Journal of Biosciences

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.

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.

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.

The phenology of organisms worldwide is shifting in response to changes in environmental conditions. There is growing concern that resulting timing mismatches among interacting species will negatively impact system-level properties, yet there is no general framework for evaluating community responses to changes in phenology. To address this gap, we developed a mathematical framework based on local stability analysis and used it to assess the resilience implications of phenological perturbations with a multi-year, highly time-resolved empirical dataset on subalpine plant-pollinator communities. The forecasted effects of phenological perturbations were largely independent of perturbations to species densities, indicating the potential for even small changes in phenology to disrupt the functioning of ecosystems that are otherwise highly stable.

Many eukaryotic cells produce attractant molecules to which they themselves are also attracted. For example, neutrophils produce leukotriene B4 while swarming. These autoattractants create a secondary signalling layer that can coordinate collective cell behaviour during chemotaxis. Here we use a hybrid agent-based computational model to examine how immune cells migrating along a self-generated gradient may communicate with each other using autoattractants. We find that autoattractant signals strongly enhance cells responses to primary attractant. Efficient removal of autoattractants is also crucial, through depletion by cells, chemical instability, or enzymatic breakdown. Consequently, autoattractants have a lifetime, determined by a balance between production and removal rates. We find that optimal lifetimes exist, and that these are determined by cell speed and attractant diffusion, but are remarkably independent of cell density and primary attractant concentration. We further show that autoattractants whose removal is governed by inherent instability rather than breakdown by cells coordinate migration less efficiently, but work more robustly across different environments. Finally, we find that autoattractant signalling without direct breakdown by the cells involved establishes a characteristic optimal cell-cell distance: too little communication leaves cells uncoordinated, while excessive communication causes cells to aggregate into slow-moving clumps. Strikingly, the conditions that produce optimal chemotaxis lie very close to those that trigger aggregation, suggesting that many autoattractant systems operate near a critical boundary.

The architecture of the genotype-phenotype-fitness map (GPFM) is a key determinant of evolutionary dynamics. One salient feature of biological GPFMs is variational modularity, where each mutation affects only a small subset of functional traits. Variational modularity may constrain the dynamics of trait evolution, but these constraints are not well understood. Here, we use several extensions of the Fishers geometric model with two functional traits to investigate these constrains. We find that on GPFMs with universal pleiotropy, populations evolve along the fitness gradient, which implies that the trait under stronger selection is optimized exponentially faster than the trait under weaker selection. In contrast, on modular GPFMs, populations approach a quasi-steady state that we term a "module-selection balance" where both traits improve at the same rate and their ratio remains constant. We demonstrate that the existence of a module-selection balance is robust with respect to the details of evolutionary dynamics and GPFMs themselves, as long as they are variationally modular. Our theory predicts that variationally modular organisms should exhibit stereotypical bi-phasic dynamics of genome evolution, especially in the strong clonal interference regime, and we find support for this prediction in metagenomic data from Lenskis long-term evolution experiment in bacterium Escherichia coli. We propose that module-selection balance is an inherent feature of variationally modular GPFMs, which imposes an important constraint on long-term trait evolution.

Predicting the composition and dynamics of ecological communities is challenging because complexity increases rapidly with species richness. A common strategy is to adopt a reductionist framework in which community dynamics are inferred from simpler components, such as population-level parameters or organismal traits. However, it remains unclear at which level of biological organization ecological predictability emerges. Here we experimentally test this reductionist cascade in marine phytoplankton communities. We first ask whether multispecies dynamics can be quantitatively predicted from demographic parameters measured in monocultures and species pairs. We then test whether these predictive parameters can themselves be inferred from organismal traits, focusing on cell size. We find that community composition is highly reproducible and can be accurately predicted from population-level parameters measured in simpler experimental settings. In contrast, these parameters do not show systematic relationships with cell size and cannot be predicted from this commonly used trait. These results demonstrate that ecological predictability emerges at the population level, where demographic parameters capture the combined effects of underlying biological processes, but resist further reduction to simple trait-based descriptions, suggesting that ecological interactions reshape organismal performance across levels of organisation.

