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Determinative developmental cell lineages are robust to cell deaths.

ABSTRACT: All forms of life are confronted with environmental and genetic perturbations, making phenotypic robustness an important characteristic of life. Although development has long been viewed as a key component of phenotypic robustness, the underlying mechanism is unclear. Here we report that the determinative developmental cell lineages of two protostomes and one deuterostome are structured such that the resulting cellular compositions of the organisms are only modestly affected by cell deaths. Several features of the cell lineages, including their shallowness, topology, early ontogenic appearances of rare cells, and non-clonality of most cell types, underlie the robustness. Simple simulations of cell lineage evolution demonstrate the possibility that the observed robustness arose as an adaptation in the face of random cell deaths in development. These results reveal general organizing principles of determinative developmental cell lineages and a conceptually new mechanism of phenotypic robustness, both of which have important implications for development and evolution.

SUBMITTER: Yang JR

PROVIDER: S-EPMC4110091 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Determinative developmental cell lineages are robust to cell deaths.

Yang Jian-Rong JR Ruan Shuxiang S Zhang Jianzhi J

PLoS genetics 20140724 7

All forms of life are confronted with environmental and genetic perturbations, making phenotypic robustness an important characteristic of life. Although development has long been viewed as a key component of phenotypic robustness, the underlying mechanism is unclear. Here we report that the determinative developmental cell lineages of two protostomes and one deuterostome are structured such that the resulting cellular compositions of the organisms are only modestly affected by cell deaths. Seve ...[more]

PMID: 25058586

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Project description:Boolean networks have been widely used as models for gene regulatory networks, signal transduction networks, or neural networks, among many others. One of the main difficulties in analyzing the dynamics of a Boolean network and its sensitivity to perturbations or mutations is the fact that it grows exponentially with the number of nodes. Therefore, various approaches for simplifying the computations and reducing the network to a subset of relevant nodes have been proposed in the past few years. We consider a recently introduced method for reducing a Boolean network to its most determinative nodes that yield the highest information gain. The determinative power of a node is obtained by a summation of all mutual information quantities over all nodes having the chosen node as a common input, thus representing a measure of information gain obtained by the knowledge of the node under consideration. The determinative power of nodes has been considered in the literature under the assumption that the inputs are independent in which case one can use the Bahadur orthonormal basis. In this article, we relax that assumption and use a standard orthonormal basis instead. We use techniques of Hilbert space operators and harmonic analysis to generate formulas for the sensitivity to perturbations of nodes, quantified by the notions of influence, average sensitivity, and strength. Since we work on finite-dimensional spaces, our formulas and estimates can be and are formulated in plain matrix algebra terminology. We analyze the determinative power of nodes for a Boolean model of a signal transduction network of a generic fibroblast cell. We also show the similarities and differences induced by the alternative complete orthonormal basis used. Among the similarities, we mention the fact that the knowledge of the states of the most determinative nodes reduces the entropy or uncertainty of the overall network significantly. In a special case, we obtain a stronger result than in previous works, showing that a large information gain from a set of input nodes generates increased sensitivity to perturbations of those inputs.

Project description:BackgroundObstructive sleep apnea (OSA) is recognized to increase the risk of dyslipidemia; however, the specific sleep traits in OSA that influence dyslipidemia are poorly understood. This study sought to determine which sleep traits are independently associated with dyslipidemia and serum lipid profiles in patients with OSA.MethodsIn this cohort study, 5239 participants were included from the Sleep Heart Health Study. Further, OSA was diagnosed via polysomnography with an AHI ≥ 5 events/h. Sleep traits were assessed using polysomnographic data and questionnaires. Then, logistic regression was used to identify sleep traits that predict dyslipidemia in OSA patients. Non-linear associations between sleep traits and dyslipidemia were evaluated using restricted cubic splines. The potential mediating effect of body mass index (BMI) was also calculated. Later, linear regression analysis identified sleep traits that were independently linked to lipid levels.ResultsAfter adjusting for confounding factors, logistic regression identified sleep latency (OR: 1.005, 95% CI: 1.002-1.009, P = 0.001), rapid eye movement (REM) stage (OR: 0.987, 95% CI: 0.977-0.998, P = 0.022), REM latency (OR: 1.001, 95% CI: 1.000-1.002, P = 0.027), mean oxygen saturation (meanSpO2) (OR: 0.961, 95% CI: 0.926-0.996, P = 0.031), percentage of time with oxygen saturation below 95% (T95) (OR: 1.003, 95% CI: 1.001-1.005, P = 0.005), and time to fall asleep (OR: 1.004, 95% CI: 1.000-1.007, P = 0.042) as variables independently associated with dyslipidemia. No significant non-linear associations were found (all P >0.05). BMI mediated the association between REM stage, meanSpO2, T95, and dyslipidemia risk. Linear regression analysis identified T95 as a consistent independent determinant of all lipid parameters. Additionally, the meanSpO2 and sleep latency were significant independent determinants of most lipid parameters.ConclusionsSleep latency, sleep architecture, and nocturnal hypoxemia are key factors in dyslipidemia among patients with OSA. These insights suggest potential biomarkers and targeted interventions for the management of lipid-related complications of OSA.

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Determinative developmental cell lineages are robust to cell deaths.

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Determinative developmental cell lineages are robust to cell deaths.

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