its transmission was outlined across 20 countries/territories as per genomic similarity. Our results demonstrate faster evolving variations in the genomic structure of SARS-CoV-2 when compared to the isolates from early stages of the pandemic. Genomic alterations were predominantly located and mapped onto the reported vaccine candidates of structural genes, which are the main targets for vaccine candidates. S protein showed 34, N protein 25, E protein 2, and M protein 3 amino acid variations in 246 genomes among 540. Among identified mutations, 23 in S protein, 1 in E, 2 from M, and 7 from N protein were mapped with the reported vaccine candidates explaining the possible implications on universal vaccines. Hence, potential target regions for vaccines would be ideally chosen from the structural regions of the genome that lack high variation. The increasing variations in the genome of SARS-CoV-2 together with our observations in structural genes have important implications for the efficacy of a successful universal vaccine against SARS-CoV-2.

BackgroundTP53 mutations occur in more than 50% of cancers. We sought to determine the effect of the intragenic P72R single nucleotide polymorphism (SNP; rs1042522) on the oncogenic properties of mutant p53.MethodsP72R allelic selection in tumors was determined from genotype calls and a Gaussian distributed mixture model. The SNP effect on mutant p53 was determined in p53-negative cancer cell lines. RNA-sequencing, chromatin immunoprecipitation, and survival analysis were performed to describe the SNP effect. All statistical tests were 2-sided.ResultsAmong 409 patients with germline heterozygous P72R SNP who harbored somatic mutations in TP53, we observed a selection bias against missense TP53 mutants encoding the P72 SNP (P = 1.64 x 10-13). Exogenously expressed hotspot p53 mutants with the P72 SNP were negatively selected in cancer cells. Gene expression analyses showed the enrichment of p53 pathway genes and inflammatory genes in cancer cells transduced with mutants encoding P72 SNP. Immune gene signature is enriched in patients harboring missense TP53 mutations with homozygous P72 SNP. These patients have improved overall survival as compared with those with the R72 SNP (P = .04).ConclusionThis is the largest study demonstrating a selection against the P72 SNP. Missense p53 mutants with the P72 SNP retain partial wild-type tumor-suppressive functions, which may explain the selection bias against P72 SNP across cancer types. Ovarian cancer patients with the P72 SNP have a better prognosis than with the R72 SNP. Our study describes a previously unknown role through which the rs1042522 SNP modifies tumor suppressor activities of mutant p53 in patients.

Regulatory T cells (Tregs) are a subset of immune cells that suppress the immune response. Treg therapy for inflammatory diseases is being tested in the clinic, with moderate success. However, it is difficult to isolate and expand Tregs to sufficient numbers. Engineered Tregs (eTregs) can be generated in larger quantities by genetically manipulating conventional T cells to express FOXP3. These eTregs can suppress in vitro and in vivo but not as effectively as endogenous Tregs. We hypothesized that ectopic expression of the transcription factor Helios along with FOXP3 is required for optimal eTreg immunosuppression. To test this theory, we generated eTregs by retrovirally transducing total human T cells (CD4+ and CD8+) with FOXP3 alone or with each of the 2 predominant isoforms of Helios. Expression of both FOXP3 and the full-length isoform of Helios was required for eTreg-mediated disease delay in a xenogeneic graft-versus-host disease model. In vitro, this corresponded with superior suppressive function of FOXP3 and full-length Helios-expressing CD4+ and CD8+ eTregs. RNA sequencing showed that the addition of full-length Helios changed gene expression in cellular pathways and the Treg signature compared with FOXP3 alone or the other major Helios isoform. Together, these results show that functional human CD4+ and CD8+ eTregs can be generated from total human T cells by coexpressing FOXP3 and full-length Helios.

Oral microbiota are believed to play important roles in systemic diseases, including cancer. We collected oral swabs and at least one pancreatic tissue or intestinal samples from 52 subjects, and characterized 16S ribosomal RNA genes using high-throughput DNA sequencing in a total 324 samples. We identified a total of 73 unique Amplicon Sequence Variants (ASVs) that were shared between oral and pancreatic or intestinal samples. Accounting for pairing and within-subject correlation, 7 ASVs showed significant concordance (Kappa statistics) and 5 ASVs exhibited significant or marginally significant Pairwise Stratified Association (PASTA) between oral samples and pancreatic tissue or intestinal samples. Of these, two specific bacterial species (Gemella morbillorum and Fusobacterium nucleatum subsp. vincentii) showed consistent presence or absence patterns between oral and intestinal or pancreatic samples. Lastly, our microbial co-abundance analyses showed several distinct ASVs clusters and complex correlation-networks between ASV clusters in buccal, saliva, duodenum, jejunum, and pancreatic tumor samples. Toghether, our findings suggest that oral, intestinal, and pancreatic microbiomes are correlated and bacteria of oral origin exhibit co-abundance relationships and demonstrate complex correlation patterns in the intestinal and pancreatic tumor samples. Future prospective studies should aim to uncover the co-abundance of specific microbial communities for studying etiology of microbiota-driven carcinogenesis.

Background In silico functional genomics have become a driving force in the way we interpret and use gene expression data, enabling researchers to understand which biological pathways are likely to be affected by the treatments or conditions being studied. There are many approaches to functional genomics, but a number of popular methods determine if a set of modified genes has a higher than expected overlap with genes known to function as part of a pathway (functional enrichment testing). Recently, researchers have started to apply such analyses in a new way: to ask if the data they are collecting show similar disruptions to biological functions compared to reference data. Examples include studying whether similar pathways are perturbed in smokers vs. users of e-cigarettes, or whether a new mouse model of schizophrenia is justified, based on its similarity in cytokine expression to a previously published model. However, there is a dearth of robust statistical methods for testing hypotheses related to these questions and most researchers resort to ad hoc approaches. The goal of this work is to develop a statistical approach to identifying gene pathways that are equivalently (or inversely) changed across two experimental conditions. Results We developed Equivalent Change Enrichment Analysis (ECEA). This is a new type of gene enrichment analysis based on a statistic that we call the equivalent change index (ECI). An ECI of 1 represents a gene that was over or under-expressed (compared to control) to the same degree across two experiments. Using this statistic, we present an approach to identifying pathways that are changed in similar or opposing ways across experiments. We compare our approach to current methods on simulated data and show that ECEA is able to recover pathways exhibiting such changes even when they exhibit complex patterns of regulation, which other approaches are unable to do. On biological data, our approach recovered pathways that appear directly connected to the condition being studied. Conclusions ECEA provides a new way to perform gene enrichment analysis that allows researchers to compare their data to existing datasets and determine if a treatment will cause similar or opposing genomic perturbations.

Assessing DNA methylation profiles in human blood has become a major focus of epidemiologic inquiry. Understanding variability in CpG-specific DNA methylation over moderate periods of time is a critical first step in identifying CpG sites that are candidates for DNA methylation-based etiologic, diagnostic and prognostic predictors of pathogenesis. Using the Illumina MethylationEPIC [850K] BeadArray, DNA methylation was profiled in paired whole blood samples collected approximately 1 year apart from 35 healthy women enrolled in the Nurses Study II cohort. The median intraclass correlation coefficient (ICC) across all CpG loci was 0.19 [Interquartile Range (IQR) 0.00–0.50]; 74.8% of ICCs were in the low range (0–0.5), 16.9% in the mid-range of ICCs (0.5–0.8), and 8.3% in the high-range of ICCs (0.8–1). ICCs were similar for CpG probes on the 450K Illumina array (median 0.17) and the new probes added to the 850K array (median 0.21). ICCs for CpG loci on the sex chromosomes and known metastable epialleles were high (median 0.71, 0.97, respectively), and ICCs among methylation quantitative trait loci (mQTL) CpGs were significantly higher as compared to non-mQTL CpGs (median 0.73, 0.16, respectively, P

NEW FINDINGS What is the central question of this study? Do peripheral sensory neurons metabolize fat-based fuel sources, and does a ketogenic diet modify these processes? What is the main finding and its importance We show that peripheral axons from mice fed a ketogenic diet respond to fat-based fuel sources with reduced respiration and H2 O2 emission compared with mice fed a control diet. These results add to our understanding of the responses of sensory neurons to neuropathy associated with poor diet, obesity and metabolic syndrome. These findings should be incorporated into current ideas of axonal protection and might identify how dietary interventions may change mitochondrial function in settings of sensory dysfunction. ABSTRACT Metabolic syndrome and obesity are increasing epidemics that significantly impact the peripheral nervous system and lead to negative changes in sensation and peripheral nerve function. Research to understand the consequences of diet, obesity and fuel usage in sensory neurons has commonly focused on glucose metabolism. Here, we tested whether mouse sensory neurons and nerves have the capacity to metabolize fat-based fuels (palmitoyl-CoA) and whether these effects are altered by feeding of a ketogenic (90% kcal fat) diet compared with a control diet (14% kcal fat). Male C57Bl/6 mice were placed on the diets for 10 weeks, and after the mice were killed, the dorsal root ganglion (DRG) and sciatic nerve (SN) were placed in an Oroboros oxygraph-2K to examine diet-induced alterations in metabolism (respiration) of palmitoyl-CoA and H2 O2 emission (fluorescence). In addition, RNAseq was performed on the DRG of mice fed a control or a ketogenic diet for 12 weeks, and genes associated with mitochondrial respiratory function were analysed. Our results suggest that the sciatic nerves from mice fed a ketogenic diet display reduced O2 respiration and H2 O2 emission when metabolizing palmitoyl-CoA compared with mice fed a control diet. Assessments of changes in mRNA gene expression reveal alterations in genes encoding the NADH dehydrogenase complex and complex IV, which could alter production of reactive oxygen species. These new findings highlight the ability of sensory neurons and axons to oxidize fat-based fuel sources and show that these mechanisms are adaptable to dietary changes.

