Biology subjects

Marshall, W. F.

Publications and source records attributed to Marshall, W. F..

6 recordsLinked to original sources

bioRxiv2018

Modeling meiotic homolog pairing: increased fidelity from a tug of war between telomere forces and a pairing-based Brownian ratchet

Meiotic homolog pairing involves associations between homologous DNA regions scattered along the length of a chromosome. When homologs associate, they tend to do so by a processive zippering processive, which apparently results from avidity effects. Using a computational model, we show that this avidity-driven processive zippering reduces the selectivity of pairing. When active random forces are applied to telomeres, this drop in selectivity is eliminated in a force-dependent manner. Further simulations suggest that active telomere forces are engaged in a tug-of-war against zippering, which can be interpreted as a Brownian ratchet with a stall force that depends on the dissociation constant of pairing. When perfectly homologous regions of high affinity compete with homeologous regions of lower affinity, the affinity difference can be amplified through this tug of war effect provided the telomere force acts in a range that is strong enough to oppose zippering of homeologs while still permitting zippering of correct homologs. The degree of unzippering depends on the radius of the nucleus, such that complete unzippering of homeologous regions can only take place if the nucleus is large enough to pull the two chromosomes completely apart. A picture of meiotic pairing thus emerges that is fundamentally mechanical in nature, possibly explaining the purpose of active telomere forces, increased nuclear diameter, and the presence of \"Maverick\" chromosomes in meiosis.

biophysics↗

bioRxiv2017

The Transcriptional Program of Regeneration in the Giant Single Cell, Stentor coeruleus

The giant ciliate Stentor coeruleus is a classical model system for studying regeneration and morphogenesis at the level of a single cell. Stentor are polarized cells with a complex subcellular architecture. The anterior of the cell is marked by an array of cilia, known as the oral apparatus. This feeding organelle can be induced to shed and regenerate in a series of reproducible morphological steps, previously shown to require transcription. We used RNAseq to assay the dynamic changes in Stentos transcriptome during regeneration with high temporal resolution, allowing us to identify five distinct waves of gene expression. We show that the oral apparatus is a model for organelle regeneration, as well as for centriole assembly and ciliogenesis as many conserved genes involved in those processes are induced. Additionally, we find genes involved in signaling, cell cycle regulation, transcription, and RNA binding to be expressed at distinct stages of organelle regeneration, suggesting that the morphological steps of regeneration are driven by a complex regulatory system.

cell biology↗

bioRxiv2017

Inferring cell state by quantitative motility analysis reveals a dynamic state system and broken detailed balance

Cell populations display heterogeneous phenotypic states at multiple scales. Similar to molecular features commonly used to explore cell heterogeneity, cell behavior is a rich phenotypic space that may allow for identification of relevant cell states. Inference of cell state from cell behavior across a time course may enable the investigation of dynamics of transitions between heterogeneous cell states, a task difficult to perform with destructive molecular observations. Cell motility is one such easily observed cell behavior with known biomedical relevance. To investigate cell heterogeneity through the lens of cell behavior, we developed Heteromotility, a software tool to extract quantitative motility features from timelapse cell images. In mouse embryonic fibroblasts (MEFs), myoblasts, and muscle stem cells (MuSCs), Heteromotility analysis identifies multiple motility phenotypes within the population. In all three systems, the motility state identity of individual cells is dynamic. Quantification of state transitions reveals that MuSCs undergoing activation transition through progressive motility states toward the myoblast phenotype. By probability flux analysis, we find that this MuSC motility state system breaks detailed balance, while the MEF and myoblast systems do not. Our data indicate that the system regulating cell behavior can be decomposed into a set of attractor states which depend on the identity of the cell, together with a set of transitions between states governed by inputs from signaling pathways such as oncogenes and growth factors. Within one state, equilibrium formalisms can capture variation in behavior, while switching between states violates equilibrium conditions and would require an external driving force. These results support a conceptual view of the cell as a non-deterministic state automaton, responding to inputs from signaling pathways and generating outputs in the form of observable motile behaviors.

cell biology↗

bioRxiv2017

A large kinome in a large cell: Stentor coeruleus possesses highly expanded kinase families and novel domain architectures

