Network Modeling of Crohn's Disease Incidence

Transcriptional Variabilities in Human hiPSC-derived Cardiomyocytes: All Genes Are Not Equal and Their Robustness May Foretell Donor's Disease Susceptibility

Human induced pluripotent stem cells (hiPSCs) are frequently used to study disease-associated variations. We characterized transcriptional variability from a hiPSC-derived cardiomyocyte (hiPSC-CM) study of left ventricular hypertrophy (LVH) using donor samples from the HyperGEN study. Multiple hiPSC-CM differentiations over reprogramming events (iPSC generation) across 7 donors were used to assess variabilities from reprogramming, differentiation, and donor LVH status. Variability arising from pathological alterations was assessed using a cardiac stimulant applied to the hiPSC-CMs to trigger hypertrophic responses. We found that for most genes (73.3%~85.5%), technical variability was smaller than biological variability. Further, we identified and characterized lists of "noise" genes showing greater technical variability and "signal" genes showing greater biological variability. Together, they support a "genetic robustness" hypothesis of disease-modeling whereby cellular response to relevant stimuli in hiPSC-derived somatic cells from diseased donors tends to show more transcriptional variability. Our findings suggest that hiPSC-CMs can provide a valid model for cardiac hypertrophy and distinguish between technical and disease-relevant transcriptional changes.

Network location and clustering of genetic mutations determine chronicity in a stylized model of genetic diseases

In a highly simplified view, a disease can be seen as the phenotype emerging from the interplay of genetic predisposition and fluctuating environmental stimuli. We formalize this situation in a minimal model, where a network (representing cellular regulation) serves as an interface between an input layer (representing environment) and an output layer (representing functional phenotype). Genetic predisposition for a disease is represented as a loss of function of some network nodes. Reduced, but non-zero, output indicates disease. The simplicity of this genetic disease model and its deep relationship to percolation theory allows us to understand the interplay between disease, network topology and the location and clusters of affected network nodes. We find that our model generates two different characteristics of diseases, which can be interpreted as chronic and acute diseases. In its stylized form, our model provides a new view on the relationship between genetic mutations and the type and severity of a disease.

Crohn's Disease: Is the Cold Chain Hypothesis Still Hot?

Crohn's disease [CD] is an inflammatory bowel disease of unknown aetiology. During recent decades, significant technological advances led to development of -omic datasets allowing a detailed description of the disease. Unfortunately these have not, to date, resolved the question of the aetiology of CD. Thus, it may be necessary to [re]consider hypothesis-driven approaches to resolve the aetiology of CD. According to the cold chain hypothesis, the development of industrial and domestic refrigeration has led to frequent exposure of human populations to bacteria capable of growing in the cold. These bacteria, at low levels of exposure, particularly those of the genus Yersinia, are believed to be capable of inducing exacerbated inflammation of the intestine in genetically predisposed subjects. We discuss the consistency of this working hypothesis in light of recent data from epidemiological, clinical, pathological, microbiological, and molecular studies.