149 research outputs found
Evolution at the edge of expanding populations
Predicting evolution of expanding populations is critical to control
biological threats such as invasive species and cancer metastasis. Expansion is
primarily driven by reproduction and dispersal, but nature abounds with
examples of evolution where organisms pay a reproductive cost to disperse
faster. When does selection favor this 'survival of the fastest?' We searched
for a simple rule, motivated by evolution experiments where swarming bacteria
evolved into an hyperswarmer mutant which disperses faster but
pays a growth cost of to make many copies of its flagellum. We
analyzed a two-species model based on the Fisher equation to explain this
observation: the population expansion rate () results from an interplay of
growth () and dispersal () and is independent of the carrying capacity:
. A mutant can take over the edge only if its expansion rate
() exceeds the expansion rate of the established species (); this
simple condition () determines the maximum cost in slower growth
that a faster mutant can pay and still be able to take over. Numerical
simulations and time-course experiments where we tracked evolution by imaging
bacteria suggest that our findings are general: less favorable conditions delay
but do not entirely prevent the success of the fastest. Thus, the expansion
rate defines a traveling wave fitness, which could be combined with trade-offs
to predict evolution of expanding populations
A simple rule for the evolution of fast dispersal at the edge of expanding populations
Evolution by natural selection is commonly perceived as a process that favors those that replicate faster to leave more offspring; nature, however, seem to abound with examples where organisms forgo some replicative potential to disperse faster. When does selection favor invasion of the fastest? Motivated by evolution experiments with swarming bacteria we searched for a simple rule. In experiments, a fast hyperswarmer mutant that pays a reproductive cost to make many copies of its flagellum invades a population of mono-flagellated bacteria by reaching the expanding population edge; a two-species mathematical model explains that invasion of the edge occurs only if the invasive species' expansion rate, v₂, which results from the combination of the species growth rate and its dispersal speed (but not its carrying capacity), exceeds the established species', v₁. The simple rule that we derive, v₂ > v₁, appears to be general: less favorable initial conditions, such as smaller initial sizes and longer distances to the population edge, delay but do not entirely prevent invasion. Despite intricacies of the swarming system, experimental tests agree well with model predictions suggesting that the general theory should apply to other expanding populations with trade-offs between growth and dispersal, including non-native invasive species and cancer metastases.First author draf
Tissue fusion over non-adhering surfaces
Tissue fusion eliminates physical voids in a tissue to form a continuous
structure and is central to many processes in development and repair. Fusion
events in vivo, particularly in embryonic development, often involve the
purse-string contraction of a pluricellular actomyosin cable at the free edge.
However in vitro, adhesion of the cells to their substrate favors a closure
mechanism mediated by lamellipodial protrusions, which has prevented a
systematic study of the purse-string mechanism. Here, we show that monolayers
can cover well-controlled mesoscopic non-adherent areas much larger than a cell
size by purse-string closure and that active epithelial fluctuations are
required for this process. We have formulated a simple stochastic model that
includes purse-string contractility, tissue fluctuations and effective friction
to qualitatively and quantitatively account for the dynamics of closure. Our
data suggest that, in vivo, tissue fusion adapts to the local environment by
coordinating lamellipodial protrusions and purse-string contractions
Metabolic origins of spatial organization in the tumor microenvironment.
