Pressures Created by Strong Water Power Coordinate entire Body Tissue Renovating

Elise Maynard^*
*Correspondence: Elise Maynard, Department of Land, Air and Water Resources, Guilin University of Technology, Guilin 541000, China, Email:

Introduction

We established a high-throughput live imaging method that monitors larva-polyp morphogenesis in a controlled microenvironment for ∼7 days at 5-min time resolution [1]. Due to the asynchronous development between individuals, we used changes in circularity (a perfect circle is 1) of the whole-organism shape contours as a geometrical feature to classify the developmental stages into larva (circularity >0.8), larva-polyp transition (circularity of 0.3-0.8), or polyp (circularity <0.3) [2]. We found that animal circularity decreases dramatically during larva-polyp transition, which is attributed to a 3- to 4-fold change in the length of the body and the development of oral tentacles [3].

Description

The population scale dynamics of larva-polyp transition were highly variable, and the overall time for development ranged from 16 to 30 h in most cases. This variability was independent of the disparity in body lengths. To quantify morphological changes, we used the estimated body column volume and length/diameter body aspect ratio as features to monitor the changes in organismal size and shape, respectively (see STAR Methods). Mapping these two features together reveals a morphospace that animals explore during larva-polyp morphogenesis. Although larvae generally have a smaller body column volume and aspect ratio compared with primary polyps, transitioning animals explore a wide range of combinations of different volumes and aspect ratios. To investigate whether animals change their size and shape simultaneously or separately, we defined four categories of morphodynamics that reflect different phases during larva-polyp transition: (1) a change in size without a major aspect ratio increase, termed “isotropic expansion” (IE), (2) an increase in aspect ratio without major volume increase, termed “axial stretching” (AS), (3) a combined increase of both body column volume and aspect ratio, termed “anisotropic expansion” (AE), and (4) no substantial positive change in neither volume nor aspect ratio, termed “no elongation” (NE). Mapping morphodynamics over time shows that all four categories are deployed but that AE and AS are most frequently deployed during elongation. However, the sequence and duration of different morphodynamics varied across developing animals. Together, these data suggest that larva-polyp morphogenesis is guided by a relatively plastic developmental program. Marine invertebrate larvae typically undergo settlement that marks a shift from a free-swimming to a sessile form that can adhere to a substrate. To study this behavioral change and link it to developmental dynamics, we tracked motility by measuring body displacement during all time points of the transition stage (n = 707 animals) [4,5].

Conclusion

We defined a low motility state when displacement is ≤130 μm per 5-min time interval and a high motility state if displacement is >130 μm. Interestingly, the rate of body elongation was significantly higher in the low motility state compared with the high motility state. To investigate motility behavior at the level of individual animals, we mapped their displacement during the transition stage and classified each animal as sessile (n = 517 out of 707; median displacement ≤130 μm) or motile (n = 190 out of 707; median displacement >130 μm). In addition to differences in the elongation rate, sessile and motile animals were markedly different in their morphospace and morphodynamics. Sessile animals showed a developmental period dominated by initial AE followed by AS. This sequential pattern was absent in motile animals. As a result, sessile animals typically had a higher aspect ratio at the same body column volume compared with motile animals, which led to noticeable differences in polyp shape.

Conflict of Interest

The authors declare that there is no conflict of interest associated with this manuscript.

References

1. Gitelson, Anatoly A, Yoram J Kaufman and Don Rundquist. "Novel algorithms for remote estimation of vegetation fraction." Remote Sens Environ 80 (2002): 76-87.

Google Scholar, Crossref, Indexed at

2. Small, Christopher. "Estimation of urban vegetation abundance by spectral mixture analysis." Int J Remote Sens 22 (2001): 1305-1334.

Google Scholar, Crossref, Indexed at

3. Richardson Arthur J and James H. Everitt. "Using spectral vegetation indices to estimate rangeland productivity." Geocarto Int 7 (1992): 63-69.

Google Scholar, Crossref, Indexed at

4. Wiegand, C. L., and A. J. Richardson. "Leaf area, light interception, and yield estimates from spectral components analysis 1." J Agron 76 (1984): 543-548.

Google Scholar, Crossref, Indexed at

5. Roujean, Jean-Louis and François Marie Breon. "Estimating PAR absorbed by vegetation from bidirectional reflectance measurements." Remote Sens Environ 51 (1995): 375-384.

Google Scholar, Crossref, Indexed at