Biology subjects

Liu, S.

Publications and source records attributed to Liu, S..

At least 91 records · Page 5Linked to original sources

bioRxiv2026

Methodological pitfalls in plant pangenome gene family identification may lead to biased evolutionary inferences

Pangenome-level gene family identification often applies sequence similarity clustering without phylogenetic or synteny information, which risks biologically misleading evolutionary inferences. Using five transcription factor families (bHLH, MYB, NAC, WRKY, MADS-box) across 401 rice pangenome accessions, we compared clustering strategies: OrthoFinder alone, cd-hit alone, MMseqs2 alone, and OrthoFinder-informed refinement by cd-hit or MMseqs2. Methods solely based on sequence similarity merged distinct orthogroups and generated fewer orthogroups than approaches incorporating graph-based orthology. Conflicting cluster assignments, measured against OrthoFinder, varied strongly among families, from approximately 14% in MADS-box to approximately 57% in MYB, and were associated with protein length differences. Core, shell, and cloud gene classifications shifted substantially depending on the method, especially in MYB, NAC, and WRKY families. Critically, Ka/Ks distributions for core genes were highly method-sensitive, with orthology-aware methods yielding more convergent and less variable estimates of selective pressure, whereas noncore gene estimates remained robust. These findings demonstrate that neglecting graph-based orthogroup inference inflates methodological artifacts. We recommend a two-step strategy: initial graph-based orthogroup delineation followed by sequence similarity refinement to balance evolutionary accuracy and resolution in pangenome-scale gene family studies.

genomics↗

bioRxiv2026

Mapping of Stripe Rust and Leaf Rust Resistance Genes in the Hard Red Winter Wheat Population Green Hammer/Lonerider

Stripe rust and leaf rust, caused by Puccinia striiformis f. sp. tritici and P. triticina, respectively, are the most destructive wheat diseases in the southern Great Plains. Green Hammer is a hard red winter wheat (HRWW) cultivar released by Oklahoma State University in 2018 and has demonstrated a stable adult plant resistance to stripe rust and race-specific seedling resistance to leaf rust. To identify and map rust resistance loci, 109 doubled haploid (DH) lines derived from the cross between Green Hammer and another HRWW cultivar, Lonerider, were developed. Lonerider showed adult plant resistance to stripe rust but was susceptible to multiple P. triticina races. The DH lines were evaluated for stripe rust at the adult plant stage in greenhouse and field environments across Oklahoma, Kansas, and Washington, and for leaf rust at the seedling stage against seven U.S. P. triticina races and at the adult plant stage in Oklahoma and Texas. Genotyping-by-sequencing generated 6,078 polymorphic single-nucleotide polymorphisms used for genetic mapping. Quantitative trait loci (QTL) analysis identified 14 stripe rust and 8 leaf rust resistance QTL. For stripe rust, a major QTL in Green Hammer, QYr.osughln-2AS, was identified in the proximity of the 2NvS translocation. Three other major stripe rust resistance QTL were identified in Lonerider on chromosomes 2AL (two QTL) and 2BS (one QTL). For leaf rust, QLr.osughln-1DS and QLr.osughln-2DS.1 were the two major QTL identified in Green Hammer and most likely correspond to the all-stage resistance genes Lr21 and Lr39, respectively. In this study, we identified previously characterized genes as well as unknown genes that can be utilized in wheat breeding programs to enhance resistance to leaf rust and stripe rust.

genetics↗

bioRxiv2026

PARP1 catalytic domain mutations drive high-level resistance to saruparib while preserving DNA damage response vulnerabilities

