Journal of Biosciences

PremiseLong-term phenological patterns are increasingly being examined from the perspective of climate change and its potential effects. Climatic effects on plant phenology could involve the direct responses to changes in temperature, precipitation and solar irradiance, or could be mediated by these variables through exogenous teleconnections such as the El Nino Southern Oscillation (ENSO). The effects of climatic fluctuations on inter-annual variation in tropical phenology remain understudied. MethodsWe examined long-term patterns of tree flowering and fruiting intensity in a tropical forest site in the Eastern Himalayas between 2011 and 2024. Species-specific patterns were examined for 36 species. Long-term patterns were quantified using Generalized Additive Models, and splines were visualized to infer trends. Through Generalized Linear Mixed Models, we determined if there was a lagged phenological response to ENSO and temperature, precipitation and solar irradiance, and whether ENSO effects were being mediated through the latter group of variables or plant traits. ResultsBetween 2011 and 2019, trends in flowering and fruiting were significant for 17 and 23 species respectively. Flowering increased for 7 species, while fruiting declined for 8 species. Flowering peaked during El Nino, but this association did not appear to be mediated through climate variables, whereas fruiting showed a three-month positive lagged response to solar irradiance, independent of ENSO. The peak season of reproduction was the only trait determining species-specific responses to climate variables. ConclusionOur study highlights nonlinearity in long-term patterns of reproductive phenology, and the importance of solar irradiance in determining inter-annual fruit production.

Phase synchronized population dynamics of various species constituting a complex ecosystem elevates the risk of their extinction due to both environmental stochasticity and simulateneous low density fluctuations. Therefore, an extremely vital approach to measure the extinction risk of an ecosystem as a whole is to quantify the phase synchrony among the species populations co-habiting and interacting with each other in an ecosystem. Generally, in models describing population dynamics of ecosystems, both trophic and non-trophic inter-species interactions are modelled as interactions between two species. This approach contradicts the fact with such a large number of species living in close proximity, more than two species must partake in the same interaction influencing the population dynamics of each other. To address this, higher-order interactions need to be incorporated in the models describing population dynamics of an ecosystem. Consequently, their effect on phase synchronization of populations also need to be investigated. In this study, we model a species-rich ecosystem as a complex phase oscillator network and examine the phase dynamics of the total population. Each node of this network represents a constituent species, modelled as a Sakugachi-Kuramoto phase oscillator coupled non-linearly to the other nodes through both first-order and higher-order inter-species interactions. These interactions can be both mutualistic (positive) and antagonistic (negative) in nature. Along with the higher-order interactions, we also incorporate inherent asymmetry among the nodes to account for habitat heterogeneity. Further, we investigate the effects of both higher-order coupling and asymmetry on the phase synchronization of the total population. Our findings demonstrate that higher-order interactions above a threshold amplitude enforces a transition from synchronous to asynchronous dynamics of the ecosystem. Further, we find that increase in the size and diversity of the ecosystem leads to an increase in the threshold value of higher order coupling required to reach asynchronous dynamics. We also demonstrate that this higher-order induced asynchrony is further promoted by high asymmetry among the individual nodes. Importantly, negative inter-species interactions, if existing to a high degree also induce asynchrony in the system. Moreover, the size of the network also plays a role in deciding the threshold value of higher order coupling required to induce asynchrony.

In relatively aseasonal tropical rainforests, few studies have explored long-term phenological patterns of a high diversity of tree species in relation to climate, phylogeny, and functional traits. In these systems, short-duration seasonal pulses of irradiance and water deficit are expected to provide narrower windows for leafing and flowering and wider windows for fruiting across prolonged wet seasons, potentially mediated by functional traits and phylogenetic relatedness. Here, we document leafing, flowering, and fruiting phenology of 50 tree species (920 - 1077 trees, 10 - 42 trees/species) monitored monthly over an 9-y period (2017 - 26) in a relatively aseasonal south Asian tropical rainforest in the Anamalai Hills, Western Ghats, India. We examined correlations between climatic variables (irradiance, daylength, temperature, precipitation) and tree phenology and Mantel correlations among similarity in monthly phenology, functional traits (wood density, seed size, and maximum height), and phylogenetic relatedness. We then investigated the phylogenetic signal of phenological traits (frequency, amplitude, duration, and peak month) using Pagels {lambda}. Leaf flushing and flowering showed distinct seasonality and negative associations with daylength and precipitation, whereas fruiting showed greater temporal spread and weaker associations with climate. Functional traits or phylogeny did not significantly influence leaf flushing and flowering, whereas dissimilarity in fruiting was correlated with phylogenetic distance and peak fruiting month showed a significant phylogenetic signal ({lambda} = 0.96). The results indicate that in relatively aseasonal tropical rainforests, proximate climatic cues more strongly influence leaf flushing and flowering, whereas phylogenetic constraints affect timing of fruiting and may cause lineage-specific vulnerabilities to climate change.

