Journal of Biosciences

Lymphocyte-extracellular matrix (ECM) interactions occur intermittently throughout the lymphocytes life cycle. Alterations in blood insulin levels following feeding modulates naive lymphocyte trafficking and adhesion to fibronectin via a pathway involving insulin-like growth factor-1 receptor (IGF-1R), phospholipase C gamma 1 (PLC-{gamma}1) and {beta}2 integrin activation. Lymphocytes exert traction forces, on the ECM during the process of extravasation. While these forces are essential for several homeostatic processes, the role of insulin in modulating lymphocyte-derived traction forces upon ECM adhesion is unknown. The aim of the current study was to investigate the effect of insulin on the traction generated by lymphocytes when adhered onto a fibronectin-coated substrate. Jurkat T-cells were placed on a fibronectin layer (50{micro}g/ml, 100{micro}m thickness) coated on polyacrylamide gels of stiffness 400Pa with red fluorescence beads as fiduciary markers. The cellular force generated by Jurkat T-cells was mapped using traction force microscopy. To elucidate the role of PLC-{gamma}1 in cellular force generation, the traction of Jurkat T-cells lacking PLC-{gamma}1, as well as those of a knockout cell where PLC-{gamma}1 was restored were quantified and compared with wild-type Jurkat T-cells. Lack of PLC-{gamma}1 attenuated adhesion when compared to wild-type Jurkat T-cells. Additionally, the traction force generated by each cell type decreased with increasing concentration of extracellular calcium. Treatment of adherent Jurkat T-cells with insulin increased traction in lower extracellular calcium condition while a dip was observed when a high extracellular calcium was present, in comparison to the untreated cells. However, the effect of insulin treatment was lost in the case of Jurkat T-cells lacking PLC-{gamma}1. Together these results indicate that insulin regulates traction force generated by adherent Jurkat T-cells via a process involving PLC-{gamma}1, in a calcium dependent manner.

Predicting protein-ligand interactions in the modern drug discovery has revolved from the involvement of artificial intelligence and structural bioinformatics using Graph Neural Networks (GNNs). The limited explainability of GNN models presents an important encumbrance in biomedical research, but it has achieved a high degree of accuracy in determining and identifying binding affinity and active compounds, as evidenced by [1] [2] [3] [4]. Here this research focuses on the interpretation of protein-ligand interactions at a molecular level, a rapidly developing area within Graph Neural Networks (GNNs). Now days modern study handling techniques such as visualization techniques, attention mechanism and model-based feature ascription by model to boost, and make robust and decrease false predictions on binding. Along with some approaches include like graph pooling strategies, message-passing optimization, self-supervised learning, transfer learning and contrastive learning are rapidly utilized to enhance the representative learnings. Furthermore, integration of molecular docking simulations, hybrid deep learning architectures and protein language model gives more reliable & biological predictions of protein-ligand interactions. That focuses on given process that identifies key ligand atoms and binding residues, as well as physicochemical factors influencing affinity, through chemical thought processes. Here this research work identified the challenges of developing biologically significant explanations, transparency, and the corollary dataset biases on interpretability. The research work conducted an in-depth investigation into the consolidation of protein language models to establish more reliable pathways for future research, examining hybrid architectures, transparent and energy-efficient GNNs, and scientifically grounded AI models for drug discovery. My research work highlights that XGNNs establishes a connection between Deep Learning and Biochemical expertise with increased confidence, which will enhance the accuracy of predictive models and computational models.

In tissue engineering, it is important to conceive and construct artificial bio-mimetic scaffolds able to foster cell migration as this is a fundamental process in wound healing and tissue regeneration. In order to do that, cubically symmetric and triply periodic porous structures have been identified as promising candidates for instance for the reconstruction of artificial cartilages and bones, also due to their tunable mechanical characteristics and highly inter-connected porous architectures that mimic the trabecular bone hyperboloidal topography. We propose here a mathematical approach that might be helpful to identify what are the best geometrical characteristics of such scaffolds, in order to promote cell migration into the porous structures and speed-up their re-population. The method is based on the observation that cell nucleus deformations should be avoided, yet assuring a good possibility for the cell to reach the wall of the porous structure. Mathematically speaking, this leads to the problem of identifying the size of the largest sphere that can pass, without being stuck, through the pores of the bio-mimetic scaffold.

