Background: Amyloidogenic proteins are most often associated with neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and Huntington's disease, but there are more than two dozen human proteins known to form amyloid fibrils associated with disease. Lysozyme is an antimicrobial protein that is used as a general model to study amyloid fibril formation. Studies aimed at elucidating the process of amyloid formation of lysozyme tend to focus on partial unfolding of the native state due to the relative instability of mutant amyloidogenic variants. While this is well supported, the data presented here suggest the native structure of the variants may also play a role in primary nucleation. Results: Three-dimensional structural analysis identified lysozyme residues 21, 62, 104, and 122 as displaced in both amyloidogenic variants compared to wild type lysozyme. Residue interaction network (RIN) analysis found greater clustering of residues 112-117 in amyloidogenic variants of lysozyme compared to wild type. An analysis of the most energetically favored predicted dimers and trimers provided further evidence for a role for residues 21, 62, 104, 122, and 112-117 in amyloid formation. Conclusions: This study used lysozyme as a model to demonstrate the utility of combining 3D structural analysis with RIN analysis for studying the general process of amyloidogenesis. Results indicated that binding of two or more amyloidogenic lysozyme mutants may be involved in amyloid nucleation by placing key residues (21, 62, 104, 122, and 112-117) in proximity before partial unfolding occurs. Identifying residues in the native state that may be involved in amyloid formation could provide novel drug targets to prevent a range of amyloidoses.

The ecological dynamics of co-flowering communities are largely mediated by pollinators. However, current understanding of pollinator-mediated interactions primarily relies on how co-flowering plants influence attraction of shared pollinators, and much less is known about plant–plant interactions that occur via heterospecific pollen (HP) transfer. Invaded communities in particular can be highly affected by the transfer of alien pollen, but the strength, drivers and fitness consequences of these interactions at a community scale are not well understood. Here we analyse HP transfer networks in nine coastal communities in the Yucatan Mexico that vary in the relative abundance of invasive flowers to evaluate how HP donation and receipt varies between native and alien plants. We further evaluate whether HP donation and receipt are mediated by floral traits (e.g. display, flower size) or pollinator visitation rate. Finally, we evaluated whether post-pollination success (proportion of pollen tubes produced) was affected by alien HP receipt and whether the effect varied between native and alien recipients. HP transfer networks exhibit relatively high connectance (c. 15%), suggesting high HP transfer within the studied communities. Significant network nestedness further suggests the existence of species that predominantly act as HP donors or recipients in the community. Species-level analyses showed that natives receive 80% more HP compared to alien species, and that alien plants donate 40% more HP than natives. HP receipt and donation were mediated by different floral traits and such effects were independent of plant origin (native or alien). The proportion of alien HP received significantly affected conspecific pollen tube success in natives, but not that of alien species. Synthesis. Our results suggest that HP transfer in invaded communities is widespread, and that native and alien species play different roles within HP transfer networks, which are mediated by a different suite of floral traits. Alien species, in particular, play a central role as HP donors and are more tolerant to HP receipt than natives—a finding that points to two overlooked mechanisms facilitating alien plant invasion and success within native co-flowering communities.

