BioSystems

Phenotypic diversity is often thought to arise from the evolutionary modification of developmental processes. However, developmental processes are tightly coupled in space and time, with each process beginning from conditions set by the one before it. While we know from dynamical systems theory that initial conditions can significantly affect a systems out-come, their importance as a source of phenotypic evolvability has been largely overlooked. Here we show for the first time, that phenotypic evolution can proceed through changes in developmental initial conditions while the underlying developmental process remains conserved. Somitogenesis is the process by which vertebral precursors, known as somites, are periodically patterned in the pre-somitic mesoderm (PSM). Somitic count (total number of somites) is thought to diversify through the evolution of components of somitogenesis such as the tempo of the segmentation clock or the mechanisms driving axial morphogenesis. Using two closely related species of Lake Malawi cichlid fishes that differ in vertebral counts, we show that somite count evolution has happened without changes to somitogenesis itself, but instead, by altering the size of the PSM at the onset of this process. This work will expand what we consider developmental drivers of phenotypic evolution and highlight the importance of comparative studies to understand the diversification of phenotypes.

Predicting protein-ligand interactions in the modern drug discovery has revolved from the involvement of artificial intelligence and structural bioinformatics using Graph Neural Networks (GNNs). The limited explainability of GNN models presents an important encumbrance in biomedical research, but it has achieved a high degree of accuracy in determining and identifying binding affinity and active compounds, as evidenced by [1] [2] [3] [4]. Here this research focuses on the interpretation of protein-ligand interactions at a molecular level, a rapidly developing area within Graph Neural Networks (GNNs). Now days modern study handling techniques such as visualization techniques, attention mechanism and model-based feature ascription by model to boost, and make robust and decrease false predictions on binding. Along with some approaches include like graph pooling strategies, message-passing optimization, self-supervised learning, transfer learning and contrastive learning are rapidly utilized to enhance the representative learnings. Furthermore, integration of molecular docking simulations, hybrid deep learning architectures and protein language model gives more reliable & biological predictions of protein-ligand interactions. That focuses on given process that identifies key ligand atoms and binding residues, as well as physicochemical factors influencing affinity, through chemical thought processes. Here this research work identified the challenges of developing biologically significant explanations, transparency, and the corollary dataset biases on interpretability. The research work conducted an in-depth investigation into the consolidation of protein language models to establish more reliable pathways for future research, examining hybrid architectures, transparent and energy-efficient GNNs, and scientifically grounded AI models for drug discovery. My research work highlights that XGNNs establishes a connection between Deep Learning and Biochemical expertise with increased confidence, which will enhance the accuracy of predictive models and computational models.

During embryogenesis, as cells proliferate and assemble into tissues, they undergo a sequence of transitions between distinct molecular states eventually giving rise to a cellular population consisting of an appropriate distribution of specific functional cell types. Recent progress on the dynamics underlying decision-making in developmental landscape makes it feasible to start analysing the amount of information involved in constructing such systems. To explore this we introduce the notion of potency of a developmental landscape and attempt to calculate it for two development systems of current interest, in-vitro differentiation of epiblast-like cells into neural and mesodermal progenitors and the worm vulva patterning system. Our approach integrates concepts from developmental biology, information theory and dynamical systems to estimate both the number and identity of signalling regimes that give rise to distinguishable temporal response patterns.