While the regulation of bacterial cell size is widely studied across generations, the stochastic nature of cell volume growth remains elusive within a cell cycle. Here, we investigate the fluctuations of cell volume growth and report a deviation from standard white-noise models: the random growth rate exhibits subdiffusive dynamics. Specifically, the mean square displacement of the growth-rate noise scales as {Delta}t with an anomalous exponent {approx} 0.27. This low exponent implies strong negative temporal correlations in growth rate noise on timescales of minutes, which are significantly faster than those of gene expression dynamics. We attribute this phenomenon to the physical mechanics of the cell wall. By modeling the peptidoglycan network as a complex viscoelastic material with power-law-distributed relaxation times, we successfully recapitulate the observed subdiffusive behavior. Our results suggest that the heterogeneous mechanical constraints of the peptidoglycan network, rather than biological regulatory programs,govern the short-timescale fluctuations of bacterial growth.

Asymmetric self-organization is a hallmark of cell polarity, yet the diversity of observed polarization patterns is frequently attributed to specialized, complex biochemical mechanisms motifs beyond simple positive feedback. Here, we demonstrate that spatial heterogeneity alone fundamentally reshapes polarization dynamics within minimal stochastic reaction-diffusion processes. We show that weak differences in reaction rates between distinct spatial domains strongly bias polarization timing and determine which region ultimately polarizes. In systems containing two distant favored regions, a "stochastic winner-takes-all" mechanism--driven by long-range competition mediated by a shared cytoplasmic pool--induces stochastic switching that manifests as pole-to-pole oscillations. By relaxing the assumption of a perfectly mixed cytoplasm and incorporating finite cytoplasmic diffusion, we reveal a qualitative shift in this competitive dynamic. Specifically, as the total particle abundance increases, the system transitions from monopolar to bipolar activation, capturing the essence of the New-End Take-Off (NETO) phenomenon during cell growth and provides a physical basis for pole coexistence. These results demonstrate that spatial heterogeneity alone can strongly influence polarization dynamics in minimal models, highlighting the potential importance of quenched spatial variability in biological reaction-diffusion systems. Author summaryCells often need to choose a specific site for growth, division, or shape change. This process, known as cell polarization, is a fundamental organizing principle in biology. The wide variety of polarization patterns seen in living cells is often explained by proposing complex biochemical mechanisms beyond basic positive feedback among signaling molecules. In this work, we asked whether some of this diversity could instead arise from a simpler source: fixed spatial differences within the cell. Using minimal stochastic reaction-diffusion models, we found that even small local differences can strongly influence where polarization appears and how quickly it develops. When two favored sites are present, they can compete for a shared pool of molecules in cytoplasm, so that one site dominates at a time and the polarized state can switch stochastically between them. We also found that this competition changes when the shared molecular pool does not mix instantly: under these conditions, two polarized sites can start to coexist. This behavior offers a simple physical explanation for phenomena such as the appearance of a new growth site during cell development. Our results show that spatial heterogeneity alone can generate behaviors that might otherwise seem to require much more complicated biochemical mechanisms.

This study presents a multidisciplinary approach to evaluate the structure formation and digestion of lupin protein crosslinked with transglutaminase (TG). TG was applied at 0-10 U/g protein, and structural development was assessed by oscillatory rheology (G, G"), while SDS-PAGE and o-phthaldialdehyde (OPA) assays were used to evaluate protein participation and the reduction of free {varepsilon}-amino groups, respectively. Proteomics was further employed to characterise molecular features associated with crosslinking behaviour. Lupin protein showed a clear dose-dependent increase in gel strength during incubation, with G values reaching 214 {+/-} 43.9 Pa at 10 U/g TG, compared to 7.2 {+/-} 0.6 Pa in the untreated control. Across all conditions, G remained higher than G" throughout frequency sweeps, and low tan {delta} values confirmed the formation of elastic networks driven by covalent crosslinks. SDS-PAGE and OPA results consistently demonstrated efficient crosslink formation, which increased with both incubation time and TG dosage, with SDS-PAGE indicating involvement of specific protein fractions. Proteomic analysis revealed disordered structural domains in the protein are preferred regions to form crosslinks. Furthermore, TG treatment was found to slow the digestibility of the crosslinked lupin protein. Overall, this work demonstrates how integrating proteomic insights with functional measurements can guide the selection and optimisation of plant proteins for enzymatic structuring. The approach offers a rational pathway to enhance the functionality of alternative protein sources such as lupin, supporting the development of sustainable food systems, including applications in meat and dairy analogues.

