Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “interaction”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed2026

The Lipid Interactome: an interactive and open access platform for exploring cellular lipid-protein interactions.

SUMMARY: Lipid-protein interactions play essential roles in cellular signaling and membrane dynamics, yet their systematic characterization has long been hindered by the inherent biochemical properties of lipids. Recent advances in functionalized lipid probes-equipped with photoactivatable crosslinkers, affinity handles, and photocleavable protecting groups-have enabled proteomics-based identification of lipid interacting proteins with unprecedented specificity and resolution. Despite the growing number of published lipid interactomes, there remains no centralized effort to harmonize, compare, or integrate these datasets. The Lipid Interactome addresses this gap by providing a structured, interactive web portal that adheres to FAIR data principles-ensuring that lipid interactome studies are Findable, Accessible, Interoperable, and Reusable. Through standardized data formatting, interactive visualizations, and direct cross-study comparisons, this resource enables researchers to systematically explore the protein-binding partners of diverse bioactive lipids. By consolidating and curating lipid interactome proteomics data from multiple studies, the Lipid Interactome database serves as a critical tool for deciphering the biological functions of lipids in cellularsystems. AVAILABILITY AND IMPLEMENTATION: This site can be viewed at LipidInteractome.org. All data are available for download. No user information is collected or necessary for data navigation, interaction, or download.

Proteins↗
PubMed2026

Library-based, multiplexed strategy for mapping protein interaction networks via crosslinking.

BACKGROUND: Protein-protein interactions are fundamental to cellular function, yet resolving their interaction interfaces and dynamic behaviors in native biological contexts remains challenging, particularly for weak or transient interactions. Crosslinking strategies based on noncanonical amino acids offer an effective means to capture such interactions; however, traditional single-site incorporation provides limited coverage and may overlook critical interaction hotspots. RESULTS: By employing a mutagenesis library, multiple interaction partners and cross-linking sites of a target protein can be simultaneously screened in a single experiment, without prior knowledge of its precise structural or functional features, enabling effective and unbiased analysis of its interaction network. In this study, we constructed an amber codon-scanning mutagenesis library of PSMD10, facilitating independent incorporation of the photocrosslinking ncAA p-azido-phenylalanine at multiple distinct residues. This approach allowed us to systematically interrogate and precisely map potential interaction regions across the protein surface. Coupled with crosslinking mass spectrometry, we identified multiple residues involved in intermolecular interactions, as well as previously unreported interaction partners, including T2FA, TBA1C, and ATRIP. CONCLUSIONS: These findings expand our understanding of PSMD10-associated proteasome interactome, demonstrate a multiplexed strategy for in situ mapping of protein interaction interfaces with broad coverage, and offer a valuable platform for developing therapeutics that target protein-protein interactions.

Protein Interaction Mapping↗
PubMed2025

Immune-mediated indirect interaction between gut microbiota and bacterial pathogens.

BACKGROUND: In many animals, survival during infection depends on the ability to coordinate interactions between the host immune system and gut microbiota. These tripartite interactions, in turn, potentially shape pathogen virulence evolution. A key regulator of the immune system and, hence, bipartite interactions in insects is the immune deficiency (Imd) pathway, which modulates gut microbiota and pathogens by synthesizing antimicrobial peptides (AMPs) through the NF-κB transcription factor Relish. However, whether Imd-dependent AMPs mediate indirect interactions between gut microbiota and pathogens in a tripartite context remains unclear. Using RNAi-mediated knockdown of Tenebrio molitor Relish (TmRelish), we hypothesized that Imd-dependent AMPs influence indirect interaction between Providencia burhodogranariea_B (P. b_B) infection and the gut microbiota. RESULTS: TmRelish knockdown altered bipartite interactions by disrupting gut microbiota load and composition, increasing pathogen load, and ultimately leading to higher host mortality during infection. However, we did not find support for our tripartite hypothesis that Imd-dependent AMPs mediate indirect interactions between the gut microbiota and P. b_B infection, suggesting the involvement of alternative regulatory pathways or Imd-independent mechanisms. Nevertheless, our investigations of tripartite interactions showed a positive effect of P. b_B infection on gut microbiota load, which in turn stimulated the expression of a subset of AMPs. However, this upregulation of AMPs did not result in reduced P. b_B load. Notably, the gut microbiota did not affect pathogen load but promoted host survival during P. b_B infection, indicating a role in increasing host tolerance rather than resistance. CONCLUSIONS: These findings suggest that while Imd-dependent AMPs may not mediate tripartite interactions in our system, microbiota-host interactions, such as microbiota-mediated immune priming and changes in microbiota load, can shape infection outcomes. These effects on infection outcomes almost certainly exert important selective pressures on the evolution of bacterial virulence.

