Biology subjects

Kelsey, K. T.

Publications and source records attributed to Kelsey, K. T..

5 recordsLinked to original sources

bioRxiv2021

Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling

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, B cells, CD4+ and CD8+ naive and memory cells, natural killer, and T regulatory cells). Including derived variables, our method provides up to 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data both for current and retrospective 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 the immune system in human health and disease.

bioinformatics↗

bioRxiv2019

A Bayesian framework for identifying consistent patterns of microbial abundance between body sites

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.

microbiology↗

bioRxiv2019

Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data

BackgroundUmbilical cord blood (UCB) is commonly used in epigenome-wide association studies of prenatal exposures. Accounting for cell type composition is critical in such studies as it reduces confounding due to the cell specificity of DNA methylation (DNAm). In the absence of cell sorting information, statistical methods can be applied to deconvolve heterogeneous cell mixtures. Among these methods, reference-based approaches leverage age appropriate cell-specific DNA-methylation profiles to estimate cellular composition. In UCB, four reference datasets comprising DNAm signatures profiled in purified cell populations have been published using the Illumina 450K and 850K EPIC arrays. These datasets are biologically and technically different, and currently there is no consensus on how to best apply them. Here, we systematically evaluate and compare these datasets and provide recommendations for reference-based UCB deconvolution.\n\nResultsWe first evaluated the four reference datasets to ascertain both the purity of the samples and the potential cell cross-contamination. We filtered samples and combined datasets to obtain a joint UCB reference. We selected deconvolution libraries using two different approaches: automatic selection using the top differentially methylated probes from the function pickCompProbes in minfi and a standardized library selected using the IDOL (Identifying Optimal Libraries) iterative algorithm. We compared the performance of each reference separately and in combination, using the two approaches for reference library selection, and validated the results in an independent cohort (Generation R Study, n=191) with matched FACS measured cell counts. Strict filtering and combination of the references significantly improved the accuracy and efficiency of cell type estimates. Ultimately, the IDOL library outperformed the library from the automatic selection method implemented in pickCompProbes.\n\nConclusionThese results have important implications for epigenetic studies in UCB as implementing this method will optimally reduce confounding due to cellular heterogeneity. This work provides guidelines for future reference-based UCB deconvolution and establishes a framework for combining reference datasets in other tissues.

bioinformatics↗

bioRxiv2018

Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health

Tobacco smoking is a risk factor for multiple diseases, including cardiovascular disease and diabetes. Many smoking-associated signals have been detected in the blood methylome, but the extent to which these changes are widespread to metabolically relevant tissues, and impact gene expression or cardio-metabolic health, remains unclear.\n\nWe investigated smoking-associated DNA methylation and gene expression variation in adipose tissue from 542 healthy female twins with available well-characterized cardio-metabolic phenotype profiles. We identified 42 smoking-methylation and 42 smoking-expression signals, where five genes (AHRR, CYP1A1, CYP1B1, CYTL1, F2RL3) were both hypo-methylated and up-regulated in smokers. We replicated and validated a proportion of the signals in blood, adipose, skin, and lung tissue datasets, identifying tissue-shared effects. Smoking leaves systemic imprints on DNA methylation after smoking cessation, with stronger but shorter-lived effects on gene expression. We tested for associations between the observed smoking signals and several adiposity phenotypes that constitute cardio-metabolic disease risk. Visceral fat and android/gynoid ratio were associated with methylation at smoking-markers with functional impacts on expression, such as CYP1A1, and in signals shared across tissues, such as NOTCH1. At smoking-signals BHLHE40 and AHRR DNA methylation and gene expression levels in current smokers were predictive of future gain in visceral fat upon smoking cessation.\n\nOur results provide the first comprehensive characterization of coordinated DNA methylation and gene expression markers of smoking in adipose tissue, a subset of which link to human cardio-metabolic health and may give insights into the wide ranging risk effects of smoking across the body.\n\nAuthor SummaryTobacco smoking is the strongest environmental risk factor for human disease. Here, we investigate how smoking systemically changes methylome and transcriptome signatures in multiple tissues in the human body. We observe strong and coordinated epigenetic and gene expression changes in adipose tissue, some of which are mirrored in blood, skin, and lung tissue. Smoking leaves a strong short-lived impact on gene expression levels, while methylation changes are long-lasting after smoking cessation. We investigated if these changes observed in a metabolically-relevant (adipose) tissue had impacts on human disease, and observed strong associations with cardio-metabolic disease traits. Some of the smoking signals could predict future gain in obesity and cardio-metabolic disease risk in current smokers who subsequently go on to quit smoking. Our results provide novel insights into understanding the widespread health consequence of smoking outside the lung.

genomics↗

bioRxiv2017

The Microbiomes of Pancreatic Tissue in Pancreatic Cancer and Non-Cancer Subjects

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.\n\nDesign77 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.\n\nResultsNumber 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, including Porphyromonas.\n\nConclusionsBacterial 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.

epidemiology↗