As the primary antagonist to the tumorigenic PI3K/AKT pathway, PTEN is classified as a tumor suppressor. The inactivation of PTEN through genetic or post-translational modifications is a critical step in the tumorigenesis of many breast cancers (BCs). SIPL1 is a novel protein which was identified as a PTEN negative regulator. To further explore SIPL1-mediated PTEN inactivation, we analyzed 17 datasets covering 3484 BC cases and 228 normal individuals. SIPL1 gene amplification and increased mRNA expression correlates with the progression and poor prognosis of ER and/or PR positive tumors. Furthermore, examination of a BC tissue microarray containing 224 tumor cases revealed elevated SIPL1 protein expression in ER+ and PR+ tumors and was associated with greater AKT activation. Additionally, ectopic expression of SIPL1 in CHO-K1 cells resulted in increased AKT activation and cell proliferation, and cytoskeleton reorganization alongside with PTEN downregulation. SIPL1 contributes to the linear polyubiquitination of NEMO, suggesting a role for SIPL1 in PTEN ubiquitination. Indeed, it was SIPL1, not the SIPL1-∆UBL (a PTEN-binding defective mutant) which robustly induced PTEN polyubiquitination in a lysine (K) 63-dependent but K48-independent manner. While K48-linked polyubiquitin chains direct protein degradation, K63-linked chains regulate a variety of protein functions. SIPL1 binds polyubiquitinated PTEN with significantly higher affinity than non-ubiquitinated PTEN. A SIPL1 mutant, SIPL1-TFLV, is unable to cause PTEN ubiquitination but is capable of PTEN association. Collectively, our results reveal that SIPL1 interacts with PTEN with a low affinity, which results in PTEN polyubiquitination, and that the modification may stabilize the association between SIPL1 and PTEN. We propose a model where SIPL1 mediates the K63-linked ubiquitination of PTEN inactivating it. The downregulation of PTEN, when paired with the tumor-promoting effects of ER and/or PR, stimulates breast tumorigenesis. SIPL1 is an important BC marker and future research should focus on its potential as a therapeutic target. / Thesis / Doctor of Philosophy (PhD)

Protein-protein interactions (PPI) represent a challenging target class in contemporary small molecule drug discovery. The difficulty arises because PPI sites are structurally and physicochemically different from conventional drug binding sites. Moreover, we currently lack a good understanding of the druggability of PPI targets: that is, how the structure and properties of a PPI interface site relates to the properties of small molecules that can bind to that site with high affinity. Efforts to achieve potent drug-like small molecule inhibitors of PPI interfaces, involving a wide range targets, historically have largely been unsuccessful, leading to the conclusion that new inhibitor chemotypes are needed to inhibit this class of target. In this thesis, I describe the application of two approaches to identify inhibitors of the PPI interface between Kelch-like ECH associated protein 1 (KEAP1) and Nuclear factor (erythroid-derived 2)-like 2 (Nrf2): (i) screening a library of synthetic macrocycles, and (ii) fragment-based lead discovery. I validate and characterize the hit compounds obtained. In the case of the fragment hits, I investigate what features of the compounds are required for binding to the target (Chapter Two). In parallel, I investigate the structure of the hot spot ensemble at the KEAP1/Nrf2 binding interface using three complementary methods: alanine scanning mutagenesis, fragment screening, and in silico probe mapping using the FTMap algorithm (Chapter Three). This analysis brings insight into the druggability of KEAP1, and advances our understanding of the utility and limitations of those three widely used methods for characterizing the hot spot ensembles at PPI interfaces (Chapter Three). Finally, to gain additional insight into the energetics of KEAP1/Nrf2 binding, I probe the additivity of combinations of alanine mutants (Chapter Four). I use the results to propose a quantitative approach to categorizing the various degrees of additivity that can be observed at PPI interfaces, and discuss the possible structural basis for these behaviors. The model potentially provides a more general framework for understanding the binding energetics at PPI interfaces using combinations of mutations.

