- Associate Professor, Molecular and Cellular Biology
- Associate Professor, Applied BioSciences - GIDP
- Associate Professor, Applied Mathematics - GIDP
- Associate Professor, Cancer Biology - GIDP
- Associate Professor, Genetics - GIDP
- B.S. Molecular Biology
- University of Science and Technology of China (USTC), Hefei, China
- Ph.D. Oncology/Cancer Biology
- University of Wisconsin - Madison, Madison, Wisconsin
- Dissertation: "Understanding the Ah Receptor Regulatory Network"
As a systems biologist, I focus my research on gene regulation and cell fate decision by integrating experiments with computer modeling, aiming to dissect complex biological systems to advance our understanding and treatment of human diseases, particularly cancer and aging. To this end, my lab is currently working with modelers, statisticians, computer scientists, optical and mechanical engineers, and clinical physicians to study gene networks that control the life and death decisions of cells, particularly, the Rb-E2F gene network switch that controls the balance between cell proliferation and quiescence.
Bioinfo+ Func Genomic AnlMCB 416A (Spring 2021)
Bioinfo+ Func Genomic AnlMCB 516A (Spring 2021)
Directed RsrchMCB 392 (Spring 2021)
DissertationMCB 920 (Spring 2021)
Lab Presentations & DiscussionMCB 696A (Spring 2021)
Stem Cells and Human HealthMCB 295D (Spring 2021)
CBIO GIDP Seminar SeriesCBIO 596H (Fall 2020)
Directed RsrchMCB 392 (Fall 2020)
DissertationMCB 920 (Fall 2020)
Lab Presentations & DiscussionMCB 696A (Fall 2020)
ResearchGENE 900 (Fall 2020)
ThesisGENE 910 (Fall 2020)
Bioinfo+ Func Genomic AnlMCB 416A (Spring 2020)
Bioinfo+ Func Genomic AnlMCB 516A (Spring 2020)
Directed RsrchMCB 392 (Spring 2020)
DissertationMCB 920 (Spring 2020)
Lab Presentations & DiscussionMCB 696A (Spring 2020)
CBIO GIDP Seminar SeriesCBIO 596H (Fall 2019)
Directed RsrchMCB 392 (Fall 2019)
DissertationMCB 920 (Fall 2019)
Honors Independent StudyNSCS 399H (Fall 2019)
Lab Presentations & DiscussionMCB 696A (Fall 2019)
Directed ResearchPSIO 492 (Spring 2019)
DissertationMCB 920 (Spring 2019)
Honors ThesisPSIO 498H (Spring 2019)
Independent StudyECOL 499 (Spring 2019)
Lab Presentations & DiscussionMCB 696A (Spring 2019)
Senior CapstoneBIOC 498 (Spring 2019)
CBIO GIDP Seminar SeriesCBIO 596H (Fall 2018)
DissertationMCB 920 (Fall 2018)
Honors ThesisPSIO 498H (Fall 2018)
Introduction to ResearchMCB 795A (Fall 2018)
Lab Presentations & DiscussionMCB 696A (Fall 2018)
Senior CapstoneBIOC 498 (Fall 2018)
ThesisMCB 910 (Fall 2018)
ThesisMCB 910 (Summer I 2018)
Directed ResearchBIOC 492 (Spring 2018)
DissertationMCB 920 (Spring 2018)
Genetic & Molecular NetworksMCB 546 (Spring 2018)
Honors Independent StudyMCB 399H (Spring 2018)
Lab Presentations & DiscussionMCB 696A (Spring 2018)
ThesisMCB 910 (Spring 2018)
CBIO GIDP Seminar SeriesCBIO 596H (Fall 2017)
DissertationMCB 920 (Fall 2017)
Lab Presentations & DiscussionMCB 696A (Fall 2017)
The Biology of CancerMCB 325 (Fall 2017)
ThesisMCB 910 (Fall 2017)
Topic Molec BiologyMCB 595A (Fall 2017)
ThesisMCB 910 (Summer I 2017)
DissertationMCB 920 (Spring 2017)
Genetic & Molecular NetworksMCB 546 (Spring 2017)
Honors Independent StudyMCB 499H (Spring 2017)
Honors ThesisMCB 498H (Spring 2017)
Lab Presentations & DiscussionMCB 696A (Spring 2017)
Molecular GeneticsMCB 304 (Spring 2017)
ResearchMCB 900 (Spring 2017)
ThesisMCB 910 (Spring 2017)
CBIO GIDP Seminar SeriesCBIO 596H (Fall 2016)
DissertationMCB 920 (Fall 2016)
Honors ThesisMCB 498H (Fall 2016)
Lab Presentations & DiscussionMCB 696A (Fall 2016)
ResearchMCB 900 (Fall 2016)
The Biology of CancerMCB 325 (Fall 2016)
ThesisMCB 910 (Fall 2016)
Directed ResearchECOL 492 (Spring 2016)
DissertationMCB 920 (Spring 2016)
Genetic & Molecular NetworksMCB 546 (Spring 2016)
Introduction to ResearchMCB 795A (Spring 2016)
Lab Presentations & DiscussionMCB 696A (Spring 2016)
Molecular GeneticsMCB 304 (Spring 2016)
ThesisMCB 910 (Spring 2016)
- Kwon, J., Wang, X., & Yao, G. (2017). Study Quiescence Heterogeneity by Coupling Single-Cell Measurements and Computer Modeling. In Methods in Molecular Biology.