Clinical poly (ADP)-ribose polymerase (PARP) inhibitors (PARPi) are limited by toxicities associated with inhibition of multiple PARP family proteins and acquired resistance. As PARP1-specific inhibitors, like saruparib (AZD5305), move toward standard-of-care status for BRCA and HR-deficient cancers replacing less specific PARPi, defining mechanisms of intrinsic and acquired resistance is essential for developing effective treatment strategies. Here, we established 5 saruparib-resistant (SR) cell lines from BRCA1-deficient MDA-MB-436 triple negative breast cancer (TNBC) cells using a selection strategy of high-level dosing consistent with clinical exposure, yielding models that are >1,000-fold resistant to saruparib. Whole genome sequencing identified PARP1 catalytic domain mutations in all SR cell lines, and in vitro reconstitution of these PARP1 mutants confirmed them as drivers of saruparib resistance, in contrast to HR restoration as observed in the case of less-selective PARPi. PARP1 mutations also induce altered saruparib-dependent PARP1 trapping and PARylation inhibition. While these mutations render cells highly resistant to saruparib, differential sensitivity to other PARPi was observed and SR cell lines retain, and in some cases, increase, sensitivity to alternative clinical PARPi and DNA damage response (DDR)-targeted therapeutics. Our findings demonstrate that high-intensity selection pressure favors target-site mutation over pathway restoration as a primary escape mechanism from PARP1-selective inhibition. This study provides a first-in-class characterization of saruparib resistance and maps a clear therapeutic path forward. By identifying these specific PARP1 mutations and their collateral DDR vulnerabilities, we provide the molecular framework necessary to monitor and treat patients who progress on next-generation PARP1-selective inhibitors.

cancer biology↗

bioRxiv2026

Bacteria rewire fungal antimicrobial gene expression in microbial arms races

Microbial communities are shaped by antagonistic interactions, yet how competitors evade antimicrobial strategies in a community context remains unclear. Fungi deploy antimicrobial proteins (AMPs) to suppress bacterial niche competitors, but how bacteria counteract these defences is unknown. Here we show that soil microbiota suppress fungal AMP expression, revealing a previously unrecognized mode of intermicrobial interference. In the soil-borne pathogen Verticillium dahliae, AMP genes are highly conserved yet broadly repressed in the presence of natural microbial communities. This repression extends across diverse fungi, suggesting a widespread phenomenon. Correlation analyses of AMP expression and community compositions linked the bacterial genera Leptothrix and Devosia to repression of the V. dahliae AMPs VdL1 and VdD1, respectively. Both AMPs antagonize these bacterial taxa, and microbiota-mediated repression of VdL1 reduces its contribution to fungal soil colonization. Together, our findings reveal that bacteria can evade fungal antagonism by suppressing AMP deployment, highlighting community-level control of microbial competition.

microbiology↗

bioRxiv2026

Macrophage metabolism directs regenerative versus fibrotic healing through BMP signaling in the mouse digit tip

Macrophages play a central role in determining the outcomes of healing, coordinating regeneration in some injuries and scar formation in others. In both cases, this coordination involves the cross-talk between macrophages and surrounding cells. But what drives the different cross-communication pathways to determine healing outcomes is not well known. In this study, we make use of the mouse digit tip amputation model, in which an amputation through the third phalangeal element (P3) is able to completely regenerate whereas an amputation through the second phalangeal element (P2) forms a scar. We identify a population of macrophages that is specific to the P3 regenerating digit. By integrating single-cell RNAseq, spatial transcriptomics, and metabolomic analyses, we show that this population localizes specifically to the growing bone front, express BMP ligands that drive downstream BMP activation in neighboring osteoblasts and is governed by a two-part metabolic switch involving increased fatty acid oxidation coupled with reduced glycolytic activity. This spatially restricted, BMP-expressing macrophage population is entirely absent in the scar-forming P2 injury, and our data indicate that environmental conditions unique to the regenerating digit are responsible for its emergence. Together these findings identify a regeneration-specific macrophage signaling center for patterned bone formation and suggest that targeting the metabolic conditions that drive this population could improve the efficacy of regenerative therapies.

developmental biology↗

bioRxiv2026

Eleven deep-sea coral genome assemblies unveil insights into evolution, adaptation, and coral biodiversity