Animals↗
PubMed2025

Structure-informed theoretical modeling defines principles governing avidity in bivalent protein interactions.

In signaling cascades, where domain-motif interactions tend to interact with relatively low affinity (allowing for reversibility), signaling proteins often encode multiple domains or motifs, which present the possibility of avidity - drastically increasing the interaction strength and duration as a result of multivalent binding. However, given the large combinatorial space, predicting and validating multivalent interactions that interact with avidity is a challenge. Here, we integrate mechanistic modeling, structure-based analysis, and experimental approaches as a framework for defining the conditions under which avidity plays a role. We explore the tandem SH2 domain family of interactions with bisphosphorylated partners as a multivalent archetype, which encompasses key secondary messengers in tyrosine kinase signaling networks. While certain multivalent interactions have been shown to be necessary in immune receptor recruitment of partners, bivalent recruitment of tandem SH2 domains more broadly is poorly understood. Theoretical modeling suggests that maximum avidity occurs with closely spaced or flexibly linked phosphotyrosine sites, combined with moderate monovalent affinities - exactly around the innate range of SH2 domain affinity. Surprisingly, despite sequence diversity, structure-based analysis showed remarkably conserved three-dimensional spacing between SH2 domains across all tandem SH2 families, which we corroborate experimentally, suggesting evolutionary optimization for avidity interactions. The combination of structure-based analysis of domain spacing with available monovalent experimental data appears to be sufficiently accurate to predict and rank order high affinity interactions of tandem SH2 domain recruitment to the EGFR C-terminal tail. These approaches lay the groundwork for larger utility in multivalent prediction and testing to help better understand protein interactions that drive cell signaling.

BLI↗
PubMed2025

Roles of microbial interactions in determining the establishment and function of synthetic consortium inoculants for soil applications.

Synthetic microbial consortium inoculants are emerging nature-based solutions for promoting sustainable agriculture and mitigating environmental challenges. However, despite promising results in simpler lab-scale trials, many inoculants fail to establish or perform satisfactorily in field conditions. One most critical yet least understood factor influencing inoculant effectiveness is the complex microbial interactions, both within consortium inoculants ("within-community" interactions) and between consortium inoculants and native soil communities ("cross-community" interactions). Here, we first discuss major negative and positive "within-community" interactions and highlight the importance to design consortium inoculants with positive interactions for improved stability and functionality. We then examine the bidirectional "cross-community" interactions once introducing consortium inoculants to soils. Soil native communities often create strong resistance to the invasion of inoculants. We discuss major drivers controlling the invasibility of native communities and various strategies increasing the invasiveness of consortium inoculants. We then discuss how consortium inoculants can reshape native communities, with implications for long-term ecosystem resilience and functioning. We propose future research efforts including advancing strategies for harnessing natural species from relatively untapped soil reservoirs and using high-throughput interaction profiling with multi-omics and computational tools to build compatible synthetic consortia with desirable functions; leveraging positive interactions and prebiotics to facilitate inoculant establishment; and assessing fully soil functional resilience over longer terms, including recognizing the importance of rare keystone taxa. By integrating with ecological theory, this review provides a comprehensive insight into microbial interactions to advance the design, application, and monitoring of synthetic consortium inoculants for enhancing soil health and ecosystem sustainability.

establishment↗
PubMed2026

Direct interaction between RSV polymerase L and active Rab11a mediates viral ribonucleoprotein transport to assembly sites.

