Large-scale gene expression studies are widely used to identify genes that are differentially expressed between phenotypes relevant to disease. Often thousands of differentially expressed genes (DEGs) are found using this type of analysis, which complicates the interpretation of the data. In this project we treat DEGs as windows into the biological processes that underlie disease. In order to find these processes, we put DEGs into the context in which they perform their functions - through the interactions of their protein products. Protein-protein interactions can provide biological context to DEGs in the form of functional modules. These modules are groups of proteins that together perform cellular functions. In this thesis we have refined a functional module detection process that consists of two steps. Firstly, community detection methods are applied to protein interaction networks (PINs) to detect groups of interacting proteins, and secondly, the biological coherence of the proteins grouped together is evaluated to select communities that represent potential functional modules. Two features that are central to this work are the detection of modules at different scales of network organization, and CommWalker, a module evaluation method that we developed which is able to detect signals of poorly-studied functions. By integrating these methods into our functional module detection process, we were able to obtain a good coverage of potential functional modules. Testing for enrichment of DEGs on these functional modules can uncover biological processes that are involved in the contrasted phenotypes and merit further investigation. We have applied our pipeline to find differentially regulated functions between hypoxic and normoxic breast cancer cell lines, and between M1 and M2 macrophages. Our results generate biological hypotheses of cellular functions that are differentially regulated in the investigated phenotypes, and proteins that are involved in these functions. We were able to validate several proteins in enriched modules which did not correspond to DEGs that were input into the pipeline, which suggests our methodology can reveal new biological insight.

京都大学 / 新制・課程博士 / 博士(人間健康科学) / 甲第23827号 / 人健博第98号 / 新制||人健||7(附属図書館) / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 澤本 伸克, 教授 林 悠, 教授 武藤 学 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM

A multimodal network (MMN) is a novel mathematical construct that captures the structure of biological networks, computational network models, and relationships from biological databases. An MMN subsumes the structure of graphs and hypergraphs, either undirected or directed. Formally, an MMN is a triple (V,E,M) where V is a set of vertices, E is a set of modal hyperedges, and M is a set of modes. A modal hyperedge e=(T,H,A,m) in E is an ordered 4-tuple, in which T,H,A are subsets of V and m is an element of M. The sets T, H, and A are the tail, head, and associate of e, while m is its mode. In the context of biology, each vertex is a biological entity, each hyperedge is a relationship, and each mode is a type of relationship (e.g., 'forms complex' and 'is a'). Within the space of multimodal networks, structural operations such as union, intersection, hyperedge contraction, subnetwork selection, and graph or hypergraph projections can be performed. A denotational semantics approach is used to specify the semantics of each hyperedge in MMN in terms of interaction among its vertices. This is done by mapping each hyperedge e to a hyperedge code algo:V(e), an algorithm that details how the vertices in V(e) get used and updated. A semantic MMN-based model is a function of a given schedule of evaluation of hyperedge codes and the current state of the model, a set of vertex-value pairs. An MMN-based computational system is implemented as a proof of concept to determine empirically the benefits of having it. This system consists of an MMN database populated by data from various biological databases, MMN operators implemented as database functions, graph operations implemented in C++ using LEDA, and mmnsh, a shell scripting language that provides a consistent interface to both data and operators. It is demonstrated that computational network models may enrich the MMN database and MMN data may be used as input to other computational tools and environments. A simulator is developed to compute from an initial state and a schedule of hyperedge codes the resulting state of a semantic MMN model. / Ph. D.