Animals sense and integrate complex external cues to make developmental decisions that help them better survive and adapt to their natural habitats. Under environmental adversity, nematodes can enter an alternative developmental pathway to form a diapautic and stress-resistant stage, termed the dauer larvae. While dauer formation has been well characterized in Caenorhabditis elegans, how environmental factors influence analogous stages in other nematode species remains largely unexplored. This study examines how symbiotic bacteria, temperature, and pheromones affect the formation of the infective juvenile (IJ), a dauer-like stage, of the insect-parasitic nematode Steinernema hermaphroditum. In contrast to C. elegans, where dauer entry is promoted by heat, IJ development in S. hermaphroditum development is enhanced by reduced temperature. Moreover, the presence and absence of live symbiotic bacterium Xenorhabdus griffiniae functions as an ON-and-OFF switch that regulates the host IJ formation. Crude pheromone extracts from S. hermaphroditum liquid culture do not robustly induce IJ formation in a dose-responsive manner, unlike the potent pheromone-driven dauer entry observed in C. elegans. Nutrient-rich liver-kidney media that mimics host insect environment showed IJ entry induction in a pheromone-dependent manner. These data suggest that external cues, such as temperature, microbial diet, and pheromone, are perceived differently by S. hermaphroditum in comparison to that of C. elegans, reflecting species-specific adaptations to distinct ecological niches and life history strategies.

In this study, resource-constrained learning methods were developed as a model for the learning behavior of the fly brain, specifically the mushroom body. Recent research on the mushroom bodies of flies shows that unfamiliar odors activate certain output neurons (MBONs); however, these effects are rapidly suppressed upon repeated exposure to the same odor. Such MBON behaviors appear to reflect odor learning. We investigated how flies continue learning about odors throughout their lives despite their small brains. Researchers have suggested that learning about new odors can help flies forget existing memories. Therefore, we hypothesized that the main reason for continual learning is that it serves as a strategy for forgetting. To test the validity of this hypothesis, we designed three models using a kernel perceptron. This approach is suitable for estimating ongoing learning capacity within a budget. According to the results of computer simulations and theoretical analysis, the model demonstrated the importance of forgetting mechanisms for two reasons: first, to prepare for subsequent learning sessions, and second, to reduce the negative effects of deleting memories. Author summaryDrosophila mushroom body output neurons (MBONs) in the 3 compartment of the fruit fly brain are highly activated by novel odors, and their activation triggers alerting behavior. Interestingly, these specific neurons react only to unfamiliar odor information, suggesting they constantly undergo incremental learning of new odors. This study was aimed at constructing three incremental learning models of the MBON 3 neurons. Although there have been numerous studies on complex circuit designs to reproduce activation waveforms, herein we constructed a fundamental learning model based on a kernelized learning method. Since kernelized learning models interpret Hebbian learning as the addition or subtraction of kernel functions, the model is easy to analyze theoretically. Consequently, we conclude that the forgetting property observed in the MBON 3 neurons is essential for reducing error when learning occurs within a brain of limited capacity.

Studies of appendage regeneration in vertebrates have shown that the fundamental building block of any regenerative tissue is a blastema. The blastema is a cone-shaped accumulation that forms at the site of amputation post wound-healing and is the result of a highly coordinated process involving a cluster of cells capable of growth, migration, and differentiation. Although several key signaling pathways involved in regeneration have been identified, which cellular processes they control and how these processes are coordinated across space and time are not yet fully understood. This study introduces a computational tool to examine how the outgrowth results from the interaction of two tissue layers: the bulk (mesenchyme) and the overlying epithelium. We developed a novel hybrid agent-based modeling framework and an accompanying parameter inference pipeline to uncover the cellular properties in the epithelium and the mesenchyme driving the formation of a normal regenerative blastema with a morphology similar to that observed in experiments. Using our model, we report two conditions for blastema formation: retained local softening of the epithelial layer at the site of injury, which was confirmed experimentally with atomic force microscopy (AFM) measurements, and the involvement of the Wnt signaling pathway in the directed migration of mesenchyme cells towards the distal tip. Taken together, this combined experimental-theoretical approach provides a framework for understanding how the Wnt signaling pathway influences the formation of the early blastema at multiple levels of organization and how key cellular behaviors contribute to its formation. Author SummaryA small number of tetrapods have retained the ancestral ability to regenerate tissues and even limbs. Indifferent of species or tissue, the decisive initial stage of limb regeneration is the formation of a specialized structure called the blastema, a heterogeneous mass of mesenchymal cells, in a relatively short timescale of 2-14 days post injury or amputation. To study the mechanical and cellular conditions for limb blastema formation in the axolotl model organism, we developed a novel hybrid agent-based modeling framework and accompanying kinetic parameter inference pipeline. By recapitulating blastema morphometrics of healthy and stalled regenerative states, our model finds two conditions for blastema formation: retained local softening of the epithelial layer at the injury site post wound-healing, which we confirmed with atomic force microscopy measurements, and that the Wnt signaling pathway plays a role in the migration of mesenchyme cells to the distal tip in order to produce the blastema.