BackgroundTo control leishmaniasis, chemotherapy drugs are currently under development. However, these drugs often exhibit poor efficacy and are associated with toxicity, adverse effects, and drug resistance. At present, no specific drug is available for the treatment of leishmaniasis. Meanwhile, vaccine research is ongoing. Recent studies have analysed some experimental vaccines using mathematical models. AimIn previous work, drug targeting was focused on the entire human body rather than specifically addressing infected macrophages and parasites. In our current approach, we aim to eliminate infected macrophages and parasites through nano-drug design. Specifically, we utilise two types of nanoparticles: iron oxide and citric acid-coated iron oxide. Moving forward, we plan to advance this strategy using mathematical modelling of macrophage-parasite interactions. MethodsWe design PDE-based models of macrophages and parasites, incorporating cytokine dynamics, to support nano-drug development. Drug efficacy is estimated using posterior distributions to analyse phenotypic fluctuations of macrophages and parasites during the design phase. We investigate implicit and semi-implicit treatment schemes, focusing on energy decay properties. To model drug flow during treatment, we introduce a three-phase moving boundary problem. Comparative analyses are conducted to evaluate macrophage and parasite behaviour with and without treatment. Finally, the entire framework is implemented within a virtual lab environment. ResultsThe results show that the nano-drug exhibits better efficacy compared to combined drug doses. We analysed and compared two types of nano-drug particles: iron oxide and citric acid-coated iron oxide. We discuss how the drug effectively targets and eliminates infected macrophages and parasites. ConclusionOur models results and simulations will support researchers conducting further studies in nano-drug design for leishmaniasis. These simulations are performed within a virtual lab environment.

The size structure of phytoplankton communities plays a key role in the fate of carbon fixed by photosynthesis. Whether phytoplankton cells sink, enter the microbial loop, or are consumed by larger organisms is generally determined by their size. Grazing has been advanced as a factor determining size structure, but sources of mortality other than grazing, such as viruses also are recognized to be important. Based on the observation that cell size and genome size are related in phytoplankton, we hypothesize that viruses can also play a role in shaping the size structure of the phytoplankton community. Because cell size is related to genome size, we suggest that phytoplankton species with larger genomes will have a more developed immune system to defend against viral infection. As a first step to test this hypothesis, we screened the published transcriptomes of 125 phytoplankton species for expressed viral and immune-response related genes. We found a significant negative correlation between host-cell size and viral-gene diversity, and a positive correlation between host-cell size and the number of immune-response related genes. Our hypothesis supported by preliminary findings opens new pathways to explore whether we should consider viruses as an additional evolutionary driver for larger phytoplankton size, along with grazing and nutrients.

Transition metal based compounds are promising therapeutic agents, particularly in cancer treatment. However, predicting their binding sites remains a major challenge. In this work, we investigate the applicability of two tools, Metal3D and Metal1D, for this purpose. Although originally trained to predict zinc ion binding sites only, both predictors successfully identify several experimentally observed binding sites for transition metal complexes directly from apo protein structures. At the same time, we highlight current limitations, such as the sensitivity to side-chain conformations, and discuss possible strategies for improvement. This work provides a first step toward establishing a robust computational pipeline in which rapid and low-cost predictors are able to identify putative hotspots for transition metal binding, which can then be refined using more accurate but computationally demanding methods. Author summaryTransition metals play a crucial role as therapeutic agents, especially in cancer therapy. However, the prediction of their binding site locations is challenging, as accurate computational methods often require time-consuming simulations, making them impractical when many possible binding sites must be explored. In this work, we explored the capability of two binding site predictors, originally developed to locate metal ions in proteins, to identify binding sites for more complex covalently-bound transition metal based agents. We found that these tools can often identify the experimentally-known binding regions, even when starting from the apo structure, in which the protein does not already contain the metal compound. At the same time, our results show clear limitations in more challenging cases, particularly when the binding involves only a single amino acid or when the binding site undergoes major structural rearrangements upon binding. Overall, our study shows that fast predictors can provide valuable early insights in the investigation of the binding sites of covalently-bound transition metal based compounds. When combined with more accurate simulation techniques, they can help focus computational efforts and ultimately support the rational design of transition metal based drugs.

AbstractBeneficial associations between bobtail squids (Cephalopoda: Sepiolidae) and Vibrio bacteria encompass a unique association where symbionts are obtained environmentally from the surrounding environment. Vibrio symbionts are susceptible to a number of ecological pressures such as protozoan grazing whilst in their free-living state. Impacts of grazing have several consequences for symbiosis characteristics such as biofilm formation, a trait crucial for survival both in and outside the squid. Therefore, in order to ascertain how biotic factors such as grazing in the environment effect symbiotic success, two V. fischeri strains, ES114 and ETBB1-C were experimentally evolved in separate biofilm grazing experiments with the amoeba, Acanthamoeba castellanii and ciliate Tetrahymena pyriformis. Both ES114 and ETBB1-C biofilms were evolved up to 50 generations through serial passaging. At 50 generations, ES114 biofilms displayed variability in response to predation by both predators, whereas ETBB1-C biofilms were more stable across generations of grazing. A. castellanii decreased in population numbers when co-inoculated with ETBB1-C, whereas T. pyriformis increased in numbers with biofilm growth. Growth of V. fischeri biofilms in the presence of grazers such as T. pyriformis has an important role in inducing biofilm growth by acting as a chaperone for recycling nutrients back into the environment. Additionally, V. fischeri colonization fitness in the host was dependent on which grazer was used to evolve the biofilms. Such variation in response by V. fischeri to different types of predation demonstrates the versatility of this symbiont in its free living state and has subsequent impacts on the eventual association with squids. ImportanceThis manuscript demonstrates the importance of biotic factors (such as protozoan grazing) in the environment that effect host colonization in a beneficial symbiosis. Using an experimental evolution approach, this work demonstrates how symbiotic biofilms can adapt to pressures such as grazing that subsequently influences the ability to colonize its invertebrate host.

Although there has been considerable progress in understanding the factors that determine the invasiveness of plasmodium falciparum merozoites, the collective role of the biophysical characteristics of erythrocyte deformability in the invasion process is poorly understood. Cell shape, cytoplasmic viscosity, and membrane stability are the main determinants of erythrocyte deformability, but it remains unknown how these properties affect the merozoite invasiveness. This study aimed to investigate computationally (i) the role of erythrocyte morphology and merozoite-induced erythrocyte membrane damage in merozoite invasion and (ii) the suitability of mechanical markers of merozoite-induced erythrocyte membrane damage for screening of invasion-blocking antimalarial drugs. Finite element models were developed to represent a human erythrocyte and a spherocyte, their invasion by a malaria merozoite, and erythrocyte compression and nanoindentation as mechanical assays for membrane damage. Smoothed particle hydrodynamics represented the erythrocyte cytoplasm, and merozoite-induced erythrocyte membrane damage was implemented with a constitutive model. The invasiveness of the merozoite decreases with increased erythrocyte sphericity associated with genetic disorders such as hereditary spherocytosis. The invasiveness is larger when membrane damage is induced in the erythrocyte at an early invasion stage than throughout the invasion process. The minimum force required for a malaria merozoite to invade a human erythrocyte was predicted to be 11 pN. The findings on the invasion mechanics can guide future studies into the invasiveness of the merozoite. The nanoindentation simulations point to the potential of nanoindentation to determine erythrocyte membrane damage for screening novel invasion-blocking anti-malaria drugs.