Deep-sea corals are vital in maintaining coral ecosystem biodiversity, yet their genetic characteristics remain largely unexplored. Here, we present 11 deep-sea coral genome assemblies, including four Hexacorallia and seven Octocorallia species, significantly contributing new genomic information across two orders. Our analysis reveals the historical dynamics of coral speciation and the influence of environmental factors on the evolution of coral reef ecosystems.Total of 126 horizontal gene transfer (HGT) events were detected, among which genes from the ancestor of symbiodiniaceae indicate that the ancestors of deep-sea corals may have inhabited shallow-sea environments. Notably, several of these HGTs are involved in phosphorus (PhnX/PhnW) and cholesterol (DHCR7) metabolisms within corals, indicating that HGTs may serve as an adaptive survival strategy for the coral holobionts. Deep-sea corals also rely on symbiotic bacteria to synthesize 10 essential amino acids (such as valine and tyrosine), retaining only partial amino acid synthesis capacity. In addition, we investigated the evolution of key biological rhythm genes and temperature adaptation in corals. The loss of key rhythm genes (e.g., clock and cry) in deep-sea corals and copy number difference of genes related to heat stress (e.g., Cbl-b and Rchy) revealed genetic difference between deep-sea and shallow-sea corals. Our new genome assemblies enhance the understanding of deep-sea coral evolution, biodiversity, and adaptation, providing a genetic foundation for coral conservation.

genomics↗

bioRxiv2026

Novel linkage disequilibrium-based genotype-by-environmental interaction method for genomic prediction of cotton yield and fibre quality traits

Genomic prediction (GP) across diverse environments has a potential to accelerate genetic gain in cotton breeding programs. A major challenge in GP is modelling genotype-by-environment interactions (GEI), which is essential for selecting stable and high-performing genotypes under variable production conditions. However, incorporating GEI into GP models increases the dimensionality and computational complexity, risking complex models that are impractical to use on commercial breeding-scale data sets because of run times and computational demands. This study addresses two primary aims. Firstly, we evaluate the practical benefits of GEI-informed GP for predicting economically important cotton traits. Second, advanced statistical modelling strategies are developed and assessed for integrating genomic and environmental data at scale. We propose a dimensionality reduction approach that combines linkage disequilibrium network analysis with principal component techniques to reduce redundancy while preserving informative variation. Using this reduced dataset, we implement Bayesian linear regression models and, for comparison, deep residual neural networks for genomic prediction. Analyses were conducted on a large multi-environment dataset from the CSIRO cotton breeding program, comprising 3,236 breeding lines, 54 environmental covariates, and 8,049 yield and fibre quality phenotype records collected over 10 years and 9 locations representing 41 year-location combinations. Results demonstrate that generally Bayesian linear regression approaches outperform BG-BLUP models, with all three linear/linear mixed methods providing clearly more reliable performance than the deep learning models. These findings highlight the value of using interpretable statistical models for integrating genomic and environmental information to support selection decisions under diverse environmental conditions.

plant biology↗

bioRxiv2026

Inhibition of oogenic JNK preserves fertility and ovarian hormones during DNA-damaging cancer therapy

Primary ovarian insufficiency (POI) and related infertility, early menopause, and endocrine disorders due to hormonal deficiency are major side effects in young female cancer patients undergoing cancer therapy. Current strategies preserving the fertility and hormonal functions of the ovary remain imperfect due to concerns of feasibility, efficacy, or safety. Herein, we identified c-Jun N-terminal kinase (JNK) as a pivotal regulator of the DNA damage response (DDR) signaling in oocytes of primordial follicles in response to DNA-damaging cancer therapy. Using pharmacological JNK inhibition and a genetically modified mouse model with oocyte-specific JNK deletion, together with histological, bioinformatic, and molecular approaches, we demonstrated that JNK inhibition prevented chemotherapy-induced oocyte apoptosis and POI, and preserved long-term reproductive cycles and fertility. Mechanistically, JNK was activated in response to chemotherapy-induced DNA damage in oocytes of primordial follicles, causing activation of transcription factor TAp63 and subsequent oocyte apoptosis, ultimately resulting in diminished ovarian reserve and POI. A more clinically relevant breast cancer-bearing mouse model revealed that JNK inhibition preserved the ovarian reserve without compromising anti-cancer efficacy of chemotherapy. Together, our study identifies oocyte-intrinsic JNK as a promising target for developing ovarian protectants and safeguarding reproductive health and fertility in young female cancer survivors.