- Lee, T., Yao, G., & You, L. (2012). Cell cycle transition: principles of the restriction point. In Encyclopedia of Systems Biology. Springer.
- Yao, G., & Bradfield, C. (2003). The Ah receptor. In PAS Proteins: Regulators and Sensors of Development and Physiology(pp 149-182). Boston, MA: Kluwer Academic Publishers.
- Fujimaki, K., Li, R., Chen, H., Della Croce, K., Zhang, H. H., Xing, J., Bai, F., & Yao, G. (2019). Graded regulation of cellular quiescence depth between proliferation and senescence by a lysosomal dimmer switch. Proceedings of the National Academy of Sciences of the United States of America, 116(45), 22624-22634.More infoThe reactivation of quiescent cells to proliferate is fundamental to tissue repair and homeostasis in the body. Often referred to as the G0 state, quiescence is, however, not a uniform state but with graded depth. Shallow quiescent cells exhibit a higher tendency to revert to proliferation than deep quiescent cells, while deep quiescent cells are still fully reversible under physiological conditions, distinct from senescent cells. Cellular mechanisms underlying the control of quiescence depth and the connection between quiescence and senescence are poorly characterized, representing a missing link in our understanding of tissue homeostasis and regeneration. Here we measured transcriptome changes as rat embryonic fibroblasts moved from shallow to deep quiescence over time in the absence of growth signals. We found that lysosomal gene expression was significantly up-regulated in deep quiescence, and partially compensated for gradually reduced autophagy flux. Reducing lysosomal function drove cells progressively deeper into quiescence and eventually into a senescence-like irreversibly arrested state; increasing lysosomal function, by lowering oxidative stress, progressively pushed cells into shallower quiescence. That is, lysosomal function modulates graded quiescence depth between proliferation and senescence as a dimmer switch. Finally, we found that a gene-expression signature developed by comparing deep and shallow quiescence in fibroblasts can correctly classify a wide array of senescent and aging cell types in vitro and in vivo, suggesting that while quiescence is generally considered to protect cells from irreversible arrest of senescence, quiescence deepening likely represents a common transition path from cell proliferation to senescence, related to aging.
- Tian, X., Tu, X., Della Croce, K., Yao, G., Cai, H., Brock, N., Pau, S., & Liang, R. (2019). Multi-wavelength quantitative polarization and phase microscope. Biomedical optics express, 10(4), 1638-1648.More infoWe introduce a snapshot multi-wavelength quantitative polarization and phase microscope (MQPPM) for measuring spectral dependent quantitative polarization and phase information. The system uniquely integrates a polarized light microscope and a snap-shot quantitative phase microscope in a single system, utilizing a novel full-Stokes camera operating in the red, green, and blue (RGB) spectrum. The linear retardance and fast axis orientation of a birefringent sample can be measured simultaneously in the visible spectra. Both theoretical analysis and experiments have been performed to demonstrate the capability of the proposed microscope. Data from liquid crystal and different biological samples are presented. We believe that MQPPM will be a useful tool in measuring quantitative polarization and phase information of live cells.
- Zhang, J., Chen, H., Li, R., Taft, D. A., Yao, G., Bai, F., & Xing, J. (2019). Spatial clustering and common regulatory elements correlate with coordinated gene expression. PLoS computational biology, 15(3), e1006786.More infoMany cellular responses to surrounding cues require temporally concerted transcriptional regulation of multiple genes. In prokaryotic cells, a single-input-module motif with one transcription factor regulating multiple target genes can generate coordinated gene expression. In eukaryotic cells, transcriptional activity of a gene is affected by not only transcription factors but also the epigenetic modifications and three-dimensional chromosome structure of the gene. To examine how local gene environment and transcription factor regulation are coupled, we performed a combined analysis of time-course RNA-seq data of TGF-β treated MCF10A cells and related epigenomic and Hi-C data. Using Dynamic Regulatory Events Miner (DREM), we clustered differentially expressed genes based on gene expression profiles and associated transcription factors. Genes in each class have similar temporal gene expression patterns and share common transcription factors. Next, we defined a set of linear and radial distribution functions, as used in statistical physics, to measure the distributions of genes within a class both spatially and linearly along the genomic sequence. Remarkably, genes within the same class despite sometimes being separated by tens of million bases (Mb) along genomic sequence show a significantly higher tendency to be spatially close despite sometimes being separated by tens of Mb along the genomic sequence than those belonging to different classes do. Analyses extended to the process of mouse nervous system development arrived at similar conclusions. Future studies will be able to test whether this spatial organization of chromosomes contributes to concerted gene expression.