Bacterial cells detached from Staphylococcus epidermidis biofilms are found to release predominantly as small oblate clusters ([~]1.9 {micro}m) in both untreated biofilms and biofilms treated with matrix-targeted disruptors. Quantitative image analysis common to colloidal science was applied to quantitatively evaluate the physical properties of 9,147 bacterial clusters detached from S. epidermidis biofilms with and without targeted disruption of individual matrix components (polysaccharides, proteins, extracellular DNA) or solubilization of the extracellular polymeric substances (EPS). Concentrations of S. epidermidis biofilm-detached cells are highest after matrix-targeted disruption of polysaccharides. K-means clustering, an unsupervised machine learning technique, was used to reveal that S. epidermidis biofilm-detached cells are released in five distinct phenotypes: small oblate, mid-sized oblate, large oblate, small spherical, and mid-sized prolate clusters. S. epidermidis biofilm detached cell clusters are predominantly oblate across three size groups (79.5%), with the small oblate phenotype representing 60.1% of cell clusters that have 3.1 {+/-} 1.2 cells per cluster, Euclidean diameters of 1.9 {+/-} 0.4 {micro}m, anisotropy indices of 0.98 {+/-} 0.05, and asphericities of -1.75 {+/-} 0.31 on average. The proportion of S. epidermidis cell clusters within each biofilm-detached cell phenotype differs between matrix-targeted disruptors. There are also variations in the abundance of S. epidermidis biofilm detached cells after matrix-targeted disruption between growth conditions and strains. Evaluating the physical properties of biofilm-detached cells after matrix-targeted disruption is critical to understanding their translocation in fluid flow and susceptibility to the host immune response as well as in evaluating matrix-targeted disruption for biofilm control.

Phylogenomic datasets assembled to resolve the Tree of Life now routinely span thousands of loci comprising millions of characters. Yet the persistence of incongruent topologies across such datasets reveals a fundamental truth of phylogenetics: not all data are equally informative. Here we derive analytical approaches that predict the relative impacts of phylogenetic signal, stochastic noise, and systematic bias on phylogenetic inference. We show that these three components exhibit divergent scaling properties with character sampling: signal and bias accumulate linearly, while noise accumulates nonlinearly with a concave trajectory. For some phylogenetic problems, substantial amounts of phylogenetic noise may eventually be overwhelmed by signal. For other phylogenetic problems--especially those involving deep divergences, short internodes, or constrained character-state space--the slope of signal accumulation can be so shallow that even signal from genome-scale data may never practically exceed noise. Moreover, linear accumulation of phylogenetic bias can in principle continuously overwhelm accumulation of signal at a lower slope with additional characters, regardless of dataset size. Applying our theory to empirical datasets, we show that anchored hybrid enrichment and ultraconserved element loci, like any loci, can exhibit signal that is overwhelmed by noise, and that character acquisition biases in some loci can further confound inference. Given the pervasive nature of incongruence in the phylogenomic era, our work provides a theoretical foundation for understanding the limits of inference, improving experimental design, and guiding efficient and accurate resolution of the Tree of Life.

A mild impairment of mitochondrial function activates the hypoxia inducible factor (HIF-1)-mediated hypoxia stress response pathway leading to a HIF-1-dependent increase in lifespan. Lifespan extension resulting from HIF-1 stabilization is dependent on activation of flavin-containing monooxygenase-2 (FMO-2). In this work, we explored the role of fmo-2 in the long lifespan of genetic mitochondrial mutants in C. elegans. We found that fmo-2, but not other fmo genes, are specifically upregulated in the long-lived mitochondrial mutants clk-1, isp-1 and nuo-6. Disruption of fmo-2 through RNA interference or genetic mutation shortens the lifespan of these mitochondrial mutants indicating that fmo-2 is required for lifespan extension in these worms. Moreover, signaling molecules that have been shown to be involved in upregulation of fmo-2 are also required for the long life of clk-1, isp-1 and nuo-6 mutants including HLH-30, NHR-49 and MDT-15. Finally, we examined the effect of multiple lifespan-promoting pathways in clk-1 mutants on the expression of fmo-2. We found that in all cases, genes required for clk-1 longevity are also required for the upregulation of fmo-2 in clk-1 worms. These genes included DAF-16, PMK-1, SKN-1, CEH-23, AAK-2, HIF-1 and ELT-2. Combined, this work advances our understanding of the molecular mechanisms contributing to longevity in the long-lived mitochondrial mutants and identifies FMO-2 as a common downstream effector of multiple pathways that modulate longevity.

