893 research outputs found
sj-docx-2-tct-10.1177_15330338221106855 - Supplemental material for TJP1, a Membrane-Expressed Protein, is a Potential Therapeutic and Prognostic Target for Lung Cancer
No full textSupplemental material, sj-docx-2-tct-10.1177_15330338221106855 for TJP1, a Membrane-Expressed Protein, is a Potential Therapeutic and Prognostic Target for Lung Cancer by Junyi Pu, Tao Ai, Weining Weng, Lijun Wang, Yuan Yang, Linjie Ma, Zhiping Hu, and Xun Meng in Technology in Cancer Research & Treatment</p
sj-docx-1-tct-10.1177_15330338221106855 - Supplemental material for TJP1, a Membrane-Expressed Protein, is a Potential Therapeutic and Prognostic Target for Lung Cancer
No full textSupplemental material, sj-docx-1-tct-10.1177_15330338221106855 for TJP1, a Membrane-Expressed Protein, is a Potential Therapeutic and Prognostic Target for Lung Cancer by Junyi Pu, Tao Ai, Weining Weng, Lijun Wang, Yuan Yang, Linjie Ma, Zhiping Hu, and Xun Meng in Technology in Cancer Research & Treatment</p
Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project
No full text Job Dekker and Zhiping Weng are part of the ENCODE Project Consortium.We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function
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A Study of Legal Tradition of China from a Culture Perspective ::Searching for Harmony in the Natural Order /
No full textProfessor Zhiping Liang offers a new understanding of Chinese legal tradition in this profoundly influential book. Unlike the available literature using the usual method of legal history research, this book attempts to illustrate ancient Chinese legal tradition through cultural interpretation. The author holds that both the concept and practice of law are meaningful cultural symbols. The law reveals not only the life pattern in a specific time and space but also the world of the mind of a specific group of people. Therefore, just as cultures have different types, laws embedded in different societies and cultures also have different characters and spirits. Believing that human experience is often condensed into concepts, categories, and classifications, the author begins his discussion with the analysis of relevant terms and then seeks to understand history by interpreting the interaction and interconnectedness of the words, ideas, and practices. Based on thesame understanding, the author uses modern concepts reflectively and critically, consciously exploiting the differences between ancient and contemporary Chinese and Western concepts to achieve a more realistic understanding of history while avoiding the ethnocentrism and modern-centrism common in historical studies
Gq Protein-Coupled Membrane-Initiated Estrogen Signaling Rapidly Excites Corticotropin-Releasing Hormone Neurons in the Hypothalamic Paraventricular Nucleus in Female Mice
No full textCRH neurons in the hypothalamic paraventricular nucleus (PVN) play a central role in regulating the hypothalamus-pituitary-adrenal (HPA) axis and are directly influenced by 17β-estradiol (E2). Although compelling evidence has suggested the existence of membrane-associated estrogen receptors (mERs) in hypothalamic and other central nervous system neurons, it remains unknown whether E2 impacts CRH neuronal excitability through this mechanism. The purpose of the current study is to examine the existence and function of mER signaling in PVN CRH neurons. Whole-cell recordings were made from CRH neurons identified by Alexa Fluor 594 labeling and post hoc immunostaining in ovariectomized female mice. E2 (100nM) rapidly suppressed the M-current (a voltage-dependent K(+) current) and potentiated glutamatergic excitatory postsynaptic currents. The putative Gq-coupled mER (Gq-mER) characterized in hypothalamic proopiomelanocortin neurons initiates a phospholipase C-protein kinase C-protein kinase A pathway; therefore, we examined the involvement of this pathway using selective inhibitors. Indeed, the ER antagonist ICI 182780 and inhibitors of Gq-phospholipase C-protein kinase C-protein kinase A blocked E2's actions, suggesting dependence on the Gq-mER. Furthermore, STX, a selective ligand for the Gq-mER, mimicked E2's actions. Finally, to examine the in vivo effect of Gq-mER activation, E2 or STX injection increased c-fos expression in CRH neurons in the PVN, suggesting CRH neuronal activation. This corresponded to an increase in plasma corticosterone. We conclude that the Gq-mER plays a critical role in the rapid regulation of CRH neuronal activity and the HPA axis. Our findings provide a potential underlying mechanism for E2's involvement in the pathophysiology of HPA-associated mood disorders.Peer reviewe
Computational analyses of small silencing RNAs
No full textHigh-throughput sequencing is a powerful tool to study diverse aspects of biology and applies to genome, transcriptome, and small RNA profiling. Ever increasing sequencing throughput and more specialized sequencing assays demand more sophisticated bioinformatics approaches. In this thesis, I present 4 studies for which I developed computational methods to handle high-throughput sequencing data to gain insights into biology.