Respiratory syncytial virus (RSV) is an enveloped, negative-sense, single-stranded RNA virus whose ribonucleoproteins (vRNPs) must be transported from cytoplasmic viral factories to the plasma membrane for efficient virion assembly. Viral vRNPs comprise genomic RNA encapsidated by nucleoprotein N and associated with the polymerase complex (L, P, and M2-1). It was previously demonstrated that newly synthesized vRNPs are transported along microtubules by hijacking Rab11a, a small GTPase involved in the regulation of recycling endosomes. In our previous study, we showed an interaction between Rab11a and vRNPs in infected cells by immunoprecipitation assays, nevertheless the molecular mechanisms underlying Rab11a viral hijacking remained unknown. Here, we provide the first comprehensive characterization of the interaction between RSV vRNPs and Rab11a using immunoprecipitation, immunofluorescence colocalization, GST pull-down assays, and biolayer interferometry. We demonstrate that the viral polymerase L is the sole vRNPs component responsible for Rab11a recognition: immunoprecipitation of L specifically co-precipitates HA-tagged Rab11a, whereas other vRNPs proteins show no interaction. In vitro binding studies confirm that L interacts directly and specifically with the active, GTP-bound form of Rab11a with sub-micromolar affinity. Domain mapping using truncated constructs reveals that this interaction requires the C-terminal methyltransferase and CTD domains of L (residues 1756-2165) and depends on Rab11a's Switch I region, known to mediate interactions with cellular Rab11a partners. Mutagenesis further highlights leucine 1860 in the L polymerase as critical for Rab11a binding. Competitive inhibition of the interaction between Rab11a and L using the minimal Rab11a-binding domain significantly impairs vRNP dynamics during infection, indicating that Rab11a-L binding is involved in the transport of vRNPs. Together, these findings establish RSV polymerase L as the key mediator of Rab11a engagement, define the molecular interface of their interaction, and reveal a potentially conserved viral strategy for genome transport. Targeting the L-Rab11a interaction could therefore be a promising strategy for the development of RSV-specific or broad-spectrum antiviral therapies.

rab GTP-Binding Proteins↗
PubMed2025

TRAIT: A Comprehensive Database for T-cell Receptor-antigen Interactions.

Comprehensive and integrated resources on interactions between T-cell receptors (TCRs) and antigens are still lacking for adoptive T-cell-based immunotherapies, highlighting a significant gap that must be addressed to fully understand the mechanisms of antigen recognition by T cells. In this study, we present the T-cell receptor-antigen interaction database (TRAIT), a comprehensive database that profiles the interactions between TCRs and antigens. TRAIT stands out due to its comprehensive description of TCR-antigen interactions by integrating sequences, structures, and affinities. It provides millions of experimentally validated TCR-antigen pairs, resulting in an exhaustive landscape of antigen-specific TCRs. Notably, TRAIT emphasizes single-cell omics as a major reliable data source for TCR-antigen interactions and includes millions of reliable non-interactive TCRs. Additionally, it thoroughly demonstrates the interactions between mutations of TCRs and antigens, thereby benefiting affinity optimization of engineered TCRs as well as vaccine design. TCRs on clinical trials are innovatively provided. With the significant efforts made toward elucidating the complex interactions between TCRs and antigens, TRAIT is expected to ultimately contribute superior algorithms and substantial advancements in the field of T-cell-based immunotherapies. TRAIT is freely accessible at https://pgx.zju.edu.cn/traitdb.

Receptors, Antigen, T-Cell↗
PubMed2026

SARS-CoV-2 Orf3a protein interaction mapping using unnatural amino acid incorporation.

Mapping transient protein-protein interactions remain a major challenge in studying viral host-pathogen interfaces. While some virus-host interactions are stable and readily captured, the majority are highly dynamic, reflecting the need for viral proteins to engage distinct host factors at different stages of the life cycle. Here, we employ a protein engineering strategy based on the site-specific incorporation of the unnatural acid p-azido-L-phenylalanine (AzF) to enable photo-crosslinking proteomic analysis of the SARS-CoV-2 accessory protein Orf3a in live cells. Genetic installation of AzF at residue K198 of Orf3a permitted UV-induced covalent capture of proximal host interacting proteins, overcoming challenges associated with membrane localization and limited protein abundance. A total of 248 high-confidence Orf3a-interacting proteins were reproducibly identified and subjected to gene ontology analysis, revealing enrichment in innate immune signaling, antiviral defense, RNA processing, and viral replication-associated pathways. Orf3a is an accessory protein that functions as a viroporin and traffics across multiple cellular compartments, and was found to interact with host RNA helicases, RNA-binding proteins, immune regulators, and metabolic enzymes implicated in SARS-CoV-2 infection. Together, these results demonstrate that genetically encoded, site-specific photo-crosslinking enables selective capture of transient interactions that are often missed by nonspecific 254 nm UV crosslinking approaches and highlights Orf3a as a multifunctional protein that engages diverse host pathways. More broadly, this study establishes a generalizable framework for leveraging unnatural amino acid-based protein engineering approaches to interrogate dynamic host-pathogen interactions.