Spatio-temporal interactions shape microbial community dynamics. Metabolism, through competition and cross-feeding, is a foundational mechanism of these interactions. Flux balance analysis enables efficient simulation of steady-state metabolism. Integrating these simulations through time, using dynamic flux balance analysis, provides temporal predictions of growth and metabolism. Incorporating spatial context, through partial differential equations, enables spatio-temporal simulation of microbial communities. In this chapter, we step through this sequential process, moving from steady-state, to temporal, to spatio-temporal simulation of microbial community metabolism. We provide an illustrative example using the modeling software COMETS (Computation of Microbial Ecosystems in Time and Space) to simulate interacting bacterial colonies of Bifidobacterium longum subsp. infantis and Anaerobutyricum hallii (previously Eubacterium hallii). Within this simulation, both competition and cross-feeding influenced the production of butyrate leading to an intermediate optimal interaction distance for metabolite production. We outline each step and provide open-source code such that this simulation can serve as a template for future spatio-temporal simulations of microbial community metabolism.

Experimental studies show that tumor cells adopt migratory or proliferative phenotypes depending on the local extracellular matrix (ECM). In this work, we propose a minimal go-or-grow invasion model, comprising two specialist cell phenotypes: proliferating and migratory, with phenotypic switching and cell migration depending on local ECM density. Numerical simulations of this model reveal that the spatial arrangement of proliferative and migratory cells depends on the choice of phenotypic switching function. We then ask whether this specialist cell-phenotype model can be reduced to a generalist cell-phenotype model. We derive a relationship between the reduced model and go-or-grow model in the fast phenotypic switching regime. We observe that the reduced model captures the dynamics of the original model, for a range of realistic phenotypic switching functions. We analytically derive the minimum traveling wave speed of the reduced model in a homogeneous ECM bed. Moreover, using linear stability analysis on the go-or-grow model, we recover the same wave speed expression. In addition, we numerically explore how the key parameters influence the traveling wave speed profile. Our analysis indicated the counter-intuitive result that the wave speed is independent of the matrix degradation rate, and our simulations show that, at most, the speed is weakly dependent on this parameter.

In view of the outstanding progress of machine learning (ML) and growing cost of health systems, it is a current challenge to incorporate artificial intelligence tools into actual medical practice. Here we explored the feasibility and reliability of using machine learning to perform an important immunological investigation that currently requires experienced biologists : Anti-nuclear cytoplasmic antibodies (ANCAs) are important markers for vasculitis and they may be evidenced by microscopic examination of cells labeled with patients' sera. The use of a reliable ML classifier to discriminate between positive and negative samples would increase the rapidity and decrease the cost of immunofluorescence-based ANCA detection. Here, we tested seven well-documented ML algorithms, ranging from simple models such as k nearest neighbors to more complex convolutional neural networks involving millions of adjustable parameter. We studied the feasibility and reliability of classifying 1114 serum samples that had been collected for about 3 years and assayed with conventional procedure. We compared four strategies consisting of assaying either whole microscope fields or individual cell images, and natural images or histograms. The following conclusions were obtained : (i) Several different strategies allowed us to build models stable enough to discriminate between positive and negative samples collected during about 27 months, with a comparison to human classification yielding a kappa index of about 0.7, that may be considered as fairly good and intermediate between the performance of junior and senior biologists. (ii) Simpler ML models combined with theoretical thinking might provide the most rapid and efficient way of developing a reliable test within the framework of a single institution. (iii) In addition, the interpretability of the simplest model provided some theoretical insight into important classification parameters. (iv) An important point and caveat is that the multiplicity and versatility of currently available tools make it an essential requirement to test repeatedly a given model, that must be chosen as simple as possible, to achieve a reliability compatible with medical use. It is concluded that our study provides a strong incentive to incorporate ML tools in well defined medical tests, which might reduce the risk of human errors and pave the way to fully automatic procedures.