Recent progress in mathematical kinship modelling has allowed one to predict the probable numbers of kin for a typical population member. In the models, kin may be structured by age and sex, both in static or time-variant demographies. Knowing the probable numbers of kin in different stages - such as parity, health status, or geographic location - however, remains an open challenge in Kinship Demography. Knowing how population structure delimits kin to distinct stages is an advance - for instance, the probability of having one sister at home and one sister away has different social implications from the probability of having two sisters. We present a novel analytical framework, grounded in branching process theory, that provides kin-number distributions jointly structured by age and stage. Using recursive compositions of probability generating functions (PGFs), we derive the joint age, stage, and age x stage kin-number distributions. All marginal distributions over either dimension naturally emerge. Simple extensions of the PGF approach additionally yield: the joint distribution of an individuals own stage and their kins stage; the probable numbers of kin deaths, both in total and by generation number; and the probabilities of being kinless and/or orphaned. We demonstrate the framework through novel results in an application using UK parity-specific fertility and mortality data. HighlightsO_LIA new method calculates probability generating functions for the number of kin structured by age and stage C_LIO_LIThe model allows predicting the probable numbers of kin organised by age and stage C_LIO_LIRecursive nesting of probability generating functions in branching processes is used C_LIO_LIAn application is presented highlighting the novel results C_LI

The survival of eukaryotes during starvation depends on effective nutrient recycling via autophagy. Accordingly, loss of autophagy-related (ATG) proteins, including the nutrient-sensing ATG1 kinase complex, typically results in reduced fitness or lethality under nutrient limitation. The green alga Chlamydomonas reinhardtii provides a tractable model for autophagy studies, primarily because its ATG repertoire is encoded by single-copy genes. We generated a full panel of ATG deletion mutants and examined their growth and autophagy during starvation. Surprisingly, starvation-induced autophagy occurred independently of the ATG1 complex components (ATG1, ATG11, ATG13, and ATG101), challenging the canonical ATG1-dependent model and suggesting an alternative pathway.

Toll-like receptors (TLRs) are vital components of the innate immune system, recognizing both exogenous pathogens signals (PAMPs) and internal stress signals (DAMPs). TLR2 is unique among the human (Homo sapiens) TLR family members, as it contains a large cavity for binding hydrophobic ligands, such as lipoteichoic acid (LTA) and di/triacyl lipopeptides (Pam2/3CSK4). This study analyzed the structural phylogeny of cavity presence in the TLR2 lineage in vertebrates (vTLR) enabled by AI protein structure predictions and explored the potential convergent evolution of similar features in invertebrates (iTLRs). Analysis of AI models of TLR2s shows that this cavity is consistently present in TRL2 orthologs across jawed vertebrates (Gnathostomata). In jawless vertebrates (Cyclostomatha), these cavities were found in lamprey (Petromyzon marinus) TLR2 model, but only in some extant hagfish (Myxini), suggesting an ancestral origin in basal vertebrates followed by lineage-specific losses. TLR2 paralogs were found in several species, with a similar central cavity but potentially different ligand specificities. In silico ligand docking showed Pam2CSK4 binds to this cavity in all TLRs and paralogs consistently, demonstrating the conserved function of the ligand-binding pocket in gram-positive bacteria recognition across TLR2 branches. Changes in the TLR2 cavity size and shape in some vertebrate groups show the evolution of this DAMP recognition mechanism adapted to its respective pathogens. iTLRs form a separate phylogenetic branch with distinct structural features, but in literature some are considered to be TLR2 orthologs. Indeed, TLRs from some species of Helobdella and Ciona, contain a cavity with some similarity to that in the vTLR2 lineage. However, detailed structural comparisons of their location in the LRR domain and the structural details of the models suggest that their cavities have developed independently from that in TLR2s. Smaller cavities are present in other branches of the LRR family, but show different locations, shapes, and features, indicating that the binding of small ligands in the internal cavities within the LRR domains evolved multiple times in the LRR domain family history.