pharmacology and toxicology↗

bioRxiv2026

From Generalist to Specialist: Evolution of PS2 α-integrins and Implications for Drug Targeting

Integrins are heterodimeric transmembrane receptors that mediate cell-cell and cell- extracellular matrix interactions and play essential roles in development and disease. Within the PS2 -integrin subfamily, four paralogs (IIb, 5, 8, and V) share a conserved RGD-binding motif yet exhibit diverse functional specializations. Integrins have been widely targeted therapeutically for various clinical conditions, though achieving subtype specificity remains a major challenge. Here, we performed an integrative evolutionary analysis of 114 PS2 -integrin sequences across 28 vertebrate species, combining phylogenetic reconstruction, time calibration, ancestral sequence inference, and structural mapping. Our time-calibrated phylogeny indicates that the PS2 lineage originated [~]862 Mya, with diversification of the four paralogs occurring prior to vertebrate radiation. Ancestral state reconstruction reveals that fibronectin and vitronectin binding are ancestral traits, whereas fibrinogen binding and {beta}3 pairing arose independently in the IIb and V lineage. Evolutionary rate analysis shows domain-specific divergence, with the {beta}-propeller acting as a hotspot of evolutionary change, likely driven by combined pressures from ligand binding and {beta}-subunit interaction. These pressures vary across paralogs: IIb exhibits accelerated evolution in ligand-binding regions, while V displays elevated rates in {beta}-subunit interaction domains. Mapping sequence variation onto structural interfaces identifies lineage-specific substitutions underlying functional divergence, including distinct molecular solutions for fibrinogen binding in IIb and V. These findings collectively demonstrate that PS2 -integrins evolved from a generalist ancestor through neofunctionalization and lineage-specific specialization. This work provides an evolutionary framework for identifying subtype-specific functional sites and highlights the potential of evolution-informed strategies to guide the development of more selective integrin-targeting therapeutics.

bioinformatics↗

bioRxiv2026

ExMODE: A Multi-Omics Repository for Extremophile Adaptation and Bioprospecting

Extreme environments, though hostile to most life forms, host specialized extremophile communities that have redefined biological cognition and emerged as vital biotechnological resources, with their unique adaptive traits and bioactive molecules driving advances in multiple scientific and industrial fields. However, research on extremophiles is hindered by limitations in culture-based methods, fragmented multi-omics data with non-uniform annotation standards across repositories, the lack of cross-extreme comparative research in existing resources, and the singularity of data dimensionality that neglects key structural information, all of which restrict the functional interpretation of extremophile microbes and the exploitation of their bioprospecting potential. To tackle these challenges, we developed ExMODE (https://db.genomics.cn/exmode/), a comprehensive multi-omics database platform dedicated to extremophiles. It centrally integrates multi-omics data from diverse extreme habitats with a standardized annotation framework, resolving data fragmentation and enabling systematic cross-environment comparative analyses to elucidate extremophile adaptive mechanisms. Moreover, ExMODE aggregates multi-dimensional datasets including genes, genomes, secondary metabolite sequences and protein structures, overcoming the constraints of single-dimensional data and significantly improving the efficiency of biotechnological resource discovery from extreme microorganisms.

microbiology↗

bioRxiv2026

Distinct HPO axis responses and ovarian aging trajectories to chronic unpredictable mild stress in reproductively young versus middle-aged female mice