Shortest-path network analysis was employed to identify novel genes that modulate longevity in the baker’s yeast Saccharomyces cerevisiae. Based upon a set of previously reported genes associated with increased life span, a shortest path network algorithm was applied to a pre-existing protein-protein interaction dataset in order to construct a shortest-path longevity network. To validate this network, the replicative aging potential of 88 single gene deletion strains corresponding to predicted components of the shortest path longevity network was determined. The 88 single-gene deletion strains identified by a network approach are significantly enriched for mutation conferring both increased and decreased replicative life span when compared to a randomly selected set of 564 single-gene deletion strains or to the current data set available for the entire haploid deletion collection. In addition, previously unknown longevity genes were identified, several of which function in a longevity pathway believed to mediate life span extension in response to dietary restriction. This study represents the first biologically validated application of a network construct to the study of aging and rigorously demonstrates, also for the first time, that shortest path network analysis is a potentially powerful tool for predicting genes that function as potential modulators of aging.

Gene expression analysis provides genome-wide insights into the transcriptional activity of a cell. One of the first computational steps in exploration and analysis of the gene expression data is clustering. With a number of standard clustering methods routinely used, most of the methods do not take prior biological information into account. Here, we propose a new approach for gene expression clustering analysis. The approach benefits from a new deep learning architecture, Robust Autoencoder, which provides a more accurate high-level representation of the feature sets, and from incorporating prior system-wide biological information into the clustering process. We tested our approach on two gene expression datasets and compared the performance with two widely used clustering methods, hierarchical clustering and k-means, and with a recent deep learning clustering approach. Our approach outperformed all other clustering methods on the labeled yeast gene expression dataset. Furthermore, we showed that it is better in identifying the functionally common clusters than k-means on the unlabeled human gene expression dataset. The results demonstrate that our new deep learning architecture can generalize well the specific properties of gene expression profiles. Furthermore, the results confirm our hypothesis that the prior biological network knowledge is helpful in the gene expression clustering.

Biological motifs are defined as overly recurring sub-patterns in biological systems. Sequence motifs and network motifs are the examples of biological motifs. Due to the wide range of applications, many algorithms and computational tools have been developed for efficient search for biological motifs. Therefore, there are more computationally derived motifs than experimentally validated motifs, and how to validate the biological significance of the ‘candidate motifs’ becomes an important question. Some of sequence motifs are verified by their structural similarities or their functional roles in DNA or protein sequences, and stored in databases. However, biological role of network motifs is still invalidated and currently no databases exist for this purpose. In this thesis, we focus not only on the computational efficiency but also on the biological meanings of the motifs. We provide an efficient way to incorporate biological information with clustering analysis methods: For example, a sparse nonnegative matrix factorization (SNMF) method is used with Chou-Fasman parameters for the protein motif finding. Biological network motifs are searched by various clustering algorithms with Gene ontology (GO) information. Experimental results show that the algorithms perform better than existing algorithms by producing a larger number of high-quality of biological motifs. In addition, we apply biological network motifs for the discovery of essential proteins. Essential proteins are defined as a minimum set of proteins which are vital for development to a fertile adult and in a cellular life in an organism. We design a new centrality algorithm with biological network motifs, named MCGO, and score proteins in a protein-protein interaction (PPI) network to find essential proteins. MCGO is also combined with other centrality measures to predict essential proteins using machine learning techniques. We have three contributions to the study of biological motifs through this thesis; 1) Clustering analysis is efficiently used in this work and biological information is easily integrated with the analysis; 2) We focus more on the biological meanings of motifs by adding biological knowledge in the algorithms and by suggesting biologically related evaluation methods. 3) Biological network motifs are successfully applied to a practical application of prediction of essential proteins.

