Comprehensive profiling of neutralizing polyclonal sera targeting coxsackievirus B3.

Despite their fundamental role in resolving viral infections, our understanding of how polyclonal neutralizing antibody responses target non-enveloped viruses remains limited. To define these responses, we obtained the full antigenic profile of multiple human and mouse polyclonal sera targeting the capsid of a prototypical picornavirus, coxsackievirus B3. Our results uncover significant variation in the breadth and strength of neutralization sites targeted by individual human polyclonal responses, which contrasted with homogenous responses observed in experimentally infected mice. We further use these comprehensive antigenic profiles to define key structural and evolutionary parameters that are predictive of escape, assess epitope dominance at the population level, and reveal a need for at least two mutations to achieve significant escape from multiple sera. Overall, our data provide a comprehensive analysis of how polyclonal sera target a non-enveloped viral capsid and help define both immune dominance and escape at the population level.

Random field calibration with data on irregular grid for regional analyses: A case study on the bare carrying capacity of bats in Africa.

Many applications in science and engineering involve data defined at specific geospatial locations, which are often modeled as random fields. The modeling of a proper correlation function is essential for the probabilistic calibration of the random fields, but traditional methods were developed with the assumption to have observations with evenly spaced data. Available methods dealing with irregularly spaced data generally require either interpolation or computationally expensive solutions. Instead, we propose a simple approach based on least square regression to estimate the autocorrelation function. We first tested our methodology on an artificially produced dataset to assess the performance of our method. The accuracy of the method and its robustness to the level of noise in the data indicate that it is suitable for use in realistic problems. In addition, the methodology was used on a major application, the modeling of animal species connected with zoonotic diseases. Understanding the population dynamics of reservoirs of zoonotic diseases, such as bats, is a crucial first step to predict and prevent potential spillover of deadly viruses like Ebola. Due to the limited data on bats across Africa, their density and migrations can only be studied with probabilistic numerical models based on samples of the ecological bare carrying capacity (K0). For this purpose, the bare carrying capacity was modeled as a random field and its statistics calibrated with the available data. The bare carrying capacity of bats was found to be denser in central Africa. This is because climatic and environmental conditions are more suitable for the survival of bats. The proposed methodology for random field calibration was shown to be a promising approach, which can cope with large gaps in data and with complex applications involving large geographical areas and high resolution.

Estimation of Ebola's spillover infection exposure in Sierra Leone based on sociodemographic and economic factors.

Zoonotic diseases spread through pathogens-infected animal carriers. In the case of Ebola Virus Disease (EVD), evidence supports that the main carriers are fruit bats and non-human primates. Further, EVD spread is a multi-factorial problem that depends on sociodemographic and economic (SDE) factors. Here we inquire into this phenomenon and aim at determining, quantitatively, the Ebola spillover infection exposure map and try to link it to SDE factors. To that end, we designed and conducted a survey in Sierra Leone and implement a pipeline to analyze data using regression and machine learning techniques. Our methodology is able (1) to identify the features that are best predictors of an individual's tendency to partake in behaviors that can expose them to Ebola infection, (2) to develop a predictive model about the spillover risk statistics that can be calibrated for different regions and future times, and (3) to compute a spillover exposure map for Sierra Leone. Our results and conclusions are relevant to identify the regions in Sierra Leone at risk of EVD spillover and, consequently, to design and implement policies for an effective deployment of resources (e.g., drug supplies) and other preventative measures (e.g., educational campaigns).

Bacterial degrons in synthetic circuits.

Bacterial proteases are a promising post-translational regulation strategy in synthetic circuits because they recognize specific amino acid degradation tags (degrons) that can be fine-tuned to modulate the degradation levels of tagged proteins. For this reason, recent efforts have been made in the search for new degrons. Here we review the up-to-date applications of degradation tags for circuit engineering in bacteria. In particular, we pay special attention to the effects of degradation bottlenecks in synthetic oscillators and introduce mathematical approaches to study queueing that enable the quantitative modelling of proteolytic queues.

ANISE: an application to design mechanobiology simulations of planar epithelia.

SUMMARY: TiFoSi (Tissues Forces & Signaling) is an efficient computational tool for performing mechanobiology simulations of planar epithelia. A drawback of this tool is that it relies on an XML configuration file (input data) that can be cumbersome to set up and/or decode due to the endless possibilities of the software. Moreover, some modeling know-how is needed in order to provide equations that describe gene regulatory interactions. These factors limit the usability of this tool for users with a weak computational and/or mathematical background. Here, we introduce ANISE (grAphical coNfigurator of TiFoSi In Silico Experiments), a web-app that allows to easily setup the configuration of mechanobiology simulations using TiFoSi. The application covers all the configuration modules in TiFoSi comprehensively (from basic to advanced editing options) and uses a graphical approach (e.g. to build the modeling equations of gene regulatory networks). AVAILABILITY AND IMPLEMENTATION: http://github.com/lsym-uveg/anise (server: http://lsymserver.uv.es/lsym/ANISE).

