Protein-protein interactions: network analysis and applications in drug discovery

Physical interactions among proteins constitute the backbone of cellular function, making them an attractive source of therapeutic targets. Although the challenges associated with targeting protein-protein interactions (PPIs) -in particular with small molecules are considerable, a growing number of functional PPI modulators is being reported and clinically evaluated. An essential starting point for PPI inhibitor screening or design projects is the generation of a detailed map of the human interactome and the interactions between human and pathogen proteins. Different routes to produce these biological networks are being combined, including literature curation and computational methods. Experimental approaches to map PPIs mainly rely on the yeast two-hybrid (Y2H) technology, which have recently shown to produce reliable protein networks. However, other genetic and biochemical methods will be essential to increase both coverage and resolution of current protein networks in order to increase their utility towards the identification of novel disease-related proteins and PPIs, and their potential use as therapeutic targets

Bet v 1 triggers antiviral-type immune signalling in birch-pollen-allergic individuals

Background In allergic patients, clinical symptoms caused by pollen remind of symptoms triggered by viral respiratory infections, which are also the main cause of asthmatic exacerbations. In patients sensitized to birch pollen, Bet v 1 is the major symptom-causing allergen. Immune mechanisms driving Bet v 1-related responses of human blood cells have not been fully characterized. Objective To characterize the immune response to Bet v 1 in peripheral blood in patients allergic to birch pollen. Methods The peripheral blood mononuclear cells of birch-allergic (n = 24) and non-allergic (n = 47) adolescents were stimulated ex-vivo followed by transcriptomic profiling. Systems-biology approaches were employed to decipher disease-relevant gene networks and deconvolution of associated cell populations. Results Solely in birch-allergic patients, co-expression analysis revealed activation of networks of innate immunity and antiviral signalling as the immediate response to Bet v 1 stimulation. Toll-like receptors and signal transducer transcription were the main drivers of gene expression patterns. Macrophages and dendritic cells were the main cell subsets responding to Bet v 1. Conclusions and clinical relevance In birch-pollen-allergic patients, the activated innate immune networks seem to be, in part, the same as those activated during viral infections. This tendency of the immune system to read pollens as viruses may provide new insight to allergy prevention and treatment.Peer reviewe

The Modular Organization of Protein Interactions in Escherichia coli

Escherichia coli serves as an excellent model for the study of fundamental cellular processes such as metabolism, signalling and gene expression. Understanding the function and organization of proteins within these processes is an important step towards a ‘systems’ view of E. coli. Integrating experimental and computational interaction data, we present a reliable network of 3,989 functional interactions between 1,941 E. coli proteins (∼45% of its proteome). These were combined with a recently generated set of 3,888 high-quality physical interactions between 918 proteins and clustered to reveal 316 discrete modules. In addition to known protein complexes (e.g., RNA and DNA polymerases), we identified modules that represent biochemical pathways (e.g., nitrate regulation and cell wall biosynthesis) as well as batteries of functionally and evolutionarily related processes. To aid the interpretation of modular relationships, several case examples are presented, including both well characterized and novel biochemical systems. Together these data provide a global view of the modular organization of the E. coli proteome and yield unique insights into structural and evolutionary relationships in bacterial networks

The potential of text mining in data integration and network biology for plant research : a case study on Arabidopsis

Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein-protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies

Comparative genomics and transcriptomics elucidate virulence mechanisms and host responses in infectious diseases

The main thematic area of the present thesis is the development and application of bioinformatics pipelines, namely whole-genome sequence (WGS) analysis and transcriptome profile analysis. These pipelines were applied to study the fungal pathogen Aspergillus fumigatus (Manuscripts I, III, and IV) and the early human immune mechanisms activated in response to different types of pathogens (bacteria, fungi, and co-infections) in sepsis patients (Manuscript II). The comparative genomic and transcriptomic analyses applied in my thesis have significantly improved our understanding of fungal pathogenicity as well as the pathogen-specific immune response mechanisms of the human host. Next to a number of novel insights, my work included in this thesis has generated a large number of new hypotheses based on big-data analysis, offering the scientific community the possibility to design exciting new research to confirm them in future experimental studies and bring us closer to actual precision medicine for infectious diseases

Corynebacterium glutamicum regulation beyond transcription: Organizing principles and reconstruction of an extended regulatory network incorporating regulations mediated by small RNA and protein-protein interactions

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory machinery. The study of such machinery at the global scale has been challenged by the lack of data integration. Here, we report three regulatory network models for C. glutamicum: strong (3040 interactions) constructed solely with regulations previously supported by directed experiments; all evidence (4665 interactions) containing the strong network, regulations previously supported by non-directed experiments, and protein-protein interactions with a direct effect on gene transcription; and sRNA (5222 interactions) containing the all evidence network and sRNA-mediated regulations. Compared to the previous version (2018), the strong and all evidence networks increased by 75 and 1225 interactions, respectively. We analyzed the system-level components of the three networks to identify how they differ and compared their structures against those for the networks of more than 40 species. The inclusion of the sRNAs regulations changed the proportions of the system-level components and increased the number of modules but decreased their size. The C. glutamicum regulatory structure contrasted with other bacterial regulatory networks. Finally, we used the strong networks of three model organisms to provide insights and future directions of the C. glutamicum regulatory network characterization.Comment: 32 pages, 4 figures, 1 supplementary materia

Network Analyses in Systems Biology: New Strategies for Dealing with Biological Complexity

The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity

Network analyses in systems biology: new strategies for dealing with biological complexity

The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity