Biology subjects

Zapien-Campos, R.

Publications and source records attributed to Zapien-Campos, R..

4 recordsLinked to original sources

bioRxiv2023

Dynamic cell differentiation in multicellularity with specialized cell types

The specialization of cells is a hallmark of complex multicellularity. Cell differentiation enables the emergence of specialized cell types that carry out separate functions previously executed by a multifunctional ancestor cell. One view is that initial cell differentiation occurred randomly, especially for genetically identical cells, exposed to the same life history environment. How such a change in differentiation probabilities can affect the evolution of differentiation patterns is still unclear. We develop a theoretical model to investigate the effect of stage-dependent cell differentiation - cells change their developmental trajectories during a single round of development via cell divisions - on the evolution of optimal differentiation patterns. We found that irreversible differentiation - a cell type gradually losing its differentiation capability to produce other cell types - is more favored under stage-dependent than stage-independent cell differentiation in relatively small organisms with limited differentiation probability variations. Furthermore, we discovered that irreversible differentiation of germ cells, which is the gradual loss of germ cells ability to differentiate, is a prominent pattern among irreversible differentiation patterns under stage-dependent cell differentiation. In addition, large variations in differentiation probabilities prohibit irreversible differentiation from being the optimal differentiation pattern. Author summaryThe differentiation of cells into different branches is a characteristic feature of multicellular organisms. To understand its origin, the mechanism of division of labour was proposed, where cells are specialized at distinct tasks. In previous models, a cell type is usually assumed to produce another cell type with a fixed probability which is referred to as stage-independent differentiation. However, it has been argued that cell differentiation is a dynamic process in which cells possess changing differentiation capabilities during the different stages of an organisms development. Stage-dependent differentiation exhibits more diverse patterns of development than differentiation with fixed probabilities, thus it can lead to novel targets of selection. How does stage-dependent differentiation impact the evolution of optimal differentiation patterns compared with stage-independent one? To address this question, we built a stage-dependent cell differentiation model and classified differentiation patterns based on the cells differentiation capability in their last cell division. We investigate how stage-dependent differentiation probabilities impact the evolution of the optimal differentiation pattern, which acts on the fitness of an organism. As we take the growth rate as a proxy of an organisms fitness, we seek the "optimal strategy" that leads to the fastest growth. Our numerical results show that irreversible differentiation which gradually loses its differentiation capability, is favored over stage-independent differentiation in small organisms. Meanwhile, irreversible differentiation wont be optimal when there are no constraints on the changes of stage-dependent differentiation probabilities between successive cell divisions.

evolutionary biology↗

bioRxiv2023

Inferring interactions from microbiome data

Parameter inference of high-dimensional data is challenging and microbiome time series data is no exception. Methods aimed at predicting from point estimates exist, but often even fail to recover the true parameters from simulated data. Computational methods to robustly infer and quantify the uncertainty in model parameters are needed. Here, we propose a computational workflow addressing such challenges - allowing us to compare mechanistic models and identify the values and the certainty of inferred parameters. This approach allows us to infer which kind of interactions occur in the microbial community. In contrast to point-estimate inference, the distribution for the parameters, our outcome, reflects their uncertainty. To achieve this, we consider as many equations for the statistical moments of the microbiome as parameters. Our inference workflow, which builds upon a mechanistic foundation of microscopic processes, can take into account that commonly metagenomic datasets only provide information on relative abundances and hosts ensembles. With our framework, we move from qualitative prediction to quantifying the likelihood of certain interaction types in microbiomes.

ecology↗

bioRxiv2021

On the effect of inheritance of microbes in commensal microbiomes

BackgroundOur current view of nature depicts a world where macroorganisms dwell in a landscape full of microbes. Some of these microbes not only transit but establish themselves in or on hosts. Although hosts might be occupied by microbes for most of their lives, a microbe-free stage during their prenatal development seems to be the rule for many hosts. The questions of who the first colonizers of a newborn host are and to what extent these are obtained from the parents follow naturally. ResultsWe have developed a mathematical model to study the effect of the transfer of microbes from parents to offspring. Even without selection, we observe that microbial inheritance is particularly effective in modifying the microbiome of hosts with a short lifespan or limited colonization from the environment, for example by favouring the acquisition of rare microbes. ConclusionBy modelling the inheritance of commensal microbes to newborns, our results suggest that, in an eco-evolutionary context, the impact of microbial inheritance is of particular importance for some specific life histories.

ecology↗

bioRxiv2021

The effect of microbial selection on the occurrence-abundance patterns of microbiomes

Theoretical models are useful to investigate the drivers of community dynamics. Notable are models that consider the events of death, birth, and immigration of individuals assuming they only depend on their abundance - thus, all types share the same parameters. The community level expectations arising from these simple models and their agreement to empirical data have been discussed extensively, often suggesting that in nature, rates might indeed be neutral or their differences not important. But, how robust are these model predictions to type-specific rates? And, what are the consequences at the level of types? Here, we address these questions moving from simple to diverse communities. For this, we build a model where types are differently adapted to the environment. We adapt a computational method from the literature to compute equilibrium distributions of the abundance. Then, we look into the occurrence-abundance pattern often reported in microbial communities. We observe that large immigration and biodiversity - common in microbial systems - lead to such patterns, regardless of whether the rates are neutral or non-neutral. We conclude by discussing the implications to interpret and test empirical data.

ecology↗