Humans↗
PubMed2025

In silico prediction method for plant Nucleotide-binding leucine-rich repeat- and pathogen effector interactions.

Plant Nucleotide-binding leucine-rich repeat (NLR) proteins play a crucial role in effector recognition and activation of Effector triggered immunity following pathogen infection. Genome sequencing advancements have led to the identification of a myriad of NLRs in numerous agriculturally important plant species. However, deciphering which NLRs recognize specific pathogen effectors remains challenging. Predicting NLR-effector interactions in silico will provide a more targeted approach for experimental validation, critical for elucidating function, and advancing our understanding of NLR-triggered immunity. In this study, NLR-effector protein complex structures were predicted using AlphaFold2-Multimer for all experimentally validated NLR-effector interactions reported in literature. Binding affinities- and energies were predicted using 97 machine learning models from Area-Affinity. We show that AlphaFold2-Multimer predicted structures have acceptable accuracy and can be used to investigate NLR-effector interactions in silico. Binding affinities for 58 NLR-effector complexes ranged between -8.5 and -10.6 log(K), and binding energies between -11.8 and -14.4 kcal/mol-1, depending on the Area-Affinity model used. For 2427 "forced" NLR-effector complexes, these estimates showed larger variability, enabling identification of novel NLR-effector interactions with 99% accuracy using an Ensemble machine learning model. The narrow range of binding energies- and affinities for "true" interactions suggest a specific change in Gibbs free energy, and thus conformational change, is required for NLR activation. This is the first study to provide a method for predicting NLR-effector interactions, applicable to all pathosystems. Finally, the NLR-Effector Interaction Classification (NEIC) resource can streamline research efforts by identifying NLRs important for plant-pathogen resistance, advancing our understanding of plant immunity.

Plant Proteins↗
PubMed2026

BaGGLS: a Bayesian shrinkage framework for interpretable modeling of interactions in high-dimensional biological data.

MOTIVATION: Biological data is often high dimensional, noisy, and governed by complex interactions among sparse signals. This poses major challenges for interpretability and reliable feature selection. Tasks such as identifying motif interactions in genomics exemplify these difficulties, as only a small subset of biologically relevant features (e.g. motifs) are typically active, and their effects are often non-linear and context-dependent. While statistical approaches often result in more interpretable models, deep learning models have proven effective in modeling complex interactions and prediction accuracy, yet their black-box nature limits interpretability. RESULTS: We introduce BaGGLS, a flexible and interpretable probabilistic binary regression model designed for high-dimensional biological inference involving feature interactions. BaGGLS incorporates a Bayesian group global-local shrinkage prior, aligned with the group structure introduced by interaction terms. This prior encourages sparsity while retaining interpretability, helping to isolate meaningful signals and suppress noise. To enable scalable inference, we employ a partially factorized variational approximation that captures posterior skewness and supports efficient learning even in large feature spaces. In extensive simulations, we compare BaGGLS to frequentist probit regressions (unconstrained and with L1-penalty) as well as a probit model with Markov Chain Monte Carlo (MCMC) sampling under a horseshoe prior. We can show that BaGGLS outperforms the other methods with regard to interaction detection and is many times faster than MCMC sampling under the horseshoe prior. We also demonstrate the usefulness of BaGGLS in the context of interaction discovery from motif scanner outputs (e.g. Find Individual Motif Occurrences (FIMO)) and noisy attribution scores from deep learning models. This shows that BaGGLS is a promising approach for uncovering biologically relevant interaction patterns, with potential applicability across a range of high-dimensional tasks in computational biology. AVAILABILITY: Code is available at gitlab.com/dacs-hpi/baggls.

Bayes Theorem↗
PubMed2025

The signed two-space proximity model for learning representations in protein-protein interaction networks.