- Fujimaki, K., & Yao, G. (2018). Crack the state of silence: tune the depth of cellular quiescence for cancer therapy. Mol Cell Oncol..
- Yao, G. (2018). ATP-Dependent Dynamic Protein Aggregation Regulates Bacterial Dormancy Depth Critical for Antibiotic Tolerance. Molecular Cell.
- Yao, G. (2018). Graded regulation of cellular quiescence depth between proliferation and senescence by a lysosomal dimmer switch. bioRxiv (PNAS under revision).
- Dai, J., Miller, M., Everetts, N., Wang, X., Li, P., Li, Y., Xu, J., & Yao, G. (2016). Elimination of quiescent slow-cycling cells via reducing quiescence depth by natural compounds purified from Ganoderma Lucidum. Oncotarget.
- Geoff, M., Kwon, J., Della Croce, K., & Yao, G. (2017). Exit from quiescence displays a positional growth memory. Nature Communications.
- Kwon, J., Everetts, N., Della Croce, K., & Yao, G. (2017). Controlling depth of cellular quiescence by an Rb-E2F bistable switch. Cell Reports.
- Shat, I., Deng, M., Davidovich, A., Zhang, C., Kwon, J., Manandhar, D., Gordan, R., Yao, G., & You, L. (2017). Expression level is a key determinant of E2F1-mediated cell fate. Cell Death Differ..
- Chen, C., Li, P., Li, Y., Yao, G., & Xu, J. (2016). Antitumor effects and mechanisms of Ganoderma extracts and spores oil.. Oncol Lett..
- Zhang, W., Wei, L., Li, L., Yang, B., Kong, B., Yao, G., & Zheng, W. (2015). Ovarian serous carcinogenesis from tubal secretory cells. Histology and Histopathology.
- Mondal, D., Dougherty, E., Mukhopadhyay, A., Carbo, A., Yao, G., & Xing, J. (2014). Systematic reverse engineering of network topologies: a case study of resettable bistable cellular responses. PloS one, 9(8), e105833.More infoA focused theme in systems biology is to uncover design principles of biological networks, that is, how specific network structures yield specific systems properties. For this purpose, we have previously developed a reverse engineering procedure to identify network topologies with high likelihood in generating desired systems properties. Our method searches the continuous parameter space of an assembly of network topologies, without enumerating individual network topologies separately as traditionally done in other reverse engineering procedures. Here we tested this CPSS (continuous parameter space search) method on a previously studied problem: the resettable bistability of an Rb-E2F gene network in regulating the quiescence-to-proliferation transition of mammalian cells. From a simplified Rb-E2F gene network, we identified network topologies responsible for generating resettable bistability. The CPSS-identified topologies are consistent with those reported in the previous study based on individual topology search (ITS), demonstrating the effectiveness of the CPSS approach. Since the CPSS and ITS searches are based on different mathematical formulations and different algorithms, the consistency of the results also helps cross-validate both approaches. A unique advantage of the CPSS approach lies in its applicability to biological networks with large numbers of nodes. To aid the application of the CPSS approach to the study of other biological systems, we have developed a computer package that is available in Information S1.
- Srimani, J. K., Yao, G., Neu, J., Tanouchi, Y., Lee, T. J., & You, L. (2014). Linear population allocation by bistable switches in response to transient stimulation. PloS one, 9(8), e105408.More infoMany cellular decision processes, including proliferation, differentiation, and phenotypic switching, are controlled by bistable signaling networks. In response to transient or intermediate input signals, these networks allocate a population fraction to each of two distinct states (e.g. OFF and ON). While extensive studies have been carried out to analyze various bistable networks, they are primarily focused on responses of bistable networks to sustained input signals. In this work, we investigate the response characteristics of bistable networks to transient signals, using both theoretical analysis and numerical simulation. We find that bistable systems exhibit a common property: for input signals with short durations, the fraction of switching cells increases linearly with the signal duration, allowing the population to integrate transient signals to tune its response. We propose that this allocation algorithm can be an optimal response strategy for certain cellular decisions in which excessive switching results in lower population fitness.
