Tissue Regeneration: Regional differences
The skin is a complex landscape containing regions in which hair follicles exhibit different types of behavior.
- Tsinghua University, China
- National Institute of Biological Sciences, China
The regeneration of tissue is a tightly controlled process which ensures that adult stem cells generate an appropriate number of daughter cells in order to maintain healthy tissue in the face of daily wear and tear. Tissue regeneration lasts throughout an animal’s lifetime, but when it goes wrong the end results can include chronic wounds, premature aging and cancer (Fuchs and Chen, 2013). Skin tissue has a fast turnover rate, and many of the fundamental principles that govern tissue regeneration have been discovered in experiments on hair follicle stem cells in mice (Fuchs, 2016).
A hair follicle cycles through three phases: growth, regression and resting. Growth is powered by stem cells being activated to proliferate and generate daughter cells, and the extent to which this happens depends on the relative levels of activating signals (such as WNT) and inhibitory signals (such as BMP; Plikus and Chuong, 2014). However, we know relatively little about the mechanisms that control the levels of these signaling molecules in the first place. Now, in eLife, Qing Nie and Maksim Plikus of the University of California, Irvine and colleagues – including Qixuan Wang and Ji Won Oh as joint first authors – report new insights into these mechanisms based on a combination of simulations and experiments (Wang et al., 2017).
Wang et al. – who are based at Irvine and various institutes in the United States, Korea, China, Australia and Poland – started by using simulations of a simple model to show that certain features of the hair cycle in mice were not observed in the model if it was assumed that all the hair follicles were identical. This indicated that the hair follicles in the different regions of the skin expressed different levels of signal molecules, and that the diffusion of, say, WNT from regions with high levels of WNT influenced the behavior of neighboring regions that had lower levels of WNT to begin with. When the model was adjusted so that dorsal skin hair follicles went through the cycles slower than ventral skin hair follicles, the simulations produced hair cycle patterns similar to those seen in real mice, including waves of hair cycles spreading from ventral skin to dorsal skin.
To test whether or not there exist multiple regions of skin with distinct hair cycles, Wang et al. used a reporter line to detect the level of WNT activity in the skin of live mice in real time. This revealed that hair follicles in ventral chin skin have a shorter growth phase and a faster turnover rate than hair follicles in dorsal skin. To understand the molecular mechanism behind this difference, the researchers discovered that dorsal skin in its resting phase has enriched BMP ligands, depleted BMP antagonists and decreased WNT ligands compared to ventral chin skin. Experiments on transgenic mice confirmed that hair cycles were changed in regions in which BMP or WNT signaling had been perturbed: moreover, perturbing these signals also altered the waves of hair cycles that spread from ventral skin to dorsal skin.
The simulations also predicted that hair follicles would be in an extended resting phase when inhibitor levels are extremely high. Wang et al. observed such behavior in ear skin: the hair follicles in ear skin exist in a dormant resting phase for an extended period of time, with limited or no response to methods that would normally lead to hair growth. The researchers then discovered that the cartilage/muscle complex that is only found in the ear expresses high levels of BMP ligands and multiple WNT antagonists, and went on to show that dampening the BMP signal or increasing the WNT signal can partially activate the growth of hair follicles in the ear.
These experiments confirm that the BMP and WNT pathways control the regional pattern of hair regeneration that is observed, and that these pathways also mediate the interaction among these different regions. This is consistent with the well-known role of BMP/WNT signaling in the skin (Kandyba et al., 2013), but we do not know what gives rise to the intrinsically different levels of BMP and WNT found in the various regions.
Why is it important to understand regional tissue regeneration? Conventional studies often focus on a single hair follicle or a population of hair follicles within one region. These reductionist approaches were able to reveal some of the core principles governing tissue regeneration, but they also missed out on a whole dimension of regulation. Most tissues are composed of heterogeneous cell populations (Donati and Watt, 2015): different regions of skin, for example, differ in hair follicle length, epidermis thickness and pigmentation patterns, all of which serve unique functions (Chang, 2009; Chuong et al., 2013).
Likewise, such regional behavior is evident in many human diseases: for instance, non-segmental vitiligo involves the loss of skin melanocytes from patches of skin on both the left and right sides of the body (Taïeb and Picardo, 2009). These region-specific features suggest that tissue regeneration is regulated by some sort of 'molecular area code'. The next challenge is to work out how this area code works.
References
-
Module-based complexity formation: Periodic patterning in feathers and hairsWiley Interdisciplinary Reviews: Developmental Biology 2:97–112.https://doi.org/10.1002/wdev.74
-
Stem cell heterogeneity and plasticity in epitheliaCell Stem Cell 16:465–476.https://doi.org/10.1016/j.stem.2015.04.014
-
Epithelial skin biology: Three decades of developmental biology, a hundred questions answered and a thousand new ones to addressCurrent Topics in Developmental Biology 116:357–374.https://doi.org/10.1016/bs.ctdb.2015.11.033
-
Macroenvironmental regulation of hair cycling and collective regenerative behaviorCold Spring Harbor Perspectives in Medicine 4:a015198.https://doi.org/10.1101/cshperspect.a015198
-
Clinical practice VitiligoNew England Journal of Medicine 360:160–169.https://doi.org/10.1056/NEJMcp0804388
Article and author information
Author details
Publication history
Copyright
© 2017, Yu et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,236
- views
-
- 154
- downloads
-
- 2
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Tissue Regeneration: Regional differences
eLife 6:e30249.
https://doi.org/10.7554/eLife.30249
Further reading
-
- Computational and Systems Biology
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.
-
- Computational and Systems Biology
The RAS-MAPK system plays an important role in regulating various cellular processes, including growth, differentiation, apoptosis, and transformation. Dysregulation of this system has been implicated in genetic diseases and cancers affecting diverse tissues. To better understand the regulation of this system, we employed information flow analysis based on transfer entropy (TE) between the activation dynamics of two key elements in cells stimulated with EGF: SOS, a guanine nucleotide exchanger for the small GTPase RAS, and RAF, a RAS effector serine/threonine kinase. TE analysis allows for model-free assessment of the timing, direction, and strength of the information flow regulating the system response. We detected significant amounts of TE in both directions between SOS and RAF, indicating feedback regulation. Importantly, the amount of TE did not simply follow the input dose or the intensity of the causal reaction, demonstrating the uniqueness of TE. TE analysis proposed regulatory networks containing multiple tracks and feedback loops and revealed temporal switching in the reaction pathway primarily responsible for reaction control. This proposal was confirmed by the effects of an MEK inhibitor on TE. Furthermore, TE analysis identified the functional disorder of a SOS mutation associated with Noonan syndrome, a human genetic disease, of which the pathogenic mechanism has not been precisely known yet. TE assessment holds significant promise as a model-free analysis method of reaction networks in molecular pharmacology and pathology.