Les espèces qui cooccurrent partagent plus que l'espace physique, elles partagent aussi des interactions biotiques. Les interactions trophiques sont un type particulier d'interactions antagonistes qui représentent le flux de biomasse d'une espèce proie vers son prédateur. Les réseaux trophiques, dans leur forme la plus simple, sont composés de producteurs, de consommateurs et de décomposeurs. En raison de la nature des interactions qui composent les réseaux trophiques, leur structure nous révèle les processus gouvernant l'assemblage des communautés, la distribution de la biodiversité, le fonctionnement des écosystèmes et des services qu'ils nous rendent. Bien que l'étude des réseaux trophiques à large échelle spatiale ait été, jusqu’à présent, limitée par la disponibilité des données, une nouvelle ère de recherche en biogéographie et en écologie des réseaux trophiques est en train d’émerger. Pendant mon doctorat, j'ai ainsi étudié la diversité spatiale des réseaux trophiques des espèces européennes de tétrapodes dans le but de fusionner la biogéographie, l’écologie des reseaux trophiques et la biologie de la conservation. J'ai commencé par combiner l’information sur la distribution des espèces et leurs interactions potentielles pour cartographier la diversité des réseaux trophiques européens. Cela m'a permis d’explorer comment les gradients de milieu, qu’ils soient climatiques ou d’utilisation du sol, influencent et structurante la composition et la distribution des réseaux trophiques en Europe. Ensuite, dans un effort de compréhension de la structure des réseaux, j'ai identifié les espèces jouant un rôle clé dans les réseaux via l’utilisation d’indices de centralité. En effet, la perte d'espèces dites ‘centrales’ dans un réseau trophique peut grandement perturber la structure et la dynamique des communautés, jusqu’à provoquer des extinctions secondaires. À l'aide de trois mesures de centralité (degré, betweenness et eigenvector centrality), j'ai examiné la relation entre la centralité des espèces et leurs caractéristiques fonctionnelles et leur place dans la phylogénie des espèces de tetrapods européens. J'ai découvert que la centralité était conservée au long de la phylogénie et que les espèces qui chassent activement des petites proies étaient plus susceptibles de jouer un rôle central dans un réseau trophique. Étant donné le rôle primordial des espèces centrales pour la persistance des communautés, j’ai ensuite testé l’efficacité du réseau d’aires protégées européen pour leur conservation. J'ai ainsi exploré la relation entre le risque d'extinction des espèces et leur centralité ainsi que leur niveau trophique. J'ai constaté que le lien entre le risque d'extinction et les caractéristiques trophiques des espèces était faible ou inexistant. Par une analyse des lacunes (‘gap analysis’) des espaces protégés, j’ai aussi pu montrer par une que les espèces dites centrales dans les réseaux trophiques européens étaient d’ailleurs généralement bien protégées en Europe et n’étaient donc pas forcément les plus vulnérables. Pour conclure, en combinant la biogéographie, l'écologie des réseaux trophiques et la biologie de la conservation, ma thèse offre l'une des premières études approfondies sur la distribution à grande échelle de la structure trophique des tétrapodes, ainsi que les facteurs responsable de cette distribution, sur les dépendances entre caractéristiques trophiques, fonctionnelles et phylogénétiques des espèces, et leur statut de conservation. / Co-occurring species share more than just physical space, they share also biotic interactions. Trophic interactions depict the flow of biomass from a prey species to its predator. Food webs, in their simplest form, are composed by producers, consumers and decomposers. Because of the nature of the interactions that food webs depict, their structure can be informative about the underlying processes responsible for the assembly of communities, the organization of biodiversity, the functioning of ecosystems and the services they provide to humans. Although the study of food webs across large spatial scales has been limited by data availability, a new generation of biogeography and food web ecology research is rising. In my PhD, I investigated the spatial diversity of food webs of European tetrapod species with the objective of merging biogeography, food web ecology and conservation biology. I started by combining two different sources of information, species distributions and an expert-based database of species interactions, to map the diversity of European food webs. This allowed me to uncover the spatial interactions between food web diversity, composition, environmental and land-use gradients. I found that the spatial diversity of food webs was highly linked with climatic conditions. Then, I used centrality indices to identify trophic keystone species. Losing central species in a food web can affect a community disproportionally, by disrupting its trophic structure and potentially causing other extinctions. Using three measures of centrality (degree, betweenness and eigenvector centralities), I investigated how these centrality indices correlated with the traits and phylogeny of European tetrapod species. I found that centrality was highly conserved through phylogeny, and active hunters of small prey were more likely to be central within a food web. Because centrality was restricted to few species, assessing the efficiency of current protected areas for these central species is crucial to persistence of European communities. Thus, I investigated the correlation between species extinction risk, centrality and also trophic level. On a positive note, I found weak to no links between extinction risk and species trophic features. I also performed a gap analysis to search for any links between species centrality, trophic level and their spatial protection, which showed that key species are generally well protected in Europe. My thesis brings together the three fields of biogeography, food web ecology, and conservation biology into one of the first comprehensive studies on the large-scale patterns of trophic structure and its drivers, and the dependencies between species trophic, functional and phylogenetic characteristics, finally providing an option to bring trophic information into conservation applications.