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.

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.

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.

Environmental change has caused dramatic global declines in biodiversity, with some species showing abrupt and often irreversible changes in population abundance. These regime shifts can occur when environmental thresholds, known as tipping points, are passed. Many species can respond to environmental change via phenotypic plasticity with the expectation that strong phenotypic plasticity reduces the risk of regime shifts by enabling a species to rapidly respond to environmental change, potentially mitigating the risks of population collapse. Testing the theory that phenotypic plasticity buffers against regime shifts requires a novel whole population approach that robustly considers the feedback mechanisms between environment, phenotype and population density, common to the life-history of many species. For this purpose we develop a tractable mathematical framework, and demonstrate, counter-intuitively, that phenotypic plasticity can induce tipping points, due to the inclusion of feedback mechanisms that operate at both the level of the organism and population. Consequently, predicting the existence of potentially devastating tipping points and so understanding ecosystem collapse is more nuanced than current thinking suggests.

Behcets disease (BD) is a systemic inflammatory disease. It is considered as an autoinflammatory disease triggered by innate immunity rather than adaptive immunity. Human leukocyte antigen-B51 (HLA-B51) is the strongest genetic factor associated with BD. This study investigated how HLA class 1 molecules interact with innate immune cells and induce cytokine secretion. For this purpose, 293T cells transfected with a plasmid encoding HLA-B51 were cultured with natural killer (NK) cells obtained from healthy human donors. Within 24 h, the concentrations of interleukin-4 (IL-4), IL-8, and interferon-{gamma} (IFN-{gamma}) in the medium increased, indicating that NK cells secreted cytokines without undergoing cellular expansion for cytolysis. NK cells stimulated by nonself HLA-B51 produced IFN-{gamma} levels comparable to those produced by NK cells stimulated by self HLA-B51. NK cells carrying HLA-B51 were accurately recognized by overexpressing HLA-B51 on 293T cells. Moreover, ample intracellular IFN-{gamma} levels were detected in NK cells after stimulation with phorbol 12-myristate-13-acetate (PMA) plus ionomycin. KLRK1 (CD314)-positive cells mainly primarily accounted for IFN-{gamma}-producing cells, whereas KLRK1-negative cells did not. In contrast, both NCR1 (CD335)-positive and -negative cells contributed to IFN-{gamma} production. We next investigated whether HLA-B51 on the surface of 293T cells stimulates KLRK1 as a ligand causing IFN-{gamma} secretion. In masking experiments using anti-KLRK1 antibodies, NK cells with high levels of cell surface KLRK1 decreased the production of IFN-{gamma}. Conversely, human NK cell line KHYG1 cells also produced IFN-{gamma} in culture with 293T cells, but did not increase IFN-{gamma} through HLA-B51 stimulation. The mRNA expression of the signal adaptor protein HCST (DAP10) in KHYG1 cells was lower than that in NK cells, whereas the relative expression of IL-2RA in KHYG1 cells was higher than that in NK cells. These findings suggest that HLA-B51 can interact with KLRK1 on the NK cells inducing IFN-{gamma} secretion, whereas IL-2 signals outweigh HLA-51 stimulation in KHYG1 cells.

The ATP molecule is the universal energy currency across all living organisms. There are two fundamental pathways of ATP synthesis: substrate-level and oxidative phosphorylation. While substrate-level phosphorylation generates ATP directly, in oxidative phosphorylation, proton motive force (PMF) is required to power ATP synthesis via the F1Fo ATP synthase. Using Escherichia coli, we show that due to simultaneous use of both pathways, the strength of coupling between ATP and PMF strongly depends on growth conditions: coupling is weak when requirements for independent generation of ATP and PMF are met, and becomes essential when not. We determine the conditions, under which PMF-ATP coupling becomes essential and show that PMF is required for bacterial growth irrespective of its ATP synthesis function. We propose that the main role of F1Fo in Escherichia coli, contrary to the canonical view, is not to generate ATP but to provide an auxiliary pathway that allows both, ATP and PMF, to be produced.

