Nuclear Scaling Is Coordinated among Individual Nuclei in Multinucleated Muscle Fibers

Optimal cell performance depends on cell size and the appropriate relative size, i.e., scaling, of the nucleus. How nuclear scaling is regulated and contributes to cell function is poorly understood, especially in skeletal muscle fibers, which are among the largest cells, containing hundreds of nuclei. Here, we present a Drosophila in vivo system to analyze nuclear scaling in whole multinucleated muscle fibers, genetically manipulate individual components, and assess muscle function. Despite precise global coordination, we find that individual nuclei within a myofiber establish different local scaling relationships by adjusting their size and synthetic activity in correlation with positional or spatial cues. While myonuclei exhibit compensatory potential, even minor changes in global nuclear size scaling correlate with reduced muscle function. Our study provides the first comprehensive approach to unraveling the intrinsic regulation of size in multinucleated muscle fibers. These insights to muscle cell biology will accelerate the development of interventions for muscle diseases

Mechanical positioning of multiple nuclei in muscle cells.

Many types of large cells have multiple nuclei. In skeletal muscle fibers, the nuclei are distributed along the cell to maximize their internuclear distances. This myonuclear positioning is crucial for cell function. Although microtubules, microtubule associated proteins, and motors have been implicated, mechanisms responsible for myonuclear positioning remain unclear. We used a combination of rough interacting particle and detailed agent-based modeling to examine computationally the hypothesis that a force balance generated by microtubules positions the muscle nuclei. Rather than assuming the nature and identity of the forces, we simulated various types of forces between the pairs of nuclei and between the nuclei and cell boundary to position the myonuclei according to the laws of mechanics. We started with a large number of potential interacting particle models and computationally screened these models for their ability to fit biological data on nuclear positions in hundreds of Drosophila larval muscle cells. This reverse engineering approach resulted in a small number of feasible models, the one with the best fit suggests that the nuclei repel each other and the cell boundary with forces that decrease with distance. The model makes nontrivial predictions about the increased nuclear density near the cell poles, the zigzag patterns of the nuclear positions in wider cells, and about correlations between the cell width and elongated nuclear shapes, all of which we confirm by image analysis of the biological data. We support the predictions of the interacting particle model with simulations of an agent-based mechanical model. Taken together, our data suggest that microtubules growing from nuclear envelopes push on the neighboring nuclei and the cell boundaries, which is sufficient to establish the nearly-uniform nuclear spreading observed in muscle fibers

Sample gallery of the spatial nuclear patterns produced in Filter 1.

<p>A: Equilibrium positions for the 36 model classes (examples). Combinations of forces (att: attractive, rep: repulsive, dec: decreasing, inc: increasing, con: constant). We show results of varying the character of internuclear and nuclear-cell side forces. For each force combination, we show predicted patterns in a narrow (VL4) and wide (VL3) cell (black rectangles). Nuclei at their equilibrium positions are shown as blue and red discs for valid and non-valid patterns respectively. Outside of the green box, the models have not passed the first filtering step for any parameter values. The examples are shown for parameter values <i>c</i>_<i>N</i> = <i>c</i>_<i>S</i> = <i>c</i>_<i>P</i> = ∞, <i>σ</i>_<i>P</i> = 1, <i>α</i>_<i>P</i> = −1 and <i>M</i>_<i>s</i> = <i>M</i>_<i>p</i> = 1. Green box: model classes that passed the first filtering step for some parameter combination (positions shown represent valid patterns). For one particular combination (grey, dashed box), further details of scanning the parameter space are shown in B. B: Equilibrium positions for the (wide) VL3 cell for the force combination marked in gray in A, for all combinations of force ratios (<i>M</i>_<i>s</i>, <i>M</i>_<i>p</i>) for distance independent repulsive internuclear forces (with range <i>c</i>_<i>N</i> = 50), and for repulsive side and pole forces falling with distance (with an infinite range). Blue nuclei indicate valid patterns. For all non-valid patterns (red nuclei), symbols indicate the type of violation: △—nuclei stick to the edges, ◻—nuclei are too close together, ▼ nuclei don’t form two files, ○—nuclei do not stretch 2/3 of the cell’s length, ◼—mean nearest neighbor distance is less than 30<i>μm</i>. For parameters see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006208#pcbi.1006208.t001" target="_blank">Table 1</a>, for details see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006208#sec021" target="_blank">Methods</a>.</p

Force types and force screen.