Bisphenol A (BPA) and Bisphenol S (BPS) exposure disrupt ovarian function and granulosa cell (GC) steroidogenesis. Extracellular vesicles (EVs) and their miRNA cargo, as mediators of cellular response to environmental stimuli, might be involved in fertility and folliculogenesis. This study explored modulation of microRNA expression after 48h BPA or BPS exposure (10 {micro}M) in ovine primary GC and EVs from corresponding conditioned medium (CM EVs). Small RNA sequencing of control (0h) and 48h treated GC, CM EVs as well as follicular fluid EVs allowed identification of 533 ovine miRNAs, including 129 new sequences. BPA did not alter miRNA expression in GC, while BPS decreased cellular oar-24b miR. In contrast, BPA modified expression of 4 miRNAs in CM-EVs, including 3 new sequences, and two miRNAs were modified by BPS. Both compounds reduced expression of sequence homologous to miR-1306. Further studies are required to decipher their roles in bisphenol toxicity in GC.

Inflammation is a fundamental immune response but, when dysregulated, contributes to the pathogenesis of numerous inflammatory disorders. Although there are several conventional anti-inflammatory drugs which are effective, their long term use is often associated with adverse side effects, which highlights the need for safer alternative therapeutic drugs. Probiotic derived membrane vesicles (MVs) have recently emerged as biologically active nanostructures capable of modulating host immune responses. In the present study, MVs isolated from Lactobacillus acidophilus MTCC 10307 were evaluated for their anti-inflammatory efficacy and safety profile using in vitro and in vivo models. In RAW 264.7 macrophages, L. acidophilus MVs significantly attenuated lipopolysaccharide induced expression of the pro-inflammatory mediators Il-1{beta}, Il-6, and iNOS, accompanied by reduced nitric oxide and reactive oxygen species production which was abolished in the proteinase K treated MVs. The protein levels of NF{kappa}B and IL1{beta} were also reduced in the treatment groups. Repeated dose oral toxicity studies revealed no adverse effects, as evidenced by body weight and histopathological evaluation of major organs. The anti-inflammatory properties of L. acidophilus MVs were further validated in an in vivo hind paw edema model, which shows inflammation resolution demonstrated by molecular and histological analysis. Proteomic analysis using LC-MS/MS identified the presence of surface-layer protein A (SlpA) which is a potential bioactive component which might contribute to the observed immunomodulatory effects. Collectively, these findings demonstrate that L. acidophilus MVs exert potent anti-inflammatory activity while maintaining an excellent safety profile using integrated in vitro and in vivo models.

Astyanax Baird & Girard, 1854 is a widely distributed and species-rich genus of Acestrorhamphidae, whose abundant populations in Neotropical basins play a crucial ecological role at the trophic level. Taxonomic uncertainties persist within the genus, as seen in Astyanax sp. (formerly designated as A. fasciatus) from the Magdalena basin in Colombia. Concerns about its genetic status are heightened due to ecological threats posed by hydroelectric dams, from habitat loss to river connectivity. We isolated and characterized 17 microsatellite loci to assess the population genetics of this species in a broad sample from the middle and lower sections of the Cauca River, now interrupted by the Ituango dam. Furthermore, a multidisciplinary approach integrating phylogenetic analyses of mitochondrial (COI) and nuclear (rag2) markers with geometric morphometric analyses was employed to evaluate potential cryptic diversity within Astyanax sp. Microsatellites revealed two genetic groups in the studied area, strongly supported as distinct lineages by phylogenetic analyses. Unexpectedly, one of these lineages of Astyanax sp. was recovered in an unresolved clade with samples of A. microlepis and allopatric samples of A. viejita from the Maracaibo Lake basin. Each genetic group showed high genetic diversity, but also evidence of recent bottleneck events and significant-high values of inbreeding. Morphometric analyses provided evidence of significant phenotypic differentiation among A. microlepis, Astyanax sp. 1 (Asp1), and Astyanax sp. 2 (Asp2). Morphological patterns ranged from the robust profile of A. microlepis to the streamlined shape of Astyanax sp. 2 (Asp2), with Astyanax sp. 1 (Asp1) displaying intermediate traits and localized differences in head length and fin placement. Statistical support from permutation tests and a high overall classification accuracy (95.65%) underscore the existence of distinct morphospecies, suggesting that phenotypic differentiation is well-established, despite the complex evolutionary history of the group. This study suggests the presence of cryptic diversity within Astyanax sp. and provides valuable genetic information for the conservation and management of their populations in the Magdalena basin.