Psychosocial stressors are key contributors to ovarian functional decline. Chronic unpredictable mild stress (CUMS) is widely used to model stress-induced premature ovarian insufficiency (POI) in mice; however, current animal models do not adequately reflect middle-aged women, who represent a key population exposed to chronic psychosocial stress, nor do they capture the dynamic progression toward POI. Here, female C57BL/6 mice aged 2 or 6 months were subjected to CUMS for 8 or 12 weeks. Estrous cyclicity, endocrine profiles, ovarian histology, and transcriptomic changes in HPO axis-related tissues were systematically analyzed. After 8 weeks of exposure, 2-month-old mice exhibited impaired pituitary responsiveness to estradiol negative feedback, as evidenced by dysregulated FSH secretion, indicating reduced stress tolerance compared with 6-month-old mice. Following 12 weeks of CUMS exposure, both age groups showed significant reductions in ovarian size and follicle numbers across all developmental stages. These findings demonstrate that CUMS induces an age-dependent progression toward POI, with short-term exposure eliciting compensatory phases preceding overt ovarian insufficiency, accompanied by distinct endocrine and reproductive alterations and differential responsiveness of the HPO axis. Transcriptomic analyses revealed age-dependent stress responses: ovaries of 2-month-old mice displayed marked activation of inflammatory and immune-related pathways, whereas 6-month-old mice showed sustained upregulation of protein kinase-related signaling networks. Notably, the 6-month-old CUMS model more closely recapitulates stress-associated reproductive aging in adult women. In briefCUMS has been widely used to establish mouse models of psychosocial stress-induced POI. However, current animal models do not adequately reflect middle-aged women, who represent a key population exposed to chronic psychosocial stress, nor do they capture the dynamic progression toward premature ovarian insufficiency (POI). In this study, we demonstrate that different durations of CUMS exposure induce distinct stages of ovarian dysfunction in both young and middle-aged mice, with short-term exposure driving age-dependent compensatory phases and prolonged exposure leading to overt POI, both accompanied by divergent endocrine and reproductive alterations, alongside age-dependent changes in HPO axis responsiveness to CUMS. Notably, the 6-month-old CUMS model shows greater clinical relevance in recapitulating chronic psychosocial stress and stress-related reproductive aging in adult women.

physiology↗

bioRxiv2026

Macronutrient Composition and Genetic Background Determine the Response to a Ketogenic Diet

While standard high fat diets cause hyperphagia and obesity in mice, high fat-low carbohydrate ketogenic diets (KDs) reduce food intake and body weight. Because the basis for this difference is still unclear, we systematically altered the macronutrient content of a standard KD and found that feeding C57BL/6J (B6J) mice a KD with 5% protein resulted in hypophagia, weight loss, and hypoglycemia, whereas the same diet with 10% protein led to increased adiposity and glucose intolerance. However, these effects were strain-dependent as C57BL/6NJ (B6NJ) weighed similar amounts on the two diets leading us to investigate the molecular mechanisms. When fed the KD-5% diet, B6J but not B6NJ mice showed increased levels of two anorexigenic factors, GDF15 and LCN2, and loss of function of either blunted the weight loss of B6J mice fed the diet. B6J mice harbor mutations in Nnt (Nicotinamide nucleotide transhydrogenase) and Nlrp12 (NLR family pyrin domain containing 12), both of which are wildtype in B6NJ mice. B6J mice fed the KD-5% diet showed the RNA signature of oxidative and integrated stress responses (ISR) and restoring NNT function in liver reduced the levels of GDF15. RNA-seq also revealed that B6J but not B6NJ mice had the RNA signature for hepatic inflammation and a knockout of Nlrp12 led B6NJ mice to lose weight on the KD-5% diet with increased levels of LCN2. Suppression of oxidative stress with N-acetylcysteine (NAC) reduced expression of both GDF15 and LCN2 and prevented the weight loss associated with the KD-5% protein diet in B6J mice, whereas inhibition of the integrated stress response with ISRIB only attenuated the GDF15 axis. Collectively, these findings explain why B6J mice lose weight on a ketogenic diet and reveal a critical interplay between macronutrient composition and genetic background leading to increased levels of GDF15 and LCN2 to induce hypophagia. Finally, these data suggest that the response to different diets among humans might be similarly variable based on genetic variation and macronutrient composition, suggesting the possible need for personalized dietary interventions.