Genome-wide studies of diseases and chronic conditions frequently fail to uncover marked or consistent differences in RNA or protein concentrations. However, the developing field of kinetic proteomics has made promising discoveries in differences in the turnover rate of these same proteins, even when concentrations were not necessarily different. The situation can theoretically be modeled mathematically using bifurcation equations, but uncovering the proper form of these is difficult. To this end, we developed TWIG, a method for characterizing bifurcations that leverages information geometry to identify drivers of complex systems. Using this, we characterized the bifurcation and stability properties of all 132 possible 3- and 22,662 possible 4-node subgraphs (motifs) of protein-protein interaction networks. Analyzing millions of real world protein networks indicates that natural selection has little preference for motifs that are stable per se, but a great preference for motifs who have parameter regions that are exclusively stable, rather than poorly constrained mixtures of stability and instability. We apply this knowledge to mice on calorie restricted (CR) diets, demonstrating that changes in their protein turnover rates do indeed make their protein networks more stable, explaining why CR is the most robust way known to extend lifespan.

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京都大学 / 新制・課程博士 / 博士(理学) / 甲第23720号 / 理博第4810号 / 新制||理||1688(附属図書館) / 京都大学大学院理学研究科化学専攻 / (主査)教授 寺嶋 正秀, 教授 林 重彦, 教授 渡邊 一也 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

The negative effects from rising carbon levels have created the need to find alternative energy sources that are more carbon neutral. One such alternative energy source is to use the biomass derived from forest trees to fulfill the need for a renewable alternative fuel. Through increased understanding and optimization of regulatory mechanisms that control wood development the potential exists to increase biomass yield. Transcription factors (TFs) are DNA-binding regulatory proteins capable of either activation or repression by binding to a specific region of DNA, normally located in the 5-prime upstream promoter region of the gene. In the first section of this work, six DNA promoters from wood formation-related genes were screened by the Yeast One-Hybrid (Y1H) assay in efforts to identify novel interacting TFs involved in wood formation. The promoters tested belong to genes involved in lignin biosynthesis, programmed cell death, and cambial zone associated TFs. The promoters were screened against a mini-library composed of TFs expressed 4-fold or higher in differentiating xylem vs phloem-cambium. The Y1H results identified PtrRAD1 with interactions involving several of the promoters screened. Further testing of PtrRAD1 by Yeast Two-Hybrid (Y2H) assay identified a protein-protein interaction (PPI) with poplar DIVARACATA RADIALIS INTERACTING FACTOR (DRIF1). PtrDRIF1 was then used in the Y2H assay and formed PPIs with MYB/SANT domain proteins, homeodomain family (HD) TFs, and cytoskeletal-related proteins. In the second section of this work, PPIs involving PtrDRIF1s' interaction partners were further characterized. PtrDRIF1 is composed of two separate domains, an N-terminal MYB/SANT domain that interacted with the MYB/SANT domain containing PtrRAD1 and PtrDIVARICATA-like proteins, and a C-terminal region containing a Domain of Unknown Function 3755 (DUF3755). The DUF3755 domain interacted with HD family members belonging to the ancient WOX clade and Class II KNOX domain TFs. In addition, PtrDRIF1 was able to form a complex between PtrRAD1 and PtrWOX13c in a Y2H bridge assay. PtrDRIF1 may function as a regulatory module linking cambial cell proliferation, lignification, and cell expansion during growth. Combined, these findings support a role for PtrDRIF1 in regulating aspects of wood formation that may contribute to altering biomass yield. / Ph. D.