A quantitative biophysical principle to explain the 3D cellular connectivity in curved epithelia.

Epithelial cell organization and the mechanical stability of tissues are closely related. In this context, it has been recently shown that packing optimization in bended or folded epithelia is achieved by an energy minimization mechanism that leads to a complex cellular shape: the "scutoid". Here, we focus on the relationship between this shape and the connectivity between cells. We use a combination of computational, experimental, and biophysical approaches to examine how energy drivers affect the three-dimensional (3D) packing of tubular epithelia. We propose an energy-based stochastic model that explains the 3D cellular connectivity. Then, we challenge it by experimentally reducing the cell adhesion. As a result, we observed an increment in the appearance of scutoids that correlated with a decrease in the energy barrier necessary to connect with new cells. We conclude that tubular epithelia satisfy a quantitative biophysical principle that links tissue geometry and energetics with the average cellular connectivity.

Mechanics and self-organization in tissue development.

Self-organization is an all-important feature of living systems that provides the means to achieve specialization and functionality at distinct spatio-temporal scales. Herein, we review this concept by addressing the packing organization of cells, the sorting/compartmentalization phenomenon of cell populations, and the propagation of organizing cues at the tissue level through traveling waves. We elaborate on how different theoretical models and tools from Topology, Physics, and Dynamical Systems have improved the understanding of self-organization by shedding light on the role played by mechanics as a driver of morphogenesis. Altogether, by providing a historical perspective, we show how ideas and hypotheses in the field have been revisited, developed, and/or rejected and what are the open questions that need to be tackled by future research.

The complex three-dimensional organization of epithelial tissues.

Understanding the cellular organization of tissues is key to developmental biology. In order to deal with this complex problem, researchers have taken advantage of reductionist approaches to reveal fundamental morphogenetic mechanisms and quantitative laws. For epithelia, their two-dimensional representation as polygonal tessellations has proved successful for understanding tissue organization. Yet, epithelial tissues bend and fold to shape organs in three dimensions. In this context, epithelial cells are too often simplified as prismatic blocks with a limited plasticity. However, there is increasing evidence that a realistic approach, even from a reductionist perspective, must include apico-basal intercalations (i.e. scutoidal cell shapes) for explaining epithelial organization convincingly. Here, we present an historical perspective about the tissue organization problem. Specifically, we analyze past and recent breakthroughs, and discuss how and why simplified, but realistic, in silico models require scutoidal features to address key morphogenetic events.

Mycobacterium Phage Butters-Encoded Proteins Contribute to Host Defense against Viral Attack.

A diverse set of prophage-mediated mechanisms protecting bacterial hosts from infection has been recently uncovered within cluster N mycobacteriophages isolated on the host, Mycobacterium smegmatis mc2155. In that context, we unveil a novel defense mechanism in cluster N prophage Butters. By using bioinformatics analyses, phage plating efficiency experiments, microscopy, and immunoprecipitation assays, we show that Butters genes located in the central region of the genome play a key role in the defense against heterotypic viral attack. Our study suggests that a two-component system, articulated by interactions between protein products of genes 30 and 31, confers defense against heterotypic phage infection by PurpleHaze (cluster A/subcluster A3) or Alma (cluster A/subcluster A9) but is insufficient to confer defense against attack by the heterotypic phage Island3 (cluster I/subcluster I1). Therefore, based on heterotypic phage plating efficiencies on the Butters lysogen, additional prophage genes required for defense are implicated and further show specificity of prophage-encoded defense systems.IMPORTANCE Many sequenced bacterial genomes, including those of pathogenic bacteria, contain prophages. Some prophages encode defense systems that protect their bacterial host against heterotypic viral attack. Understanding the mechanisms undergirding these defense systems is crucial to appreciate the scope of bacterial immunity against viral infections and will be critical for better implementation of phage therapy that would require evasion of these defenses. Furthermore, such knowledge of prophage-encoded defense mechanisms may be useful for developing novel genetic tools for engineering phage-resistant bacteria of industrial importance.

Self-sustained planar intercalations due to mechanosignaling feedbacks lead to robust axis extension during morphogenesis.

