Cells of the freshwater cnidarian Hydra possess an exceptional regeneration ability. In small groups of these cells, organizer centers emerge spontaneously and instruct the patterning of the ...surrounding population into a new animal. This property makes them an excellent model system to study the general rules of self-organization. A small tissue fragment or a clump of randomly aggregated cells can form a hollow spheroid that is able to establish a body axis de novo. Interestingly, mechanical oscillations (inflation/deflation cycles of the spheroid) driven by osmosis accompany the successful establishment of axial polarity. Here we describe different approaches for generating Hydra tissue spheroids, along with imaging and image analysis techniques to investigate their mechanical behavior.
The coefficient of determination
quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest. However, estimating
for ...generalized linear mixed models (GLMMs) remains challenging. We have previously introduced a version of
that we called Formula: see text for Poisson and binomial GLMMs, but not for other distributional families. Similarly, we earlier discussed how to estimate intra-class correlation coefficients (ICCs) using Poisson and binomial GLMMs. In this paper, we generalize our methods to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data. While expanding our approach, we highlight two useful concepts for biologists, Jensen's inequality and the delta method, both of which help us in understanding the properties of GLMMs. Jensen's inequality has important implications for biologically meaningful interpretation of GLMMs, whereas the delta method allows a general derivation of variance associated with non-Gaussian distributions. We also discuss some special considerations for binomial GLMMs with binary or proportion data. We illustrate the implementation of our extension by worked examples from the field of ecology and evolution in the
environment. However, our method can be used across disciplines and regardless of statistical environments.
Chromatin is partitioned on multiple length scales into subcompartments that differ from each other with respect to their molecular composition and biological function. It is a key question how these ...compartments can form even though diffusion constantly mixes the nuclear interior and rapidly balances concentration gradients of soluble nuclear components. Different biophysical concepts are currently used to explain the formation of “chromatin bodies” in a self-organizing manner and without consuming energy. They rationalize how soluble protein factors that are dissolved in the liquid nuclear phase, the nucleoplasm, bind and organize transcriptionally active or silenced chromatin domains. In addition to cooperative binding of proteins to a preformed chromatin structure, two different mechanisms for the formation of phase-separated chromatin subcompartments have been proposed. One is based on bridging proteins that cross-link polymer segments with particular properties. Bridging can induce a collapse of the nucleosome chain and associated factors into an ordered globular phase. The other mechanism is based on multivalent interactions among soluble molecules that bind to chromatin. These interactions can induce liquid-liquid phase separation, which drives the assembly of liquid-like nuclear bodies around the respective binding sites on chromatin. Both phase separation mechanisms can explain that chromatin bodies are dynamic spherical structures, which can coalesce and are in constant and rapid exchange with the surrounding nucleoplasm. However, they make distinct predictions about how the size, density, and stability of chromatin bodies depends on the concentration and interaction behavior of the molecules involved. Here, we compare the different biophysical mechanisms for the assembly of chromatin bodies and discuss experimental strategies to distinguish them from each other. Furthermore, we outline the implications for the establishment and memory of functional chromatin state patterns.
The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered ...by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure–activity relationships (QSARs). Herein, we develop QSAR models that quantitatively predict both thermodynamic- and kinetic-based binding parameters of small molecules and the HIV-1 transactivation response (TAR) RNA model system. Small molecules bearing diverse scaffolds were screened against TAR using surface plasmon resonance. Multiple linear regression (MLR) combined with feature selection afforded robust models that allowed direct interpretation of the properties critical for both binding strength and kinetic rate constants. These models were validated with new molecules, and their accurate performance was confirmed via comparison to ensemble tree methods, supporting the general applicability of this platform.
•Traditional drug screening model systems are inadequate.•Organoid-on-a-chip platforms stand to significantly advance drug and toxicology screening.•A wide variety or tissue types have been ...represented by organoids-on-a-chip.•3D organoid systems better mimic responses of the human body to drug and toxins.•Cutting edge multi-organ “body-on-a-chip” systems are currently being developed.
In recent years, advances in tissue engineering and microfabrication technologies have enabled rapid growth in the areas of in vitro organoid development as well as organoid-on-a-chip platforms. These 3D model systems often are able to mimic human physiology more accurately than traditional 2D cultures and animal models. In this review, we describe the progress that has been made to generate organ-on-a-chip platforms and, more recently, more complex multi-organoid body-on-a-chip platforms and their applications. Importantly, these systems have the potential to dramatically impact biomedical applications in the areas of drug development, drug and toxicology screening, disease modeling, and the emerging area of personalized precision medicine.
Organoid-on-a-chip and multi-organoid body-on-a-chip platforms stand to significantly advance our capabilities in drug and toxicology screening, disease modeling, and the emerging area of personalized precision medicine.
Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body ...fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.
When cells measure concentrations of chemical signals, they may average multiple measurements over time in order to reduce noise in their measurements. However, when cells are in an environment that ...changes over time, past measurements may not reflect current conditions—creating a new source of error that trades off against noise in chemical sensing. What statistics in the cell’s environment control this trade-off? What properties of the environment make it variable enough that this trade-off is relevant? We model a single eukaryotic cell sensing a chemical secreted from bacteria (e.g., folic acid). In this case, the environment changes because the bacteria swim—leading to changes in the true concentration at the cell. We develop analytical calculations and stochastic simulations of sensing in this environment. We find that cells can have a huge variety of optimal sensing strategies ranging from not time averaging at all to averaging over an arbitrarily long time or having a finite optimal averaging time. The factors that primarily control the ideal averaging are the ratio of sensing noise to environmental variation and the ratio of timescales of sensing to the timescale of environmental variation. Sensing noise depends on the receptor-ligand kinetics, while environmental variation depends on the density of bacteria and the degradation and diffusion properties of the secreted chemoattractant. Our results suggest that fluctuating environmental concentrations may be a relevant source of noise even in a relatively static environment.
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