Protein interaction data is being generated at unprecedented rates thanks to advancements made in high throughput techniques such as mass spectrometry and DNA microarrays. Biomedical researchers, operating under budgetary constraints, have found it difficult to scale their efforts to keep up with the ever-increasing amount of available data. They often lack the resources and manpower required to analyze the data using existing methodologies. These research deficiencies impede our ability to understand diseases, delay the advancement of clinical therapeutics, and ultimately costs lives. One of the most commonly used techniques to analyze protein interaction data is the construction and visualization of protein interaction networks. This research investigated the effectiveness and efficiency of novel domain-specific algorithms for visualizing protein interaction networks. The existing domain-agnostic algorithms were compared to the novel algorithms using several performance, aesthetic, and biological relevance metrics. The graph drawing algorithms proposed here introduced novel domain-specific forces to the existing force-directed graph drawing algorithms. The innovations include an attractive force and graph coarsening policy based on semantic similarity, and a novel graph refinement algorithm. These experiments have demonstrated that the novel graph drawing algorithms consistently produce more biologically meaningful layouts than the existing methods. Aggregated over the 480 tests performed, and quantified using the Biological Evaluation Percentage metric defined in the Methodology chapter, the novel graph drawing algorithms created layouts that are 237 percent more biologically meaningful than the next best algorithm. This improvement came at the cost of additional edge crossings and smaller minimum angles between adjacent edges, both of which are undesirable aesthetics. The aesthetic and performance tradeoffs are experimentally quantified in this study, and dozens of algorithmically generated graph drawings are presented to visually illustrate the benefits of the novel algorithms. The graph drawing algorithms proposed in this study will help biomedical researchers to more efficiently produce high quality interactive protein interaction network drawings for improved discovery and communication.

Species' floral traits and flowering times are known to be the major drivers of pollinator-mediated plant–plant interactions in diverse co-flowering communities. However, their simultaneous role in mediating plant community assembly and plant–pollinator interactions is still poorly understood. Since not all species flower at the same time, inference of facilitative and competitive interactions based on floral trait distribution patterns should account for fine phenological structure (intensity of flowering overlap) within co-flowering communities. Such an approach may also help reveal the simultaneous action of competitive and facilitative interactions in structuring co-flowering communities. Here we used modularity within a co-flowering network context, as a novel approach to detect convergent and/or over-dispersed patterns in floral trait distribution and pollinator sharing. Specifically, we evaluate differences in floral trait and pollinator distribution patterns within (high temporal flowering overlap) and among co-flowering modules (low temporal flowering overlap). We further evaluate the consistency of observed floral trait and pollinator sharing distribution patterns across space (three geographical regions) and time (dry and rainy seasons). We found that floral trait similarity was significantly higher in plant species within co-flowering modules than in species among them. This suggests pollinator facilitation may lead to floral trait convergence, but only within co-flowering modules. However, our results also revealed seasonal and spatial shifts in the underlying interactions (facilitation or competition) driving co-flowering assembly, suggesting that the prevalent dominant interactions are not static. Synthesis. Overall, we provide strong evidence showing that the use of flowering time and floral trait distribution alone may be insufficient to fully uncover the role of pollinator-mediated interactions in community assembly. Integrating this information along with patterns of pollinator sharing will greatly help reveal the simultaneous action of facilitative and competitive pollinator-mediated interactions in co-flowering communities. The spatial and temporal variation in flowering and trait distribution patterns observed further emphasize the importance of adopting a more dynamic view of community assembly processes.

The ecological dynamics of co-flowering communities are largely mediated by pollinators. However, current understanding of pollinator-mediated interactions primarily relies on how co-flowering plants influence attraction of shared pollinators, and much less is known about plant–plant interactions that occur via heterospecific pollen (HP) transfer. Invaded communities in particular can be highly affected by the transfer of alien pollen, but the strength, drivers and fitness consequences of these interactions at a community scale are not well understood. Here we analyse HP transfer networks in nine coastal communities in the Yucatan Mexico that vary in the relative abundance of invasive flowers to evaluate how HP donation and receipt varies between native and alien plants. We further evaluate whether HP donation and receipt are mediated by floral traits (e.g. display, flower size) or pollinator visitation rate. Finally, we evaluated whether post-pollination success (proportion of pollen tubes produced) was affected by alien HP receipt and whether the effect varied between native and alien recipients. HP transfer networks exhibit relatively high connectance (c. 15%), suggesting high HP transfer within the studied communities. Significant network nestedness further suggests the existence of species that predominantly act as HP donors or recipients in the community. Species-level analyses showed that natives receive 80% more HP compared to alien species, and that alien plants donate 40% more HP than natives. HP receipt and donation were mediated by different floral traits and such effects were independent of plant origin (native or alien). The proportion of alien HP received significantly affected conspecific pollen tube success in natives, but not that of alien species. Synthesis. Our results suggest that HP transfer in invaded communities is widespread, and that native and alien species play different roles within HP transfer networks, which are mediated by a different suite of floral traits. Alien species, in particular, play a central role as HP donors and are more tolerant to HP receipt than natives—a finding that points to two overlooked mechanisms facilitating alien plant invasion and success within native co-flowering communities.

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.

No description available.

