Mapping Dynamic Histone Acetylation Patterns to Gene Expression in Nanog-depleted Murine Embryonic Stem Cells

Embryonic stem cells (ESC) have the potential to self-renew indefinitely and to differentiate into any of the three germ layers. The molecular mechanisms for self-renewal, maintenance of pluripotency and lineage specification are poorly understood, but recent results point to a key role for epigenetic mechanisms. In this study, we focus on quantifying the impact of histone 3 acetylation (H3K9,14ac) on gene expression in murine embryonic stem cells. We analyze genome-wide histone acetylation patterns and gene expression profiles measured over the first five days of cell differentiation triggered by silencing Nanog, a key transcription factor in ESC regulation. We explore the temporal and spatial dynamics of histone acetylation data and its correlation with gene expression using supervised and unsupervised statistical models. On a genome-wide scale, changes in acetylation are significantly correlated to changes in mRNA expression and, surprisingly, this coherence increases over time. We quantify the predictive power of histone acetylation for gene expression changes in a balanced cross-validation procedure. In an in-depth study we focus on genes central to the regulatory network of Mouse ESC, including those identified in a recent genome-wide RNAi screen and in the PluriNet, a computationally derived stem cell signature. We find that compared to the rest of the genome, ESC-specific genes show significantly more acetylation signal and a much stronger decrease in acetylation over time, which is often not reflected in an concordant expression change. These results shed light on the complexity of the relationship between histone acetylation and gene expression and are a step forward to dissect the multilayer regulatory mechanisms that determine stem cell fate.Comment: accepted at PLoS Computational Biolog

Feature Fusion and Detection in Alzheimer’s Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Voxel-based morphometry provides an opportunity to study Alzheimer’s disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10−6) and cell adhesion molecules (corrected p-value = 5.44 × 10−4). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.Peer reviewedFinal Published versio

Evolutionary History Of Subterranean Termites In The Geographic And Ecological Context Of The Appalachian Mountains In The United States

Termites in the genus Reticulitermes (Blattodea: Rhinotermitidae) are distributed across the eastern United States, including the southern Appalachian Mountains, a region incredibly rich in biodiversity. The eastern subterranean termite, Reticulitermes flavipes, has been uninentionally introduced to South America and Europe, and is predicted to further expand its geographic range. My goal was to determine how eco-evolutionary processes, operating at both long and short timescales, may have contributed to R. flavipes becoming an invasive species. I examined geographic and environmental influences at historical and contemporary timescales. To do this, I first determined the extent of niche divergence among three geographically overlapping Reticulitermes species, R. flavipes, R. malletei, and R. virginicus, and also identified the geographic areas and environmental conditions in which R. flavipes occurs to the exclusion of the other two species. Then, I assessed evidence for the influence of glacial-interglacial cycles on changes in the geographic distribution of R. flavipes, as well as potential genetic divergence within the species resulting from these past distributional shifts. In addition to historical eco-evolutionary processes, at the contemporary timescale I investigated how epigenetic mechanisms–specifically, DNA methylation–facilitate rapid responses to human-mediated disturbance of forest ecosystems. Finally, I developed a new landscape connectivity metric, MSconn, to help understand the effect spatial heterogeneity of environments plays on biological diversity at multiple levels of organization, from alleles to communities. In principle, MSconn can be integrated into an eco-evolutionary framework, making it possible to quantify the effect of biotic and abiotic environments on gene flow between populations, and vice versa, the effect of gene flow on species interactions within and between communities

Feature generation for optimization of marketing campaign

Abstract. Utilizing the gaming data for optimizing the entire gaming paradigm has revolutionized the thought process of developers and gamers alike. The significance of the gaming data can be judged from the fact that it is being used productively by the marketing agencies to develop algorithms that could predict the behavior of a certain gamer and the reaction to updates. The core idea behind the solution proposed and implemented in this thesis is focused on making the marketing campaigns more impactful. According to the facts from credible online resources, i.e., Statista.com, the business-to-business (B2B) organizations spent over $12.3 billion on marketing campaigns. Since one of the major aims of a marketing campaign is customer acquisition, which is also referred to as demand generation, measuring the success rate of the marketing campaign is also of great importance. Besides, the conventional Customer Relation Managers (CRMs) don’t have such features using which, the businesses can monitor the effectiveness of the marketing campaigns. The system this thesis proposes aims to analyze the gaming data, which can be used to extract features for refined marketing campaigns. To analyze and precisely classify the gaming data, this thesis proposes an algorithm running behind a full-fledged marketing campaign that can yield optimal results and which can be further refined to predict the future purchase behavior of the users in such marketing campaigns. To accomplish this task, the Random Forest Classifier is the one, which this thesis proposes and has been implemented to optimize feature selection in order to enhance the profit revenue of the business. The promising results of empirical research and studies have proven the capability of the random forest classifier, and after employing it in the research, it has been established that the mentioned classifier is absolutely capable of extracting significant features on the basis of the gaming data sets that were provided. More importantly, this study has indicated that the Random Forest classifier gives better results in predicting the purchase likelihood, which is an essential milestone for our project. It should be noted that the solution we have proposed does not only serve to predict the purchase likelihood, but it can also be preferably utilized for other aims and objectives which are related to optimizing the marketing campaigns

Prediction of drug-drug interaction potential using machine learning approaches

Drug discovery is a long, expensive, and complex, yet crucial process for the benefit of society. Selecting potential drug candidates requires an understanding of how well a compound will perform at its task, and more importantly, how safe the compound will act in patients. A key safety insight is understanding a molecule\u27s potential for drug-drug interactions. The metabolism of many drugs is mediated by members of the cytochrome P450 superfamily, notably, the CYP3A4 enzyme. Inhibition of these enzymes can alter the bioavailability of other drugs, potentially increasing their levels to toxic amounts. Four models were developed to predict CYP3A4 inhibition: logistic regression, random forests, support vector machine, and neural network. Two novel convolutional approaches were explored for data featurization: SMILES string auto-extraction and 2D structure auto-extraction. The logistic regression model achieved an accuracy of 83.2%, the random forests model, 83.4%, the support vector machine model, 81.9%, and the neural network model, 82.3%. Additionally, the model built with SMILE string auto-extraction had an accuracy of 82.3%, and the model with 2D structure auto-extraction, 76.4%. The advantages of the novel featurization methods are their ability to learn relevant features from compound SMILE strings, eliminating feature engineering. The developed methodologies can be extended towards predicting any structure-activity relationship and fitted for other areas of drug discovery and development