The primary goal of translational research is to generate and apply knowledge that can improve human health. Although research conducted within the confines of a single discipline has helped us to achieve this goal in many settings, this unidisciplinary approach may not be optimal when disease causation is complex and health decisions are pressing. To address these issues, we suggest that transdisciplinary approaches can facilitate the progress of translational research, and we review publications that demonstrate what these approaches can look like. These examples serve to (1) demonstrate why transdisciplinary research is useful, and (2) stimulate a conversation about how it can be further promoted. While we note that open-minded communication is a prerequisite for germinating any transdisciplinary work and that epidemiologists can play a key role in promoting it, we do not propose a rigid protocol for conducting transdisciplinary research, as one really does not exist. These achievements were developed in settings where typical disciplinary and institutional barriers were surmountable, but they were not accomplished with a single predetermined plan. The benefits of cross-disciplinary communication are hard to predict a priori and a detailed research protocol or process may impede the realization of novel and important insights. Overall, these examples demonstrate that enhanced cross-disciplinary information exchange can serve as a starting point that helps researchers frame better questions, integrate more relevant evidence, and advance translational knowledge more effectively. Specifically, we discuss examples where transdisciplinary approaches are helping us to better explore, assess, and intervene to improve human health.

Background

Gestational diabetes mellitus (GDM) affects approximately 10 % of pregnancies in the United States and increases the risk of adverse health outcomes in the offspring. These adult disease propensities may be set by anatomical and molecular alterations in the placenta associated with GDM.

Results

To assess the mechanistic aspects of fetal programming, we measured genome-wide methylation (Infinium HumanMethylation450 BeadChips) and expression (Affymetrix transcriptome microarrays) in placental tissue of 41 GDM cases and 41 matched pregnancies without maternal complications from the Harvard Epigenetic Birth Cohort. Specific transcriptional and epigenetic perturbations associated with GDM status included alterations in the major histocompatibility complex (MHC) region, which were validated in an independent cohort, the Rhode Island Child Health Study. Gene ontology enrichment among gene regulation influenced by GDM revealed an over-representation of immune response pathways among differential expression, reflecting these coordinated changes in the MHC region. This differential methylation and expression may be capturing shifts in cellular composition, reflecting physiological changes in the placenta associated with GDM.

Conclusions

Our study represents the largest investigation of transcriptomic and methylomic differences associated with GDM, providing comprehensive insight into how GDM shapes the intrauterine environment, which may have implications for fetal (re)programming.

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.

Glioblastoma multiforme (GBM) is the most aggressive of all brain tumors, with a median survival of less than 1.5 years. Recently, epigenetic alterations were found to play key roles in both glioma genesis and clinical outcome, demonstrating the need to integrate genetic and epigenetic data in predictive models. To enhance current models through discovery of novel predictive biomarkers, we employed a genome-wide, agnostic strategy to specifically capture both methylation-directed changes in gene expression and alternative associations of DNA methylation with disease survival in glioma. Human GBM-associated DNA methylation, gene expression, IDH1 mutation status, and survival data were obtained from The Cancer Genome Atlas. DNA methylation loci and expression probes were paired by gene, and their subsequent association with survival was determined by applying an accelerated failure time model to previously published alternative and expression-based association equations. Significant associations were seen in 27 unique methylation/expression pairs with expression-based, alternative, and combinatorial associations observed (10, 13, and 4 pairs, respectively). The majority of the predictive DNA methylation loci were located within CpG islands, and all but three of the locus pairs were negatively correlated with survival. This finding suggests that for most loci, methylation/expression pairs are inversely related, consistent with methylation-associated gene regulatory action. Our results indicate that changes in DNA methylation are associated with altered survival outcome through both coordinated changes in gene expression and alternative mechanisms. Furthermore, our approach offers an alternative method of biomarker discovery using a priori gene pairing and precise targeting to identify novel sites for locus-specific therapeutic intervention.