Mass-conserved reaction-diffusion systems are used as mathematical models for various phenomena such as cell polarity. Numerical simulations of this system present transient dynamics in which multiple stripe patterns converge to spatially monotonic patterns. Previous studies indicated that the transient dynamics are driven by a mass conservation law and by variations in the amount of substance contained in each pattern, which we refer to as "pattern flux". However, it is challenging to mathematically investigate these pattern dynamics. In this study, we introduce a reaction-diffusion compartment model to investigate the pattern dynamics in view of the conservation law and the pattern flux. This model is defined on multiple intervals (compartments), and diffusive couplings are imposed on each boundary of the compartments. Corresponding to the transient dynamics in the original system, we consider the dynamics around stripe patterns in the compartment model. We derive ordinary differential equations describing the pattern dynamics of the compartment model and analyze the existence and stability of equilibria for the reduced ODE with respect to the boundary parameters. For a specific parameter setting, we obtained results consistent with previous studies. Moreover, we present that the stripe patterns in the compartment model are potentially stabilized by changing the parameter, which is not observed in the original system. We expect that the methodology developed in this paper is extendable to various directions, such as membrane-induced pattern control.

Vertebrate gastrulating mesoderm is a prototypic example of a mesenchymal-like tissue undergoing extensive remodelling. While the tissue may be globally represented as a viscoelastic material, the actual biological material is intrinsically complex. To get to a real understanding of its properties, one needs to move to the mesoscale, linking cellular properties to collective phenomena. Vertebrate embryos also display a remarkable variability in mechanical properties, despite which they robustly complete gastrulation. This study attempts to explore these aspects by dissecting Xenopus mesoderm cell behaviour in a minimal system, using aspiration through a microfluidic system to impose controlled stress to a mesoderm aggregate. We show that beyond estimating global rheology at the tissue scale, it is possible to infer a wealth of information based on cell morphology and dynamics. Our data are consistent with collective behaviour being mostly dictated by the balance between the capacity of cells to stretch and the resistance to cell-cell contacts, which limits cell-cell intercalation and thus tissue remodelling. Importantly, tissues are not only able to transmit stress over a distance, they also clearly react to it through actively reinforcing cell-cell mechanical coupling. This adaptative property is found through a broad range of tissue stiffness, and adhesion strength appears to scale with the elastic modulus, suggesting that cell stiffness may ultimately be the key parameter setting mesoderm rheology and accounting for the large differences observed between embryo batches.

Variability in gene expression among single cells and growing cell populations can arise from the stochastic nature of protein synthesis, which often occurs in random bursts. This study investigates the variability in the expression of a growth-sustaining protein, whose concentration is regulated by a negative feedback loop due to cell growth-induced dilution. We model the distribution of protein concentration using a Chapman-Kolmogorov equation for single cells and a population balance equation for growing cell populations. For single cells, we derive an explicit solution for the protein concentration distribution in state space and represent it as a Bessel function in Laplace space. For growing populations, we find that the distribution satisfies a Heun differential equation with singular boundary conditions. By addressing the central connection problem for the Heun equation, we quantify the population-level protein distribution and determine the Mathusian parameter, which characterizes population growth. This work provides a comprehensive analytical framework for understanding how stochastic protein synthesis impacts gene expression variability and population dynamics.

