Qiang Yu

Bio: Qiang Yu is an academic researcher from Tianjin University. The author has contributed to research in topics: Spiking neural network & Cancer. The author has an hindex of 61, co-authored 206 publications receiving 12489 citations. Previous affiliations of Qiang Yu include Boston Medical Center & Max Planck Society.

Pharmacologic disruption of Polycomb-repressive complex 2-mediated gene repression selectively induces apoptosis in cancer cells

Abstract: Polycomb-repressive complex 2 (PRC2)-mediated histone methylation plays an important role in aberrant cancer gene silencing and is a potential target for cancer therapy. Here we show that S-adenosylhomocysteine hydrolase inhibitor 3-Deazaneplanocin A (DZNep) induces efficient apoptotic cell death in cancer cells but not in normal cells. We found that DZNep effectively depleted cellular levels of PRC2 components EZH2, SUZ12, and EED and inhibited associated histone H3 Lys 27 methylation (but not H3 Lys 9 methylation). By integrating RNA interference (RNAi), genome-wide expression analysis, and chromatin immunoprecipitation (ChIP) studies, we have identified a prominent set of genes selectively repressed by PRC2 in breast cancer that can be reactivated by DZNep. We further demonstrate that the preferential reactivation of a set of these genes by DZNep, including a novel apoptosis affector, FBXO32, contributes to DZNep-induced apoptosis in breast cancer cells. Our results demonstrate the unique feature of DZNep as a novel chromatin remodeling compound and suggest that pharmacologic reversal of PRC2-mediated gene repression by DZNep may constitute a novel approach for cancer therapy.

Apoptosis, autophagy, necroptosis, and cancer metastasis

Abstract: Metastasis is a crucial hallmark of cancer progression, which involves numerous factors including the degradation of the extracellular matrix (ECM), the epithelial-to-mesenchymal transition (EMT), tumor angiogenesis, the development of an inflammatory tumor microenvironment, and defects in programmed cell death. Programmed cell death, such as apoptosis, autophagy, and necroptosis, plays crucial roles in metastatic processes. Malignant tumor cells must overcome these various forms of cell death to metastasize. This review summarizes the recent advances in the understanding of the mechanisms by which key regulators of apoptosis, autophagy, and necroptosis participate in cancer metastasis and discusses the crosstalk between apoptosis, autophagy, and necroptosis involved in the regulation of cancer metastasis.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Model-based Analysis of ChIP-Seq (MACS)

Abstract: We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Molecular mechanisms of epithelial–mesenchymal transition

Abstract: The transdifferentiation of epithelial cells into motile mesenchymal cells, a process known as epithelial-mesenchymal transition (EMT), is integral in development, wound healing and stem cell behaviour, and contributes pathologically to fibrosis and cancer progression. This switch in cell differentiation and behaviour is mediated by key transcription factors, including SNAIL, zinc-finger E-box-binding (ZEB) and basic helix-loop-helix transcription factors, the functions of which are finely regulated at the transcriptional, translational and post-translational levels. The reprogramming of gene expression during EMT, as well as non-transcriptional changes, are initiated and controlled by signalling pathways that respond to extracellular cues. Among these, transforming growth factor-β (TGFβ) family signalling has a predominant role; however, the convergence of signalling pathways is essential for EMT.

Hypoxia-inducible factor 1 is a basic-helix-loop-helix-PAS heterodimer regulated by cellular O2 tension

Abstract: Hypoxia-inducible factor 1 (HIF-1) is found in mammalian cells cultured under reduced O2 tension and is necessary for transcriptional activation mediated by the erythropoietin gene enhancer in hypoxic cells. We show that both HIF-1 subunits are basic-helix-loop-helix proteins containing a PAS domain, defined by its presence in the Drosophila Per and Sim proteins and in the mammalian ARNT and AHR proteins. HIF-1 alpha is most closely related to Sim. HIF-1 beta is a series of ARNT gene products, which can thus heterodimerize with either HIF-1 alpha or AHR. HIF-1 alpha and HIF-1 beta (ARNT) RNA and protein levels were induced in cells exposed to 1% O2 and decayed rapidly upon return of the cells to 20% O2, consistent with the role of HIF-1 as a mediator of transcriptional responses to hypoxia.