Genetic variants can alter the profile of heritable molecules such as small RNAs in sperm and oocytes, and in this manner ancestral genetic variants can have a significant effect on offspring phenotypes even if they are not themselves inherited. Here we show that wild type female mice descended from ancestors with a mutation in the mammalian germ cell gene Khdc3 have hepatic metabolic defects that persist over multiple generations. We find that genetically wild type females descended from Khdc3 mutants have transcriptional dysregulation of critical hepatic metabolic genes, which persist over multiple generations and pass through both female and male lineages. This was associated with dysregulation of hepatically-metabolized molecules in the blood of these wild type mice with mutational ancestry. The oocytes of Khdc3-null females, as well as their wild type descendants, had dysregulation of multiple small RNAs, suggesting that these epigenetic changes in the gametes transmit the phenotype between generations. Our results demonstrate that ancestral mutation in Khdc3 can produce transgenerational inherited phenotypes, potentially indefinitely.
The advent of high-throughput DNA methylation profiling techniques has enabled the possibility of accurate identification of differentially methylated genes for cancer research. The large number of measured loci facilitates whole genome methylation study, yet posing great challenges for differential methylation detection due to the high variability in tumor samples.We have developed a novel probabilistic approach, D: ifferential M: ethylation detection using a hierarchical B: ayesian model exploiting L: ocal D: ependency (DM-BLD), to detect differentially methylated genes based on a Bayesian framework. The DM-BLD approach features a joint model to capture both the local dependency of measured loci and the dependency of methylation change in samples. Specifically, the local dependency is modeled by Leroux conditional autoregressive structure; the dependency of methylation changes is modeled by a discrete Markov random field. A hierarchical Bayesian model is developed to fully take into account the local dependency for differential analysis, in which differential states are embedded as hidden variables. Simulation studies demonstrate that DM-BLD outperforms existing methods for differential methylation detection, particularly when the methylation change is moderate and the variability of methylation in samples is high. DM-BLD has been applied to breast cancer data to identify important methylated genes (such as polycomb target genes and genes involved in transcription factor activity) associated with breast cancer recurrence.A Matlab package of DM-BLD is available at http://www.cbil.ece.vt.edu/software.htm CONTACT: Xuan@vt.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Although the anticancer properties of oligomeric proanthocyanidins (OPCs) from grape seeds have been well recognized, the molecular mechanisms by which they exert anticancer effects are poorly understood. In this study, through comprehensive RNA-sequencing-based gene expression profiling in multiple colorectal cancer cell lines, we for the first time illuminate the genome-wide effects of OPCs from grape seeds in colorectal cancer. Our data revealed that OPCs affect several key cancer-associated genes. In particular, genes involved in cell cycle and DNA replication were most significantly and consistently altered by OPCs across multiple cell lines. Intriguingly, our in vivo experiments showed that OPCs were significantly more potent at decreasing xenograft tumor growth compared with the unfractionated grape seed extract (GSE) that includes the larger polymers of proanthocyanidins. These findings were further confirmed in colorectal cancer patient-derived organoids, wherein OPCs more potently inhibited the formation of organoids compared with GSE. Furthermore, we validated alteration of cell cycle and DNA replication-associated genes in cancer cell lines, mice xenografts as well as patient-derived organoids. Overall, this study provides an unbiased and comprehensive look at the mechanisms by which OPCs exert anticancer properties in colorectal cancer.