Many environmental bacteria do not readily grow under laboratory conditions and population establishment often occurs stochastically. Although the scout hypothesis has been proposed to explain stochastic population establishment in environmental bacteria, how stochastic population establishment is shaped by individual cell growth behaviors in environmental isolates remains unclear. In the present study, we focused on the ammonia-oxidizing bacterium Nitrosomonas sp. PY1 and showed that environmentally responsive individual cell growth behavior, incorporating time-dependent stochastic growth initiation, shapes both deterministic and stochastic population establishment dynamics. Using single-cell observation, we revealed that PY1 altered cell growth behavior in response to surrounding biomass production ({Delta}Vt). These {Delta}Vt-dependent changes in growth behavior were suppressed by the addition of its own cell-free supernatant (CFS), indicating the presence of a growth regulation mechanism via cell-cell communication. Replicate cultures under the same conditions showed that the population establishment of PY1 was stochastic, whereas the model strain Nitrosomonas europaea exhibited synchronized population establishment, consistent with previous reports. This stochasticity in PY1 was also eliminated by the addition of CFS. Finally, a simulation model based on {Delta}Vt-dependent cell growth behavior of PY1 successfully reproduced synchronized population establishment in the presence of CFS. By contrast, the stochastic population establishment observed in the absence of CFS was successfully reproduced by a model incorporating {Delta}Vt-independent growth initiation following a Weibull distribution. Such environmentally responsive changes in population establishment dynamics may contribute to the low isolation success of environmental bacteria and sudden blooms of the rare biosphere.

Historically, venom potencies have been assessed using measures of lethality, such as the median lethal dose (LD50). However, venoms may be selected primarily for their ability to rapidly incapacitate rather than cause mortality, meaning LD50 may not capture the efficacy of venoms in an ecological and evolutionary context. To capture this context, recent studies have adapted measures that assess venoms ability to rapidly incapacitate, such as the median effective dose (ED50). However, while ED50 values are expected to provide a more proximate assessment of ecological variation in venom potency, it is unknown whether historically available LD50 values are still useful proxies of ecologically relevant potency or whether they capture independent axes of venom variation. Here, we test the relationship between LD50 and ED50 in spider venoms by experimentally estimating LD50 and ED50 for 12 species and collating additional potency data for 40 species retrieved from the literature. We observed an isometric relationship between LD50 and ED50 in both analyses, showing these potency measures are both strongly coupled, with an increase in paralysis efficiency associated with a similar increase in lethality. Our results suggest that the functional aspects of venom potency, paralysis and lethality, are intrinsically linked, and due to this strong mechanistic coupling, historically available LD50 values may be used to compare general venom potencies in spiders, provided that they are based on the same prey model.

Standard target-mediated drug disposition (TMDD) models are widely used to describe nonlinear pharmacokinetics driven by high-affinity drug-target interactions. However, their reliance on instantaneous binding limits their ability to capture delayed and history-dependent dynamics observed in vivo. Here, we introduce a fractional TMDD model that incorporates memory effects through a fractional derivative, thereby generalizing the standard TMDD (sTMDD) framework. Although this fractional TMDD (fTMDD) formulation increases modeling flexibility, it also exacerbates parameter identifiability challenges under typical experimental conditions where only drug concentration data are available. To address this limitation, we derive a fractional quasi-steady-state approximation (fQSSA) that reduces model dimensionality while preserving essential nonlinear and memory-dependent pharmacokinetic dynamics. We further establish an explicit validity condition that quantifies the approximation error of both fTMDD and fQSSA without requiring numerical simulation. This condition reveals that the initial drug-to-target ratio is the primary determinant of QSSA validity, whereas the fractional order has a comparatively minor influence. Application of the proposed framework to recombinant human erythropoietin (rhEPO) data demonstrates that fractional dynamics play a population-dependent role, improving model performance in adults but not in infants. Together, this work provides the first systematic derivation of a QSSA framework for fractional TMDD models, along with rigorous and computable applicability conditions. Our results establish a principled foundation for incorporating memory effects into pharmacokinetic modeling and offer a generalizable framework for nonlinear PK-PD systems involving binding-mediated dynamics. Author summaryMany drugs interact strongly with their biological targets, leading to complex and nonlinear pharmacokinetics that are commonly described using target-mediated drug disposition (TMDD) models. However, these models assume that drug-target interactions occur instantaneously, which limits their ability to capture delayed and history-dependent behaviors observed in real biological systems. In this study, we develop a new modeling framework that incorporates such memory effects by extending TMDD models using fractional calculus. To make the model more practical and computationally efficient, we derive a simplified version based on a quasi-steady-state approximation (QSSA) and provide a clear mathematical condition that determines when this simplification is valid. Our analysis shows that the accuracy of the simplified model is primarily controlled by the initial ratio of drug to target, while the influence of memory effects is comparatively smaller. When applied to experimental data for erythropoietin, our model reveals that memory effects are important in adults but negligible in infants, suggesting that these effects may reflect underlying physiological differences. Overall, this work provides a systematic and interpretable framework for incorporating memory effects into pharmacokinetic modeling, with potential applications to a wide range of drug systems involving complex binding dynamics.