- Wang, Y., Wang, Y., Li, D., Li, L., Zhang, W., Yao, G., Jiang, Z., & Zheng, W. (2014). IMP3 signatures of fallopian tube: a risk for pelvic serous cancers. Journal of hematology & oncology, 7, 49.More infoRecent advances suggest fallopian tube as the main cellular source for women's pelvic serous carcinoma (PSC). In addition to TP53 mutations, many other genetic changes are involved in pelvic serous carcinogenesis. IMP3 is an oncofetal protein which has recently been observed to be overexpressed in benign-looking tubal epithelia. Such findings prompted us to examine the relationship between IMP3 over-expression, patient age and the likelihood of development of PSC.
- Yao, G. (2014). Modelling mammalian cellular quiescence. Interface focus, 4(3), 20130074.More infoCellular quiescence is a reversible non-proliferating state. The reactivation of 'sleep-like' quiescent cells (e.g. fibroblasts, lymphocytes and stem cells) into proliferation is crucial for tissue repair and regeneration and a key to the growth, development and health of higher multicellular organisms, such as mammals. Quiescence has been a primarily phenotypic description (i.e. non-permanent cell cycle arrest) and poorly studied. However, contrary to the earlier thinking that quiescence is simply a passive and dormant state lacking proliferating activities, recent studies have revealed that cellular quiescence is actively maintained in the cell and that it corresponds to a collection of heterogeneous states. Recent modelling and experimental work have suggested that an Rb-E2F bistable switch plays a pivotal role in controlling the quiescence-proliferation balance and the heterogeneous quiescent states. Other quiescence regulatory activities may crosstalk with and impinge upon the Rb-E2F bistable switch, forming a gene network that controls the cells' quiescent states and their dynamic transitions to proliferation in response to noisy environmental signals. Elucidating the dynamic control mechanisms underlying quiescence may lead to novel therapeutic strategies that re-establish normal quiescent states, in a variety of hyper- and hypo-proliferative diseases, including cancer and ageing.
- Chen, C., Li, J., Yao, G., Chambers, S. K., & Zheng, W. (2013). Tubal origin of ovarian low-grade serous carcinoma. American journal of clinical and experimental obstetrics and gynecology, 1(1), 13-36.
- Wong, J. V., Yao, G., Nevins, J. R., & You, L. (2011). Using noisy gene expression mediated by engineered adenovirus to probe signaling dynamics in mammalian cells. Methods in Enzymology, 497, 221-237.More infoPMID: 21601089;Abstract: Perturbations from environmental, genetic, and pharmacological sources can generate heterogeneous biological responses, even in genetically identical cells. Although these differences have important consequences on cell physiology and survival, they are often subsumed in measurements that average over the population. Here, we describe in detail how variability in adenoviral-mediated gene expression provides an effective means to map dose responses of signaling pathways. Cellcell variability is inherent in gene delivery methods used in cell biology, which makes this approach adaptable to many existing experimental systems. We also discuss strategies to quantify biologically relevant inputs and outputs. © 2011 Elsevier Inc. All rights reserved.
- Wong, J. V., Yao, G., Nevins, J. R., & You, L. (2011). Using noisy gene expression mediated by engineered adenovirus to probe signaling dynamics in mammalian cells. Methods in enzymology, 497, 221-37.More infoPerturbations from environmental, genetic, and pharmacological sources can generate heterogeneous biological responses, even in genetically identical cells. Although these differences have important consequences on cell physiology and survival, they are often subsumed in measurements that average over the population. Here, we describe in detail how variability in adenoviral-mediated gene expression provides an effective means to map dose responses of signaling pathways. Cell-cell variability is inherent in gene delivery methods used in cell biology, which makes this approach adaptable to many existing experimental systems. We also discuss strategies to quantify biologically relevant inputs and outputs.
- Wong, J. V., Yao, G., Nevins, J. R., & You, L. (2011). Viral-Mediated Noisy Gene Expression Reveals Biphasic E2f1 Response to MYC. Molecular Cell, 41(3), 275-285.More infoPMID: 21292160;PMCID: PMC3044932;Abstract: Gene expression mediated by viral vectors is subject to cell-to-cell variability, which limits the accuracy of gene delivery. When coupled with single-cell measurements, however, such variability provides an efficient means to quantify signaling dynamics in mammalian cells. Here, we illustrate the utility of this approach by mapping the E2f1 response to MYC, serum stimulation, or both. Our results revealed an underappreciated mode of gene regulation: E2f1 expression first increased, then decreased as MYC input increased. This biphasic pattern was also reflected in other nodes of the network, including the miR-17-92 microRNA cluster and p19Arf. A mathematical model of the network successfully predicted modulation of the biphasic E2F response by serum and a CDK inhibitor. In addition to demonstrating how noise can be exploited to probe signaling dynamics, our results reveal how coordination of the MYC/RB/E2F pathway enables dynamic discrimination of aberrant and normal levels of growth stimulation. © 2011 Elsevier Inc.
