Retromer and Its Role in Regulating Signaling at Endosomes.

The retromer complex is a key element of the endosomal protein sorting machinery being involved in trafficking of proteins from endosomes to the Golgi and also endosomes to the cell surface. There is now accumulating evidence that retromer also has a prominent role in regulating the activity of many diverse signaling proteins that traffic through endosomes and this activity has profound implications for the functioning of many different cell and tissue types from neuronal cells to cells of the immune system to specialized polarized epithelial cells of the retina. In this review, the protein composition of the retromer complex will be described along with many of the accessory factors that facilitate retromer-mediated endosomal protein sorting to detail how retromer activity contributes to the regulation of several distinct signaling pathways

Robust simplifications of multiscale biochemical networks

<p>Abstract</p> <p>Background</p> <p>Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed.</p> <p>Results</p> <p>We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-<it>κ</it>B pathway.</p> <p>Conclusion</p> <p>Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.</p

Incorporating concepts of inequality and inequity into health benefits analysis

BACKGROUND: Although environmental policy decisions are often based in part on both risk assessment information and environmental justice concerns, formalized approaches for addressing inequality or inequity when estimating the health benefits of pollution control have been lacking. Inequality indicators that fulfill basic axioms and agree with relevant definitions and concepts in health benefits analysis and environmental justice analysis can allow for quantitative examination of efficiency-equality tradeoffs in pollution control policies. METHODS: To develop appropriate inequality indicators for health benefits analysis, we provide relevant definitions from the fields of risk assessment and environmental justice and consider the implications. We evaluate axioms proposed in past studies of inequality indicators and develop additional axioms relevant to this context. We survey the literature on previous applications of inequality indicators and evaluate five candidate indicators in reference to our proposed axioms. We present an illustrative pollution control example to determine whether our selected indicators provide interpretable information. RESULTS AND CONCLUSIONS: We conclude that an inequality indicator for health benefits analysis should not decrease when risk is transferred from a low-risk to high-risk person, and that it should decrease when risk is transferred from a high-risk to low-risk person (Pigou-Dalton transfer principle), and that it should be able to have total inequality divided into its constituent parts (subgroup decomposability). We additionally propose that an ideal indicator should avoid value judgments about the relative importance of transfers at different percentiles of the risk distribution, incorporate health risk with evidence about differential susceptibility, include baseline distributions of risk, use appropriate geographic resolution and scope, and consider multiple competing policy alternatives. Given these criteria, we select the Atkinson index as the single indicator most appropriate for health benefits analysis, with other indicators useful for sensitivity analysis. Our illustrative pollution control example demonstrates how these indices can help a policy maker determine control strategies that are dominated from an efficiency and equality standpoint, those that are dominated for some but not all societal viewpoints on inequality averseness, and those that are on the optimal efficiency-equality frontier, allowing for more informed pollution control policies

Cleavage modification did not alter blastomere fates during bryozoan evolution

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.The study was funded by the core budget of the Sars Centre and by The European Research Council Community’s Framework Program Horizon 2020 (2014–2020) ERC grant agreement 648861 to A

Crosstalk between senescent bone cells and the bone tissue microenvironment influences bone fragility during chronological age and in diabetes

Bone is a complex organ serving roles in skeletal support and movement, and is a source of blood cells including adaptive and innate immune cells. Structural and functional integrity is maintained through a balance between bone synthesis and bone degradation, dependent in part on mechanical loading but also on signaling and influences of the tissue microenvironment. Bone structure and the extracellular bone milieu change with age, predisposing to osteoporosis and increased fracture risk, and this is exacerbated in patients with diabetes. Such changes can include loss of bone mineral density, deterioration in micro-architecture, as well as decreased bone flexibility, through alteration of proteinaceous bone support structures, and accumulation of senescent cells. Senescence is a state of proliferation arrest accompanied by marked morphological and metabolic changes. It is driven by cellular stress and serves an important acute tumor suppressive mechanism when followed by immune-mediated senescent cell clearance. However, aging and pathological conditions including diabetes are associated with accumulation of senescent cells that generate a pro-inflammatory and tissue-destructive secretome (the SASP). The SASP impinges on the tissue microenvironment with detrimental local and systemic consequences; senescent cells are thought to contribute to the multimorbidity associated with advanced chronological age. Here, we assess factors that promote bone fragility, in the context both of chronological aging and accelerated aging in progeroid syndromes and in diabetes, including senescence-dependent alterations in the bone tissue microenvironment, and glycation changes to the tissue microenvironment that stimulate RAGE signaling, a process that is accelerated in diabetic patients. Finally, we discuss therapeutic interventions targeting RAGE signaling and cell senescence that show promise in improving bone health in older people and those living with diabetes