The advent of various types of high-throughput genomic data has enabled researchers to investigate complex biological systems in a systemic way and started to shed light on the underlying molecular mechanisms in cancers. To analyze huge amounts of genomic data, effective statistical and machine learning tools are clearly needed; more importantly, integrative approaches are especially needed to combine different types of genomic data for a network or pathway view of biological systems. Motivated by such needs, we make efforts in this dissertation to develop integrative framework for pathway analysis. Specifically, we dissect the molecular pathway into two parts: protein-DNA interaction network and protein-protein interaction network. Several novel approaches are proposed to integrate gene expression data with various forms of biological knowledge, such as protein-DNA interaction and protein-protein interaction for reliable molecular network identification. The first part of this dissertation seeks to infer condition-specific transcriptional regulatory network by integrating gene expression data and protein-DNA binding information. Protein-DNA binding information provides initial relationships between transcription factors (TFs) and their target genes, and this information is essential to derive biologically meaningful integrative algorithms. Based on the availability of this information, we discuss the inference task based on two different situations: (a) if protein-DNA binding information of multiple TFs is available: based on the protein-DNA data of multiple TFs, which are derived from sequence analysis between DNA motifs and gene promoter regions, we can construct initial connection matrix and solve the network inference using a constraint least-squares approach named motif-guided network component analysis (mNCA). However, connection matrix usually contains a considerable amount of false positives and false negatives that make inference results questionable. To circumvent this problem, we propose a knowledge based stability analysis (kSA) approach to test the conditional relevance of individual TFs, by checking the discrepancy of multiple estimations of transcription factor activity with respect to different perturbations on the connections. The rationale behind stability analysis is that the consistency of observed gene expression and true network connection shall remain stable after small perturbations are applied to initial connection matrix. With condition-specific TFs prioritized by kSA, we further propose to use multivariate regression to highlight condition-specific target genes. Through simulation studies comparing with several competing methods, we show that the proposed schemes are more sensitive to detect relevant TFs and target genes for network inference purpose. Experimentally, we have applied stability analysis to yeast cell cycle experiment and further to a series of anti-estrogen breast cancer studies. In both experiments not only biologically relevant regulators are highlighted, the condition-specific transcriptional regulatory networks are also constructed, which could provide further insights into the corresponding cellular mechanisms. (b) if only single TF's protein-DNA information is available: this happens when protein-DNA binding relationship of individual TF is measured through experiments. Since original mNCA requires a complete connection matrix to perform estimation, an incomplete knowledge of single TF is not applicable for such approach. Moreover, binding information derived from experiments could still be inconsistent with gene expression levels. To overcome these limitations, we propose a linear extraction scheme called regulatory component analysis (RCA), which can infer underlying regulation relationships, even with partial biological knowledge. Numerical simulations show significant improvement of RCA over other traditional methods to identify target genes, not only in low signal-to-noise-ratio situations and but also when the given biological knowledge is incomplete and inconsistent to data. Furthermore, biological experiments on Escherichia coli regulatory network inferences are performed to fairly compare traditional methods, where the effectiveness and superior performance of RCA are confirmed. The second part of the dissertation moves from protein-DNA interaction network up to protein-protein interaction network, to identify dys-regulated protein sub-networks by integrating gene expression data and protein-protein interaction information. Specifically, we propose a statistically principled method, namely Metropolis random walk on graph (MRWOG), to highlight condition-specific PPI sub-networks in a probabilistic way. The method is based on the Markov chain Monte Carlo (MCMC) theory to generate a series of samples that will eventually converge to some desired equilibrium distribution, and each sample indicates the selection of one particular sub-network during the process of Metropolis random walk. The central idea of MRWOG is built upon that the essentiality of one gene to be included in a sub-network depends on not only its expression but also its topological importance. Contrasted to most existing methods constructing sub-networks in a deterministic way and therefore lacking relevance score for each protein, MRWOG is capable of assessing the importance of each individual protein node in a global way, not only reflecting its individual association with clinical outcome but also indicating its topological role (hub, bridge) to connect other important proteins. Moreover, each protein node is associated with a sampling frequency score, which enables the statistical justification of each individual node and flexible scaling of sub-network results. Based on MRWOG approach, we further propose two strategies: one is bootstrapping used for assessing statistical confidence of detected sub-networks; the other is graphic division to separate a large sub-network to several smaller sub-networks for facilitating interpretations. MRWOG is easy to use with only two parameters need to be adjusted, one is beta value for performing random walk and another is Quantile level for calculating truncated posteriori mean. Through extensive simulations, we show that the proposed scheme is not sensitive to these two parameters in a relatively wide range. We also compare MRWOG with deterministic approaches for identifying sub-network and prioritizing topologically important proteins, in both cases MRWG outperforms existing methods in terms of both precision and recall. By utilizing MRWOG generated node/edge sampling frequency, which is actually posteriori mean of corresponding protein node/interaction edge, we illustrate that condition-specific nodes/interactions can be better prioritized than the schemes based on scores of individual node/interaction. Experimentally, we have applied MRWOG to study yeast knockout experiment for galactose utilization pathways to reveal important components of corresponding biological functions; we also applied MRWSOG to study breast cancer patient prognostics problems, where the sub-network analysis could lead to an understanding of the molecular mechanisms of antiestrogen resistance in breast cancer. Finally, we conclude this dissertation with a summary of the original contributions, and the future work for deepening the theoretical justification of the proposed methods and broadening their potential biological applications such as cancer studies. / Ph. D.

