The Parkinson's-disease-associated mutation LRRK2-G2019S alters dopaminergic differentiation dynamics via NR2F1.

Increasing evidence suggests that neurodevelopmental alterations might contribute to increase the susceptibility to develop neurodegenerative diseases. We investigate the occurrence of developmental abnormalities in dopaminergic neurons in a model of Parkinson's disease (PD). We monitor the differentiation of human patient-specific neuroepithelial stem cells (NESCs) into dopaminergic neurons.

Neural Stem Cells of Parkinson's Disease Patients Exhibit Aberrant Mitochondrial Morphology and Functionality.

Emerging evidence suggests that Parkinson's disease (PD), besides being an age-associated disorder, might also have a neurodevelopment component. Disruption of mitochondrial homeostasis has been highlighted as a crucial cofactor in its etiology. Here, we show that PD patient-specific human neuroepithelial stem cells (NESCs), carrying the LRRK2-G2019S mutation, recapitulate key mitochondrial defects previously described only in differentiated dopaminergic neurons.

Natural variation of chronological aging in the species reveals diet-dependent mechanisms of life span control.

Aging is a complex trait of broad scientific interest, especially because of its intrinsic link with common human diseases. Pioneering work on aging-related mechanisms has been made in , mainly through the use of deletion collections isogenic to the S288c reference strain. In this study, using a recently published high-throughput approach, we quantified chronological life span (CLS) within a collection of 58 natural strains across seven different conditions.

Discovering pair-wise genetic interactions: an information theory-based approach.

Phenotypic variation, including that which underlies health and disease in humans, results in part from multiple interactions among both genetic variation and environmental factors. While diseases or phenotypes caused by single gene variants can be identified by established association methods and family-based approaches, complex phenotypic traits resulting from multi-gene interactions remain very difficult to characterize. Here we describe a new method based on information theory, and demonstrate how it improves on previous approaches to identifying genetic interactions, including both synthetic and modifier kinds of interactions.

Describing the complexity of systems: multivariable "set complexity" and the information basis of systems biology.

Context dependence is central to the description of complexity. Keying on the pairwise definition of "set complexity," we use an information theory approach to formulate general measures of systems complexity. We examine the properties of multivariable dependency starting with the concept of interaction information.

Relations between the set-complexity and the structure of graphs and their sub-graphs.

: We describe some new conceptual tools for the rigorous, mathematical description of the "set-complexity" of graphs. This set-complexity has been shown previously to be a useful measure for analyzing some biological networks, and in discussing biological information in a quantitative fashion. The advances described here allow us to define some significant relationships between the set-complexity measure and the structure of graphs, and of their component sub-graphs.