We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA addresses this limitation by embedding the search domain into a toroidal geometry, thereby eliminating artificial boundaries and enabling continuous cyclic exploration. Beyond boundary handling, TSA uses winding numbers to capture the history of agent movement across periodic dimensions, which are exploited to adaptively refine local search. A modified sigmoid control function regulates the transition between global and local search. Performance of TSA is evaluated on a collection of unimodal and multimodal benchmark functions at various dimensions. It consistently outperforms established metaheuristics. Notably, TSA demonstrates exceptional robustness to increasing dimensionality, maintaining fast convergence and low variance where competing methods deteriorate. To assess real-world applicability, we apply TSA to an inverse problem from mathematical oncology. With both synthetic and clinical data, TSA reliably recovers physiologically plausible parameters with greater stability and predictive accuracy than competing algorithms. These results demonstrate that TSA is a powerful and robust tool for large-scale global optimization in computational modelling applications. Striking Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/709766v1_ufig1.gif" ALT="Figure 1"> View larger version (131K): org.highwire.dtl.DTLVardef@1b2c994org.highwire.dtl.DTLVardef@d045a8org.highwire.dtl.DTLVardef@18d296corg.highwire.dtl.DTLVardef@9a972d_HPS_FORMAT_FIGEXP M_FIG C_FIG Image generated with Google Gemini.

The challenge of stratifying patients for combination therapy is both technically demanding and clinically crucial. In previous work, we introduced a multi-objective optimization framework for identifying optimally synergistic combination protocols that are robust to competing definitions of additivity. This manuscript extends this methodology to quantify how inter-individual variability in drug sensitivity influences the combination doses that optimally balance the competing objectives of synergy of efficacy and synergy of potency (a proxy measure of toxicity). For this methodology, we introduce a voxel-based stratification approach to characterize individuals (model parameterizations) into subgroups based on sensitivity to each drug as a monotherapy and in combination. As a case study, we apply the method to a preclinical dataset of murine response to the combination of an immune checkpoint inhibitor and an antiangiogenic agent. We demonstrate that the algorithm can quantify how the robustly optimal combination therapies vary across different treatment response subgroups and how the algorithm can identify subpopulations for which no meaningfully efficacious combination exists. As applying the methodology requires knowledge of specific parameter values for which measurable biomarkers may be unavailable, we also propose an initiation protocol that permits identification of the parameters necessary to place an individual in a subgroup. This methodology is a step in the direction of determining the right combination therapy for a subgroup and finding the right subgroup for an existing therapy.

