bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.05.06.723284

Temperate phage microdiversity reflects infant gut microbiome maturation independent of chronic undernutrition

Abstract

The assembly and maturation of the infant gut microbiome is a critical developmental process. Yet the dynamics of the viral community, particularly in the context of stunting (chronic malnutrition) remain underexplored. Leveraging longitudinal fecal metagenomes from Zimbabwean infants with normal and stunted growth trajectories, we characterized the development of the gut bacterial and temperate phage communities from birth to 18 moths old. We found that infant gut temperate phages target hallmark early-life bacterial taxa, such as Bifidobacteriaceae, and exhibit an age-dependent maturation that parallels bacterial succession. Notably, both bacterial and temperate phage alpha diversity increased with age. This contrasts with previous studies focused on the extracellular viral fraction and highlights a strong coupling between prophage early-life dynamics and during bacterial gut colonization. Using abundance-based maturation models, we identified successional phases of colonization for both bacteria and their associated temperate viral clusters. Importantly, a viral microdiversity maturation model provided a stronger prediction of chronological age than viral abundance-based model, revealing within-phage genomic variation as a key signal of virome assembly, particularly around weaning. Contrary to findings in wasting (or severe acute malnutrition), stunted growth trajectories were not associated with a significant delay in either bacterial or temperate phage maturation. These results demonstrate that viral genomic variation is a new, informative dimension of early-life gut microbial assembly and that stunting may not impair infants gut maturation process. ImportanceThe early-life period represents a critical window for the establishment of the gut microbial communities, a process that is often affected by environmental factors such as diet. While severe acute malnutrition (SAM) is known to delay bacterial maturation, the impact of chronic, moderate undernutrition, such as stunting is poorly understood. Stunting is a highly prevalent global health condition with irreversible consequences on long-term host health, yet its implications on gut microbiome assembly remain unclear. Our study provides novel insights into the maturation of temperate phages, which to prime the infant gut by colonizing alongside their bacterial hosts and acting as drivers of bacterial evolution via lysogeny. By demonstrating that viral strain-level (genomic) variation captures a stronger age-related signal than viral abundance, we identified an underexplored dimension of microbial assembly. The finding that stunting, in contrast to SAM, does not impact microbial maturation provides essential context for public health interventions and future studies addressing this condition.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Camelo Valera, L. C. C., Reyes, A., Maurice, C. F.. 2026-05-10. Temperate phage microdiversity reflects infant gut microbiome maturation independent of chronic undernutrition. https://doi.org/10.64898/2026.05.06.723284

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

A meta-interaction basis for cell-cell communication in tissues

Tissue function depends on signals exchanged between cells and the responses they elicit. Yet whether diverse cell-cell interactions in situ form recurrent sender-receiver programs remains unclear. We present SpiderNet, an interpretable representation-learning framework that discovers such directed programs as a compact basis of cell-cell meta-interactions (MIs) from spatial transcriptomics. SpiderNet jointly learns which sender regulators, ligand-receptor pairs, and receiver targets define each MI and where each program is active across neighboring cell pairs. The resulting representation traces multicellular relays and links communication to cell states, perturbation responses, and phenotypes. SpiderNet recovers ground-truth MIs and their molecular components in simulations and, in real tissues, shows stronger direction-specific agreement with independently curated regulatory programs in senders and receivers than alternative methods. Across more than 5.8 million spatially profiled cells, SpiderNet resolves an SPP1-THBS relay linking monocytes, fibroblasts, and tumor cells within an immune-suppressive ovarian cancer niche, predicts T-cell responses to held-out melanoma-cell perturbations, and identifies a T-cell-associated brain-aging program and age-predictive signals that transfer across regions and platforms. It reveals a recurrent pan-cancer COLLAGEN-linked fibroblast-tumor program whose projected abundance in independent cohorts is associated with poorer survival and non-response to immunotherapy. SpiderNet thus establishes MIs as a reusable organizational layer between molecular interactions and tissue phenotypes, providing a framework to resolve, compare, trace, and perturb multicellular regulation in situ.

bioinformatics↗

Heterogeneous Graph Contrastive Learning for Drug-Gene-Disease Motif Prediction

Drug repurposing and target discovery offer critical strategies for advancing therapeutic development by uncovering the potential biological pathways and novel associations among drugs, genes, and diseases. However, experimental discovery remains expensive and time-consuming, which limits the scalability of large-scale studies. In addition, existing computational approaches often struggle to effectively integrate heterogeneous biomedical data, capture the complex higher-order topological signatures of biological interactomes, and generalize to unseen entities. Here, we present HANAMI (Heterogeneous grAph coNtrastive leArning for drug-gene-disease Motif predIction), a multi-view deep graph learning framework designed to model complex interactions among drugs, genes, and diseases. HANAMI integrates diverse heterogeneous biomedical knowledge, including chemical structures, genomic sequences, and clinical phenotypes, and leverages relation-aware topology encoding, structure-aware aggregation, and contrastive learning to enable accurate motif prediction with biological context from the network. Systematic evaluation on benchmark datasets shows that HANAMI achieves up to 6% improvements over existing state-of-the-art methods in predicting drug-gene-disease motifs. The framework further demonstrates strong inductive generalization, maintaining an [~]18% performance advantage in zero-shot settings involving previously unseen entities. Beyond predictive performance, HANAMI effectively prioritizes drug-disease relationships investigated in Phase II or III trials while identifying candidate genes that suggest plausible mechanistic links. Together, HANAMI provides a computational framework for interpreting complex biomedical interactions, offering a scalable foundation to accelerate drug repurposing and therapeutic innovation.

bioinformatics↗

PTMExplorer: A Multi-Dimensional Integrative Visualization Platform for Protein Post-Translational Modification Function and Structure

Deciphering the functions of post-translational modifications (PTMs) is a critical bridge connecting large-scale modification proteomics data to mechanistic studies. However, most existing tools for visualizing PTM omics data are limited to site catalogs or single-dimensional feature displays. They lack the capability to simultaneously map user-derived differential modification sites onto multi-dimensional contexts, including protein three-dimensional (3D) structure, evolutionary conservation, functional sites, and disease associations. This limitation makes it difficult for researchers to rapidly assess the biological importance of candidate sites from among a vast number of differentially modified sites. Here, we present PTMExplorer, an interactive platform for the multi-dimensional visualization of protein PTMs. PTMExplorer comprises three core modules: PTM Inspector, built upon ProtVista, provides a multi-track, sequence-feature integrated view incorporating intrinsically disordered region (IDR) prediction (via flDPnn), surface accessibility calculation (via FreeSASA), and UniProt functional annotations; PTM 3D Locator, leveraging the Nightingale/Mol* engine, anchors modification sites onto AlphaFold/Protein Data Bank (PDB) 3D structures through residue mapping via PDBe-SIFTS; and PTM Overview, utilizing the R circlize package, presents a panoramic polar circos plot illustrating modification distribution and inter-group differential regulation. Additionally, three major disease-associated modification databases (PTMD, qPTM, and PhosCancer) are integrated as PTM-Disease Nexus, enabling co-localization comparison between user-defined differential sites and reported disease-related sites. PTMExplorer currently supports eight model organisms, accepts user-uploaded differential analysis results, and provides multi-dimensional annotations and various visualization options (https://www.bioladder.cn/PTMExplorer/). Using a multi-omics dataset from hepatocellular carcinoma (18 patients, 9 modification types) as a case study, we demonstrate the practical utility of PTMExplorer in screening potential biomarkers, revealing multi-modification coordination mechanisms, and distinguishing between absolute and relative quantification patterns.

bioinformatics↗