Consumption of green tea (GT) extracts or purified catechins has shown the ability to prevent oral and other cancers and inhibit cancer progression in rodent models, but the evidence for this in humans is mixed. Working with humans, we sought to understand the source of variable responses to GT by examining its effects on oral epithelium. Lingual epithelial RNA and lingual and gingival microbiota were measured before and after 4 weeks of exposure in tobacco smokers, whom are at high risk of oral cancer. GT consumption had on average inconsistent effects on miRNA expression in the oral epithelium. Only analysis that examined paired miRNAs, showing changed and coordinated expression with GT exposure, provided evidence for a GT effect on miRNAs, identifying miRNAs co-expressed with two hubs, miR-181a-5p and 301a-3p. An examination of the microbiome on cancer prone lingual mucosa, in contrast, showed clear shifts in the relative abundance of Streptococcus and Staphylococcus, and other genera after GT exposure. These data support the idea that tea consumption can consistently change oral bacteria in humans, which may affect carcinogenesis, but argue that GT effects on oral epithelial miRNA expression in humans vary between individuals.

Background Metastatic progress is the primary cause of death in most cancers, yet the regulatory dynamics driving the cellular changes necessary for metastasis remain poorly understood. Multi-omics approaches hold great promise for addressing this challenge; however, current analysis tools have limited capabilities to systematically integrate transcriptomic, epigenomic, and cistromic information to accurately define the regulatory networks critical for metastasis. Results To address this limitation, we use a purposefully generated cellular model of colon cancer invasiveness to generate multi-omics data, including expression, accessibility, and selected histone modification profiles, for increasing levels of invasiveness. We then adopt a rigorous probabilistic framework for joint inference from the resulting heterogeneous data, along with transcription factor binding profiles. Our approach uses probabilistic graphical models to leverage the functional information provided by specific epigenomic changes, models the influence of multiple transcription factors simultaneously, and automatically learns the activating or repressive roles of cis-regulatory events. Global analysis of these relationships reveals key transcription factors driving invasiveness, as well as their likely target genes. Disrupting the expression of one of the highly ranked transcription factors JunD, an AP-1 complex protein, confirms functional relevance to colon cancer cell migration and invasion. Transcriptomic profiling confirms key regulatory targets of JunD, and a gene signature derived from the model demonstrates strong prognostic potential in TCGA colorectal cancer data. Conclusions Our work sheds new light into the complex molecular processes driving colon cancer metastasis and presents a statistically sound integrative approach to analyze multi-omics profiles of a dynamic biological process.

Differential gene expression in bulk transcriptomics data can reflect regulated change of transcript abundance within a cell type and/or change in the proportion of cell types within the sample. To differentiate these scenarios, bulk expression deconvolution methods have been developed, which reveal cell type proportions and transcriptomes at the larger scales afforded by bulk RNA-seq compared to single-cell RNA-seq. However, the accuracy of these methods is highly sensitive to technical and biological differences between bulk profiles and the cell type-signatures required as references during deconvolution. We present BEDwARS, a Bayesian deconvolution method specifically designed to address potential differences between reference signatures and true but unknown signatures underlying the bulk transcriptomic profiles. Through extensive benchmarking utilizing eight different datasets derived from pancreas and brain, and by generating additional noisy reference signatures, we demonstrate that BEDwARS outperforms leading in-class methods for estimating cell type proportions and is more robust to noise in reference signatures. Furthermore, it more accurately estimates true cell type signatures compared to the state-of-the-art method. Application of BEDwARS to newly generated RNA-seq and scRNA-seq data on a rare pediatric condition (Dihydropyridine Dehydrogenase deficiency) revealed the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder.

Differential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportions of cell types. Expression deconvolution methods can help differentiate these scenarios. BEDwARS is a Bayesian deconvolution method designed to address differences between reference signatures of cell types and corresponding true signatures underlying bulk transcriptomic profiles. BEDwARS is more robust to noisy reference signatures and outperforms leading in-class methods for estimating cell type proportions and signatures. Application of BEDwARS to dihydropyridine dehydrogenase deficiency identified the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder.

Superwarfarin toxicity may be a serious problem. It needs high clinical suspicious in patients with bleeding diathesis without hematologic or liver diseases even in patients with apparent negative history of warfarin or other anticoagulant accessibility. Here we reported a patient with a negative history of any medical diseases or drug administration who was referred with generalized ecchymosis. Increased international normalized ratio and decreased vitamin K-dependent coagulation factors were detected in this patient. His hematologic and liver evaluations were normal. Clinical pharmacist emphasis in taking history revealed using anticoagulant rodenticide all over the farm the patient lived in that might result in unaware intoxication in this patient who suffered dementia.