Using a novel mouse model, a mitochondrial-nuclear exchange model termed MNX, we tested the hypothesis that inherited mitochondrial haplotypes alter primary tumor latency and metastatic efficiency. Male FVB/N-Tg(MMTVneu)202Mul/J (Her2) transgenic mice were bred to female MNX mice having FVB/NJ nuclear DNA with either FVB/NJ, C57BL/6J, or BALB/cJ mtDNA. Pups receiving the C57BL/6J or BALB/cJ mitochondrial genome (i.e., females crossed with Her2 males) showed significantly (P < 0.001) longer tumor latency (262 vs. 293 vs. 225 days), fewer pulmonary metastases (5 vs. 7 vs. 15), and differences in size of lung metastases (1.2 vs. 1.4 vs. 1.0 mm diameter) compared with FVB/NJ mtDNA. Although polyoma virus middle T–driven tumors showed altered primary and metastatic profiles in previous studies, depending upon nuclear and mtDNA haplotype, the magnitude and direction of changes were not the same in the HER2-driven mammary carcinomas. Collectively, these results establish mitochondrial polymorphisms as quantitative trait loci in mammary carcinogenesis, and they implicate distinct interactions between tumor drivers and mitochondria as critical modifiers of tumorigenicity and metastasis. Cancer Res; 77(24); 6941–9. ©2017 AACR.

Mitochondrial DNA (mtDNA) mutations and polymorphisms contribute to many complex diseases, including cancer. Using a unique mouse model that contains nDNA from one mouse strain and homoplasmic mitochondrial haplotypes from different mouse strain(s)—designated Mitochondrial Nuclear Exchange (MNX)—we showed that mtDNA could alter mammary tumor metastasis. Because retrograde and anterograde communication exists between the nuclear and mitochondrial genomes, we hypothesized that there are differential mtDNA-driven changes in nuclear (n)DNA expression and DNA methylation. Genome-wide nDNA methylation and gene expression were measured in harvested brain tissue from paired wild-type and MNX mice. Selective differential DNA methylation and gene expression were observed between strains having identical nDNA, but different mtDNA. These observations provide insights into how mtDNA could be altering epigenetic regulation and thereby contribute to the pathogenesis of metastasis. Cancer Res; 77(22); 6202–14. ©2017 AACR.

Dysfunctional mitochondria and generation of reactive oxygen species (ROS) promote chronic diseases, which have spurred interest in the molecular mechanisms underlying these conditions. Previously, we have demonstrated that disruption of post-translational modification of proteins with β-linked N-acetylglucosamine (O-GlcNAcylation) via overexpression of the O-GlcNAc-regulating enzymes O-GlcNAc transferase (OGT) or O-GlcNAcase (OGA) impairs mitochondrial function. Here, we report that sustained alterations in O-GlcNAcylation either by pharmacological or genetic manipulation also alter metabolic function. Sustained O-GlcNAc elevation in SH-SY5Y neuroblastoma cells increased OGA expression and reduced cellular respiration and ROS generation. Cells with elevated O-GlcNAc levels had elongated mitochondria and increased mitochondrial membrane potential, and RNA-sequencing analysis indicated transcriptome reprogramming and down-regulation of the NRF2-mediated antioxidant response. Sustained O-GlcNAcylation in mouse brain and liver validated the metabolic phenotypes observed in the cells, and OGT knockdown in the liver elevated ROS levels, impaired respiration, and increased the NRF2 antioxidant response. Moreover, elevated O-GlcNAc levels promoted weight loss and lowered respiration in mice and skewed the mice toward carbohydrate-dependent metabolism as determined by indirect calorimetry. In summary, sustained elevation in O-GlcNAcylation coupled with increased OGA expression reprograms energy metabolism, a finding that has potential implications for the etiology, development, and management of metabolic diseases.

Genetic and epigenetic alterations are required for carcinogenesis and the mutation burden across tumor types has been investigated. Here, we investigate epigenetic alterations with a novel measure of global DNA methylation dysregulation, the methylation dysregulation index (MDI), across 14 cancer types in The Cancer Genome Atlas (TCGA) database. DNA methylation data—obtained using Illumina HumanMethylation450 BeadChip—was accessed from TCGA. We calculated the MDI in 14 tumor types (n = 5,592 tumors), using adjacent normal tissues (n = 701) from each tumor site. Copy number alteration, and mutation burden were retrieved from cBioportal (n = 5,152). We tested the relation of subject MDI across tumors and with age, gender, tumor stage, estimated tumor purity, and copy number alterations for both overall MDI and genomic-context-specific MDI. We also investigated the top most dysregulated loci shared across tumor types. There was a broad range of extent in methylation dysregulation across tumor types (P < 2.2E-16). However, a consistent pattern of methylation dysregulation stratified by genomic context was observed across tumor types where the highest dysregulation occurred at non-CpG island regions. Considering other summary measures of somatic alteration, MDI was correlated with copy number alterations but not with mutation burden. Using the top dysregulated CpG sites in common across tumors, 4 classes of cancer types were observed, and the functional consequences of these alterations to gene expression were confirmed. This work identified the global DNA methylation dysregulation patterns across 14 cancer types showing a higher impact for the non-CpG island areas. The most dysregulated loci across cancer types identified common clusters across cancer types that may have implications for future treatment and prevention measures.

Recent studies have found that the microbiome in both gut and mouth are associated with diseases of the gut, including cancer. If resident microbes could be found to exhibit consistent patterns between the mouth and gut, disease status could potentially be assessed non-invasively through profiling of oral samples. Currently, there exists no generally applicable method to test for such associations. Here we present a Bayesian framework to identify microbes that exhibit consistent patterns between body sites, with respect to a phenotypic variable. For a given operational taxonomic unit (OTU), a Bayesian regression model is used to obtain Markov-Chain Monte Carlo estimates of abundance among strata, calculate a correlation statistic, and conduct a formal test based on its posterior distribution. Extensive simulation studies demonstrate overall viability of the approach, and provide information on what factors affect its performance. Applying our method to a dataset containing oral and gut microbiome samples from 77 pancreatic cancer patients revealed several OTUs exhibiting consistent patterns between gut and mouth with respect to disease subtype. Our method is well powered for modest sample sizes and moderate strength of association and can be flexibly extended to other research settings using any currently established Bayesian analysis programs.