MOTIVATION: Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expensive. Thus, attention has been recently drawn to machine learning approaches. Furthermore, insufficient effort has been made toward analyzing signed PPI networks, which capture both activating (positive) and inhibitory (negative) interactions. To accurately represent biological relationships, we present the Signed Two-Space Proximity Model (S2-SPM) for signed PPI networks, which explicitly incorporates both types of interactions, reflecting the complex regulatory mechanisms within biological systems. This is achieved by leveraging two independent latent spaces to differentiate between positive and negative interactions while representing protein similarity through proximity in these spaces. Our approach also enables the identification of archetypes representing extreme protein profiles. RESULTS: S2-SPM's superior performance in predicting the presence and sign of interactions in SPPI networks is demonstrated in link prediction tasks against relevant baseline methods. Additionally, the biological prevalence of the identified archetypes is confirmed by an enrichment analysis of Gene Ontology (GO) terms, which reveals that distinct biological tasks are associated with archetypal groups formed by both interactions. This study is also validated regarding statistical significance and sensitivity analysis, providing insights into the functional roles of different interaction types. Finally, the robustness and consistency of the extracted archetype structures are confirmed using the Bayesian Normalized Mutual Information (BNMI) metric, proving the model's reliability in capturing meaningful SPPI patterns. AVAILABILITY: S2-SPM is implemented and freely available under the MIT license at https://github.com/Nicknakis/S2SPM.

Protein Interaction Mapping↗
PubMed2026

Structure-informed theoretical modeling defines principles governing avidity in bivalent protein interactions.

In signaling cascades, signaling proteins often encode multiple domains or motifs, which presents the possibility for avidity -- where multivalent binding drastically increases interaction strength and duration. However, predicting and validating multivalent interactions that interact with avidity is a challenge. Here, we integrate mechanistic modeling, structure-based analysis, and experimental approaches as a framework for defining the conditions under which avidity plays a role. We explore the tandem SH2 domain family of interactions with bisphosphorylated partners as a multivalent archetype, which encompasses key secondary messengers in tyrosine kinase signaling networks. Theoretical modeling suggests that maximum avidity occurs with closely spaced tyrosine phosphorylation sites combined with moderate monovalent affinities - exactly around the innate range of SH2 domain affinity - or with phosphorylation sites separated by sufficiently flexible linkers. Surprisingly, despite sequence diversity, structure-based analysis showed relatively conserved three-dimensional spacing between SH2 domains across all tandem SH2 families, which we corroborate experimentally, suggesting evolutionary optimization for avidity interactions. The combination of structure-based analysis of domain spacing with available monovalent experimental data appears, along with iterative experimental refinement of biophysical parameters, can identify high affinity interactions of tandem SH2 domain recruitment to the EGFR C-terminal tail. Using these principles, we extended bivalent predictions into the full phosphoproteome space and structural parameterization of other partners of SH2 domain binding, providing resources and methods for more rapid expansion of bivalent analysis. These approaches lay the groundwork for larger utility in multivalent prediction and testing to help better understand protein interactions that drive cell signaling.

BLI↗
PubMed2019

Residues on Adeno-associated Virus Capsid Lumen Dictate Interactions and Compatibility with the Assembly-Activating Protein.

The adeno-associated virus (AAV) serves as a broadly used vector system for in vivo gene delivery. The process of AAV capsid assembly remains poorly understood. The viral cofactor assembly-activating protein (AAP) is required for maximum AAV production and has multiple roles in capsid assembly, namely, trafficking of the structural proteins (VP) to the nuclear site of assembly, promoting the stability of VP against multiple degradation pathways, and facilitating stable interactions between VP monomers. The N-terminal 60 amino acids of AAP (AAPN) are essential for these functions. Presumably, AAP must physically interact with VP to execute its multiple functions, but the molecular nature of the AAP-VP interaction is not well understood. Here, we query how structurally related AAVs functionally engage AAP from AAV serotype 2 (AAP2) toward virion assembly. These studies led to the identification of key residues on the lumenal capsid surface that are important for AAP-VP and for VP-VP interactions. Replacing a cluster of glutamic acid residues with a glutamine-rich motif on the conserved VP beta-barrel structure of variants incompatible with AAP2 creates a gain-of-function mutant compatible with AAP2. Conversely, mutating positively charged residues within the hydrophobic region of AAP2 and conserved core domains within AAPN creates a gain-of-function AAP2 mutant that rescues assembly of the incompatible variant. Our results suggest a model for capsid assembly where surface charge/neutrality dictates an interaction between AAPN and the lumenal VP surface to nucleate capsid assembly.IMPORTANCE Efforts to engineer the AAV capsid to gain desirable properties for gene therapy (e.g., tropism, reduced immunogenicity, and higher potency) require that capsid modifications do not affect particle assembly. The relationship between VP and the cofactor that facilitates its assembly, AAP, is central to both assembly preservation and vector production. Understanding the requirements for this compatibility can inform manufacturing strategies to maximize production and reduce costs. Additionally, library-based approaches that simultaneously examine a large number of capsid variants would benefit from a universally functional AAP, which could hedge against overlooking variants with potentially valuable phenotypes that were lost during vector library production due to incompatibility with the cognate AAP. Studying interactions between the structural and nonstructural components of AAV enhances our fundamental knowledge of capsid assembly mechanisms and the protein-protein interactions required for productive assembly of the icosahedral capsid.