physiology↗

bioRxiv2026

Structure-guided, physics-aware reconstruction expands scan-limited multiphoton imaging

Multiphoton microscopy is increasingly pushed toward deep, large-scale and fast functional imaging, where acquisition speed has become a bottleneck. Here we present SPARC, a structure-guided, physics-aware reconstruction framework for scan-limited multiphoton imaging. Rather than treating undersampled recovery as generic super-resolution, SPARC formulates it as a constrained reconstruction problem tailored to point-scanning microscopy by incorporating a sample-matched structural reference from the same field of view and the anisotropic acquisition physics of scan-limited imaging. SPARC jointly integrates denoising and deep upsampling, enabling stable recovery from noisy sparse measurements. In simulated calcium imaging with ground truth, SPARC improved spatial reconstruction fidelity and temporal signal recovery relative to existing methods. In vivo, SPARC improved temporal readout in three-photon and mesoscale two-photon calcium imaging, and enabled a more temporally informative 400-Hz voltage readout on a standard resonant-galvo two-photon microscope. These results suggest SPARC expands the functional operating range of existing multiphoton microscopes under scan-limited conditions.

neuroscience↗

bioRxiv2026

OneGenomeRice (OGR): A Genomic Foundation Model for Rice

The transition of genomics to a predictive intelligence discipline is driven by the advent of genomic foundation models. While substantial progress has been observed in all-life and human-centric models, plant species, particularly for the staple crops, remains hindered by a lack of models. Here we introduce OneGenome-Rice (OGR), a rice (Oryza sativa) genomic foundation model pre-trained on a genomic dataset comprising 422 high-quality genomes of cultivated and wild rice. OGR is engineered upon a Mixture of Experts (MoE) transformer architecture with 1.25-billion parameters and supports an ultra-long context window of up to 1 million base (Mb) pairs at single-nucleotide resolution. A comprehensive benchmark demonstrated that OGR significantly outperforms existing state-of-the-art multi-plant or all-life genome models in 11 categories (e.g. motif identification, sweep detection, etc). We further demonstrated the utility of OGR in several downstream applications, such as indica-japonica subspecies introgression analysis, identification of agronomy trait-associated functional loci and prediction of gene expression from DNA sequences. These results establish OGR as a promising foundational computational infrastructure for rice functional genomics and precision breeding. The OGR and its fine-tuned models, including pretrained weights, training code and the rice genomic benchmark suit, have been fully opened.

genomics↗

bioRxiv2026

Concordia: Spatial Domain Detection via Augmented Graphs for Population-Level Spatial Proteomics

A key step in analyzing population-level spatial proteomic data is to delineate consistently defined spatial domains across samples. Domain detection is particularly challenging for cancer tissues, which have complex spatial domains with elongated or branching geometries. To address these challenges, we present Concordia, a Graph Neural Network (GNN)-based framework that uses augmented graphs to capture complex spatial domains, and it is designed to analyze thousands of tissues simultaneously to obtain consistently defined domains. Applied to a lung cancer dataset, Concordia uncovers a spatially defined cancer associated fibroblast subset linked to clinical outcomes that cannot be identified using protein expression alone.