Background Differentiated cells that arise from stem cells in early development contain DNA methylation features that provide a memory trace of their fetal cell origin (FCO). The FCO signature was developed to estimate the proportion of cells in a mixture of cell types that are of fetal origin and are reminiscent of embryonic stem cell lineage. Here we implemented the FCO signature estimation method to compare the fraction of cells with the FCO signature in tumor tissues and their corresponding nontumor normal tissues. Methods We applied our FCO algorithm to discovery data sets obtained from The Cancer Genome Atlas (TCGA) and replication data sets obtained from the Gene Expression Omnibus (GEO) data repository. Wilcoxon rank sum tests, linear regression models with adjustments for potential confounders and non-parametric randomization-based tests were used to test the association of FCO proportion between tumor tissues and nontumor normal tissues. P-values of

The development and progression of invasive breast cancer is characterized by alterations to the genome and epigenome. However, the relationship between breast tumor characteristics, disease subtypes, and patient outcomes with the cumulative burden of these molecular alterations are not well characterized. We determined the average departure of tumor DNA methylation from adjacent normal breast DNA methylation using Illumina 450K methylation data from 700 invasive breast tumors and 90 adjacent normal breast tissues in The Cancer Genome Atlas. From this we generated a novel summary measure of altered DNA methylation, the DNA methylation dysregulation index (MDI), and examined the relation of MDI with tumor characteristics and summary measures that quantify cumulative burden of genetic mutation and copy number alterations. Our analysis revealed that MDI was significantly associated with tumor stage (P = 0.017). Across invasive breast tumor subtypes we observed significant differences in genome-wide DNA MDIs (P = 4.9E–09) and in a fraction of the genome with copy number alterations (FGA) (P = 4.6E–03). Results from a linear regression adjusted for subject age, tumor stage, and estimated tumor purity indicated a positive significant association of MDI with both MCB and FGA (P = 0.036 and P < 2.2E–16). A recursively partitioned mixture model of all 3 somatic alteration burden measures resulted in classes of tumors whose epigenetic and genetic burden profile were associated with the PAM50 subtype and mutations in TP53, PIK3CA, and CDH1. Together, our work presents a novel framework for characterizing the epigenetic burden and adds to the understanding of the aggregate impact of epigenetic and genetic alterations in breast cancer.

BackgroundDNA methylation profiling of peripheral blood leukocytes has many research applications, and characterizing the changes in DNA methylation of specific white blood cell types between newborn and adult could add insight into the maturation of the immune system. As a consequence of developmental changes, DNA methylation profiles derived from adult white blood cells are poor references for prediction of cord blood cell types from DNA methylation data. We thus examined cell-type specific differences in DNA methylation in leukocyte subsets between cord and adult blood, and assessed the impact of these differences on prediction of cell types in cord blood.ResultsThough all cell types showed differences between cord and adult blood, some specific patterns stood out that reflected how the immune system changes after birth. In cord blood, lymphoid cells showed less variability than in adult, potentially demonstrating their naïve status. In fact, cord CD4 and CD8 T cells were so similar that genetic effects on DNA methylation were greater than cell type effects in our analysis, and CD8 T cell frequencies remained difficult to predict, even after optimizing the library used for cord blood composition estimation. Myeloid cells showed fewer changes between cord and adult and also less variability, with monocytes showing the fewest sites of DNA methylation change between cord and adult. Finally, including nucleated red blood cells in the reference library was necessary for accurate cell type predictions in cord blood.ConclusionChanges in DNA methylation with age were highly cell type specific, and those differences paralleled what is known about the maturation of the postnatal immune system.

Genome-wide methylation arrays are powerful tools for assessing cell composition of complex mixtures. We compare three approaches to select reference libraries for deconvoluting neutrophil, monocyte, B-lymphocyte, natural killer, and CD4+ and CD8+ T-cell fractions based on blood-derived DNA methylation signatures assayed using the Illumina HumanMethylationEPIC array. The IDOL algorithm identifies a library of 450 CpGs, resulting in an average R2 = 99.2 across cell types when applied to EPIC methylation data collected on artificial mixtures constructed from the above cell types. Of the 450 CpGs, 69% are unique to EPIC. This library has the potential to reduce unintended technical differences across array platforms. Electronic supplementary material The online version of this article (10.1186/s13059-018-1448-7) contains supplementary material, which is available to authorized users.