The genetic and phenotypic diversity of cells within tumors is a major obstacle for cancer treatment. Because of the stochastic nature of genetic alterations, this intratumoral heterogeneity is often viewed as chaotic. Here we show that the altered metabolism of cancer cells creates predictable gradients of extracellular metabolites that orchestrate the phenotypic diversity of cells in the tumor microenvironment. Combining experiments and mathematical modeling, we show that metabolites consumed and secreted within the tumor microenvironment induce tumor-associated macrophages (TAMs) to differentiate into distinct subpopulations according to local levels of ischemia and their position relative to the vasculature. TAMs integrate levels of hypoxia and lactate into progressive activation of MAPK signaling that induce predictable spatial patterns of gene expression, such as stripes of macrophages expressing arginase 1 (ARG1) and mannose receptor, C type 1 (MRC1). These phenotypic changes are functionally relevant as ischemic macrophages triggered tube-like morphogenesis in neighboring endothelial cells that could restore blood perfusion in nutrient-deprived regions where angiogenic resources are most needed. We propose that gradients of extracellular metabolites act as tumor morphogens that impose order within the microenvironment, much like signaling molecules convey positional information to organize embryonic tissues. Unearthing embryology-like processes in tumors may allow us to control organ-like tumor features such as tissue repair and revascularization and treat intratumoral heterogeneity
Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network
Bacteria of many species rely on a simple molecule, the intracellular secondary messenger c-di-GMP (Bis-(3'-5')-cyclic dimeric guanosine monophosphate), to make a vital choice: whether to stay in one place and form a biofilm, or to leave it in search of better conditions. The c-di-GMP network has a bow-tie shaped architecture that integrates many signals from the outside world—the input stimuli—into intracellular c-di-GMP levels that then regulate genes for biofilm formation or for swarming motility—the output phenotypes. How does the ‘uninformed’ process of evolution produce a network with the right input/output association and enable bacteria to make the right choice? Inspired by new data from 28 clinical isolates of Pseudomonas aeruginosa and strains evolved in laboratory experiments we propose a mathematical model where the c-di-GMP network is analogous to a machine learning classifier. The analogy immediately suggests a mechanism for learning through evolution: adaptation though incremental changes in c-di-GMP network proteins acquires knowledge from past experiences and enables bacteria to use it to direct future behaviors. Our model clarifies the elusive function of the ubiquitous c-di-GMP network, a key regulator of bacterial social traits associated with virulence. More broadly, the link between evolution and machine learning can help explain how natural selection across fluctuating environments produces networks that enable living organisms to make sophisticated decisions
Open Science Saves Lives: Lessons from the COVID-19 Pandemic
In the last decade Open Science principles, such as Open Access, study preregistration, use of preprints, making available data and code, and open peer review, have been successfully advocated for and are being slowly adopted in many different research communities. In response to the COVID-19 pandemic many publishers and researchers have sped up their adoption of some of these Open Science practices, sometimes embracing them fully and sometimes partially or in a sub-optimal manner. In this article, we express concerns about the violation of some of the Open Science principles and its potential impact on the quality of research output. We provide evidence of the misuses of these principles at different stages of the scientific process. We call for a wider adoption of Open Science practices in the hope that this work will encourage a broader endorsement of Open Science principles and serve as a reminder that science should always be a rigorous process, reliable and transparent, especially in the context of a pandemic where research findings are being translated into practice even more rapidly
Dense active matter model of motion patterns in confluent cell monolayers
Epithelial cell monolayers show remarkable displacement and velocity
correlations over distances of ten or more cell sizes that are reminiscent of
supercooled liquids and active nematics. We show that many observed features
can be described within the framework of dense active matter, and argue that
persistent uncoordinated cell motility coupled to the collective elastic modes
of the cell sheet is sufficient to produce swirl-like correlations. We obtain
this result using both continuum active linear elasticity and a normal modes
formalism, and validate analytical predictions with numerical simulations of
two agent-based cell models, soft elastic particles and the self-propelled
Voronoi model together with in-vitro experiments of confluent corneal
epithelial cell sheets. Simulations and normal mode analysis perfectly match
when tissue-level reorganisation occurs on times longer than the persistence
time of cell motility. Our analytical model quantitatively matches measured
velocity correlation functions over more than a decade with a single fitting
parameter.Comment: updated version accepted for publication in Nat. Com
Active wetting of epithelial tissues
Development, regeneration and cancer involve drastic transitions in tissue
morphology. In analogy with the behavior of inert fluids, some of these
transitions have been interpreted as wetting transitions. The validity and
scope of this analogy are unclear, however, because the active cellular forces
that drive tissue wetting have been neither measured nor theoretically
accounted for. Here we show that the transition between 2D epithelial
monolayers and 3D spheroidal aggregates can be understood as an active wetting
transition whose physics differs fundamentally from that of passive wetting
phenomena. By combining an active polar fluid model with measurements of
physical forces as a function of tissue size, contractility, cell-cell and
cell-substrate adhesion, and substrate stiffness, we show that the wetting
transition results from the competition between traction forces and contractile
intercellular stresses. This competition defines a new intrinsic lengthscale
that gives rise to a critical size for the wetting transition in tissues, a
striking feature that has no counterpart in classical wetting. Finally, we show
that active shape fluctuations are dynamically amplified during tissue
dewetting. Overall, we conclude that tissue spreading constitutes a prominent
example of active wetting --- a novel physical scenario that may explain
morphological transitions during tissue morphogenesis and tumor progression
<i>Schinus terebinthifolia</i> leaf lectin (SteLL) has anti-infective action and modulates the response of <i>Staphylococcus aureus</i>-infected macrophages
Implications of serial measurements of natriuretic peptides in heart failure: insights from BIOSTAT‐CHF
No abstract available
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