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Abstract Gene duplication is a fundamental part of evolutionary innovation. While single-gene duplications frequently exhibit asymmetric evolutionary rates between paralogs, the extent to which this applies to multi-gene duplications remains unclear. In this study, we investigate the role of genetic context in shaping evolutionary divergence within multi-gene duplications, leveraging microsynteny to differentiate source and target copies. Using a dataset of 193 mammalian genome assemblies and a bird outgroup, we systematically analyze patterns of sequence divergence between duplicated genes and reference orthologs. We find that target copies, those relocated to new genomic environments, exhibit elevated evolutionary rates compared to source copies in the ancestral location. This asymmetry is influenced by the distance between copies and the size of the target copy. We also demonstrate that the polarization of rate asymmetry in paralogs, the “choice” of the slowly evolving copy, is biased towards collective, block-wise polarization in multi-gene duplications. Our findings highlight the importance of genetic context in modulating post-duplication divergence, where differences in cis-regulatory elements and co-expressed gene clusters between source and target copies may be responsible. This study presents a large-scale test of asymmetric evolution in multi-gene duplications, offering new insight into how genome architecture shapes functional diversification of paralogs. Significance statementAfter a gene is duplicated, reduced selective constraints can lead the two copies to rapidly diverge, with one copy often evolving faster and occasionally gaining a new function. We quantify the influence of genetic context in choosing which copy of a duplicated gene has an elevated substitution rate. In a representative dataset of 193 mammalian genomes, we found strong evidence that gene copies pasted into new genomic locations tend to evolve faster than the corresponding copies in ancestral locations, suggesting an important role for the regulatory environment. The asymmetry in evolutionary rates of duplicated genes persists even for very large multigenic duplications, up to the scale of megabases, indicating that regulatory interactions frequently reach farther than previously thought. 

The T cell receptor (TCR) is a key component of the adaptive immune system, recognizing foreign antigens (ligands) and triggering an immune response. To explain the high sensitivity and selectivity of the TCR in discriminating “self” from “non-self” ligands, most models evoke kinetic proofreading (KP) schemes, however it is unclear how competing models used for TCR triggering, such as the kinetic segregation (KS) model, influence KP performance. In this paper, we consider two different TCR triggering models and their influence on subsequent KP-based ligand discrimination by the TCR: a classic conformational change model (CC-KP), where ligand-TCR binding is strictly required for activation, and the kinetic segregation model (KS-KP), where only residence of the TCR within a close contact devoid of kinases is required for its activation. Building on previous work, our computational model permits a head-to-head comparison of these models . While we find that both models can be used to explain the probability of TCR activation across much of the parameter space, we find biologically important regions in the parameter space where significant differences in performance can be expected. Furthermore, we show that the available experimental evidence may favor the KS-KP model over CC-KP. Our results may be used to motivate and guide future experiments to determine accurate mathematical models of TCR function. Published by the American Physical Society2025 

Information theory can be used to describe the gain of evolutionary fitness that an organism obtains from sensing, processing, and acting on environmental information. This paper considers the fitness value of subjective information, i.e., the context-dependent value of different kinds of information. A simplified model is given in which the organism requires two essential nutrients, and can prioritize sensing for one or the other. It is shown that a subjective strategy, in which the organism prioritizes a less abundant nutrient for sensing, leads to higher fitness than a balanced strategy, in which total information is maximized and the meaning of the acquired information is disregarded. Using this model, the fitness advantage of subjective information admits an analytical solution, and it is shown that subjective information is more advantageous when the organism's knowledge of the environment is less precise. 

Abstract Tumors develop in a complex physical, biochemical, and cellular milieu, referred to as the tumor microenvironment. Of special interest is the set of immune cells that reciprocally interact with the tumor, the tumor-immune microenvironment (TIME). The diversity of cell types and cell–cell interactions in the TIME has led researchers to apply concepts from ecology to describe the dynamics. However, while tumor cells are known to induce immune cells to switch from anti-tumor to pro-tumor phenotypes, this type of ecological interaction has been largely overlooked. To address this gap in cancer modeling, we develop a minimal, ecological model of the TIME with immune cell conversion, to highlight this important interaction and explore its consequences. A key finding is that immune conversion increases the range of parameters supporting a co-existence phase in which the immune system and the tumor reach a stalemate. Our results suggest that further investigation of the consequences of immune cell conversion, using detailed, data-driven models, will be critical for greater understanding of TIME dynamics. 

The evolutionary consequences of quorum sensing in regulating bacterial cooperation are not fully understood. In this study, we reveal unexpected effects of regulating public good production through quorum sensing on bacterial population dynamics, showing that quorum sensing can be a collectively harmful alternative to unregulated production. We analyze a birth-death model of bacterial population dynamics accounting for public good production and the presence of non-producing cheaters. Our model demonstrates that when demographic noise is a factor, the consequences of controlling public good production according to quorum sensing depend on the cost of public good production and the growth rate of populations in the absence of public goods. When public good production is inexpensive, quorum sensing is a destructive alternative to unconditional production, in terms of the mean population extinction time. When costs are higher, quorum sensing becomes a constructive strategy for the producing strain, both stabilizing cooperation and decreasing the risk of population extinction.