- Wong, J. V., Yao, G., Nevins, J. R., & You, L. (2011). Viral-mediated noisy gene expression reveals biphasic E2f1 response to MYC. Molecular cell, 41(3), 275-85.More infoGene expression mediated by viral vectors is subject to cell-to-cell variability, which limits the accuracy of gene delivery. When coupled with single-cell measurements, however, such variability provides an efficient means to quantify signaling dynamics in mammalian cells. Here, we illustrate the utility of this approach by mapping the E2f1 response to MYC, serum stimulation, or both. Our results revealed an underappreciated mode of gene regulation: E2f1 expression first increased, then decreased as MYC input increased. This biphasic pattern was also reflected in other nodes of the network, including the miR-17-92 microRNA cluster and p19Arf. A mathematical model of the network successfully predicted modulation of the biphasic E2F response by serum and a CDK inhibitor. In addition to demonstrating how noise can be exploited to probe signaling dynamics, our results reveal how coordination of the MYC/RB/E2F pathway enables dynamic discrimination of aberrant and normal levels of growth stimulation.
- Yao, G., Tan, C., West, M., Nevins, J. R., & You, L. (2011). Origin of bistability underlying mammalian cell cycle entry. Molecular systems biology, 7, 485.More infoPrecise control of cell proliferation is fundamental to tissue homeostasis and differentiation. Mammalian cells commit to proliferation at the restriction point (R-point). It has long been recognized that the R-point is tightly regulated by the Rb-E2F signaling pathway. Our recent work has further demonstrated that this regulation is mediated by a bistable switch mechanism. Nevertheless, the essential regulatory features in the Rb-E2F pathway that create this switching property have not been defined. Here we analyzed a library of gene circuits comprising all possible link combinations in a simplified Rb-E2F network. We identified a minimal circuit that is able to generate robust, resettable bistability. This minimal circuit contains a feed-forward loop coupled with a mutual-inhibition feedback loop, which forms an AND-gate control of the E2F activation. Underscoring its importance, experimental disruption of this circuit abolishes maintenance of the activated E2F state, supporting its importance for the bistability of the Rb-E2F system. Our findings suggested basic design principles for the robust control of the bistable cell cycle entry at the R-point.
- Lee, T. J., Yao, G., Bennett, D. C., Nevins, J. R., & You, L. (2010). Stochastic E2F activation and reconciliation of phenomenological cell-cycle models. PLoS biology, 8(9).More infoThe transition of the mammalian cell from quiescence to proliferation is a highly variable process. Over the last four decades, two lines of apparently contradictory, phenomenological models have been proposed to account for such temporal variability. These include various forms of the transition probability (TP) model and the growth control (GC) model, which lack mechanistic details. The GC model was further proposed as an alternative explanation for the concept of the restriction point, which we recently demonstrated as being controlled by a bistable Rb-E2F switch. Here, through a combination of modeling and experiments, we show that these different lines of models in essence reflect different aspects of stochastic dynamics in cell cycle entry. In particular, we show that the variable activation of E2F can be described by stochastic activation of the bistable Rb-E2F switch, which in turn may account for the temporal variability in cell cycle entry. Moreover, we show that temporal dynamics of E2F activation can be recast into the frameworks of both the TP model and the GC model via parameter mapping. This mapping suggests that the two lines of phenomenological models can be reconciled through the stochastic dynamics of the Rb-E2F switch. It also suggests a potential utility of the TP or GC models in defining concise, quantitative phenotypes of cell physiology. This may have implications in classifying cell types or states.
- Mori, S., Chang, J. T., Andrechek, E. R., Matsumura, N., Baba, T., Yao, G., Kim, J. W., Gatza, M., Murphy, S., & Nevins, J. R. (2009). Anchorage-independent cell growth signature identifies tumors with metastatic potential. Oncogene, 28(31), 2796-805.More infoThe oncogenic phenotype is complex, resulting from the accumulation of multiple somatic mutations that lead to the deregulation of growth regulatory and cell fate controlling activities and pathways. The ability to dissect this complexity, so as to reveal discrete aspects of the biology underlying the oncogenic phenotype, is critical to understanding the various mechanisms of disease as well as to reveal opportunities for novel therapeutic strategies. Previous work has characterized the process of anchorage-independent growth of cancer cells in vitro as a key aspect of the tumor phenotype, particularly with respect to metastatic potential. Nevertheless, it remains a major challenge to translate these cell biology findings into the context of human tumors. We previously used DNA microarray assays to develop expression signatures, which have the capacity to identify subtle distinctions in biological states and can be used to connect in vitro and in vivo states. Here we describe the development of a signature of anchorage-independent growth, show that the signature exhibits characteristics of deregulated mitochondrial function and then demonstrate that the signature identifies human tumors with the potential for metastasis.