<p>A: Possible molecular mechanisms of nuclear interaction via MT generated forces (1-8; see key bottom left, gray circles represent nuclei). Different forces <i>f</i> (positive for repulsive, negative for attractive) as a function of distance <i>d</i> are depicted in the lower row. 1: Kinesin 5 acting between overlapping antiparallel MTs from neighboring nuclei generates a repulsive force. Greater distances between the nuclei lead to a larger MT overlap (assuming long MTs), hence the force is increasing with distance. 2: MTs growing from one nucleus push on the neighboring nucleus, resulting in a repulsive force that drops with distance, since MT ends’ density decreases with the distance. 3: Kinesin motors are localized at the nuclear membrane and push away MT plus ends from neighboring nuclei. Depending upon whether the number of MTs or the number of kinesins are limiting, the resulting repulsive force can be decreasing with distance or be distance independent. 4 and 5: As 2 and 3, but the nucleus interacts with cell boundary or with motors on the cell cortex at the cell boundary. 6 and 7: Analogous to 1 and 3, but with the motors being dyneins (or kinesin 14), rather than kinesins, resulting in an attractive force. 8: Analogous to 7, but MTs from the nucleus interact with dyneins on the cell cortex at the cell boundary. B: Structure of the force screen: Two filtering steps are followed by a calibration step. We started with all 216 possible models and made a rough scan of the each model’s parameter space using a representative thin (VL4) cell and a representative wide (VL3) cell. Filter 1: The models are filtered by their ability to produce an evenly spread single file (SF) of nuclei in the thin cell, and double file (DF) in the wide cell. We found that the vast majority of the models fail this step and can be discarded; only 12 potential model classes remained. Filter 2: We applied each of the 12 model classes to 14 imaged cells of representative width, height and number of nuclei and examined whether the models can account for two characteristics of the biological data: 1) average nuclear <i>x</i>-position increases with cell width, 2) the model behavior is robust with respect to parameter changes. After this second filter, only two model classes remained. In the final step (“calibration”), we fixed the pole forces and used the data on the nuclear positions from all 200 imaged cells to determine the best parameters for the two models.</p

Pattern along the <i>x</i>- and <i>y</i>-axis.

<p>A and B: Comparison between the measured and simulated histogram of relative <i>y</i>-positions for M1 and M2 using the calibrated parameter values (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006208#pcbi.1006208.t001" target="_blank">Table 1</a>). C: Example cell with the positions of the nuclei as measured (upper row) and determined by M1 (lower row). In the lower row, the forces felt by a nucleus near the pole (black) is shown. Blue arrow represents the pole force on the black nucleus; green arrows show internuclear forces; side forces are not depicted. The strength of internuclear forces is represented by varying shades of red. Gray shading represents the reach of the internuclear force. D: Assuming a perfect pattern of zig-zags (top row), pairwise files (middle row) or of equally spaced single files (bottom row), the resulting auto-, or cross-correlation is depicted on the right. E: The auto-correlation function predicted and measured for SF cells. F: The predicted and measured auto-correlation function for each row in DF cells. G: The predicted and measured cross-correlation between the left row and the middle points of the right row in DF cells. For details see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006208#sec021" target="_blank">Methods</a>.</p

Positioning of the myonuclei in Ventral Longitudinal (VL) muscles 3 and 4.

<p>A: Left: <i>Drosophila</i> 3^rd instar larva (anterior up), right: Dissected larva revealing the somatic musculature, labeled with phalloidin (red) to reveal actin structures (sarcomeres) in muscles. B: Left: Representative VL3 and VL4 muscles (phalloidin, red) and nuclei (<i>α</i>-Lamin, cyan), right: Measured cell and nuclear outlines and nuclear centroids. C: VL3 and VL4 muscles (phalloidin, red) showing organization of microtubules (<i>α</i>-tubulin, yellow) around myonuclei (DNA; Hoechst, gray). D: Frequency of wide vs long nuclei defined using the angle of the major axis of a fitted ellipse. E: Histogram of nearest neighbor distances (NND) in VL3 and VL4 muscles. Filled bars show the experimental values, blue for VL3, red for VL4 and dark-pink where they overlap; the red and blue curves show the computer-generated NND histogram for randomly positioned nuclei (1000 realizations for all 200 cells, thick-solid lines are the means, thin-dashed and thin-dotted lines the mean ± standard deviation for VL3 and VL4 respectively). F: Histograms of the nuclear positions along the <i>x</i>- and <i>y</i>-axes for both cell types. The half-length and half-width of the cells were normalized to 1. G: Average nuclear <i>x</i>-positions relative to the center for each cell as functions of the cell width. Shown are the averages (green lines) ± standard deviation (green shading) as well as the actual measurements for all VL3 (blue) and VL4 (red) cells.</p

Fss/Tbx6 is required for central dermomyotome cell fate in zebrafish

Summary The dermomyotome is a pool of progenitor cells on the surface of the myotome. In zebrafish, dermomyotome precursors (anterior border cells, ABCs) can be first identified in the anterior portion of recently formed somites. They must be prevented from undergoing terminal differentiation during segmentation, even while mesodermal cells around them respond to signaling cues and differentiate. T-box containing transcription factors regulate many aspects of mesoderm fate including segmentation and somite patterning. The fused somites (fss) gene is the zebrafish ortholog of tbx6. We demonstrate that in addition to its requirement for segmentation, fss/tbx6 is also required for the specification of ABCs and subsequently the central dermomyotome. The absence of Tbx6-dependent central dermomyotome cells in fss/tbx6 mutants is spatially coincident with a patterning defect in the myotome. Using transgenic fish with a heat-shock inducible tbx6 gene in the fss/tbx6 mutant background, we further demonstrate that ubiquitous fss/tbx6 expression has spatially distinct effects on recovery of the dermomyotome and segment boundaries, suggesting that the mechanism of Fss/Tbx6 action is distinct with respect to dermomyotome development and segmentation. We propose that Fss/Tbx6 is required for preventing myogenic differentiation of central dermomyotome precursors before and after segmentation and that central dermomyotome cells represent a genetically and functionally distinct subpopulation within the zebrafish dermomyotome

Parameters in the agent-based, stochastic simulation using Cytosim.

<p>Parameters in the agent-based, stochastic simulation using Cytosim.</p