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.

We investigate the target search process by proteins locating specific target sites along DNA - a phenomenon fundamental to biological functions such as gene regulation, transcription, replication, recombination, and gene-editing technologies. This process proceeds through a repetitive sequence of stochastic motions: consisting of one-dimensional (1D) sliding along the DNA contour interspersed with detachment and three-dimensional (3D) excursions in the bulk, and then reattachment to a random location on DNA. Recognizing this sequence of random events as analogous to the resetting processes widely studied in statistical physics, we employ a first-passage-renewal framework and derive general expressions for both the mean and fluctuations of the total search time. Our results are completely generic and do not depend on the detailed microscopic dynamics of either the 1D or 3D phases. Quite interestingly, we find that intermittent detachment can not only accelerate the mean search but can also regulate fluctuations around it. Our analysis reveals a universal fluctuation inequality that links the variability and mean of the sliding time to the mean excursion time, thereby identifying the fundamental conditions under which target search process becomes efficient. Notably, we find that broad distributions of sliding times emerge as a universal characteristic for optimal search efficiency--a feature emanating from the slow dynamics along the DNA. Using the facilitated diffusion mechanism as a representative example, we validate the generality of our results. These findings provide a unified theoretical framework connecting stochastic search, resetting dynamics, and biological efficiency, while also highlighting the crucial role of DNA structure such as its contour length in modulating search performance.

Cooperators that pay a cost to provide benefits to others are vulnerable to exploitation by defectors that reap the benefits while avoiding the costs. Thus selection on the individual level can lead to loss of cooperators while lowering overall population fitness. Accordingly, the maintenance of cooperation is a key problem in evolutionary biology. The puzzle of cooperation extends to viruses: cooperative viruses produce gene products that can be shared, while defector viruses produce less and instead use products made by cooperators. In coinfection, defectors are always advantaged, predicting the loss of cooperation. However, the fitness of cooperator and defector phenotypes is context dependent. Though defectors are advantaged in coinfection, they suffer reduced replication in single infections. Environmental feedback is the process by which changes in population composition alter viral densities and rates of coinfection, which in turn feed back to affect the fitness of each type. We show that environmental feedback maintains cooperation in viruses. We also find that defector emergence may interfere with phage therapy by disrupting the phage dynamics that cause host extinction, and demonstrate that the introduction of defectors lowers viral densities and drives viral extinction, suggesting that defectors that replicate alone could function as antiviral therapies.

Whole genome duplication is a common mutation in eukaryotes with far-reaching phenotypic effects. The resulting morphological, physiological, and fitness consequences and how they affect the survival probability of newly polyploid lineages are intensively studied, but very little is known about the effect of genome doubling on the short-term evolvability of populations. Understanding the effect of polyploidization on the adaptive potential of populations is of crucial importance to predict the future of polyploid populations. In this paper, I investigate the immediate consequences of genome doubling on the genetic variance of populations. To do so, I performed numerical iterations and simulations of how the genetic variance of a quantitative trait changes after polyploidization, under different genetic architectures (additivity, dominance, and epistasis). I found that genetic variance generally decreases after genome doubling. Non-additive gene actions can make autotetraploid populations genetically more diverse than their diploid progenitors in rare cases, notably with overdominance and directional epistasis. By collecting estimates from the agronomic literature, I found that both dominance and epistatic variance contribute to the genetic variance of polyploid populations. These results bring new insights into the adaptive potential of newly formed tetraploid populations, and call for further experimental investigations of how polyploidization is associated with a short-term decrease in evolvability.

