Tuberculosis is a worldwide persistent infectious disease. It is caused by bacteria from the Mycobacterium tuberculosis complex that mainly affects the lungs and can be fatal. Using an integrative systems biology approach, we study the immunopathological progression of this disease, analyzing the key interactions between the cells involved in the different phases of the infectious process. We integrated multiple in vivo and in vitro data from immunohistochemical, serological, molecular biology, and cell count assays into a mechanistic mathematical model. The ordinary differential equation (ODE) model captures the regulatory interplay between the phenotypic variation of the main cells involved in the disease progression and the inflammatory microenvironment. The model reproduces in vivo time course data of an experimental model of progressive pulmonary TB in mouse, accurately reflecting the functional adaptations of the host–pathogen interactions as the disease progresses through three phenotypically different phases. We used the model to assess the effect of genotypic variations (encoded as changes in parameters) on disease outcomes. For all genotypes, we found an all-or-nothing response, where the virtual mouse either completely clears the infection or suffers uncontrolled Tb growth. Results show that it is 84% probable that a mouse submitted to a progressive pulmonary TB assay will end up with an uncontrolled infection. The simulations also showed how the genotypic variations shape the transitions across phases, showing that 100% of the genotypes evaluated eventually progress to phase two of the disease, suggesting that adaptive immune response activation was unavoidable. All the genotypes of the network that avoided progressing to phase 3 cleared the infection. Later, by analyzing the three different phases separately, we saw that the anti-inflammatory genotype of phase 3 was the one with the highest probability of leading to uncontrolled bacterial growth, and the proinflammatory genotype associated with phase 2 had the highest probability of bacterial clearance. Forty-two percent of the genotypes evaluated showed a bistable response, with one stable steady state corresponding to infection clearance and the other one to bacteria reaching its carrying capacity. Our mechanistic model can be used to predict the outcomes of different experimental conditions through in silico assays.
Human embryonic stem cells (hESC) derive from the epiblast and have pluripotent potential. To maintain the conventional conditions of the pluripotent potential in an undifferentiated state, inactivated mouse embryonic fibroblast (iMEF) is used as a feeder layer. However, it has been suggested that hESC under this conventional condition (hESC-iMEF) is an artifact that does not correspond to the in vitro counterpart of the human epiblast. Our previous studies demonstrated the use of an alternative feeder layer of human amniotic epithelial cells (hAEC) to derive and maintain hESC. We wondered if the hESC-hAEC culture could represent a different pluripotent stage than that of naïve or primed conventional conditions, simulating the stage in which the amniotic epithelium derives from the epiblast during peri-implantation. Like the conventional primed hESC-iMEF, hESC-hAEC has the same levels of expression as the 'pluripotency core'; and does not express markers of naïve pluripotency. However, it presents a downregulation of HOX genes and genes associated with the endoderm and mesoderm and it exhibits an increase in the expression of ectoderm lineage genes, specifically in the anterior neuroectoderm. Transcriptome analysis showed in hESC-hAEC an upregulated signature of genes coding for transcription factors involved in neural induction and forebrain development, and the ability to differentiate into a neural lineage was superior in comparison with conventional hESC-iMEF. We propose that the interaction of hESC with hAEC confers hESC a biased potential that resembles the anteriorized epiblast, which is predisposed to form the neural ectoderm.
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