Kinase family proteins constitute the second largest protein class targeted in drug development efforts, most prominently to treat cancer, but also several other diseases associated with kinase dysfunction. In this work we focus on type II kinase inhibitors which bind to the "classical" inactive conformation of the protein kinase catalytic domain where the DFG motif has a "DFG-out" orientation and the activation loop is folded. Many Tyrosine kinases (TKs) exhibit strong binding affinity with a wide spectrum of type II inhibitors while serine/threonine kinases (STKs) often bind more weakly. Recent work suggests this difference is largely due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. The binding affinity of a type II inhibitor to its kinase target can be decomposed into a sum of two contributions: (1) the free energy cost to reorganize the protein from the active to inactive state, and (2) the binding affinity of the type II inhibitor to the inactive kinase conformation. In previous work we used a Potts statistical energy potential based on sequence co-variation to thread sequences over ensembles of active and inactive kinase structures. The threading function was used to estimate the free energy cost to reorganize kinases from the active to classical inactive conformation, and we showed that this estimator is consistent with the results of molecular dynamics free energy simulations for a small set of STKs and TKs. In the current study, we analyze the results of a large-scale study of the binding affinities of 50 type II inhibitors to 348 kinases, of which the results for 16 of the 50 type II inhibitors were reported in an earlier study (the "Davis dataset"); the binding data for the remaining 34 type II inhibitors to the panel of 348 kinases were recently obtained (the "Schrodinger dataset"). We use the Potts statistical energy model to investigate the contribution of protein reorganization to the selectivity of the large kinase panel against the set of 50 type II inhibitors, and find that protein reorganization makes a significant contribution to the selectivity. The AUC of the receiver-operator characteristic curve is [~]0.8. We report the results of an internal "blind test", that shows how Potts threading energies can provide more accurate estimates of kinase selectivity than corresponding predictions using experimental results of small sample size. We discuss why two STK phylogenetic kinase families, STE and CMGC, appear to contain many outliers, and how to improve the ability to predict kinase selectivity with a more complete analysis of the kinase conformational landscape. We compare the performance of Potts threading for predicting binding properties of the large set of (50) Type II inhibitors to 348 kinases, with those of a sequence-based purely machine learning model, DeepDTAGen, a publicly available machine learning model that was trained on the complete Davis dataset, including both Type I and Type II kinase inhibitors. We observe that DeepDTAGen performs well on binding predictions for the 16 type II inhibitors in the Davis dataset, but performs poorly on binding predictions for the 34 type II inhibitors against 348 kinases in the Schrodinger dataset.

Trees accumulate somatic mutations throughout their long lifespan, resulting in genetic mosaicism among branches. While recent genomic studies quantified these mutations, they were largely limited to describing static patterns of variation. In this study, we developed a mathematical model to infer the dynamic processes of somatic mutation accumulation from snapshot genomic data obtained from four tropical trees (Dipterocarpaceae), which dominate tropical rain forests in Southeast Asia. Our model focus on genetic differences between shoot apical meristems (SAMs) at branch tips and explicitly incorporate stem cell dynamics within SAMs during shoot elongation and branching, enabling us to quantify somatic genetic drift arising from stem cell lineage replacement. By comparing model predictions with empirical data from Dipterocarpaceae trees, we estimated key parameters governing stem cell dynamics and somatic mutation rates. Our results indicate that both shoot elongation and branching involve replacement of stem cell lineages, leading to a moderate degree of somatic genetic drift. Accounting for stem cell dynamics resulted in slightly lower mutation rate estimates than previous approaches that ignored these processes. Using the estimated parameters, we further performed stochastic simulations to predict patterns of somatic mutations, including features not directly observed in the sampled trees, such as occasional deviations of somatic mutation phylogenies from physical architecture. Together, our modeling framework provides insights into how genetic mosaicism is shaped within tropical trees and reveals the stem cell dynamics underlying their long-term growth and accumulation of somatic mutations. (236 words) Highlights- We built mathematical models to predict the genetic differences between branch tips by somatic mutations. - The model considers the varying dynamics of stem cells in shoot meristem during shoot elongation and branching. - We compared the model prediction with empirical data from tropical trees, Dipterocarpaceae, and estimated the dynamics of stem cells and mutation rate. - Somatic mutation dynamics were shaped by somatic genetic drift arising from stem cell lineage replacement during shoot elongation and branching. - Accounting for stem cell dynamics led to slightly smaller estimates of mutation rates compared with previous estimates that ignored the dynamics. - Our models offer insights into how genetic variability is shaped in the tropical trees and the stem cell dynamics underlying their long-term growth.