Global epistasis refers to the observation that the effect of a mutation or modification depends on the state of a biological system, not its detailed composition. Such patterns have been reported across biological scales, from proteins to organisms and ecosystems. In its simplest form, global epistasis appears as a linear relationship between the change in function or fitness due to a perturbation, and the background level of function or fitness. The mechanistic basis of global epistasis, particularly in ecological systems, remains unresolved. Here, we propose that in microbial communities, global epistasis describing the impact of adding a species to a community on function arises generically from constraints imposed by shared resource pools. We illustrate this mechanism in a single-species system growing on multiple substitutable resources, where global epistasis follows directly from nutrient limitation by an essential non-substitutable resource. We then extend this framework to multi-species communities competing for a single resource and show that the marginal effect of adding a species depends linearly on background community function, with a slope determined by the fraction of the resource claimed by the added species. We show that global epistasis persists in trophic cascades, but that facilitation and niche partitioning qualitatively break the linear dependence. This study provides a simple explanation for the appearance of global epistasis in ecosystems, and suggests that global epistasis should be a null expectation in ecosystems governed by competition. Our results propose that coupling between perturbations and shared resource pools might also help explain global epistasis at the organismal level.

Upon infecting a bacterium, temperate phages must decide between killing the cell to reproduce (lysis) or entering a symbiotic lifestyle (lysogeny). This choice is often informed by the cells state, as well as the number of infecting phage particles (MOI). Since phage gene copy numbers scale identically with MOI, an MOI-dependent decision requires a fast-acting asymmetry between the lytic and lysogenic pathways. We introduce a minimal model suggesting that only a handful of coupling mechanisms can produce such an asymmetry; for instance via a host protease, kinase, or RNase acting on one pathway. By distilling complex regulatory networks to their essential components, our model clarifies the logic of lysis-lysogeny decision mechanisms across phage species.

Collective foraging in animal groups often relies on behavioral diversity, with individuals adopting different, sometimes complementary roles during shared tasks. However, most theoretical models predict that individuals responding to similar information cues in a shared environment should converge on a single optimal behavioral strategy. Using a spatially explicit multi-agent deep reinforcement learning model embedded in a three-species food chain, we show that stable behavioral diversity can emerge spontaneously among initially naive agents. Rather than converging on a single optimum, agents differentiate along a low-dimensional manifold of sensorimotor control, reflecting tradeoffs in speed regulation, spatial exploration, and deterministic turning rules. While multiple strategies yield comparable individual energetic returns, they are not interchangeable; group performance depends on how specific strategies combine to produce spatial resource partitioning and distributed directional influence. Replacing co-learned individuals with similarly competent agents trained in other groups disrupts these interaction structures and strongly reduces total energy acquisition. These results demonstrate that coordinated collective behavior and diverse, compatible strategies can arise endogenously from shared learning histories, but that this form of collective performance is path dependent and may be fragile to changes in group composition.

Cross-scale integration remains a persistent challenge in ecology. Mechanistic network models have advanced this integration by linking individual behavior to community dynamics. Their complexity, however, often limits exploration to numerical simulations, which tend to be insufficient for fully unveiling the fundamental rules governing system behavior. Extracting these rules requires moving beyond numerical observation to establish exact, analytical constraints. Here, a complete mathematical analysis of a mechanistically detailed plant-pollinator model is presented. This cross-scale analysis decouples transient and equilibrium dynamics, proving that pollination strictly gates plant persistence while recruitment competition caps equilibrium abundance. The precise behavioral mechanisms scaling up to determine network stability are determined: nestedness stabilizes communities by generating floral reward gradients that guide adaptive foraging, whereas connectance destabilizes by eroding these rescue pathways. Additionally, native community persistence and biological invasions are conceptually unified; a single, multi-scale reward threshold (R*) is shown to govern both native survival and alien establishment. These analytical derivations are distilled into conceptual frameworks and visual summaries accessible for empiricists interested in theory and conceptual unification. By translating numerical observations into rigorous, trait-grounded proofs, this analysis demonstrates that complex, cross-scale networks are tractable, revealing the precise conditions under which communities assemble, persist, and collapse.