Background

Children born extremely preterm are at heightened risk for intellectual and social impairment, including Autism Spectrum Disorder (ASD). There is increasing evidence for a key role of the placenta in prenatal developmental programming, suggesting that the placenta may, in part, contribute to origins of neurodevelopmental outcomes.

Methods

We examined associations between placental transcriptomic and epigenomic profiles and assessed their ability to predict intellectual and social impairment at age 10 years in 379 children from the Extremely Low Gestational Age Newborn (ELGAN) cohort. Assessment of intellectual ability (IQ) and social function was completed with the Differential Ability Scales-II and Social Responsiveness Scale (SRS), respectively. Examining IQ and SRS allows for studying ASD risk beyond the diagnostic criteria, as IQ and SRS are continuous measures strongly correlated with ASD. Genome-wide mRNA, CpG methylation and miRNA were assayeds with the Illumina Hiseq 2500, HTG EdgeSeq miRNA Whole Transcriptome Assay, and Illumina EPIC/850 K array, respectively. We conducted genome-wide differential analyses of placental mRNA, miRNA, and CpG methylation data. These molecular features were then integrated for a predictive analysis of IQ and SRS outcomes using kernel aggregation regression. We lastly examined associations between ASD and the multi-omic-predicted component of IQ and SRS.

Results

Genes with important roles in neurodevelopment and placental tissue organization were associated with intellectual and social impairment. Kernel aggregations of placental multi-omics strongly predicted intellectual and social function, explaining approximately 8% and 12% of variance in SRS and IQ scores via cross-validation, respectively. Predicted in-sample SRS and IQ showed significant positive and negative associations with ASD case-control status.

Limitations

The ELGAN cohort comprises children born pre-term, and generalization may be affected by unmeasured confounders associated with low gestational age. We conducted external validation of predictive models, though the sample size (N = 49) and the scope of the available out-sample placental dataset are limited. Further validation of the models is merited.

Conclusions

Aggregating information from biomarkers within and among molecular data types improves prediction of complex traits like social and intellectual ability in children born extremely preterm, suggesting that traits within the placenta-brain axis may be omnigenic.

The potential influence of underlying differences in relative leukocyte distributions in studies involving blood-based profiling of DNA methylation is well recognized and has prompted development of a set of statistical methods for inferring changes in the distribution of white blood cells using DNA methylation signatures. However, the extent to which this methodology can accurately predict cell-type proportions based on blood-derived DNA methylation data in a large-scale epigenome-wide association study (EWAS) has yet to be examined. We used publicly available data deposited in the Gene Expression Omnibus (GEO) database (accession number GSE37008), which consisted of both blood-derived epigenome-wide DNA methylation data assayed using the Illumina Infinium HumanMethylation27 BeadArray and complete blood cell (CBC) counts among a community cohort of 94 non-diseased individuals. Constrained projection (CP) was used to obtain predictions of the proportions of lymphocytes, monocytes and granulocytes for each of the study samples based on their DNA methylation signatures. Our findings demonstrated high consistency between the average CBC-derived and predicted percentage of monocytes and lymphocytes (17.9% and 17.6% for monocytes and 82.1% and 81.4% for lymphocytes), with root mean squared error (rMSE) of 5% and 6%, for monocytes and lymphocytes, respectively. Similarly, there was moderate-high correlation between the CP-predicted and CBC-derived percentages of monocytes and lymphocytes (0.60 and 0.61, respectively), and these results were robust to the number of leukocyte differentially methylated regions (L-DMRs) used for CP prediction. These results serve as further validation of the CP approach and highlight the promise of this technique for EWAS where DNA methylation is profiled using whole-blood genomic DNA.

Abstract Background There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls. Results Here we present a method, similar to regression calibration, for inferring changes in the distribution of white blood cells between different subpopulations (e.g. cases and controls) using DNA methylation signatures, in combination with a previously obtained external validation set consisting of signatures from purified leukocyte samples. We validate the fundamental idea in a cell mixture reconstruction experiment, then demonstrate our method on DNA methylation data sets from several studies, including data from a Head and Neck Squamous Cell Carcinoma (HNSCC) study and an ovarian cancer study. Our method produces results consistent with prior biological findings, thereby validating the approach. Conclusions Our method, in combination with an appropriate external validation set, promises new opportunities for large-scale immunological studies of both disease states and noxious exposures.

Although tumor size and lymph node involvement are the current cornerstones of breast cancer prognosis, they have not been extensively explored in relation to tumor methylation attributes in conjunction with other tumor and patient dietary and hormonal characteristics. Using primary breast tumors from 162 (AJCC stage I-IV) women from the Kaiser Division of Research Pathways Study and the Illumina GoldenGate methylation bead-array platform, we measured 1,413 autosomal CpG loci associated with 773 cancer-related genes and validated select CpG loci with Sequenom EpiTYPER. Tumor grade, size, estrogen and progesterone receptor status, and triple negative status were significantly (Q-values <0.05) associated with altered methylation of 209, 74, 183, 69, and 130 loci, respectively. Unsupervised clustering, using a recursively partitioned mixture model (RPMM), of all autosomal CpG loci revealed eight distinct methylation classes. Methylation class membership was significantly associated with patient race (P<0.02) and tumor size (P<0.001) in univariate tests. Using multinomial logistic regression to adjust for potential confounders, patient age and tumor size, as well as known disease risk factors of alcohol intake and total dietary folate, were all significantly (P<0.0001) associated with methylation class membership. Breast cancer prognostic characteristics and risk-related exposures appear to be associated with gene-specific tumor methylation, as well as overall methylation patterns.

Epigenetic alterations are a common event in lung cancer and their identification can serve to inform on the carcinogenic process and provide clinically relevant biomarkers. Using paired tumor and non-tumor lung tissues from 146 individuals from three independent populations we sought to identify common changes in DNA methylation associated with the development of non-small cell lung cancer. Pathologically normal lung tissue taken at the time of cancer resection was matched to tumorous lung tissue and together were probed for methylation using Illumina GoldenGate arrays in the discovery set (n = 47 pairs) followed by bisulfite pyrosequencing for validation sets (n = 99 pairs). For each matched pair the change in methylation at each CpG was calculated (the odds ratio), and these ratios were averaged across individuals and ranked by magnitude to identify the CpGs with the greatest change in methylation associated with tumor development. We identified the top gene-loci representing an increase in methylation (HOXA9, 10.3-fold and SOX1, 5.9-fold) and decrease in methylation (DDR1, 8.1-fold). In replication testing sets, methylation was higher in tumors for HOXA9 (p < 2.2 × 10 (-16) ) and SOX1 (p < 2.2 × 10 (-16) ) and lower for DDR1 (p < 2.2 × 10 (-16) ). The magnitude and strength of these changes were consistent across squamous cell and adenocarcinoma tumors. Our data indicate that the identified genes consistently have altered methylation in lung tumors. Our identified genes should be included in translational studies that aim to develop screening for early disease detection.

Approximately 500,000 individuals diagnosed with bladder cancer in the U.S. require routine cystoscopic follow-up to monitor for disease recurrences or progression, resulting in over $2 billion in annual expenditures. Identification of new diagnostic and monitoring strategies are clearly needed, and markers related to DNA methylation alterations hold great promise due to their stability, objective measurement, and known associations with the disease and with its clinical features. To identify novel epigenetic markers of aggressive bladder cancer, we utilized a high-throughput DNA methylation bead-array in two distinct population-based series of incident bladder cancer (n = 73 and n = 264, respectively). We then validated the association between methylation of these candidate loci with tumor grade in a third population (n = 245) through bisulfite pyrosequencing of candidate loci. Array based analyses identified 5 loci for further confirmation with bisulfite pyrosequencing. We identified and confirmed that increased promoter methylation of HOXB2 is significantly and independently associated with invasive bladder cancer and methylation of HOXB2, KRT13 and FRZB together significantly predict high-grade non-invasive disease. Methylation of these genes may be useful as clinical markers of the disease and may point to genes and pathways worthy of additional examination as novel targets for therapeutic treatment.