Biology subjects

Cuesta, J. A.

Publications and source records attributed to Cuesta, J. A..

7 recordsLinked to original sources

bioRxiv2026

The role of space in explaining macroecological patterns of microbial abundance

Understanding the origin of universal macroecological patterns in microbial communities remains a central open question in ecology. A key observation is that species abundance fluctuations across diverse biomes are well described by a gamma distribution, yet the mechanism responsible for this regularity is debated. Prevailing explanations invoke exogenous stochastic forcing, while endogenous interaction-based approaches--grounded in generalized Lotka-Volterra (gLV) dynamics--have so far failed to reproduce this pattern. Here we show that incorporating spatial structure resolves this discrepancy. In a gLV model with constant migration, local abundance dynamics remain inconsistent with a gamma distribution. However, when the community is embedded in a fragmented landscape as a metacommunity--with species dispersing among patches--aggregating abundances across patches yields distributions that closely match the empirically observed gamma form. We further demonstrate that this result can be reproduced by aggregating independent realizations of the constant-migration gLV model, entailing statistical aggregation rather than a specific biological mechanism as the origin of this pattern. Our findings highlight the fundamental role of spatial structure in shaping microbial macroecology, and suggest that observed abundance distributions may largely reflect the spatial coarse-graining inherent in metagenomic sampling.

bioinformatics↗

bioRxiv2024

Collective effects in the population growth of microbials

We investigate the growth dynamics of microbial populations, challenging the conventional logistic model. By analyzing empirical data from various biomes, we demonstrate that microbial growth is better described by a generalized logistic model, the{theta} -logistic model. This accounts for different growth mechanisms and environmental fluctuations, leading to a generalized gamma distribution of abundance fluctuations. Our findings reveal that microbial growth is never sublinear, so they cannot endorse--at least in the microbial world--the recent proposal of this mechanism as a stability enhancer of highly diverse communities. These results have significant implications for understanding macroecological patterns and the stability of microbial ecosystems.

bioinformatics↗

bioRxiv2022

Chimpanzees organize their social relationships like humans

Human relationships are structured in a set of layers, ordered from higher (intimate relationships) to lower (acquaintances) emotional and cognitive intensity. This structure arises from the limits of our cognitive capacity and the different amounts of resources required by different relationships. However, it is unknown whether nonhuman primate species organize their affiliative relationships following the same pattern. We here show that the time chimpanzees devote to grooming other individuals is well described by the same model used for human relationships, supporting the existence of similar social signatures for both humans and chimpanzees. Furthermore, the relationship structure depends on group size as predicted by the model, the proportion of high intensity connections being larger for smaller groups.

animal behavior and cognition↗

bioRxiv2021

Prevalence of mutualism in a simple model of microbial co-evolution

Evolutionary transitions among ecological interactions are widely known, although their detailed dynamics remain absent for most population models. Adaptive dynamics has been used to illustrate how the parameters of population models might shift through evolution, but within an ecological regime. Here we use adaptive dynamics combined with a generalised logistic model of population dynamics to show that transitions of ecological interactions might appear as a consequence of evolution. To this purpose we introduce a two-microbial toy model in which population parameters are determined by a bookkeeping of resources taken from (and excreted to) the environment, as well as from the byproducts of the other species. Despite its simplicity, this model exhibits all sorts of ecological transitions, some of which resemble those found in nature. Overall, the model shows a clear trend toward the emergence of mutualism.

ecology↗

bioRxiv2019

Populations of genetic circuits are unable to find the fittest solution in a multilevel genotype-phenotype map