INTRODUCTION: Noting that the activated eosinophils and mast cells (MCs) that accumulate in the esophagus of eosinophilic esophagitis (EoE) patients secrete myoactive, pro-inflammatory, and cytotoxic products capable of causing the motor abnormalities and neuronal degeneration of achalasia, we proposed that an allergy-mediated, esophageal muscle-predominant form of EoE might cause achalasia. In earlier studies exploring that proposal, we found that LES muscle of achalasia patients exhibits striking MC degranulation. Although the hypercontraction and poor relaxation that characterize achalasic LES muscle have been attributed solely to its loss of inhibitory neurons, we hypothesized that abnormalities intrinsic to LES muscle cells themselves might contribute to motor disturbances in achalasia associated with MC degranulation. This study aimed to explore genetic factors that might underlie such intrinsic abnormalities. METHODS: We performed qPCR for a panel of genes known to mediate smooth muscle contraction and Ca2+ handling on LES muscle specimens obtained from 7 achalasia patients (2 type I, 4 type II, 1 type III) and 3 EGJ outflow obstruction (EGJOO) patients during Heller myotomy; control LES muscle was taken from 2 organ donors. RESULTS: Hierarchical clustering analysis revealed two discrete clusters that we call “mototype” gene patterns; one control subject grouped within each cluster (Figure 1). Mototype Cluster 1, comprised of the 2 type I and 1 type III achalasia patients as well as 2 EGJOO patients, exhibited upregulation of genes involved in smooth muscle contraction and Ca2+ handling (gene expression relative to corresponding control, Figure 2). In contrast, Mototype Cluster 2, comprised of all 4 achalasia type II patients and 1 EGJOO patient, exhibited downregulation of those same genes. Principal component analysis recapitulated this clustering of mototypes that separated achalasia types I and III from type II (Figure 3), and Rand Index calculations demonstrated that this clustering was not due to chance. CONCLUSION: We show that the genetic mototype of LES muscle can distinguish the manometric phenotypes of achalasia that are associated with MC degranulation. These findings support our proposal that there is an allergy-mediated form of achalasia, and suggest that when patients acquire this form of achalasia, their underlying genetic mototype might contribute to the resulting manometric phenotype.Figure 1.: Heat-map and hierarchical clustering with dendrogram generated from qPCR gene expression data of LES muscle for 2 control subjects, 7 achalasia patients (2 type I, 4 type II, 1 type III) and 3 EGJ outflow obstruction (EGJOO) patients reveals clustering of achalasia types I and III in mototype 1, and achalasia type II into mototype 2. Red color corresponds to high relative expression and blue color corresponds to low relative expression.Figure 2.: Heat-map and hierarchical clustering with dendrogram generated from qPCR gene expression data of LES muscle from 7 achalasia patients and 3 EGJOO patients relative to their corresponding control highlights differences between the two mototype clusters. Note the relative upregulation of the calcium handling genes PLN, RYR3, IP3R1 and the smooth muscle contraction and contractility genes RHOA, COL1A2, MYLK, and ACTA2 in mototype 2 patients and the relative downregulation of these same genes in mototype 1 patients. Red/blue colors indicate up/down-regulation with respect to corresponding control in each mototype.Figure 3.: Principal component (PC) analysis of gene expression data confirms mototype clustering of achalasia types 1 and III distinct from achalasia type II.
Recent advances in RNA sequencing (RNA-Seq) technology have offered unprecedented scope and resolution for transcriptome analysis. However, precise quantification of mRNA abundance and identification of differentially expressed genes are complicated due to biological and technical variations in RNA-Seq data. We systematically study the variation in count data and dissect the sources of variation into between-sample variation and within-sample variation. A novel Bayesian framework is developed for joint estimate of gene level mRNA abundance and differential state, which models the intrinsic variability in RNA-Seq to improve the estimation. Specifically, a Poisson-Lognormal model is incorporated into the Bayesian framework to model within-sample variation; a Gamma-Gamma model is then used to model between-sample variation, which accounts for over-dispersion of read counts among multiple samples. Simulation studies, where sequencing counts are synthesized based on parameters learned from real datasets, have demonstrated the advantage of the proposed method in both quantification of mRNA abundance and identification of differentially expressed genes. Moreover, performance comparison on data from the Sequencing Quality Control (SEQC) Project with ERCC spike-in controls has shown that the proposed method outperforms existing RNA-Seq methods in differential analysis. Application on breast cancer dataset has further illustrated that the proposed Bayesian model can 'blindly' estimate sources of variation caused by sequencing biases. We have developed a novel Bayesian hierarchical approach to investigate within-sample and between-sample variations in RNA-Seq data. Simulation and real data applications have validated desirable performance of the proposed method. The software package is available at http://www.cbil.ece.vt.edu/software.htm .
Abstract Omega-3 polyunsaturated fatty acids (n-3 PUFAs) are essential nutrients that can affect inflammatory responses. While n-3 PUFAs are generally considered beneficial for cardiovascular disease and obesity, the effects on asthma, the most common inflammatory lung disease are unclear. While prenatal dietary n-3 PUFAs decrease the risk for childhood wheezing, postnatal dietary n-3 PUFAs can worsen allergic airway inflammation. Sphingolipid metabolism is also affected by dietary n-3 PUFAs. Decreased sphingolipid synthesis leads to airway hyperreactivity, besides inflammation, a cardinal feature of asthma, and common genetic asthma risk alleles lead to lower sphingolipid synthesis. We investigated the effect of dietary n-3 PUFAs on sphingolipid metabolism and airway reactivity. Comparing a fish-oil diet with a high n-3 PUFA content (FO) to an isocaloric coconut oil-enriched diet (CO), we found an n-3 PUFA-dependent effect on increased airway reactivity, that was not accompanied by inflammation. Lung and whole blood content of dihydroceramides, ceramides, sphingomyelins, and glucosylceramides were lower in mice fed the n-3 PUFA enriched diet consistent with lower sphingolipid synthesis. In contrast, phosphorylated long chain bases such as sphingosine 1-phosphate were increased. These findings suggest that dietary n-3 PUFAs affect pulmonary sphingolipid composition to favor innate airway hyperreactivity, independent of inflammation, and point to an important role of n-3 PUFAs in sphingolipid metabolism.
Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures.The novel approach Dr Insight implements a frame-breaking statistical model for the 'hand-shake' between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug-target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks.Dr Insight R package is available at https://cran.r-project.org/web/packages/DrInsight/index.html.Supplementary data are available at Bioinformatics online.
Peripheral blood is gaining prominence as a noninvasive alternative to tissue biopsy to develop biomarkers for glioblastoma (GBM); however, widely utilized blood-based biomarkers in clinical settings have not yet been identified due to the lack of a robust detection approach. Here, we describe the application of globin reduction in RNA sequencing of whole blood (i.e., WBGR) and perform transcriptomic analysis to identify GBM-associated transcriptomic changes. By using WBGR, we improved the detection sensitivity of informatic reads and identified differential gene expression in GBM blood. By analyzing tumor tissues, we identified transcriptomic traits of GBM blood. Further functional enrichment analyses retained the most changed genes in GBM. Subsequent validation elicited a 10-gene panel covering mRNA, long noncoding RNA, and microRNA (i.e., GBM-Dx panel) that has translational potential to aid in the early detection or clinical management of GBM. Here, we report an integrated approach, WBGR, with comprehensive analytic capacity for blood-based marker identification.
Abstract Motivation NGS techniques have been widely applied in genetic and epigenetic studies. Multiple ChIP-seq and RNA-seq profiles can now be jointly used to infer functional regulatory networks (FRNs). However, existing methods suffer from either oversimplified assumption on transcription factor (TF) regulation or slow convergence of sampling for FRN inference from large-scale ChIP-seq and time-course RNA-seq data. Results We developed an efficient Bayesian integration method (CRNET) for FRN inference using a two-stage Gibbs sampler to estimate iteratively hidden TF activities and the posterior probabilities of binding events. A novel statistic measure that jointly considers regulation strength and regression error enables the sampling process of CRNET to converge quickly, thus making CRNET very efficient for large-scale FRN inference. Experiments on synthetic and benchmark data showed a significantly improved performance of CRNET when compared with existing methods. CRNET was applied to breast cancer data to identify FRNs functional at promoter or enhancer regions in breast cancer MCF-7 cells. Transcription factor MYC is predicted as a key functional factor in both promoter and enhancer FRNs. We experimentally validated the regulation effects of MYC on CRNET-predicted target genes using appropriate RNAi approaches in MCF-7 cells. Availability and implementation R scripts of CRNET are available at http://www.cbil.ece.vt.edu/software.htm. Supplementary information Supplementary data are available at Bioinformatics online.
Fabry disease is an X-linked lysosomal storage disorder caused by a deficiency of α-galactosidase A and subsequent accumulation of glycosphingolipids with terminal α-D-galactosyl residues. The molecular process through which this abnormal metabolism of glycosphingolipids causes multisystem dysfunction in Fabry disease is not fully understood. We sought to determine whether dysregulated DNA methylation plays a role in the development of this disease. In the present study, using isogenic cellular models derived from Fabry patient endothelial cells, we tested whether manipulation of α-galactosidase A activity and glycosphingolipid metabolism affects DNA methylation. Bisulfite pyrosequencing revealed that changes in α-galactosidase A activity were associated with significantly altered DNA methylation in the androgen receptor promoter, and this effect was highly CpG loci-specific. Methylation array studies showed that α-galactosidase A activity and glycosphingolipid levels were associated with differential methylation of numerous CpG sites throughout the genome. We identified 15 signaling pathways that may be susceptible to methylation alterations in Fabry disease. By incorporating RNA sequencing data, we identified 21 genes that have both differential mRNA expression and methylation. Upregulated expression of collagen type IV alpha 1 and alpha 2 genes correlated with decreased methylation of these two genes. Methionine levels were elevated in Fabry patient cells and Fabry mouse tissues, suggesting that a perturbed methionine cycle contributes to the observed dysregulated methylation patterns. In conclusion, this study provides evidence that α-galactosidase A deficiency and glycosphingolipid storage may affect DNA methylation homeostasis and highlights the importance of epigenetics in the pathogenesis of Fabry disease and, possibly, of other lysosomal storage disorders.
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