The first study describes the genome of High Five (Hi5) cells, originally derived from Trichoplusia ni eggs. The chromosome-level assembly (scaffold N50 = 14.2 Mb) contains 14,037 predicted protein-coding genes. Examination and curation of multiple gene families, pathways, and small RNA-producing loci reveal species- and order-specific features. The availability of the genome sequence, together with genome editing and single-cell cloning protocols, enables Hi5 cells as a new tool for studying small RNAs.
The second study focuses on just one type of piRNAs that are produced at the pachytene stage of mammalian spermatogenesis. Despite their abundance, pachytene piRNAs are poorly understood. I find that pachytene piRNAs cleave transcripts of protein-coding genes and further target transcripts from other pachytene piRNA loci. Subsequently, systematic investigation of piRNA targeting by integrating different types of sequencing data uncovers the piRNA targeting rule.
The third study describes computational procedures to map splicing branchpoints using high-throughput sequencing data. Screening >1.2 trillion RNA-seq reads determines >140,000 BPs for both human and mouse. Such branchpoints are compiled into BPDB (BranchPoint DataBase) to provide a comprehensive branchpoint catalog.
The final study combines novel experimental and computational procedures to handle PCR duplicates that are prevalent in high-throughput sequencing data. Incorporation of unique molecular identifiers (UMIs) to tag each read enables unambiguous identification of PCR duplicates. Both simulated and experimental datasets demonstrate that UMI incorporation increases the reproducibility of RNA-seq and small RNA-seq. Surveying 7 common variables in high-throughput sequencing reveals that the amount of starting material and sequencing depth, but not the number of PCR cycles, determine the PCR duplicate frequency. Finally, I show that removing PCR duplicates without UMIs leads to substantial bias into data analysis.2020-12-11T00:00:00
Delineation of chromatin states and transcription factor binding in mouse and tools for large-scale data integration
No full textThe goal of the ENCODE project has been to characterize regulatory elements in the human genome, such as regions bound by transcription factors (TFs), regions of open chromatin and regions with altered histone modifications. The ENCODE consortium has performed a large number of whole-genome experiments to measure TF binding, chromatin accessibility, gene expression and histone modifications, on a multitude of cell types and conditions in both human and mouse. In this dissertation I describe the analysis of numerous datasets comprising 66 epigenomes, chromatin accessibility and expression data across twelve tissues and seven time points, during mouse embryonic development. We defined chromatin states using histone modification data and performed integrative analysis on the states. We observed coordinated changes of histone mark signals at enhancers and promoters with gene expression. We detected evolutionary conserved bivalent promoters, selectively silencing ~3,400 genes, including hundreds of TFs regulating embryonic development. Second, I present a supervised method to predict TF binding across cell types, with features based on DNA sequence and patterns in DNase I cleavage data. We found that sequence and DNase read counts can outperform other features as well as state-of-the-art methods. I also describe our contribution to the ENCODE TF Binding DREAM challenge where we developed a method, using multiscale features and Extreme Boosting. Third, I describe methods, tools, and computational infrastructure that we have developed to handle large amounts of experimental data and metadata. These tools are fundamental to the selection and integration of large experimental datasets and are at the core of our pipelines, which are described in this dissertation. Finally, I present the protein docking server I developed, as well as algorithms and routines for post-processing predictions and protein structures. Collectively, this body of work encompasses computational approaches to the analyses of chromatin states, gene regulation, and the integration of large experimental datasets.2021-08-31T00:00:00
A piRNA regulation landscape in C. elegans and a computational model to predict gene functions
No full textInvestigating mechanisms that regulate genes and the genes' functions are essential to understand a biological system. This dissertation is consists of two specific research projects under these aims, which are for understanding piRNA's regulation mechanism and predicting genes' function computationally.