- Lee, T., Yao, G., Nevins, J., & You, L. (2008). Sensing and integration of Erk and PI3K signals by Myc. PLoS computational biology, 4(2), e1000013.More infoThe transcription factor Myc plays a central role in regulating cell-fate decisions, including proliferation, growth, and apoptosis. To maintain a normal cell physiology, it is critical that the control of Myc dynamics is precisely orchestrated. Recent studies suggest that such control of Myc can be achieved at the post-translational level via protein stability modulation. Myc is regulated by two Ras effector pathways: the extracellular signal-regulated kinase (Erk) and phosphatidylinositol 3-kinase (PI3K) pathways. To gain quantitative insight into Myc dynamics, we have developed a mathematical model to analyze post-translational regulation of Myc via sequential phosphorylation by Erk and PI3K. Our results suggest that Myc integrates Erk and PI3K signals to result in various cellular responses by differential stability control of Myc protein isoforms. Such signal integration confers a flexible dynamic range for the system output, governed by stability change. In addition, signal integration may require saturation of the input signals, leading to sensitive signal integration to the temporal features of the input signals, insensitive response to their amplitudes, and resistance to input fluctuations. We further propose that these characteristics of the protein stability control module in Myc may be commonly utilized in various cell types and classes of proteins.
- Mori, S., Rempel, R. E., Chang, J. T., Yao, G., Lagoo, A. S., Potti, A., Bild, A., & Nevins, J. R. (2008). Utilization of pathway signatures to reveal distinct types of B lymphoma in the Emicro-myc model and human diffuse large B-cell lymphoma. Cancer research, 68(20), 8525-34.More infoThe Emu-myc transgenic mouse has provided a valuable model for the study of B-cell lymphoma. Making use of gene expression analysis and, in particular, expression signatures of cell signaling pathway activation, we now show that several forms of B lymphoma can be identified in the Emu-myc mice associated with time of tumor onset. Furthermore, one form of Emu-myc tumor with pre-B character is shown to resemble human Burkitt lymphoma, whereas others exhibit more differentiated B-cell characteristics and show similarity with human diffuse large B-cell lymphoma in the pattern of gene expression, as well as oncogenic pathway activation. Importantly, we show that signatures of oncogenic pathway activity provide further dissection of the spectrum of diffuse large B-cell lymphoma, identifying a subset of patients who have very poor prognosis and could benefit from more aggressive or novel therapeutic strategies. Taken together, these studies provide insight into the complexity of the oncogenic process and a novel strategy for dissecting the heterogeneity of B lymphoma.
- Yao, G., Lee, T. J., Mori, S., Nevins, J. R., & You, L. (2008). A bistable Rb-E2F switch underlies the restriction point. Nature cell biology, 10(4), 476-82.More infoThe restriction point (R-point) marks the critical event when a mammalian cell commits to proliferation and becomes independent of growth stimulation. It is fundamental for normal differentiation and tissue homeostasis, and seems to be dysregulated in virtually all cancers. Although the R-point has been linked to various activities involved in the regulation of G1-S transition of the mammalian cell cycle, the underlying mechanism remains unclear. Using single-cell measurements, we show here that the Rb-E2F pathway functions as a bistable switch to convert graded serum inputs into all-or-none E2F responses. Once turned ON by sufficient serum stimulation, E2F can memorize and maintain this ON state independently of continuous serum stimulation. We further show that, at critical concentrations and duration of serum stimulation, bistable E2F activation correlates directly with the ability of a cell to traverse the R-point.
- Bild, A. H., Yao, G., Chang, J. T., Wang, Q., Potti, A., Chasse, D., Joshi, M., Harpole, D., Lancaster, J. M., Berchuck, A., Olson, J. A., Marks, J. R., Dressman, H. K., West, M., & Nevins, J. R. (2006). Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature, 439(7074), 353-7.More infoThe development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.
- Delong, M., Yao, G., Wang, Q., Dobra, A., Black, E. P., Chang, J. T., Bild, A., West, M., Nevins, J. R., & Dressman, H. (2005). DIG--a system for gene annotation and functional discovery. Bioinformatics (Oxford, England), 21(13), 2957-9.More infoWe describe a database and information discovery system named DIG (Duke Integrated Genomics) designed to facilitate the process of gene annotation and the discovery of functional context. The DIG system collects and organizes gene annotation and functional information, and includes tools that support an understanding of genes in a functional context by providing a framework for integrating and visualizing gene expression, protein interaction and literature-based interaction networks.