A key driver of bacterial infection treatment failure and relapse is the persistence of non-replicating bacterial subpopulations that emerge under stressors like nutrient starvation and immune pressure. These dormant cells evade antibiotics, fuelling recurrence and resistance. Bacteriophage therapy is a promising alternative, but its efficacy against non-replicating bacteria is poorly understood. Improving our understanding of bacteria-phage interactions under non-replicating conditions could greatly enhance phage therapeutic outcomes in clinics. By utilising various bacterial (Mycobacterium smegmatis, Mycobacterium tuberculosis, and Pseudomonas aeruginosa) and phage species, this study quantitatively demonstrates that lytic phages can infect non-replicating bacteria (under nutrient starvation, acidic pH or antibiotic pressure), persisting in a state of pseudolysogeny and resuming lysis upon bacterial regrowth. We find that the pseudolysogeny window is phage- and host-dependent, with degradation of extrachromosomal phage DNA leading to loss of pseudolysogeny. We find that Pseudomonas CRISPR defence plays a crucial role in phage DNA degradation even under non-replicating conditions, underscoring the need for its consideration in phage therapy design. We also demonstrated the in vivo relevance of pseudolysogeny and CRISPR-resistant bacteriophages in eliminating implant-associated and antibiotic-persistent Pseudomonas infections in mice. These findings highlight the need to consider phage-host dynamics and bacterial defences when designing phage-based strategies to target non-replicating bacteria and persistent infections.

Microbial communities rarely remain in a fixed physiological state. Instead, they progress through internal life cycles in which changing metabolites, spatial organization, and physiological states reshape ecological interactions over time. Despite extensive theory on coexistence with fixed interactions, we lack simple quantitative predictors of robustness for communities undergoing repeated growth and dispersal cycles. Here we show that a single quantity, the temporal co-growth of community members, predicts robustness across several models of community maturation, including chemotactic spatial patterning, cross-feeding with toxicity, and a phenomenological many-species model with prescribed growth trajectories. Communities in which different species grow at distinct times persist far longer under stochastic reseeding than communities with overlapping growth, with average community lifetime increasing approximately exponentially as co-growth decreases. Across the systems studied here, diverse mechanisms such as spatial organization, metabolic cascades, and physiological programs promote robustness insofar as they reduce the temporal overlap of rapid growth across species. These results identify co-growth as a common quantitative feature of robust dynamically maturing communities and suggest that minimizing co-growth may provide a broader organizing principle for ecological robustness.

Birds provide a unique model for ageing research, as they exhibit higher mass-adjusted metabolic rates and blood glucose levels than other vertebrate groups, yet demonstrate greater longevity and slower senescence compared to mammals of similar body size. This challenges the "pace of life syndrome" hypothesis, which predicts that high metabolic rates and elevated glucose should correlate with shorter lifespans. While the effects of glucose, glycation, and advanced glycation end-products (AGEs) on ageing are well-documented in humans and the conventional models used in biomedical research, their impact on avian physiology and ageing remains poorly understood. Some evidence suggests that birds possess adaptations mitigating the potential detrimental effects of glucose levels, which are much higher than those of all other vertebrate groups. However, previous studies indicate that elevated glucose predicts reduced lifespan, and protein glycation--varying with age--can influence survival and some fitness-related traits. This implies that glycation or AGE accumulation may have relevant effects on avian longevity. In this study, we experimentally investigated how one year of dietary supplementation with glucose or methylglyoxal affects survival and ageing markers (metabolic rate, flying performance, and beak coloration) in captive zebra finches (Taeniopygia guttata). Our results reveal a significant increase in mortality exclusively in glucose-supplemented birds. Although glucose treatment elevated albumin glycation rate and AGE formation--the latter also observed with methylglyoxal supplementation--these variables did not directly explain the increased mortality in glucose-treated birds, which was absent in methylglyoxal-treated individuals despite similar AGE accumulation. Additionally, we observed some effects on the assessed senescence markers, with an age-related constraint on seasonal metabolic adjustment, and a treatment-influenced age decline in secondary sexual traits expression. These findings support the use of these markers as proxies for senescence in zebra finches. We also discuss alternative mechanisms, independent of the glycation cascade, which may contribute to mortality. A seasonal decline in flight performance, particularly during peak mortality periods, suggests a broader deterioration of health. Thus, although we demonstrate glucose supplementation to be more deleterious than methylglyoxal, the underlying mechanisms for the observed increase in mortality induced by the treatment remain unresolved.