Tissue elongation is a necessary process in metazoans to implement their body plans that is not fully understood. Here we propose a mechanism based on the interplay between cellular mechanics and primordia patterning that results in self-sustained planar intercalations. Thus, we show that a location-dependent modulation of the mechanical properties of cells leads to robust axis extension. To illustrate the plausibility of this mechanism, we test it against different patterning models by means of computer simulations of tissues where we implemented mechano-signaling feedbacks. Our results suggest that robust elongation relies on a trade-off between cellular and tissue strains that is orchestrated through the cleavage orientation. In the particular context of axis extension in Turing-patterned tissues, we report that different directional cell activities cooperate synergetically to achieve elongation. Altogether, our findings help to understand how the axis extension phenomenon emerges from the dynamics of individual cells.

TiFoSi: an efficient tool for mechanobiology simulations of epithelia.

MOTIVATION: Emerging phenomena in developmental biology and tissue engineering are the result of feedbacks between gene expression and cell biomechanics. In that context, in silico experiments are a powerful tool to understand fundamental mechanisms and to formulate and test hypotheses. RESULTS: Here, we present TiFoSi, a computational tool to simulate the cellular dynamics of planar epithelia. TiFoSi allows to model feedbacks between cellular mechanics and gene expression (either in a deterministic or a stochastic way), the interaction between different cell populations, the custom design of the cell cycle and cleavage properties, the protein number partitioning upon cell division, and the modeling of cell communication (juxtacrine and paracrine signaling). AVAILABILITY AND IMPLEMENTATION: http://tifosi.thesimbiosys.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mechanical coordination is sufficient to promote tissue replacement during metamorphosis in Drosophila.

During development, cells coordinate to organize in coherent structures. Although it is now well established that physical forces are essential for implementing this coordination, the instructive roles of mechanical inputs are not clear. Here, we show that the replacement of the larval epithelia by the adult one in Drosophila demands the coordinated exchange of mechanical signals between two cell types, the histoblasts (adult precursors) organized in nests and the surrounding larval epidermal cells (LECs). An increasing stress gradient develops from the center of the nests toward the LECs as a result of the forces generated by histoblasts as they proliferate and by the LECs as they delaminate (push/pull coordination). This asymmetric radial coordination of expansive and contractile activities contributes to epithelial replacement. Our analyses support a model in which cell-cell mechanical communication is sufficient for the rearrangements that implement epithelial morphogenesis.

A non-linear analysis of Turing pattern formation.

Reaction-diffusion schemes are widely used to model and interpret phenomena in various fields. In that context, phenomena driven by Turing instabilities are particularly relevant to describe patterning in a number of biological processes. While the conditions that determine the appearance of Turing patterns and their wavelength can be easily obtained by a linear stability analysis, the estimation of pattern amplitudes requires cumbersome calculations due to non-linear terms. Here we introduce an expansion method that makes possible to obtain analytical, approximated, solutions of the pattern amplitudes. We check and illustrate the reliability of this methodology with results obtained from numerical simulations.

A Cell Segmentation/Tracking Tool Based on Machine Learning.

The ability to gain quantifiable, single-cell data from time-lapse microscopy images is dependent upon cell segmentation and tracking. Here, we present a detailed protocol for obtaining quality time-lapse movies and introduce a method to identify (segment) and track cells based on machine learning techniques (Fiji's Trainable Weka Segmentation) and custom, open-source Python scripts. To provide a hands-on experience, we provide datasets obtained using the aforementioned protocol.

Author Correction: Scutoids are a geometrical solution to three-dimensional packing of epithelia.

The original version of this Article contained an error in ref. 39, which incorrectly cited 'Fristrom, D. & Fristrom, J. W. in The Development of Drosophila melanogaster (eds. Bate, M. & Martinez-Arias, A.) II, (Cold spring harbor laboratory press, 1993)'. The correct reference is 'Condic, M.L, Fristrom, D. & Fristrom, J.W. Apical cell shape changes during Drosophila imaginal leg disc elongation: a novel morphogenetic mechanism. Development 111: 23-33 (1991)'. Furthermore, the last sentence of the fourth paragraph of the introduction incorrectly omitted citation of work by Rupprecht et al. The correct citation is given below. These errors have now been corrected in both the PDF and HTML versions of the Article. Rupprecht, J.F., Ong, K.H., Yin, J., Huang, A., Dinh, H.H., Singh, A.P., Zhang, S., Yu, W. & Saunders, T.E. Geometric constraints alter cell arrangements within curved epithelial tissues. Mol. Biol. Cell 28, 3582-3594 (2017).

Scutoids are a geometrical solution to three-dimensional packing of epithelia.