Background Advances in next-generation DNA sequencing technologies are now enabling detailed characterization of sequence variations in cancer genomes. With whole-genome sequencing, variations in coding and non-coding sequences can be discovered. But the cost associated with it is currently limiting its general use in research. Whole-exome sequencing is used to characterize sequence variations in coding regions, but the cost associated with capture reagents and biases in capture rate limit its full use in research. Additional limitations include uncertainty in assigning the functional significance of the mutations when these mutations are observed in the non-coding region or in genes that are not expressed in cancer tissue. Results We investigated the feasibility of uncovering mutations from expressed genes using RNA sequencing datasets with a method called Variant Detection in RNA(VaDiR) that integrates 3 variant callers, namely: SNPiR, RVBoost, and MuTect2. The combination of all 3 methods, which we called Tier 1 variants, produced the highest precision with true positive mutations from RNA-seq that could be validated at the DNA level. We also found that the integration of Tier 1 variants with those called by MuTect2 and SNPiR produced the highest recall with acceptable precision. Finally, we observed a higher rate of mutation discovery in genes that are expressed at higher levels. Conclusions Our method, VaDiR, provides a possibility of uncovering mutations from RNA sequencing datasets that could be useful in further functional analysis. In addition, our approach allows orthogonal validation of DNA-based mutation discovery by providing complementary sequence variation analysis from paired RNA/DNA sequencing datasets.

Stem cell maturation is a fundamental, yet poorly understood aspect of human development. We devised a DNA methylation signature deeply reminiscent of embryonic stem cells (a fetal cell origin signature, FCO) to interrogate the evolving character of multiple human tissues. The cell fraction displaying this FCO signature was highly dependent upon developmental stage (fetal versus adult), and in leukocytes, it described a dynamic transition during the first 5 yr of life. Significant individual variation in the FCO signature of leukocytes was evident at birth, in childhood, and throughout adult life. The genes characterizing the signature included transcription factors and proteins intimately involved in embryonic development. We defined and applied a DNA methylation signature common among human fetal hematopoietic progenitor cells and have shown that this signature traces the lineage of cells and informs the study of stem cell heterogeneity in humans under homeostatic conditions.

Current genomic studies are limited by the poor availability of fresh-frozen tissue samples. Although formalin-fixed diagnostic samples are in abundance, they are seldom used in current genomic studies because of the concern of formalin-fixation artifacts. Better characterization of these artifacts will allow the use of archived clinical specimens in translational and clinical research studies. To provide a systematic analysis of formalin-fixation artifacts on Illumina sequencing, we generated 26 DNA sequencing data sets from 13 pairs of matched formalin-fixed paraffin-embedded (FFPE) and fresh-frozen (FF) tissue samples. The results indicate high rate of concordant calls between matched FF/FFPE pairs at reference and variant positions in three commonly used sequencing approaches (whole genome, whole exome, and targeted exon sequencing). Global mismatch rates and C · G > T · A substitutions were comparable between matched FF/FFPE samples, and discordant rates were low (

Although considerable evidence suggests that in utero arsenic exposure affects children's health, these data are mainly from areas of the world where groundwater arsenic levels far exceed the World Health Organization limit of 10 μg/L. We, and others, have found that more common levels of in utero arsenic exposure may also impact children's health. However, the underlying molecular mechanisms are poorly understood. To address this issue, we analyzed the expression of key developmental genes in fetal placenta in a birth cohort of women using unregulated water supplies in a US region with elevated groundwater arsenic. We identified several genes whose expression associated with maternal arsenic exposure in a fetal sex-specific manner. In particular, expression of the HEDGEHOG pathway component, GLI3, in female placentae was both negatively associated with arsenic exposure and positively associated with infant birth weight. This suggests that modulation of GLI3 in the fetal placenta, and perhaps in other fetal tissues, contributes to arsenic's detrimental effects on fetal growth. We showed previously that arsenic-exposed NIH3T3 cells have reduced GLI3 repressor protein. Together, these studies identify GLI3 as a key signaling node that is affected by arsenic, mediating a subset of its effects on developmental signaling and fetal health., Highlights • In utero arsenic exposure associates with the expression of several key developmental genes in the fetal placenta. • There is extensive sexual dimorphism in the associations between placental gene expression and in utero arsenic exposure. • GLI3 expression in the female fetal placenta associates with arsenic exposure and may mediate its effects on fetal growth.

Mercury and arsenic are known developmental toxicants. Prenatal exposures are associated with adverse childhood health outcomes that could be in part mediated by epigenetic alterations that may also contribute to altered immune profiles. In this study, we examined the association between prenatal mercury exposure on both DNA methylation and white blood cell composition of cord blood, and evaluated the interaction with prenatal arsenic exposure. A total of 138 mother-infant pairs with postpartum maternal toenail mercury, prenatal urinary arsenic concentrations, and newborn cord blood were assessed using the Illumina Infinium Methylation450 array. White blood cell composition was inferred from DNA methylation measurements. A doubling in toenail mercury concentration was associated with a 2.5% decrease (95% CI: 5.0%, 1.0%) in the estimated monocyte proportion. An increase of 3.5% (95% CI: 1.0, 7.0) in B-cell proportion was observed for females only. Among the top 100 CpGs associated with toenail mercury levels (ranked on P-value), there was a significant enrichment of loci located in North shore regions of CpG islands (P = 0.049), and the majority of these loci were hypermethylated (85%). Among the top 100 CpGs for the interaction between arsenic and mercury, there was a greater than expected proportion of loci located in CpG islands (P = 0.045) and in South shore regions (P = 0.009) and all of these loci were hypermethylated. This work supports the hypothesis that mercury may be contributing to epigenetic variability and immune cell proportion changes, and suggests that in utero exposure to mercury and arsenic, even at low levels, may interact to impact the epigenome.