genomics↗

bioRxiv2026

Integration of proteogenomic analyses in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is still lack of clinically molecular subtyping and effective therapeutic strategies. Herein, a total of 46 paired tissue samples of esophageal squamous cell carcinoma (ESCC) were collected and subjected to a systematic proteogenomic evaluation. Consensus assessment of the ESCC-related transcriptomes and TCGA dataset revealed several consensual modes of gene expression related to ESCC specificity, with 8 plasma-detectable hub proteins that could discriminate ESCC from others. Three ESCC molecular subtypes were defined and validated based on proteome data, including pCC1 with activated immune response and best survival outcome, pCC2 as cell cycle subtype with relative worse outcome, and pCC3 with worst outcome that expressed more cell adhesion related proteins. Furthermore, we proposed potential therapeutic strategies for improving survival outcomes in patients with different ESCC molecular subtypes. This integrative proteogenomic analysis provided a novel view of ESCC-dependent molecular information.

cancer biology↗

bioRxiv2026

Identification of scavenger receptor BI as a scavenger of free heme that is essential for protection against hemolysis

Severe hemolysis is a life-threatening condition with limited therapeutic options. Although haptoglobin and hemopexin sequester hemoglobin and heme, these protective systems are rapidly saturated during acute hemolysis, leading to the accumulation of cytotoxic free heme. In this study, we identify scavenger receptor BI (SR-BI) as a critical mediator of free heme clearance. SR-BI binds heme and facilitates its hepatic uptake under pathological conditions. Mice lacking hepatic SR-BI exhibit impaired heme clearance and increased susceptibility to heme- and hemolysis-induced lethality. Pharmacological upregulation of hepatic SR-BI via imatinib or adenoviral delivery confers protection against heme toxicity. Using a humanized model of sickle cell disease (SCD), we further demonstrate that sickle hepatopathy significantly reduces hepatic SR-BI expression compared to non-SCD littermates, potentially increasing vulnerability to heme-induced injury. Notably, adenoviral-mediated SR-BI upregulation rescues SCD mice from heme toxicity. These findings reveal a previously unrecognized mechanism of heme detoxification via hepatic SR-BI and identify a promising therapeutic target for hemolytic disorders. One-Sentence SummaryIdentification of scavenger receptor BI as a targetable scavenger of heme in hemolysis

physiology↗

bioRxiv2026

Ecology of metagenomes: incorporating genotype-to-phenotype maps into ecological models

A major theoretical problem in community ecology is to understand how genes, organisms, and environments combine to shape the structure and diversity of ecological communities. However, most classic ecological models work entirely with phenotypic parameters, neglecting the central role played by genes. This limitation is particularly acute in microbial ecology, where the widespread use of sequencing technologies allows researchers to directly measure the genomic and metagenomic properties of communities. Here, we bridge this gap by incorporating genotype-to-phenotype maps into classical ecological models, including the generalized Lotka-Volterra model (GLV) and consumer resource models (CRMs). We focus on the case where genotype-to-phenotype maps are linear, which provides a tractable yet powerful framework for analyzing complex traits. Even in this simple setting, the resulting ecological dynamics give rise to novel gene-level ecological dynamics that can be recast entirely in terms of genes, allowing us to develop an ecology of metagenomes. We find that ecological interactions between genes lead to pervasive "metagenomic hitchhiking" -- low-fitness genes can survive in the ecosystem because they are integrated into genomes of high-fitness species. We also show that phylogenetic relationships between species mold the ability of closely related strains to stably coexist in complex communities. This highlights how lineage structure and competitive interactions jointly shape community composition. Our framework provides a principled foundation for interpreting metagenomic data through the lens of ecological theory. Author summaryRecent advances in sequencing technologies have transformed our ability to characterize microbial communities at the genomic level. However, most classic ecological models work entirely with phenotypic parameters, neglecting the central role played by genes. Here, we address this gap by extending classical ecological models to explicitly include genotype-to-phenotype maps. We focus on complex traits where the genotype-to-phenotype map is approximately linear. We show that the resulting ecological dynamics that can be recast entirely in terms of genes, allowing us to develop an ecology of metagenomes. Our framework provides a novel perspective for interpreting metagenomic data through the lens of ecological theory.

ecology↗