How Our Cells Become Our Selves: The Cellular Phylodynamic Biology of Growth and Development

Our lives begin with 1 cell, then 2, then 4, then the trillion cell adult, comprised of cell lineages, tissues, organs. How does this occur? Examination in numbers of cells, N, Cellular Phylodynamics, revealed two previously unappreciated processes: UNI-GROWTH, the slowing of growth that occurs as we become larger, caused by fewer cells dividing, captured by the Universal Mitotic Fraction and Universal Growth Equations, with accuracy confirmed for 13 species, including nematodes, mollusks, and vertebrates; and ALLO-GROWTH, the creation of body parts from Founder Cells, captured by the Cellular Allometric Growth Equation, which describes mitotic expansion by Cell-Heritable change in the Cell Cycle Time. These equations can generate cell lineage approximations, bringing the power of coalescent theory to developmental biology.

ECM remodeling and spatial cell cycle coordination determine tissue growth kinetics

SummaryDuring development, multicellular organisms undergo stereotypical patterns of tissue growth to yield organs of highly reproducible sizes and shapes. How this process is orchestrated remains unclear. Analysis of the temporal dynamics of tissue growth in the Drosophila abdomen reveals that cell cycle times are spatially correlated and that growth termination occurs through the rapid emergence of a population of arrested cells rather than a gradual slowing down of cell cycle time. Reduction in apical tension associated with tissue crowding has been proposed as a developmental growth termination mechanism. Surprisingly, we find that growth arrest in the abdomen occurs while apical tension increases, showing that in this tissue a reduction in tension does not underlie the mechanism of growth arrest. However, remodeling of the extracellular matrix is necessary for tissue expansion. Thus, changes in the tissue microenvironment, and a rapid exit from proliferation, control the formation of the adult Drosophila abdomen.

Life-Long Neural Stem Cells Are Fate-Specified at an Early Developmental Stage

The origin and life-long fate of quiescent neural stem cells (NSCs) in the adult mammalian brain remain largely unknown. A few neural precursor cells in the embryonic brain elongate their cell cycle time and subsequently become quiescent postnatally, suggesting the possibility that life-long NSCs are selected at an early embryonic stage. Here, we utilized a GFP-expressing lentivirus to investigate the fate of progeny from individual lentivirus-infected NSCs by identifying the lentiviral integration site. Our data suggest that NSCs become specified to two or more lineages prior to embryonic day 13.5 in mice: one NSC lineage produces cells only for the cortex and another provides neurons to the olfactory bulb. The majority of neurosphere-forming NSCs in the adult brain are relatively dormant and generate very few cells, if any, in the olfactory bulb or cortex, and this NSC population could serve as a reservoir that is occasionally reactivated later in life.

GENE-44. BRD4 DELETION LEADS TO CEREBELLAR DEFICITS AND ATAXIA

Cerebellar neuronal progenitors undergo a series of divisions before irreversibly exiting the cell cycle and differentiating into neurons. Dysfunction of this process underlies many neurological diseases including ataxia and the most common pediatric brain tumor, medulloblastoma. To better define the pathways controlling the most abundant neuronal cells in the mammalian cerebellum, cerebellar granule cell progenitors (GCPs), we performed RNA-sequencing of GCPs exiting the cell cycle. Time-series modeling of GCP cell cycle exit identified downregulation of activity of the epigenetic reader protein Brd4. Brd4 binding to the Gli1 locus is controlled by Casein Kinase 1δ (CK1 δ-dependent phosphorylation during GCP proliferation, and decreases during GCP cell cycle exit. Importantly, conditional deletion of Brd4 in vivo in the developing cerebellum induces cerebellar morphological deficits and ataxia. These studies define an essential role for Brd4 in cerebellar granule cell neurogenesis and are critical for designing clinical trials utilizing Brd4 inhibitors in neurological indications.

Coupled Reaction Networks for Noise Suppression

Noise is intrinsic to many important regulatory processes in living cells, and often forms obstacles to be overcome for reliable biological functions. However, due to stochastic birth and death events of all components in biomolecular systems, suppression of noise of one component by another is fundamentally hard and costly. Quantitatively, a widelycited severe lower bound on noise suppression in biomolecular systems was established by Lestas et. al. in 2010, assuming that the plant and the controller have separate birth and death reactions. This makes the precision observed in several biological phenomena, e.g., cell fate decision making and cell cycle time ordering, seem impossible. We demonstrate that coupling, a mechanism widely observed in biology, could suppress noise lower than the bound of Lestas et. al. with moderate energy cost. Furthermore, we systematically investigate the coupling mechanism in all two-node reaction networks, showing that negative feedback suppresses noise better than incoherent feedforward achitectures, coupled systems have less noise than their decoupled version for a large class of networks, and coupling has its own fundamental limitations in noise suppression. Results in this work have implications for noise suppression in biological control and provide insight for a new efficient mechanism of noise suppression in biology.

A Multi-stage Representation of Cell Proliferation as a Markov Process

The stochastic simulation algorithm commonly known as Gillespie’s algorithm (originally derived for modelling well-mixed systems of chemical reactions) is now used ubiquitously in the modelling of biological processes in which stochastic effects play an important role. In well-mixed scenarios at the sub-cellular level it is often reasonable to assume that times between successive reaction/interaction events are exponentially distributed and can be appropriately modelled as a Markov process and hence simulated by the Gillespie algorithm. However, Gillespie’s algorithm is routinely applied to model biological systems for which it was never intended. In particular, processes in which cell proliferation is important (e.g. embryonic development, cancer formation) should not be simulated naively using the Gillespie algorithm since the history-dependent nature of the cell cycle breaks the Markov process. The variance in experimentally measured cell cycle times is far less than in an exponential cell cycle time distribution with the same mean. Here we suggest a method of modelling the cell cycle that restores the memoryless property to the system and is therefore consistent with simulation via the Gillespie algorithm. By breaking the cell cycle into a number of independent exponentially distributed stages, we can restore the Markov property at the same time as more accurately approximating the appropriate cell cycle time distributions. The consequences of our revised mathematical model are explored analytically as far as possible. We demonstrate the importance of employing the correct cell cycle time distribution by recapitulating the results from two models incorporating cellular proliferation (one spatial and one non-spatial) and demonstrating that changing the cell cycle time distribution makes quantitative and qualitative differences to the outcome of the models. Our adaptation will allow modellers and experimentalists alike to appropriately represent cellular proliferation—vital to the accurate modelling of many biological processes—whilst still being able to take advantage of the power and efficiency of the popular Gillespie algorithm.