An expectation of a hypothesis that proposes cell-to-cell signalling pathways are redundant due to the redundancy of pathway terminal transcription factors (TFs) was tested by screening 35 signalling ligands (SLs) for rescue of a decapentaplegic (dpp) hypomorphic wing growth phenotype. The screen identified three examples of partial rescue: Hedgehog (HH), Semphorin 1a (SEMA1A) and Wnt ortholog 2 (WNT2). HH overexpression with dppGAL4 may increase the expression of DPP activity from the hypomorphic dpp alleles. However, SEMA1A and WNT2 did not phenocopy ectopic expression of HH or DPP and neither SEMA1A nor WNT2 were required for wing growth suggesting substitution of DPP for partial restoration of wing growth. The WNT2 rescue was dependent on the Frizzled 4 (FZ4) WNT receptor excluding the possibility that WNT2 weakly binds the DPP receptor. Although examples of phenotypic nonspecificity of SL function were identified, this is an expectation, and not direct proof, of the hypothesis of TF redundancy. Screen Report SummaryAn expectation of a hypothesis proposing that cell-to-cell signalling pathways are redundant due to the redundancy of the pathway terminal transcription factors was tested by screening for replacement of one signalling ligand (DPP; SLa) with another SLb for wing growth. Three non-DPP SLs were identified in the screen of 35SLs: HH, SEMA1A and WNT2. Genetic analysis of Sema1a and Wnt2 suggests functional complementation of dpp for wing growth suggesting that SEMA1A and WNT2 partially replace DPP for wing growth. Therefore, an expectation of the hypothesis is met.

To achieve a stereotypic lineage, each embryo of Caenorhabditis elegans follows an invariant cell differentiation process arising from a combination of cell polarisation, asymmetric or symmetric divisions, combined with intercellular signalling processes. This pattern of embryonic cell differentiation is driven by regulated segregation of molecules occurring at each cell division, including polarity proteins or cell fate determinants, transcription factors, p-granules and mRNAs. These distribution patterns are coupled with a robust spatio-temporal orchestration of cortical actin dynamics, which also plays a crucial role in these processes. However, compared to other molecular contents, how the actin per se is segregated from the first asymmetric division onward remains poorly understood. This study presents a thorough quantification of the intracellular distribution from the zygote to the 4-cell stage of key actors related to actin polymerisation: two nucleators (a formin and the Arp2/3 complex), a capping protein and E-cadherin. We additionally developed a novel method to assess actin polymerisation capacities from single blastomere extracts. We found that actin-related signatures arise at these early stages and that differential mechanisms of protein segregation and homeostasis occur, depending both on the cell pair and on the protein considered. Notably, if asymmetric divisions correlated with unequal partitioning of actin-related contents in a process linked with embryonic polarity, differences were revealed between AB daughter cells upon their separation. Taken together, these actin-related asymmetric distributions are adding a layer to the complexity of cell fate acquisition mechanisms in the early embryo.

Chronic wounds are persistent and difficult to treat. Often this is because they are colonized by polymicrobial communities which contribute to changes in antimicrobial susceptibilities, making these infections harder to effectively clear. We explored the role a community can play in individual members survival when challenged by antibiotics, specifically looking at a community consisting of Staphylococcus aureus, Pseudomonas aeruginosa, Enterococcus faecalis, and Acinetobacter baumannii. Our data shows that communities can contribute to both increases and decreases in susceptibilities depending on the species and the antibiotic. The changes in susceptibilities can be due to interspecies cooperation or competition, with identifiable mechanisms. We also demonstrated that current antimicrobial susceptibility testing (AST) methods used in hospitals, which focus on determining the minimum inhibitory concentration (MIC) via determination of visible turbidity breakpoints, are not able to truly indicate the clearance of bacteria, as species can persist in higher antibiotic concentrations after visible turbidity is gone. To combat decreases in antibiotic susceptibilities contributed to by the community, we used our data from individual antibiotics to determine a potentially effective antibiotic combination, similar to combinatorial therapy used in hospitals to treat recalcitrant infections. Our data proved useful, as the combination of gentamicin and cephalexin was able to overcome polymicrobial synergism and clear the desired bacteria. This demonstrates that it is possible to determine effective antibiotic treatments for polymicrobial infections, whether they be combinatorial in nature or not. One simply must account for the role of the community in order to prescribe the most effective treatment.