Evaluation of the relevance and impact of kinase dysfunction in neurological disorders through proteomics and phosphoproteomics bioinformatics

Phosphorylation is an important post-translational modification that is involved in various biological processes and its dysregulation has in particular been linked to diseases of the central nervous system including neurological disorders. The present thesis characterizes alterations in the phosphoproteome and protein abundance associated with schizophrenia and Parkinson's disease, with the goal of uncovering the underlying disease mechanisms. To support this goal, I eventually created an automated analysis pipeline in R to streamline the analysis process of proteomics and phosphoproteomics data. Mass spectrometry (MS) technology is utilized to generate proteomics and phosphoproteomics data. Study I of the thesis demonstrates an automated R pipeline, PhosPiR, created to perform multi-level functional analyses of MS data after the identification and quantification of the raw spectral data. The pipeline does not require coding knowledge to run. It supports 18 different organisms, and provides analyses of MS intensity data from preprocessing, normalization and imputation, through to figure overviews, statistical analysis, enrichment analysis, PTM-SEA, kinase prediction and activity analysis, network analysis, hub analysis, annotation mining, and homolog alignment. The LRRK2-G2019S mutation, a frequent genetic cause of late onset Parkinson's disease, was investigated in Study II and III. One study investigated the mechanism of LRRK2-G2019S function in brain, and the other identified proteins with significantly altered overall translation patterns in sporadic and LRRK2-G2019S patient samples. Specifically, study II identified that LRRK2 is localized to the small 40S ribosomal subunit and that LRRK2 activity suppresses RNA translation, as validated in cell and animal models of Parkinson's disease and in patient cells. Study III utilized bio-orthogonal non-canonical amino acid tagging to label newly translated proteins in order to identify which proteins were affected by repressed translation in patient samples, using mass spectrometry analysis. The analysis revealed 33 and 30 nascent proteins with reduced synthesis in sporadic and LRRK2-G2019S Parkinson’s cases, respectively. The biological process "cytosolic signal recognition particle (SRP)-dependent co-translational protein targeting to membrane" was functionally significantly affected in both sporadic and LRRK2-G2019S Parkinson's, while "Tubulin/FTsz C-terminal domain superfamily network" was only significantly enriched in LRRK2-G2019S Parkinson’s cases. The findings were validated bytargeted proteomics and immunoblotting. Study IV is conducted to investigate the role of JNK1 in schizophrenia. Wild type and Jnk1-/- mice were used to analyze the phosphorylation profile using LC-MS/MS analysis. 126 proteins associated with schizophrenia were identified to overlap with the significantly differentially phosphorylated proteins in Jnk1-/- mice brain. The NMDAR trafficking pathway was found to be highly enriched, and surface staining of NMDAR subunits in neurons showed that surface expression of both subunits in Jnk1-/- neurons was significantly decreased. Further behavioral tests conducted with MK801 treatment have associated the Jnk1-/- molecular and behavioral phenotype with schizophrenia and neuropsychiatric disease

An analysis of emotion-exchange motifs in multiplex networks during emergency events

In this paper, we present an analysis of the emotion-exchange patterns that arise from Twitter messages sent during emergency events. To this end, we performed a systematic structural analysis of the multiplex communication network that we derived from a data-set including more than 1.9 million tweets that have been sent during five recent shootings and terror events. In order to study the local communication structures that emerge as Twitter users directly exchange emotional messages, we propose the concept of emotion-exchangemotifs. Our findings suggest that emotion-exchange motifs which contain reciprocal edges (indicating online conversations) only emerge when users exchange messages that convey anger or fear, either in isolation or in any combination with another emotion. In contrast, the expression of sadness, disgust, surprise, as well as any positive emotion are rather characteristic for emotion-exchange motifs representing one-way communication patterns (instead of online conversations). Among other things, we also found that a higher structural similarity exists between pairs of network layers consisting of one high-arousal emotion and one low-arousal emotion, rather than pairs of network layers belonging to the same arousal dimension

Morphological and Fractal Characteristics of City Road Networks from Philippine Metropolitan Regions