The evolution of gene regulatory networks (GRNs) is of great relevance for both evolutionary and synthetic biology. Understanding the relationship between GRN structure and its function can allow us to understand the selective pressures that have shaped a given circuit. This is especially relevant when considering spatiotemporal expression patterns, where GRN models have been shown to be extremely robust and evolvable. However, previous models that studied GRN evolution did not include the evolution of protein and genetic elements that underlie GRN architecture. Here we use toyLIFE, a multilevel genotype-phenotype map, to show that not all GRNs are equally likely in genotype space and that evolution is biased to find the most common GRNs. toyLIFE rules create Boolean GRNs that, embedded in a one-dimensional tissue, develop a variety of spatiotemporal gene expression patterns. Populations of toyLIFE organisms choose the most common GRN out of a set of equally fit alternatives and, most importantly, fail to find a target pattern when it is very rare in genotype space. Indeed, we show that the probability of finding the fittest phenotype increases dramatically with its abundance in genotype space. This phenotypic bias represents a mechanism that can prevent the fixation in the population of the fittest phenotype, one that is inherent to the structure of genotype space and the genotype-phenotype map.

evolutionary biology↗

bioRxiv2019

Parsimonious scenario for the emergence of viroid-like replicons de novo

Viroids are small, non-coding, circular RNA molecules that infect plants. Different hypotheses for their evolutionary origin have been put forward, such as an early emergence in a precellular RNA World or several de novo independent evolutionary origins in plants. Here we discuss the plausibility of de novo emergence of viroid-like replicons by giving theoretical support to the likelihood of different steps along a parsimonious evolutionary pathway. While Avsunviroidae-like structures are relatively easy to obtain through evolution of a population of random RNA sequences of fixed length, rod-like structures typical of Pospiviroidae are difficult to fix. Using different quantitative approaches, we evaluate the likelihood that RNA sequences fold into a rod-like structure and bear specific sequence motifs facilitating interactions with other molecules, e.g. RNA polymerases, RNases and ligases. By means of numerical simulations, we show that circular RNA replicons analogous to Pospiviroidae emerge if evolution is seeded with minimal circular RNAs that grow through the gradual addition of nucleotides. Further, these rod-like replicons often maintain their structure if independent functional modules are acquired that impose selective constraints. The evolutionary scenario we propose here is consistent with the structural and biochemical properties of viroids described to date.

evolutionary biology↗

bioRxiv2017

Disentangling The Effects Of Selection And Loss Bias On Gene Dynamics

We combine mathematical modelling of genome evolution with comparative analysis of prokaryotic genomes to estimate the relative contributions of selection and intrinsic loss bias to the evolution of different functional classes of genes and mobile genetic elements (MGE). An exact solution for the dynamics of gene family size was obtained under a linear duplication-transfer-loss model with selection. With the exception of genes involved in information processing, particularly translation, which are maintained by strong selection, the average selection coefficient for most non-parasitic genes is low albeit positive, compatible with the observed positive correlation between genome size and effective population size. Free-living microbes evolve under stronger selection for gene retention than parasites. Different classes of MGE show a broad range of fitness effects, from the nearly neutral transposons to prophages, which are actively eliminated by selection. Genes involved in anti-parasite defense, on average, incur a fitness cost to the host that is at least as high as the cost of plasmids. This cost is probably due to the adverse effects of autoimmunity and curtailment of horizontal gene transfer caused by the defense systems and selfish behavior of some of these systems, such as toxin-antitoxin and restriction-modification modules. Transposons follow a biphasic dynamics, with bursts of gene proliferation followed by decay in the copy number that is quantitatively captured by the model. The horizontal gene transfer to loss ratio, but not the duplication to loss ratio, correlates with genome size, potentially explaining the increased abundance of neutral and costly elements in larger genomes.\n\nSIGNIFICANCEEvolution of microbes is dominated by horizontal gene transfer and the incessant host-parasite arms race that promotes the evolution of diverse anti-parasite defense systems. The evolutionary factors governing these processes are complex and difficult to disentangle but the rapidly growing genome databases provide ample material for testing evolutionary models. Rigorous mathematical modeling of evolutionary processes, combined with computer simulation and comparative genomics, allowed us to elucidate the evolutionary regimes of different classes of microbial genes. Only genes involved in key informational and metabolic pathways are subject to strong selection whereas most of the others are effectively neutral or even burdensome. Mobile genetic elements and defense systems are costly, supporting the understanding that their evolution is governed by the same factors.

evolutionary biology↗