A central goal of ecology is understanding how the architecture of food webs, which represent the structural backbone of ecosystems, affects their stability. The analysis of stability in the classical sense of population dynamics (i.e. return to equilibrium) can be successful for a single instance of an empirical food web but ignores the multiplicity of alternative states in which the system could be found as a result of intrinsic variability and fluctuations. Here we propose and test a new methodology to reconstruct, from single empirical observations of a food web, the viable ensemble of alternative realizations respecting the observed resource-consumer linkages and empirical ener-getics. The reconstruction can be handled analytically within a maximum-entropy framework which predicts how empirical food webs access a multitude of alternative states with comparable stability and reactivity. The (measurable) entropy of the reconstructed ensemble directly quantifies this multiplicity and serves as a novel proxy of system resilience, that is the rate of return to equilibrium in response to an external perturbation. We show that the associated ensemble fluctuations provide explicit predictions for the expected response of food webs to external perturbations, such as anthropogenic or climate-induced stresses. We do that by validating the proposed fluctuation-response relation on empirical soil food webs subjected to experimentally controlled perturbations, confirming that intrinsic fluctuations in the unperturbed state predict responses to subsequent stresses. The perturbed states are associated with higher entropy, indicating less likely spontaneous recovery.

Molecular mimicry between pathogen-derived and self-peptides shown by MHC molecules is one of the critical mechanisms in the pathophysiology of autoimmune diseases. Numerous studied has been conducted in this field to identify sequence similarity, but evaluating structural and dynamic similarity, systematic computational frameworks remain limited. Therefore, we created an automated multi-parameter molecular dynamics analysis workflow and used it to compare three peptides (KP1, KP2, and KP3) generated from Klebsiella pneumoniae bound to HLA-B class protein with one human self-peptide (Annexin-derived, ANX). We assessed six complementing parameters using one microsecond-scale MD simulation: radius of gyration (Rg), solvent-accessible surface area (SASA), hydrogen bonding dynamics, MM-GBSA binding free energy, root mean square fluctuation (RMSF), and root mean square deviation (RMSD) to understand time-dependent structural and dynamic behaviour of all the peptide-HLA-B complex. Additionally, hydrogen bond occupancy and molecular mechanics generalised Born surface area (MM-GBSA) binding free energy calculations were performed to provide a more comprehensive assessment of complex stability. Our analysis suggests that KP1 exhibits structural features consistent with molecular mimicry, maintaining conformational stability, surface exposure, and interaction patterns comparable to ANX. In contrast, KP2 showed reduced stability, characterised by higher RMSD values and substantial hydrogen bond loss, whereas KP3 displayed intermediate behaviour, with relatively favourable energetics but noticeable conformational variability. Overall, the multi-parameter framework enabled differentiation among the candidate peptides based on combined structural, dynamic, and energetic properties. The workflow can be adapted for the analysis of larger peptide datasets and may provide a systematic approach for investigating potential autoimmune-relevant molecular mimics in microbial proteomes, with required adjustments according to the system.

Haar-like wavelets sparsify the phylogenetic covariance matrices of large, uniformly random k-regular trees with overwhelmingly high probability. This motivates the Haar-like distance, a {beta}-diversity metric that implicitly ranks the splits of a reference phylogeny by their relevance in differentiating two microbial environments, offering an interpretation as to why the environments differ compositionally. Nevertheless, uniform binary trees exhibit statistical features distinct from those of the trees used by practitioners, leaving the extent of sparsification and the practical validity of the implied Haar-like distance speculative. To address this, our manuscript examines the sparsification of phylogenetic covariance matrices of large critical beta-splitting random trees, a model introduced to better reflect real-world phylogenies. By obtaining sharp asymptotic estimates of the first and second moments of the external path length in this ensemble, we demonstrate that the Haar-like basis also pseudo-diagonalizes the phylogenetic covariance matrix of most large trees in this more realistic framework. Additionally, we devise a test to assess the statistical significance of splits in the reference phylogeny identified by the Haar-like distance. We apply the test to a well-studied microbial mat to further substantiate the presumption that the identified splits represent genuine biological signals differentiating the top and bottom layers of the mat.