As animals develop, tissue bending contributes to shape the organs into complex three-dimensional structures. However, the architecture and packing of curved epithelia remains largely unknown. Here we show by means of mathematical modelling that cells in bent epithelia can undergo intercalations along the apico-basal axis. This phenomenon forces cells to have different neighbours in their basal and apical surfaces. As a consequence, epithelial cells adopt a novel shape that we term "scutoid". The detailed analysis of diverse tissues confirms that generation of apico-basal intercalations between cells is a common feature during morphogenesis. Using biophysical arguments, we propose that scutoids make possible the minimization of the tissue energy and stabilize three-dimensional packing. Hence, we conclude that scutoids are one of nature's solutions to achieve epithelial bending. Our findings pave the way to understand the three-dimensional organization of epithelial organs.

A Markovian Approach towards Bacterial Size Control and Homeostasis in Anomalous Growth Processes.

Regardless of the progress achieved during recent years, the mechanisms coupling growth and division to attain cell size homeostasis in bacterial populations are still not well understood. In particular, there is a gap of knowledge about the mechanisms controlling anomalous growth events that are ubiquitous even in wild-type phenotypes. Thus, when cells exceed the doubling size the divisome dynamics sets a characteristic length scale that suggests a sizer property. Yet, it has been recently shown that the size at birth and the size increment still satisfy an adder-like correlation. Herein we propose a Markov chain model, that we complement with computational and experimental approaches, to clarify this issue. In this context, we show that classifying cells as a function of the characteristic size set by the divisome dynamics provides a compelling framework to understand size convergence, growth, and division at the large length scale, including the adaptation to, and rescue from, filamentation processes. Our results reveal the independence of size homeostasis on the division pattern of long cells and help to reconcile sizer concepts at the single cell level with an adder-like behavior at a population level.

A Predictive Spatial Distribution Framework for Filovirus-Infected Bats.

Tools with predictive capabilities in regards of filovirus outbreaks are mainly anthropocentric and have disregarded the ecological dimension of the problem. Here we contribute to shift the current paradigm by studying the dynamics of the putative main zoonotic niche of filoviruses, bats, and its link to environmental drivers. We propose a framework that combines data analysis, modeling, and the evaluation of sources of variability. We implement a regression analysis using factual data to correlate environmental parameters and the presence of bats to find the distribution of resources. The information inferred by the regression is fed into a compartmental model that describes the infection state. We also account for the lack of knowledge of some parameters using a sampling/averaging technique. As a result we estimate the spatio-temporal densities of bats. Importantly, we show that our approach is able to predict where and when an outbreak is likely to appear when tested against recent epidemic data in the context of Ebola. Our framework highlights the importance of considering the feedback between the ecology and the environment in zoonotic models and sheds light on the mechanisms to propagate filoviruses geographically. We expect that our methodology can help to design prevention policies and be used as a predictive tool in the context of zoonotic diseases associated to filoviruses.

Finite cell-size effects on protein variability in Turing patterned tissues.

Herein we present a framework to characterize different sources of protein expression variability in Turing patterned tissues. In this context, we introduce the concept of granular noise to account for the unavoidable fluctuations due to finite cell-size effects and show that the nearest-neighbours autocorrelation function provides the means to measure it. To test our findings, we perform in silico experiments of growing tissues driven by a generic activator-inhibitor dynamics. Our results show that the relative importance of different sources of noise depends on the ratio between the characteristic size of cells and that of the pattern domains and on the ratio between the pattern amplitude and the effective intensity of the biochemical fluctuations. Importantly, our framework provides the tools to measure and distinguish different stochastic contributions during patterning: granularity versus biochemical noise. In addition, our analysis identifies the protein species that buffer the stochasticity the best and, consequently, it can help to determine key instructive signals in systems driven by a Turing instability. Altogether, we expect our study to be relevant in developmental processes leading to the formation of periodic patterns in tissues.

Modeling the Ebola zoonotic dynamics: Interplay between enviroclimatic factors and bat ecology.

Understanding Ebola necessarily requires the characterization of the ecology of its main enzootic reservoir, i.e. bats, and its interplay with seasonal and enviroclimatic factors. Here we present a SIR compartmental model where we implement a bidirectional coupling between the available resources and the dynamics of the bat population in order to understand their migration patterns. Our compartmental modeling approach and simulations include transport terms to account for bats mobility and spatiotemporal climate variability. We hypothesize that environmental pressure is the main driving force for bats' migration and our results reveal the appearance of sustained migratory waves of Ebola virus infected bats coupled to resources availability. Ultimately, our study can be relevant to predict hot spots of Ebola outbreaks in space and time and suggest conservation policies to mitigate the risk of spillovers.