- Dobra, A., Hans, C., Jones, B., Nevins, J., Yao, G., & West, M. (2004). Sparse graphical models for exploring gene expression data. JOURNAL OF MULTIVARIATE ANALYSIS, 90(1), 196-212.More infoWe discuss the theoretical structure and constructive methodology for large-scale graphical models, motivated by their potential in evaluating and aiding the exploration of patterns of association in gene expression data. The theoretical discussion covers basic ideas and connections between Gaussian graphical models, dependency networks and specific classes of directed acyclic graphs we refer to as compositional networks. We describe a constructive approach to generating interesting graphical models for very high-dimensional distributions that builds on the relationships between these various stylized graphical representations. Issues of consistency of models and priors across dimension are key. The resulting methods are of value in evaluating patterns of association in large-scale gene expression data with a view to generating biological insights about genes related to a known molecular pathway or set of specified genes. Some initial examples relate to the estrogen receptor pathway in breast cancer, and the Rb-E2F cell proliferation control pathway. (C) 2004 Elsevier Inc. All rights reserved.
- Yao, G., Craven, M., Drinkwater, N., & Bradfield, C. A. (2004). Interaction networks in yeast define and enumerate the signaling steps of the vertebrate aryl hydrocarbon receptor. PLoS biology, 2(3), E65.More infoThe aryl hydrocarbon receptor (AHR) is a vertebrate protein that mediates the toxic and adaptive responses to dioxins and related environmental pollutants. In an effort to better understand the details of this signal transduction pathway, we employed the yeast S. cerevisiae as a model system. Through the use of arrayed yeast strains harboring ordered deletions of open reading frames, we determined that 54 out of the 4,507 yeast genes examined significantly influence AHR signal transduction. In an effort to describe the relationship between these modifying genes, we constructed a network map based upon their known protein and genetic interactions. Monte Carlo simulations demonstrated that this network represented a description of AHR signaling that was distinct from those generated by random chance. The network map was then explored with a number of computational and experimental annotations. These analyses revealed that the AHR signaling pathway is defined by at least five distinct signaling steps that are regulated by functional modules of interacting modifiers. These modules can be described as mediating receptor folding, nuclear translocation, transcriptional activation, receptor level, and a previously undescribed nuclear step related to the receptor's Per-Arnt-Sim domain.
- Chan, W., Yao, G., Gu, Y., & Bradfield, C. (1999). Cross-talk between the aryl hydrocarbon receptor and hypoxia inducible factor signaling pathways - Demonstration of competition and compensation. JOURNAL OF BIOLOGICAL CHEMISTRY, 274(17), 12115-12123.More infoThe aryl hydrocarbon receptor (AHR) and the alpha-class hypoxia inducible factors (HIF1 alpha, HIF2 alpha, and HIF3 alpha) are basic helix-loop-helix PAS (bHLH-PAS) proteins that heterodimerize with ARNT, In response to 2,3,7,8-tetrachlorodibenzo-p-dioxin, the AHR . ARNT complex binds to "dioxin responsive enhancers" (DREs) and activates genes involved in the metabolism of xenobiotics, e.g. cytochrome P4501A1 (Cyp1a1), The HIF1 alpha . ARNT complex binds to "hypoxia responsive enhancers" and activates the transcription of genes that regulate adaptation to low oxygen, e.g, erythropoietin (Epo), We postulated that activation of one pathway would inhibit the other due to competition for ARNT or other limiting cellular factors. Using pathway specific reporters in transient transfection assays, we observed that DRE driven transcription was markedly inhibited by hypoxia and that hypoxia responsive enhancer driven transcription was inhibited by AHR agonists, When we attempted to support this cross-talk model using endogenous loci, we observed that activation of the hypoxia pathway inhibited Cyp1a1 up-regulation, but that activation of the AHR actually enhanced the induction of Epo by hypoxia. To explain this unexpected additivity, we examined the Epo gene and found that its promoter harbors DREs immediately upstream of its transcriptional start site. These experiments outline conditions where inhibitory and additive cross-talk occur between the hypoxia and dioxin signal transduction pathways and identify Epo as an AHR-regulated gene.
- Yao, G. (2016, Feb). Controlling the heterogeneous growth response of quiescent cells by a gene network switch. Department of Molecular and Cellular Physiology, University of Cincinnati.
- Yao, G. (2016, Jan). Exit from Quiescence Displays a Positional Growth Memory. UCSF Workshop on Quantifying Cell Dynamics.
- Yao, G. (2016, Jan). Modeling the Control of Quiescence Heterogeneity by an Rb-E2F Bistable Switch. Systems and Synthetic Biology Seminar Series, UC-Davis.
- Yao, G. (2016, July). Controlling the Heterogeneous Cellular Quiescent State by an Rb-E2F Network Switch. The 11th AIMS Conference on Dynamical Systems, Differential Equations and Applications..
- Yao, G. (2016, May). Controlling the heterogeneous growth response of quiescent cells by a bistable gene switch. Department of Biochemistry, University of Cailifornia, Riverside.
- Yao, G. (2016, Sept.). Drive cells to deep or shallow quiescence by controlling an Rb-E2F network switch. Cancer Biol Seminar Series, UA Cancer Center.
- Yao, G. (2015, Fall). Modeling the Control of Quiescence Heterogeneity by an Rb-E2F Bistable Switch. Centre for Integrative Systems Biology, University of Oxford, Oxford, UK.
- Yao, G. (2015, Fall). Modeling the Control of Quiescence Heterogeneity by an Rb-E2F Bistable Switch. Molecular Cell Sciences Research Centre, St. George's, University of London, UK.
- Yao, G. (2015, Jan). Heterogeneous Quiescence State Controlled by an Rb-E2F Bistable Switch. Department of Molecular, Cell and Developmental Biology, UCLA.
- Yao, G. (2015, Summer). Modeling the Control of Quiescence Heterogeneity by an Rb-E2F Bistable Switch. Center for Quantitative Biology, Peking University, Beijing, China.
- Yao, G. (2015, Summer). Modeling the Control of Quiescence Heterogeneity by an Rb-E2F Bistable Switch. Department of mathematics, Konkuk University, Seoul, South Korea.
- Yao, G. (2015, Summer). Modeling the Control of Quiescence Heterogeneity by an Rb-E2F Bistable Switch. Institute of Molecular and Cell Biology, Biopolis-A*STAR, Singapore.
- Yao, G. (2015, summer). Modeling the Control of Quiescence Heterogeneity by an Rb-E2F Bistable Switch. Institute of Biophysics, Nanjing University, Nanjing, China.
- Yao, G. (2015, summer). Modeling the Control of Quiescence Heterogeneity by an Rb-E2F Bistable Switch. School of Life Sciences, Univ. of Science and Technology of China, Hefei, China.
- Yao, G. (2015, summer). Modeling the Control of Quiescence Heterogeneity by an Rb-E2F Bistable Switch. The 9th Salk Institute Cell Cycle Meeting. Salk Institute for Biological Studies, La Jolla, CA.
- Yao, G. (2014, Apr). State of Silence. Program in Applied Mathematics, Univ. of Arizona.
- Yao, G. (2014, Oct). Modeling Cancer Cell Network. School of Information: Science, Technology, and Arts, Univ. of Arizona.
- Yao, G. (2013, April). Modeling the Bistable Control of Cellular Quiescence and Proliferation. Program in Applied Mathematics, Univ. of Arizona.
- Yao, G. (2013, Aug). The State of Silence. International Conference on Computational Cell Biology.
- Yao, G. (2013, Feb). Memory of Cellular Quiescence. Annual Winter q-bio Meeting.
- Yao, G. (2013, Mar). Memory of Silence. CSHL Meeting on Computational Cell Biology.
- Yao, G. (2013, Oct). State of Cellular Quiescence. VT Life Sciences Seminar Series, Virginia Tech, Blacksburg, VA.
- Yao, G. (2012, Aug). Cell-fate decisions and cancer. ABBS orientation.
- Yao, G. (2012, Nov). Rb-E2F Bistable Switch and Positional Memory of Quiescence. School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ.
- Yao, G. (2011, April). Modeling Gene Networks that Control Cell Proliferation and Cell-Fate Decisions. School of Information: Science, Technology, and Arts, Univ. of Arizona, Tucson, AZ.
- Yao, G. (2011, Feb). Modeling Cell Signaling Networks that Control Cell-Fate Decisions. Program in Applied Mathematics, Univ. of Arizona, Tucson, AZ.
- Yao, G. (2011, Jan). Modeling Cell Signaling Networks that Control Cell-Fate Decisions. Arizona Cancer Center, Univ. of Arizona, Tucson, AZ.
- Yao, G. (2011, July). Modeling Gene Networks that Control Cell Proliferation and Cell-Fate Decisions. CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sci., Shanghai, China.
- Yao, G. (2011, July). Modeling Gene Networks that Control Cell Proliferation and Cell-Fate Decisions. Dept. of Bioinformatics and Biostatistics, Shanghai Jiao Tong Univ., Shanghai, China.
- Yao, G. (2011, July). Modeling Gene Networks that Control Cell Proliferation. Summer Symposium on Molecular Biophysics and Interdisciplinary Sciences.
- Yao, G. (2011, March). Origin of Bistability Underlying Mammalian Cell Cycle Entry. CSHL Meeting on Computational Cell Biology.
- Yao, G. (2015, summer). Controlling the Heterogeneous Quiescent State by an Rb-E2F Bistable Switch. The Ninth q-bio Conference. Virginia Tech, Blacksburg, VA.
- Yao, G. (2014, May). Heterogeneous Quiescence State Controlled by the Activation Threshold of the Rb-E2F Bistable Switch. CSHL Meeting on the Cell Cycle.