Arsenic has wide-ranging effects on human health and there is evidence that it alters the immune response by influencing CD4+/CD8+ T cell ratios, IL-2 cytokine levels, and the expression of immune-response genes. We investigated the impact of in utero environmental arsenic exposure on immune development and function in newborns participating in a pregnancy cohort in New Hampshire, U.S., where arsenic levels have exceeded the current EPA maximum contaminant level of 10 μg/L. Our results showed that maternal urinary arsenic concentrations were inversely related to absolute total CD45RA+ CD4+ cord blood CD69+ T cell counts (N=116, p=0.04) and positively associated with CD45RA+ CD69- CD294+ cell counts (p=0.01). In placental samples (N=70), higher in utero urinary arsenic concentrations were positively associated with the expression of IL1β (p=0.03). These data provide evidence that relatively low-level arsenic exposure in utero may alter the fetal immune system and lead to immune dysregulation.

ObjectiveTo determine whether bacteria are present in the pancreas of pancreatic cancer and non-cancer subjects and examine whether bacterial profiles vary by site and disease phenotype.Design77 patients requiring surgery for pancreatic diseases, or diseases of the foregut, at the Rhode Island Hospital (RIH) were recruited into this study between 2014 and 2016. In addition, 36 whole pancreas were obtained from the National Disease Research Interchange (NDRI) from subjects who were of similar age as the RIH patients and had not died of cancer. The primary exposure of interest was the measurement of the relative abundance of bacterial taxa in all tissue specimens using 16S rRNA gene sequencing.ResultsNumber of bacterial reads per sample varied substantially across sample type and patients, but all demonstrated the presence of diverse gastrointestinal bacteria, including bacterial taxa typically identified in the oral cavity. Bacterial profiles were noted to be more similar within individuals across sites in the pancreas, than between individuals by site, suggesting that the pancreas as a whole has its own microbiome. Comparing the mean relative abundance of bacterial taxa in pancreatic cancer patients to those without cancer revealed differences in bacterial taxa previously linked to periodontal disease, includingPorphyromonas.ConclusionsBacterial taxa known to inhabit the oral cavity, as well as the intestine, were identified in pancreatic tissue of cancer and non-cancer subjects. Whether any of these bacteria play a causal role in pancreatic carcinogenesis, or are simply opportunistic in nature, needs to be further examined.

Perfluorooctanoic acid (PFOA), a perfluoroalkyl substance, is commonly detected in the serum of pregnant women and may impact fetal development via epigenetic re-programming. In a pilot study, we explored associations between serum PFOA concentrations during pregnancy and offspring peripheral leukocyte DNA methylation at delivery in women with high (n = 22, range: 12–26 ng/mL) and low (n = 22, range: 1.1–3.1 ng/mL) PFOA concentrations. After adjusting for cell type, child sex, and income, we did not find differences in CpG methylation in the two exposure groups that reached epigenome-wide significance. Among the 20 CpGs with the lowest p-values we found that seven CpG sites in three genes differed by exposure status. In a confirmatory cluster analysis, these 20 CpGs clustered into two groups that perfectly identified exposure status. Future studies with larger sample sizes should confirm these findings and determine if PFOA-associated changes in DNA methylation underlie potential health effects of PFOA.

Correcting for cell-type heterogeneity in epigenome-wide association studies: revisiting previous analyses

Background Differentially methylated regions (DMRs) within DNA isolated from whole blood can be used to estimate the proportions of circulating leukocyte subtypes. We use the term “immunomethylomics” to describe the application of these immune lineage DMRs to studying leukocyte profiles. Here, we applied this approach to peripheral blood DNA from 72 glioma patients with molecularly defined brain tumors, representing common patient groups with defined characteristic survival times and risk factors. We first estimated the proportions of leukocyte subtypes in samples using deconvolution algorithms with reference DMR libraries from isolated leukocyte populations and Illumina 450K DNA methylation data. Then, we calculated the neutrophil to lymphocyte ratio (NLR) using methylation-derived cell composition estimates (mdNLR). The NLR is considered an indicator of immunosuppressive cells in cancer patients. Results Elevated mdNLR scores were observed in glioma patients compared to mdNLR values of published controls. Significantly decreased survival times were associated with mdNLR ≥ 4.0 in Cox proportional hazards models adjusted for age, gender, tumor grade, and molecular subtype (HR 2.02, 95% CI, 1.11–3.69). We also identified five myeloid-related CpGs that were highly correlated with the mdNLR (adjusted R 2 ≥ 0.80). Each of the five myeloid CpG loci was associated with survival when adjusted for the above covariates and offer a simplified approach for utilizing fresh or archived peripheral blood samples for interrogating a very small number of methylation markers to estimate myeloid immune influences in glioma survival. Conclusions The mdNLR (based on DNA methylation) is a novel candidate methylation biomarker that represents immunosuppressive myeloid cells within the blood of glioma patients with potential application in clinical trials and future epidemiologic studies of glioma risk and survival. Electronic supplementary material The online version of this article (doi:10.1186/s13148-017-0316-8) contains supplementary material, which is available to authorized users.