Amino Acid Sequence↗
PubMed2025

Competition and cooperation: The plasticity of bacterial interactions across environments.

Bacteria live in diverse communities, forming complex networks of interacting species. A central question in bacterial ecology is whether species engage in cooperative or competitive interactions. But this question often neglects the role of the environment. Here, we use genome-scale metabolic networks from two different open-access collections (AGORA and CarveMe) to assess pairwise interactions of different microbes in varying environmental conditions (provision of different environmental compounds). By computationally simulating thousands of environments for 10,000 pairs of bacteria from each collection, we found that most pairs were able to both compete and cooperate depending on the availability of environmental resources. This modeling approach allowed us to determine commonalities between environments that could facilitate the potential for cooperation or competition between a pair of species. Namely, cooperative interactions, especially obligate, were most common in less diverse environments. Further, as compounds were removed from the environment, we found interactions tended to degrade towards obligacy. However, we also found that on average at least one compound could be removed from an environment to switch the interaction from competition to facultative cooperation or vice versa. Together our approach indicates a high degree of plasticity in microbial interactions in response to the availability of environmental resources.

Microbial Interactions↗
PubMed2025

Pooled PPIseq: Screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform.

Protein-Protein Interactions (PPIs) are a key interface between virus and host, and these interactions are important to both viral reprogramming of the host and to host restriction of viral infection. In particular, viral-host PPI networks can be used to further our understanding of the molecular mechanisms of tissue specificity, host range, and virulence. At higher scales, viral-host PPI screening could also be used to screen for small-molecule antivirals that interfere with essential viral-host interactions, or to explore how the PPI networks between interacting viral and host genomes co-evolve. Current high-throughput PPI assays have screened entire viral-host PPI networks. However, these studies are time consuming, often require specialized equipment, and are difficult to further scale. Here, we develop methods that make larger-scale viral-host PPI screening more accessible. This approach combines the mDHFR split-tag reporter with the iSeq2 interaction-barcoding system to permit massively-multiplexed PPI quantification by simple pooled engineering of barcoded constructs, integration of these constructs into budding yeast, and fitness measurements by pooled cell competitions and barcode-sequencing. We applied this method to screen for PPIs between SARS-CoV-2 proteins and human proteins, screening in triplicate >180,000 ORF-ORF combinations represented by >1,000,000 barcoded lineages. Our results complement previous screens by identifying 74 putative PPIs, including interactions between ORF7A with the taste receptors TAS2R41 and TAS2R7, and between NSP4 with the transmembrane KDELR2 and KDELR3. We show that this PPI screening method is highly scalable, enabling larger studies aimed at generating a broad understanding of how viral effector proteins converge on cellular targets to effect replication.

Humans↗
PubMed2025

Cobamide-based interactions between soil bacteria can be predicted based on monoculture growth.