Chloroplast genomes are invaluable resources for plant genomic research, providing insights into genome evolution and molecular adaptation. With the growing scientific and economic interest in Adansonia digitata, a comprehensive characterization of its chloroplast is timely and necessary. A complete chloroplast genome of A. digitata was assembled, annotated, and characterized. Comparative structural analysis was conducted against other Adansonia species, and the assembly was validated through phylogenetic placement within Malvaceae. The assembled genome exhibits the canonical quadripartite organization, spanning 160,061 bp with a GC content of 36.88%, 79 protein-coding genes, 32 tRNAs, and 4 rRNAs. Repeat analysis identified 100 simple sequence repeat motifs, predominantly A/T-rich mononucleotide types (76%), alongside 50 long sequence repeats dominated by forward (26) and palindromic (17) repeats. Comparative analysis with other Adansonia species revealed conserved genome structure, with minor IR boundary shifts involving the ndhF gene, and ycf1 duplication in A. gregorii and A. grandidieri. Average nucleotide identity exceeded 99% across all Adansonia species, with near-complete similarity (ANI {approx} 99.96%) observed with the putative A. kilima. All predicted RNA editing events were nonsynonymous, dominated by C[->]U conversions (55.02%). Codon usage showed non-random synonymous preferences biased toward A/U-ending codons, driven primarily by mutational pressure with detectable gene-specific translational selection. Nucleotide diversity ({pi}) was higher in intergenic spacers (0.00490 {+/-} 0.00574) than in coding regions (0.00167 {+/-} 0.00199), with the majority of genomic regions showing no sequence variation ({pi} = 0). Substitution patterns indicated pervasive purifying selection, with relatively high but insignificant signals in matK, ycf1, accD, and rpoB. Phylogenomic analyses placed the assembled A. digitata chloroplast genome within the Adansonia lineage, consistent with its established systematic position. This study provides detailed insight into the chloroplast genome of A. digitata, and the findings will contribute towards advancing its genomic research.

AO_SCPLOWBSTRACTC_SCPLOWProtein filaments play fundamental functions in the cell, ranging from scaffolding like in the cytoskeleton to sensing and transmitting forces and torques. Here we address the case of the nucleoprotein filaments (NPFs) of homologous recombination (HR) formed by the polymerization of the RecA protein on DNA. In contrast to the cytoskeleton filaments, the HR filaments are not known to exert or sense forces. However the stress in the stretched and unwound DNA bound to those filaments was shown to play a role in promoting DNA strand exchange during the early stage of the HR mechanism. Here we use molecular dynamics simulations to examine whether the strain in the nucleoprotein filament upon strand exchange progression and D-loop formation may influence subsequent steps of the HR process. Our results indicate that the filament mechanical properties are sensitive to the length of DNA incorporated in the D-loop. The response we observe upon increasing the D-loop length is first elastic, up to a threshold that we estimate to be 27 incorporated base pairs, after which the NPF enters a plastic stage where the protein-DNA connectivities are reorganized. Notably, the DNA displaced strand locally switches from site II to site III, a newly characterized binding site. We discuss the possible consequence of this mechanical response of the NPFs for the course of the HR process.

Ecological theory predicts that local species richness can influence biogeographic rates of speciation, extinction, and dispersal. For instance, increasing the number of competing species within a region may cause local speciation and dispersal rates to decrease but local extinction rates to increase, inducing a carrying capacity for local species richness. In this article, we introduce a fully generative, event-based phylogenetic diversification model, called DDGeoSSE, that allows diversity-dependent effects of local species richness to modulate biogeographic rates of diversification and range evolution. DDGeoSSE can accommodate and test a variety of alternative diversification scenarios that involve positive, negative, and neutral interactions among sympatric species for speciation, extinction, and dispersal. We derive mathematical and statistical properties of biogeographic outcomes generated by this model, such as the carrying capacity for a clade at equilibrium, which we validate through simulation. Because diversity-dependent phylogenetic models typically do not have tractable likelihood functions, we use deep learning with phyddle to perform parameter inference and model selection. Separately applying DDGeoSSE to Caribbean Anolis lizards and cloud forest-dwelling Viburnum plants, we find evidence that local species richness plays a significant role in shaping diversification dynamics for both clades.

The early stages of viral infection constitute a race between viral proliferation and interferon (IFN)-mediated defenses. Recent experiments on single-cell viral kinetics have demonstrated a high degree of stochasticity in the timing of viral release, but how this shapes the competition between virus and host remains unclear. We formulate a stochastic spatial model to address the question of how variability in the release of viral progeny and IFN affect the early infection dynamics. The model distinguishes between two types of timing noise: stochasticity in the initiation of release, and variability in the secretion time of individual virions. Our key result is an asymmetry in how noise affects outcomes: For the virus, stochastic initiation accelerates expansion, while for the host, effective containment via IFN benefits from precisely timed responses. For the secreting states, we find that a broader secretion profile (higher variability in particle release times) is always advantageous. In all cases, we find that stochasticity in signal timing plays a huge/central role in the early infections states.