BackgroundStentor coeruleus is a large ciliated protist, about 1mm in length, with the extraordinary ability to fully regenerate each fragment after being cut into pieces, perfectly restoring cell polarity and morphology. Single-cell regeneration in Stentor remains one of the greatest long-standing mysteries of biology, but the recently published Stentor genome now enables studies on this organism at the molecular and genetic levels. Here we characterize the complete complement of kinases, or kinome, of Stentor, in order to begin to understand the signaling capacities that underlie Stentors unique biology.\n\nResultsThe genome of S. coeruleus contains over 2000 kinases, representing 6% of the predicted proteome. Classification of the kinase genes reveals large expansions in several kinase families, particularly in the CDPKs, the DYRKs, and in several mitotic kinase families including the PLKs, NEKs, and Auroras. The large expansion of the CDPK and DYRK kinase families is an unusual feature of the Stentor kinome compared to other ciliates with sequenced genomes. The DYRK family in Stentor, notably, contains only a single pseudokinase which may suggest an important role in Stentor growth and survival, while the smaller PEK family contains a novel pseudokinase subfamily. The Stentor kinome also has examples of new domain architectures that have not been previously observed in other organisms.\n\nConclusionOur analysis provides the first gene-level view into the signaling capabilities of Stentor and will lay the foundation for unraveling how this organism can coordinate processes as complex as regeneration throughout a giant cell.

genomics↗

bioRxiv2017

Deep convolution neural networks allow analysis of cell motility during stem cell differentiation and neoplastic transformation

Cells in culture display diverse motility behaviors that may reflect differences in cell state and function, providing motivation to discriminate between different motility behaviors. Current methods to do so rely upon manual feature engineering. However, the types of features necessary to distinguish between motility behaviors can vary greatly depending on the biological context, and it is not always clear which features may be most predictive in each setting for distinguishing particular cell types or disease states. Convolutional neural networks (CNNs) are machine learning models allowing for relevant features to be learned directly from spatial data. Similarly, recurrent neural networks (RNNs) are a class of models capable of learning long term temporal dependencies. Given that cell motility is inherently spacio-temporal data, we present an approach utilizing both convolutional and long- short-term memory (LSTM) recurrent neural network units to analyze cell motility data. These RNN models provide accurate classification of simulated motility and experimentally measured motility from multiple cell types, comparable to results achieved with hand-engineered features. The variety of cell motility differences we can detect suggests that the algorithm is generally applicable to additional cell types not analyzed here. RNN autoencoders based on the same architecture are capable of learning motility features in an unsupervised manner and capturing variation between myogenic cells in the latent space. Adapting these RNN models to motility prediction, RNNs are capable of predicting muscle stem cell motility from past tracking data with performance superior to standard motion prediction models. This advance in cell motility prediction may be of practical utility in cell tracking applications.

bioinformatics↗

bioRxiv2017

Diffusion as a ruler: modeling kinesin diffusion as a length sensor for intraflagellar transport

An important question in cell biology is whether cells are able to measure size, either whole cell size or organelle size. Perhaps cells have an internal chemical representation of size that can be used to precisely regulate growth, or perhaps size is just an accident that emerges due to constraint of nutrients. The eukaryotic flagellum is an ideal model for studying size sensing and control because its linear geometry makes it essentially one-dimensional, greatly simplifying mathematical modeling. The assembly of flagella is regulated by intraflagellar transport (IFT), in which kinesin motors carry cargo adaptors for flagellar proteins along the flagellum and then deposit them at the tip, lengthening the flagellum. The rate at which IFT motors are recruited to begin transport into the flagellum is anticorrelated with the flagellar length, implying some kind of communication between the base and the tip and possibly indicating that cells contain some mechanism for measuring flagellar length. Although it is possible to imagine many complex scenarios in which additional signaling molecules sense length and carry feedback signals to the cell body to control IFT, might the already-known components of the IFT system be sufficient to allow length dependence of IFT? Here, we investigate a model in which the anterograde kinesin motors unbind after cargo delivery, diffuse back to the base, and are subsequently reused to power entry of new IFT trains into the flagellum. By modeling such a system at three different levels of abstraction we are able to show that the diffusion time of the motors can in principle be sufficient to serve as a proxy for length measurement. In all three implementations, we found that the diffusion model can not only achieve a stable steady-state length without the addition of any other signaling molecules or pathways, but also is able to produce the anticorrelation between length and IFT recruitment rate that has been observed in quantitative imaging studies.

biophysics↗