Interactions between microbes shape the structure and function of microbial communities. While studying interactions is key to understanding microbial communities as a whole, gaining a detailed mechanistic view is challenging due to the scale of co-occurring interactions. The model nutrient approach enables the study of a subset of interactions involving a single nutrient class and can shed light on broader interaction mechanisms involving other nutrients. Here, we focus on cobamides, the cobalamin (vitamin B12) family of enzyme cofactors, to study nutrient competition and nutrient-sharing interactions in co-cultures and tri-cultures. We examined bacteria that were previously isolated from a grassland soil and were characterized as "dependents" (require cobamides but cannot synthesize them) or "producers" (synthesize cobamides). The outcome of competition between a pair of dependents was predictable based on monoculture growth characteristics, with the dominant microbe determined by its adaptation to a specific cobamide concentration range. Moreover, cobamide producers could support the cobamide-dependent growth of dependents in co-culture and influenced the outcome of competition between dependents in tri-culture. We analyzed the metabolic capacity encoded in the genomes of producers and dependents and found that cobamides are likely the main shared nutrient in our co- and tri-cultures. These results highlight the utility of the model nutrient approach to characterize and predict interactions in bacterial consortia of increasing complexity.

Journal Article↗
PubMed2026

GiGCN: a network-based framework for uncovering synthetic lethal and viable genetic interactions.

Genetic interactions (GIs) underpin the functional connectivity of genes and pathways, and are important for dissecting genotype-phenotype relationships and identifying therapeutic targets for diseases. However, the scale of the human genome restricts systematic experimental interrogation of GIs. Existing computational tools focus on predicting synthetic lethality (SL) and synthetic viability (SV), the two primary forms of GIs, yet their accuracy and biological interpretability are compromised by inadequate modeling of the molecular mechanisms behind positive and negative interactions, as well as the limitation of negative samples. To overcome these challenges, we developed Genetic Interaction Graph Convolutional Network (GiGCN), a signed network modeling framework for the joint identification of gene pairs with SL and SV. We built a high-confidence signed genetic network by integrating verified GIs, and non-interacting gene pairs, together with gene semantic similarity derived from biological processes. By leveraging disentangled subspace decomposition, this framework separately models distinct functional dimensions within gene networks, enabling robust representation of context-dependent regulatory relationships and accurate discrimination of SL and SV events. Benchmark experiments demonstrate that GiGCN outperforms state-of-the-art approaches (area under receiver operating-characteristic curve: 0.978, and area under precision-recall curve: 0.944). Further analyses reveal biologically meaningful insights, including known and novel SL interactions centered on the oncogene MYC Proto-Oncogene (MYC), as well as SV interactions linked to autophagy and mitophagy pathways. This study provides a robust and interpretable network-based strategy for systematically exploring GIs. The GiGCN framework not only improves the precision of SL and SV prediction, but also offers mechanistic insights into gene functional relationships, thereby supporting the discovery of actionable therapeutic targets for cancer and other human diseases.

Humans↗
PubMed2025

Gene-environment interaction analysis in atopic eczema: evidence from large population datasets and modelling in vitro.

BACKGROUND: Environmental factors play a role in the pathogenesis of complex traits including atopic eczema (AE) and a greater understanding of gene-environment interactions (G*E) is needed to define pathomechanisms for disease prevention. We analysed data from 16 European studies to test for interaction between the 24 most significant AE-associated loci identified from genome-wide association studies and 18 early-life environmental factors. We tested for replication using a further 10 studies and in vitro modelling to independently assess findings. RESULTS: The discovery analysis showed suggestive evidence for interaction (p<0.05) between 7 environmental factors (antibiotic use, cat ownership, dog ownership, breastfeeding, elder sibling, smoking and washing practices) and at least one established variant for AE, 14 interactions in total (maxN=25,339). In replication analysis (maxN=252,040) dog exposure*rs10214237 (on chromosome 5p13.2 near IL7R) was nominally significant (ORinteraction=0.91 [0.83-0.99] P=0.025), with a risk effect of the T allele observed only in those not exposed to dogs. A similar interaction with rs10214237 was observed for siblings in the discovery analysis (ORinteraction=0.84[0.75-0.94] P=0.003), but replication analysis was under-powered ORinteraction=1.09[0.82-1.46]). Rs10214237 homozygous risk genotype is associated with lower IL-7R expression in human keratinocytes, and dog exposure modelled in vitro showed a differential response according to rs10214237 genotype. CONCLUSIONS: Interaction analysis and functional assessment provide evidence that early-life dog exposure may modify the genetic effect of rs10214237 on AE via IL7R, supporting observational epidemiology showing a protective effect for dog ownership. The lack of evidence for other G*E studied here implies that only weak effects are likely to occur.

Atopic eczema↗