In this commentary we highlight a recently proposed statistical methodology called Functional Epigenetic Modules for the integrative analysis of DNA methylation and gene expression data. We begin by providing a high-level overview of this technique, along with a discussion of the perceived strengths and limitations of this methodology. This serves as the jumping off point for dialogue on some of the frequently encountered challenges in the analysis of data from integrative ‘omic studies; particularly those involving the collection of both DNA methylation and gene expression data. We shed light on these challenges and offer our perspective on potential solutions.

Summary Background Leptin (LEP) is a hormone central for energy homeostasis and has been implicated in neurodevelopment. This adipokine is produced by the placenta and is epigenetically regulated by promoter DNA methylation. Recent evidence has suggested a role for LEP in behavioral development. In this study, we investigated associations between profiles of human newborn neurobehavior and placental LEP DNA methylation. Methods We determined LEP promoter methylation in 444 placental samples from healthy term infants and measured LEP gene expression in a random subset of these samples. Infant neurobehavior was assessed with the NICU Network Neurobehavioral Scales (NNNS) and we examined the relationship between LEP promoter methylation and profiles of infant neurobehavior derived from these scores generated using a hierarchical model-based clustering method. Results LEP methylation is negatively correlated with gene expression only in placentas from male infants (r = −0.6, P = 0.006). A 10% increase in LEP DNA methylation was associated with membership in a profile of infant neurobehavior marked by increased lethargy and hypotonicity (OR = 1.9; 95% CI: 1.07–3.4), and consistently with reduced risk of membership in a profile characterized by decreased lethargy and hypotonicity (OR = 0.54; 95% CI: 0.3–0.94) only in male infants (n = 223). No statistically significant associations were observed amongst female infants. Discussion These results suggest that increased placental LEP DNA methylation, related to reduced expression, may play a role in human newborn neurodevelopment, particularly in reactivity to various stimuli, but that these effects may be sexually dimorphic.

Recent studies have shown two distinct non-CIMP methylation clusters in colorectal cancer, raising the possibility that DNA methylation, involving non-CIMP genes, may play a role in the conventional adenoma–carcinoma pathway. A total of 135 adenomas (65 left colon and 70 right colon) were profiled for epigenome-wide DNA methylation using the Illumina HumanMethylation450 BeadChip. A principal components analysis was performed to examine the association between variability in DNA methylation and adenoma location. Linear regression and linear mixed effects models were used to identify locus-specific differential DNA methylation in adenomas of right and left colon. A significant association was present between the first principal component and adenoma location (P = 0.007), even after adjustment for subject age and gender (P = 0.009). A total of 168 CpG sites were differentially methylated between right- and left-colon adenomas and these loci demonstrated enrichment of homeobox genes (P = 3.0 × 10−12). None of the 168 probes were associated with CIMP genes. Among CpG loci with the largest difference in methylation between right- and left-colon adenomas, probes associated with PRAC(prostate cancer susceptibility candidate) gene showed hypermethylation in right-colon adenomas whereas those associated with CDX2(caudal type homeobox transcription factor 2) showed hypermethylation in left-colon adenomas. A subgroup of left-colon adenomas enriched for current smokers (OR = 6.1, P = 0.004) exhibited a methylation profile similar to right-colon adenomas. In summary, our results indicate distinct patterns of DNA methylation, independent of CIMP genes, in adenomas of the right and left colon.

Background: The peripheral blood neutrophil-to-lymphocyte ratio (NLR) is a cytologic marker of both inflammation and poor outcomes in patients with cancer. DNA methylation is a key element of the epigenetic program defining different leukocyte subtypes and may provide an alternative to cytology in assessing leukocyte profiles. Our aim was to create a bioinformatic tool to estimate NLR using DNA methylation, and to assess its diagnostic and prognostic performance in human populations. Methods: We developed a DNA methylation–derived NLR (mdNLR) index based on normal isolated leukocyte methylation libraries and established cell-mixture deconvolution algorithms. The method was applied to cancer case–control studies of the bladder, head and neck, ovary, and breast, as well as publicly available data on cancer-free subjects. Results: Across cancer studies, mdNLR scores were either elevated in cases relative to controls, or associated with increased hazard of death. High mdNLR values (>5) were strong indicators of poor survival. In addition, mdNLR scores were elevated in males, in nonHispanic white versus Hispanic ethnicity, and increased with age. We also observed a significant interaction between cigarette smoking history and mdNLR on cancer survival. Conclusions: These results mean that our current understanding of mature leukocyte methylomes is sufficient to allow researchers and clinicians to apply epigenetically based analyses of NLR in clinical and epidemiologic studies of cancer risk and survival. Impact: As cytologic measurements of NLR are not always possible (i.e., archival blood), mdNLR, which is computed from DNA methylation signatures alone, has the potential to expand the scope of epigenome-wide association studies. Cancer Epidemiol Biomarkers Prev; 26(3); 328–38. ©2016 AACR.

