Reconciling differential gene expression data with molecular interaction networks

Use of Graph Theory to Characterize Human and Arthropod Vector Cell Protein Response to Infection With Anaplasma phagocytophilum

One of the major challenges in modern biology is the use of large omics datasets for the characterization of complex processes such as cell response to infection. These challenges are even bigger when analyses need to be performed for comparison of different species including model and non-model organisms. To address these challenges, the graph theory was applied to characterize the tick vector and human cell protein response to infection with Anaplasma phagocytophilum, the causative agent of human granulocytic anaplasmosis. A network of interacting proteins and cell processes clustered in biological pathways, and ranked with indexes representing the topology of the proteome was prepared. The results demonstrated that networks of functionally interacting proteins represented in both infected and uninfected cells can describe the complete set of host cell processes and metabolic pathways, providing a deeper view of the comparative host cell response to pathogen infection. The results demonstrated that changes in the tick proteome were driven by modifications in protein representation in response to A. phagocytophilum infection. Pathogen infection had a higher impact on tick than human proteome. Since most proteins were linked to several cell processes, the changes in protein representation affected simultaneously different biological pathways. The method allowed discerning cell processes that were affected by pathogen infection from those that remained unaffected. The results supported that human neutrophils but not tick cells limit pathogen infection through differential representation of ras-related proteins. This methodological approach could be applied to other host-pathogen models to identify host derived key proteins in response to infection that may be used to develop novel control strategies for arthropod-borne pathogens.

Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions

Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to analyze these "omics" data has also increased, computational complexity and lack of documentation hinder researchers from analyzing their high-throughput data to its true potential. In this chapter we detail our data-driven, transkingdom network (TransNet) analysis protocol to integrate and interrogate multi-omics data. This systems biology approach has allowed us to successfully identify important causal relationships between different taxonomic kingdoms (e.g., mammals and microbes) using diverse types of data.

Discovering gene re-ranking efficiency and conserved gene-gene relationships derived from gene co-expression network analysis on breast cancer data

Systemic approaches are essential in the discovery of disease-specific genes, offering a different perspective and new tools on the analysis of several types of molecular relationships, such as gene co-expression or protein-protein interactions. However, due to lack of experimental information, this analysis is not fully applicable. The aim of this study is to reveal the multi-potent contribution of statistical network inference methods in highlighting significant genes and interactions. We have investigated the ability of statistical co-expression networks to highlight and prioritize genes for breast cancer subtypes and stages in terms of: (i) classification efficiency, (ii) gene network pattern conservation, (iii) indication of involved molecular mechanisms and (iv) systems level momentum to drug repurposing pipelines. We have found that statistical network inference methods are advantageous in gene prioritization, are capable to contribute to meaningful network signature discovery, give insights regarding the disease-related mechanisms and boost drug discovery pipelines from a systems point of view.

Transcription, epigenetics and ameliorative strategies in Huntington's Disease: a genome-wide perspective

Transcriptional dysregulation in Huntington’s disease (HD) is an early event that shapes the brain transcriptome by both the depletion and ectopic activation of gene products that eventually affect survival and neuronal functions. Disruption in the activity of gene expression regulators, such as transcription factors, chromatin-remodeling proteins, and noncoding RNAs, accounts for the expression changes observed in multiple animal and cellular models of HD and in samples from patients. Here, I review the recent advances in the study of HD transcriptional dysregulation and its causes to finally discuss the possible implications in ameliorative strategies from a genome-wide perspective. To date, the use of genome-wide approaches, predominantly based on microarray platforms, has been successful in providing an extensive catalog of differentially regulated genes, including biomarkers aimed at monitoring the progress of the pathology. Although still incipient, the introduction of combined next-generation sequencing techniques is enhancing our comprehension of the mechanisms underlying altered transcriptional dysregulation in HD by providing the first genomic landscapes associated with epigenetics and the occupancy of transcription factors. In addition, the use of genome-wide approaches is becoming more and more necessary to evaluate the efficacy and safety of ameliorative strategies and to identify novel mechanisms of amelioration that may help in the improvement of current preclinical therapeutics. Finally, the major conclusions obtained from HD transcriptomics studies have the potential to be extrapolated to other neurodegenerative disorders.

Epigenetic memory: the Lamarckian brain

Recent data support the view that epigenetic processes play a role in memory consolidation and help to transmit acquired memories even across generations in a Lamarckian manner. Drugs that target the epigenetic machinery were found to enhance memory function in rodents and ameliorate disease phenotypes in models for brain diseases such as Alzheimer's disease, Chorea Huntington, Depression or Schizophrenia. In this review, I will give an overview on the current knowledge of epigenetic processes in memory function and brain disease with a focus on Morbus Alzheimer as the most common neurodegenerative disease. I will address the question whether an epigenetic therapy could indeed be a suitable therapeutic avenue to treat brain diseases and discuss the necessary steps that should help to take neuroepigenetic research to the next level.