Recent breakthroughs in high-throughput biotechnology have promoted the integration of multi-platform data to investigate signal transduction pathways within a cell. In order to model complicated dynamics and heterogeneity of biological pathways, sophisticated computational models are needed to address unique properties of both the biological hypothesis and the data. In this dissertation work, we have proposed and developed methods using Markov Chain Monte Carlo (MCMC) techniques to solve complex modeling problems in human cancer research by integrating multi-platform data. We focus on two research topics: 1) identification of transcriptional regulatory networks and 2) uncovering of aberrant intracellular signal transduction pathways. We propose a robust method, called GibbsOS, to identify condition specific gene regulatory patterns between transcription factors and their target genes. A Gibbs sampler is employed to sample target genes from the marginal function of outlier sum of regression t statistic. Numerical simulation has demonstrated significant performance improvement of GibbsOS over existing methods against noise and false positive connections in binding data. We have applied GibbsOS to breast cancer cell line datasets and identified condition specific regulatory rewiring in human breast cancer. We also propose a novel method, namely Gibbs sampler to Infer Signal Transduction (GIST), to detect aberrant pathways that are highly associated with biological phenotypes or clinical information. By converting predefined potential functions into a Gibbs distribution, GIST estimates edge directions by learning the distribution of linear signaling pathway structures. Through the sampling process, the algorithm is able to infer signal transduction directions which are jointly determined by both gene expression and network topology. We demonstrate the advantage of the proposed algorithms on simulation data with respect to different settings of noise level in gene expression and false-positive connections in protein-protein interaction (PPI) network. Another major contribution of the dissertation work is that we have improved traditional perspective towards understanding aberrant signal transductions by further investigating structural linkage of signaling pathways. We develop a method called Structural Organization to Uncover pathway Landscape (SOUL), which emphasizes on modularized pathways structures from reconstructed pathway landscape. GIST and SOUL provide a very unique angle to computationally model alternative pathways and pathway crosstalk. The proposed new methods can bring insight to drug discovery research by targeting nodal proteins that oversee multiple signaling pathways, rather than treating individual pathways separately. A complete pathway identification protocol, namely Infer Modularization of PAthway CrossTalk (IMPACT), is developed to bridge downstream regulatory networks with upstream signaling cascades. We have applied IMPACT to breast cancer treated patient datasets to investigate how estrogen receptor (ER) signaling pathways are related to drug resistance. The identified pathway proteins from patient datasets are well supported by breast cancer cell line models. We hypothesize from computational results that HSP90AA1 protein is an important nodal protein that oversees multiple signaling pathways to drive drug resistance. Cell viability analysis has supported our hypothesis by showing a significant decrease in viability of endocrine resistant cells compared with non-resistant cells when 17-AAG (a drug that inhibits HSP90AA1) is applied. We believe that this dissertation work not only offers novel computational tools towards understanding complicated biological problems, but more importantly, it provides a valuable paradigm where systems biology connects data with hypotheses using computational modeling. Initial success of using microarray datasets to study endocrine resistance in breast cancer has shed light on translating results from high throughput datasets to biological discoveries in complicated human disease studies. As the next generation biotechnology becomes more cost-effective, the power of the proposed methods to untangle complicated aberrant signaling rewiring and pathway crosstalk will be finally unleashed. / Ph. D.