Model-Based Deconvolution of Cell Cycle Time-Series Data Reveals Gene Expression Details at High Resolution

In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure “just-in-time” assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract “single-cell”-like information from population-level time-series expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell

Temporal Controls of the Asymmetric Cell Division Cycle in Caulobacter crescentus

The asymmetric cell division cycle of Caulobacter crescentus is orchestrated by an elaborate gene-protein regulatory network, centered on three major control proteins, DnaA, GcrA and CtrA. The regulatory network is cast into a quantitative computational model to investigate in a systematic fashion how these three proteins control the relevant genetic, biochemical and physiological properties of proliferating bacteria. Different controls for both swarmer and stalked cell cycles are represented in the mathematical scheme. The model is validated against observed phenotypes of wild-type cells and relevant mutants, and it predicts the phenotypes of novel mutants and of known mutants under novel experimental conditions. Because the cell cycle control proteins of Caulobacter are conserved across many species of alpha-proteobacteria, the model we are proposing here may be applicable to other genera of importance to agriculture and medicine (e.g., Rhizobium, Brucella)

In Vivo Structure of the E. coli FtsZ-ring Revealed by Photoactivated Localization Microscopy (PALM)

The FtsZ protein, a tubulin-like GTPase, plays a pivotal role in prokaryotic cell division. In vivo it localizes to the midcell and assembles into a ring-like structure-the Z-ring. The Z-ring serves as an essential scaffold to recruit all other division proteins and generates contractile force for cytokinesis, but its supramolecular structure remains unknown. Electron microscopy (EM) has been unsuccessful in detecting the Z-ring due to the dense cytoplasm of bacterial cells, and conventional fluorescence light microscopy (FLM) has only provided images with limited spatial resolution (200–300 nm) due to the diffraction of light. Hence, given the small sizes of bacteria cells, identifying the in vivo structure of the Z-ring presents a substantial challenge. Here, we used photoactivated localization microscopy (PALM), a single molecule-based super-resolution imaging technique, to characterize the in vivo structure of the Z-ring in E. coli. We achieved a spatial resolution of ∼35 nm and discovered that in addition to the expected ring-like conformation, the Z-ring of E. coli adopts a novel compressed helical conformation with variable helical length and pitch. We measured the thickness of the Z-ring to be ∼110 nm and the packing density of FtsZ molecules inside the Z-ring to be greater than what is expected for a single-layered flat ribbon configuration. Our results strongly suggest that the Z-ring is composed of a loose bundle of FtsZ protofilaments that randomly overlap with each other in both longitudinal and radial directions of the cell. Our results provide significant insight into the spatial organization of the Z-ring and open the door for further investigations of structure-function relationships and cell cycle-dependent regulation of the Z-ring

A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness

Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository

The asymmetric distribution of the essential histidine kinase PdhS indicates a differentiation event in Brucella abortus

Many organisms use polar localization of signalling proteins to control developmental events in response to completion of asymmetric cell division. Asymmetric division was recently reported for Brucella abortus, a class III facultative intracellular pathogen generating two sibling cells of slightly different size. Here we characterize PdhS, a cytoplasmic histidine kinase essential for B. abortus viability and homologous to the asymmetrically distributed PleC and DivJ histidine kinases from Caulobacter crescentus. PdhS is localized at the old pole of the large cell, and after division and growth, the small cell acquires PdhS at its old pole. PdhS may therefore be considered as a differentiation marker as it labels the old pole of the large cell. Moreover, PdhS colocalizes with its paired response regulator DivK. Finally, PdhS is able to localize at one pole in other α-proteobacteria, suggesting that a polar structure associating PdhS with one pole is conserved in these bacteria. We propose that a differentiation event takes place after the completion of cytokinesis in asymmetrically dividing α-proteobacteria. Altogether, these data suggest that prokaryotic differentiation may be much more widespread than expected

Change theory in STEM higher education: a systematic review

Abstract This article systematically reviews how change theory has been used in STEM higher educational change between 1995 and 2019. Researchers are increasingly turning to theory to inform the design, implementation, and investigation of educational improvement efforts. Yet, efforts are often siloed by discipline and relevant change theory comes from diverse fields outside of STEM. Thus, there is a need to bring together work across disciplines to investigate which change theories are used and how they inform change efforts. This review is based on 97 peer-reviewed articles. We provide an overview of change theories used in the sample and describe how theory informed the rationale and assumptions of projects, conceptualizations of context, indicators used to determine if goals were met, and intervention design. This review points toward three main findings. Change research in STEM higher education almost always draws on theory about individual change, rather than theory that also attends to the system in which change takes place. Additionally, research in this domain often draws on theory in a superficial fashion, instead of using theory as a lens or guide to directly inform interventions, research questions, measurement and evaluation, data analysis, and data interpretation. Lastly, change researchers are not often drawing on, nor building upon, theories used in other studies. This review identified 40 distinct change theories in 97 papers. This lack of theoretical coherence in a relatively limited domain substantially limits our ability to build collective knowledge about how to achieve change. These findings call for more synthetic theoretical work; greater focus on diversity, equity, and inclusion; and more formal opportunities for scholars to learn about change and change theory