High-throughput whole-genome biological assays are highly intricate and difficult to interpret. The molecular interaction networks generated from evaluation of those experiments suggest that cellular functions are carried out by modules of interacting molecules. Reverse-engineering the modular structure of cellular interaction networks has the promise of significantly easing their analysis. We hypothesize that: • cellular wiring diagrams can be decomposed into overlapping modules, where each module is a set of coherently-interacting molecules and • a cell responds to a stress or a stimulus by appropriately modulating the activities of a subset of these modules. Motivated by these hypotheses, we develop models and algorithms that can reverse-engineer molecular modules from large-scale functional genomic data. We address two major problems: 1. Given a wiring diagram and genome-wide gene expression data measured after the application of a stress or in a disease state, compute the active network of molecular interactions perturbed by the stress or the disease. 2. Given the active networks for multiple stresses, stimuli, or diseases, compute a set of network legos, which are molecular modules with the property that each active network can be expressed as an appropriate combination of a subset of modules. To address the first problem, we propose an approach that computes the most-perturbed subgraph of a curated pathway of molecular interactions in a disease state. Our method is based on a novel score for pathway perturbation that incorporates both differential gene expression and the interaction structure of the pathway. We apply our method to a compendium of cancer types. We show that the significance of the most perturbed sub-pathway is frequently larger than that of the entire pathway. We identify an association that suggests that IL-2 infusion may have a similar therapeutic effect in bladder cancer as it does in melanoma. We propose two models to address the second problem. First, we formulate a Boolean model for constructing network legos from a set of active networks. We reduce the problem of computing network legos to that of constructing closed biclusters in a binary matrix. Applying this method to a compendium of 13 stresses on human cells, we automatically detect that about four to six hours after treatment with chemicals cause endoplasmic reticulum stress, fibroblasts shut down the cell cycle far more aggressively than fibroblasts or HeLa cells do in response to other treatments. Our second model represents each active network as an additive combination of network legos. We formulate the problem as one of computing network legos that can be used to recover active networks in an optimal manner. We use existing methods for non-negative matrix approximation to solve this problem. We apply our method to a human cancer dataset including 190 samples from 18 cancers. We identify a network lego that associates integrins and matrix metalloproteinases in ovarian adenoma and other cancers and a network lego including the retinoblastoma pathway associated with multiple leukemias. / Ph. D.

The purpose of this thesis was to study mitosis in Drosophila, from a network biology perspective. The primary aim was to develop and test a network-based prediction model that could integrate available data in public databases (like Flybase) and, based on that, predict potential mitotic proteins. The approach taken to design the protein interaction network included the use of a priori knowledge about the microtubule composition of the mitotic spindle and the higher likelihood of microtubule-associated proteins (MAPs) to have a putative mitotic function. The design also included the integration of different complementary datasets, from gene expression and functional RNAi screens to cross species conservation of MAPs for fitting a network-based model for predicting mitotic proteins. I begin with the creation of the MAP interactome based on a MAP dataset in Drosophila. This initial network was extended by transferring homologs and interologues of MAP datasets from four other species, i.e. human, mouse, rat and Arabidopsis. These proteins were then used as seed proteins to conduct a virtual pull-down experiment, by adding indirect interactors into the network, i.e. proteins that directly bind to two or more MAPs within the network, which completed the MAP interactome. Data from genome-wide studies in Drosophila were gathered for each node in the MAP interactome. These ‘layers’ of data were then used as features to fit a prediction model that could score each node in the network, based on the likelihood of its role in mitosis. The final model performed with 96% accuracy after 10-fold cross validation and was used to rank all the proteins in the MAP interactome. By analysing the top 100 high scoring predicted mitotic proteins, a highly connected cluster of 33 proteins was identified that was subject to experimental validation in the lab. The first approach was to conduct an in vitro analysis using an RNAi screen to test for any spindle, chromosome or centrosome phenotypes upon gene knockdown. After two independent RNAi screens, around 80% of the proteins produced mutant mitotic phenotypes strongly supporting the results of the MAP prediction model. The second approach was to conduct an in vivo analysis by expressing GFP- fusion constructs of selected genes from the subcluster. These were expressed in Drosophila early embryos to study their subcellular localization during interphase and mitosis. A variety of localizations were observed ranging from chromatin and microtubules to more generic cytoplasmic localizations. These results suggested not all predicted proteins were co-localizing with microtubules, and therefore might not necessarily be microtubule associated proteins but can possibly be functioning as microtubule associated regulator proteins. Proteomics analysis of a subset of these genes showed a large proportion of false positive interactions but also picked new interactions between member proteins that highlighted a module within the subcluster. The RNAi hits from the in vitro analysis and the members of the module within subcluster-16 from the in vivo analysis provide interesting subjects for further characterization.