Tumor microenvironments are heterogeneous and include epithelial cells, stroma, blood vessels and leukocytes. The interactions between normal and tumor epithelial cells result in responses including angiogenesis, immune regulation and tumor growth. Although flow cytometry and single cell sequencing have been used to investigate cellular composition in tumors, these approaches are dependent upon appropriate substrates that may not be routinely available. Recently, the use of molecular markers of cell type from mRNA (CIBERSORT) and DNA methylation (MethylCIBERSORT) have been used to infer specific cell proportions in tumors. Immune cell type DNA methylation signatures have been well-established for whole blood deconvolution but a challenge in tumors is the potential for carcinoma cells to have signal closely related to nontumor epithelial cells, and heterogeneity of potential tumor distinguishing deconvolution biomarkers. We aim to offer an alternative reference-based approach for tumor deconvolution with DNA methylation using ten normal cell components that we apply to TCGA tumor samples and test the relation of sample composition with patient survival. Ten magnetic sorted cell types with Illumina EPIC DNA methylation data were used as references (B cells, CD4T, CD8T and NK, monocytes, neutrophils, eosinophils, epithelial, endothelial and stromal cells). In total 1256 CpGs were selected for the reference library which was applied to deconvolute 17 TCGA tumor types (n=6417: BLCA, BRCA, COAD, CESC, ESCA, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PRAD, SKCM, STAD, THCA, UCEC). The most abundant cells were epithelial (median:36.6%), endothelial (median: 17.1%), CD8T (median: 12.1%) stromal cells (median:10.3%), and CD4T (median: 4.4%). We calculated three cell ratios: CD8T to epithelial (CD8T:epith), CD4T to epithelial (CD4T:epith) and endothelial to epithelial cells (vasc:epith) and fit Cox proportional hazards models stratified by cancer stage (I, II, III and IV), and adjusted for sex, age at diagnosis, and the three ratios. For all cancers, increased CD8T:epith was associated with worse survival HR: 1.39 (95%CI: 1.15, 1.68). For KIRC, increased CD8T:epith increased the risk of death HR: 1.54 (95%CI: 1.06, 2.22), whereas survival was improved for BRCA HR: 0.15 (95%CI: 0.03, 0.68), and CESC HR: 0.19 95%CI: 0.04, 0.90. A strong effect of increased vasc:epith ratio on risk of death was observed in BLCA HR: 8.03 (95%CI: 3.01, 21.4), and the CD4T:epith ratio reduced the risk of death HR: 0.11 95%CI: 0.02, 0.56. DNA methylation deconvolution is a promising tool for cancer prognosis and offers several technical and cost advantages. However, additional work to further stratify leukocyte signals by cell state such as CD8 T-cell exhaustion, and CD4T Treg infiltration is required. Citation Format: Lucas A. Salas, Karl T. Kelsey, Devin C. Koestler, John K. Wiencke, Brock C. Christensen. DNA methylation cytometry reveals cancer survival related to cell composition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 830.

Aims: The developmental origins of health and disease hypothesis states that later-life disease may be influenced by the quality of the in utero environment. Environmental toxicants can have detrimental effects on fetal development, potentially through effects on placental development and function. Maternal smoking during pregnancy is associated with low birth weight, preterm birth and other complications, and exposure to cigarette smoke in utero has been linked to gross pathologic and molecular changes to the placenta, including differential DNA methylation in placental tissue. The aim of this study was to investigate the relationship between maternal smoking during pregnancy, methylation changes in the placenta and gestational age. Materials & methods: We used Illumina®’s (CA, USA) Human Methylation27 BeadChip technology platform to investigate the methylation status of 21,551 autosomal, non-SNP-associated CpG loci in DNA extracted from 206 human placentas and examined loci whose variation in methylation was associated with maternal smoking during pregnancy. Results: We found that methylation patterns of a number of loci within the RUNX3 gene were significantly associated with smoking during pregnancy, and one of these loci was associated with decreased gestational age (p = 0.04). Conclusion: Our findings, demonstrating maternal smoking-induced changes in DNA methylation at specific loci, suggest a mechanism by which in utero tobacco smoke exposure could exert its detrimental effects upon the health of the fetus.

Leptin a regulator of body weight is involved in reproductive and developmental functions. Leptin promoter DNA methylation (LEP) regulates gene expression in a tissue-specific manner and has been linked to adverse pregnancy outcomes. In non-pathologic human pregnancies, we assessed LEP methylation, genotyped the single nucleotide polymorphism (SNP) rs2167270 in placental (n=81), maternal and cord blood samples (n=60), and examined the association between methylation, genotype, and perinatal factors. Maternal blood LEP methylation was lower in pre-pregnancy obese women (P=0.01). Cord blood LEP methylation was higher in small for gestational age (SGA) (P=4.6×10(-3)) and A/A genotype (P=1.6×10(-4)), lower (-1.47, P=0.03) in infants born to pre-pregnancy obese mothers and correlated (P=0.01) with maternal blood LEP. Gender was associated with placental LEP methylation (P=0.05). These results suggest that LEP epigenetic control may be influenced by perinatal factors including: maternal obesity, infant growth, genotype and gender in a tissue-specific manner and may have multigenerational implications.

The promise of epigenome-wide association studies and cancer-specific somatic DNA methylation changes in improving our understanding of cancer, coupled with the decreasing cost and increasing coverage of DNA methylation microarrays, has brought about a surge in the use of these technologies. Here, we aim to provide both a review of issues encountered in the processing and analysis of array-based DNA methylation data and a summary of the advantages of recent approaches proposed for handling those issues, focusing on approaches publicly available in open-source environments such as R and Bioconductor. We hope that the processing tools and analysis flowchart described herein will facilitate researchers to effectively use these powerful DNA methylation array-based platforms, thereby advancing our understanding of human health and disease.