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<p dir="ltr">Metabolic pathways are shaped by dynamic biotic interactions. My research delves into coevolution exemplified through two distinct projects that investigate the specialized metabolism of organisms as a consequence of biotic interactions. The first project focused on the remarkable metabolic adaptations of <i>Elysia crispata</i> morphotype clarki. This sea slug possesses the extraordinary ability to sequester and maintain functional chloroplasts (kleptoplasts) from the algae it consumes, allowing it to sustain photosynthetically active kleptoplasts for several months without feeding. To better understand the underlying molecular mechanism of this phenomenon, I generated a comprehensive 786 Mbp draft genome of <i>E. crispata</i> using a combination of ONT long reads and Illumina short reads. The resulting assembly provided a foundational resource for phylogenetic, gene family and gene expression analyses. This work advanced our understanding of the genetic underpinnings of kleptoplasty, shedding light on the evolution and maintenance of this unique metabolic strategy in sacoglossan sea slugs. I next investigated the transcriptional impacts of herbivory on maize (<i>Zea mays</i>) and green foxtail (<i>Setaria viridis</i>), induced by fall armyworm (<i>Spodoptera frugiperda</i>) and beet armyworm (<i>Spodoptera exigua</i>) feeding. This study aimed to contrast the defensive mechanisms of these grasses in response to each herbivore, and determined that green foxtail transcriptionally differentiates its responses to fall armyworm and beet armyworm herbivory. The fall armyworm has evolved a counter adaptation to lessen plant secondary metabolite production by producing a salivary protein (SFRP1) that suppresses jasmonate signaling. Investigation of the combinatorial effects of SFRP1 and beet armyworm herbivory determined the addition of endogenous SFRP1 during beet armyworm feeding is sufficient to reduce green foxtail defense responses. Results of this research shed light on host-pest reciprocal adaptations and the role of SFRP1 as an oral secretory protein. Coexpression analysis of maize and green foxtail transcriptomic responses to herbivory also identified putative genes involved in specialized metabolic pathways in green foxtail, providing insights into plant-insect interactions and potential solutions to herbivory in wild plant species. These findings highlight how gene diversification can contribute to pest resistance in grasses. Together, these seemingly unconnected projects underscore how biotic interactions influence metabolic processes across diverse organisms and reveal the fascinating intricacies of their adaptations to environmental challenges.</p>

Le cancer du sein est le deuxième cancer le plus répandu dans le monde et la principale cause de décès due à un cancer chez les femmes. L'amélioration du pronostic du cancer a été l'une des principales préoccupations afin de permettre une meilleure gestion et un meilleur traitement clinique des patients. Avec l'avancement rapide des technologies de profilage génomique durant ces dernières décennies, la disponibilité aisée d'une grande quantité de données génomiques pour la recherche médicale a motivé la tendance actuelle qui consiste à utiliser des outils informatiques tels que l'apprentissage statistique dans le domaine de la science des données afin de découvrir les biomarqueurs moléculaires en lien avec l'amélioration du pronostic. Cette thèse est conçue suivant deux directions d'approches destinées à répondre à deux défis majeurs dans l'analyse de données génomiques pour le pronostic du cancer du sein d'un point de vue méthodologique de l'apprentissage statistique : les approches basées sur le classement pour améliorer le pronostic moléculaire et les approches guidées par un réseau donné pour améliorer la découverte de biomarqueurs. D'autre part, les méthodologies développées et étudiées dans cette thèse, qui concernent respectivement l'apprentissage à partir de données de classements et l'apprentissage sur un graphe, apportent une contribution significative à plusieurs branches de l'apprentissage statistique, concernant au moins les applications à la biologie du cancer et la théorie du choix social. / Breast cancer is the second most common cancer worldwide and the leading cause of women's death from cancer. Improving cancer prognosis has been one of the problems of primary interest towards better clinical management and treatment decision making for cancer patients. With the rapid advancement of genomic profiling technologies in the past decades, easy availability of a substantial amount of genomic data for medical research has been motivating the currently popular trend of using computational tools, especially machine learning in the era of data science, to discover molecular biomarkers regarding prognosis improvement. This thesis is conceived following two lines of approaches intended to address two major challenges arising in genomic data analysis for breast cancer prognosis from a methodological standpoint of machine learning: rank-based approaches for improved molecular prognosis and network-guided approaches for enhanced biomarker discovery. Furthermore, the methodologies developed and investigated in this thesis, pertaining respectively to learning with rank data and learning on graphs, have a significant contribution to several branches of machine learning, concerning applications across but not limited to cancer biology and social choice theory.