The first project shows a piRNA regulation landscape in C. elegans. piRNAs (Piwi-interacting small RNAs) form a complex with Piwi Argonautes to maintain fertility and silence transposons in animal germlines. In C. elegans, previous studies have suggested that piRNAs tolerate mismatched pairing and in principle could target all transcripts. In this project, by computationally analyzing the chimeric reads directly captured by cross-linking piRNA and their targets in vivo, piRNAs are found to target all germline mRNAs with microRNA-like pairing rules. The number of targeting chimeric reads correlates better with binding energy than with piRNA abundance, suggesting that piRNA concentration does not limit targeting. Further more, in mRNAs silenced by piRNAs, secondary small RNAs are found to be accumulating at the center and ends of piRNA binding sites. Whereas in germline-expressed mRNAs, reduced piRNA binding density and suppression of piRNA-associated secondary small RNAs targeting correlate with the CSR-1 Argonaute presence. These findings reveal physiologically important and nuanced regulation of piRNA targets and provide evidence for a comprehensive post-transcriptional regulatory step in germline gene expression.
The second project elaborates a computational model to predict gene function. Predicting genes involved in a biological function facilitates many kinds of research, such as prioritizing candidates in a screening project. Following the “Guilt By Association” principle, multiple datasets are considered as biological networks and integrated together under a multi-label learning framework for predicting gene functions. Specifically, the functional labels are propagated and smoothed using a label propagation method on the networks and then integrated using an “Error correction of code” multi-label learning framework, where a “codeword” defines all the labels annotated to a specific gene. The model is then trained by finding the optimal projections between the code matrix and the biological datasets using canonical correlation analysis. Its performance is benchmarked by comparing to a state-of-art algorithm and a large scale screen results for piRNA pathway genes in D.melanogaster.
Finally, piRNA targeting's roles in epigenetics and physiology and its cross-talk with CSR-1 pathway are discussed, together with a survey of additional biological datasets and a discussion of benchmarking methods for the gene function prediction
Effect of miRNA Dosage in the Gene Regulation of the MeCP2 Protein
No full textThis work was produced while the author was an undergraduate student in the Summer Research Institute of the Ronald E. McNair Post Baccalaureate Degree Achievement Program at Rutgers University
Deep learning as a tool to better understand transcription factor binding across cell types and species
No full textDeep learning has transformed our everyday lives. Facebook facial recognition, Netflix personalized recommendations and ChatGPT are all powered by deep learning. Neural networks, inspired by human intelligence, are the workhorse of deep learning, and are capable of learning complex relationships and patterns within large amounts of heterogeneous data. However, they are often referred to as black boxes due to the complex nature of these patterns and the difficulty involved in discerning them. This work seeks to open this metaphorical black box and leverage the information learned by these networks to better understand transcription factor (TF) binding.
Convolutional neural networks (CNNs) excel at learning patterns from images. Nucleotide sequences are simply 1D images, a fact we use to develop a CNN-based motif discovery algorithm that outperforms classical and other deep learning-based approaches. We use our method and thousands of publicly available TF ChIP-seq experiments to annotate the binding sites of 367 human TFs. We then investigated the evolutionary conservation of these TF binding sites (TFBSs) in the mammalian lineage. We next demonstrate CNNs can be used to predict TF binding across cell types using only sequence and chromatin accessibility data. Lastly, we highlight the application of CNNs in unsupervised learning, specifically in the context of clustering brain specific regulatory elements based on sequence features. Altogether, the results presented herein highlight the importance of carefully constructing and training CNNs to achieve state of the art performance and gain the most biologically meaningful insights when trained on regulatory genomic data.Bioinformatics and Computational Biolog
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