City road networks evolve from the fundamental need to connect various locations and to subdivide available space, especially in large urban areas. No two cities are exactly the same, however, and the differences manifest themselves in the layout of roads across their geographical regions. In this work, the fractal dimensions of urban roads from the three major metropolitan regions of the Philippines were investigated, along with the distributions of dimensionless spatial metrics for characterizing roads and road-bounded blocks. The dimensionless metrics reveal the commonalities, particularly the road and block motifs found in the urban road network tapestry. On the other hand, the fractal dimensions hint at the difference in levels of urbanization of the various cities and municipalities, which are considered subject to geographical constraints. This research adds to the growing literature with a complexity perspective on urban systems by reporting on an archipelagic road network data set. From a practical perspective, this work is deemed to be a useful first step towards an even deeper quantitative analysis of these regional economic centers and its insights may be used for drafting effective policy measures for management and further development

Morphological and Fractal Characteristics of City Road Networks from Philippine Metropolitan Regions

City road networks evolve from the fundamental need to connect various locations and to subdivide available space, especially in large urban areas. No two cities are exactly the same, however, and the differences manifest themselves in the layout of roads across their geographical regions. In this work, the fractal dimensions of urban roads from the three major metropolitan regions of the Philippines were investigated, along with the distributions of dimensionless spatial metrics for characterizing roads and road-bounded blocks. The dimensionless metrics reveal the commonalities, particularly the road and block motifs found in the urban road network tapestry. On the other hand, the fractal dimensions hint at the difference in levels of urbanization of the various cities and municipalities, which are considered subject to geographical constraints. This research adds to the growing literature with a complexity perspective on urban systems by reporting on an archipelagic road network data set. From a practical perspective, this work is deemed to be a useful first step towards an even deeper quantitative analysis of these regional economic centers and its insights may be used for drafting effective policy measures for management and further development

Dyads, triads, and tetrads: a multivariate simulation approach to uncovering network motifs in social graphs

Motifs represent local subgraphs that are overrepresented in networks. Several disciplines document multiple instances in which motifs appear in graphs and provide insight into the structure and processes of these networks. In the current paper, we focus on social networks and examine the prevalence of dyad, triad, and symmetric tetrad motifs among 24 networks that represent six types of social interactions: friendship, legislative co-sponsorship, Twitter messages, advice seeking, email communication, and terrorist collusion. Given that the correct control distribution for detecting motifs is a matter of continuous debate, we propose a novel approach that compares the local patterns of observed networks to random graphs simulated from exponential random graph models. Our proposed technique can produce conditional distributions that control for multiple, lower-level structural patterns simultaneously. We find evidence for five motifs using our approach, including the reciprocated dyad, three triads, and one symmetric tetrad. Results highlight the importance of mutuality, hierarchy, and clustering across multiple social interactions, and provide evidence of “structural signatures” within different genres of graph. Similarities also emerge between our findings and those in other disciplines, such as the preponderance of transitive triads

Knowledge Extraction from Textual Resources through Semantic Web Tools and Advanced Machine Learning Algorithms for Applications in Various Domains

Nowadays there is a tremendous amount of unstructured data, often represented by texts, which is created and stored in variety of forms in many domains such as patients' health records, social networks comments, scientific publications, and so on. This volume of data represents an invaluable source of knowledge, but unfortunately it is challenging its mining for machines. At the same time, novel tools as well as advanced methodologies have been introduced in several domains, improving the efficacy and the efficiency of data-based services. Following this trend, this thesis shows how to parse data from text with Semantic Web based tools, feed data into Machine Learning methodologies, and produce services or resources to facilitate the execution of some tasks. More precisely, the use of Semantic Web technologies powered by Machine Learning algorithms has been investigated in the Healthcare and E-Learning domains through not yet experimented methodologies. Furthermore, this thesis investigates the use of some state-of-the-art tools to move data from texts to graphs for representing the knowledge contained in scientific literature. Finally, the use of a Semantic Web ontology and novel heuristics to detect insights from biological data in form of graph are presented. The thesis contributes to the scientific literature in terms of results and resources. Most of the material presented in this thesis derives from research papers published in international journals or conference proceedings