Tripartite Network Research Articles

A novel collaborative filtering model for LncRNA-disease association prediction based on the Na\xefve Bayesian classifier

BackgroundSince the number of known lncRNA-disease associations verified by biological experiments is quite limited, it has been a challenging task to uncover human disease-related lncRNAs in recent years. Moreover, considering the fact that biological experiments are very expensive and time-consuming, it is important to develop efficient computational models to discover potential lncRNA-disease associations.ResultsIn this manuscript, a novel Collaborative Filtering model called CFNBC for inferring potential lncRNA-disease associations is proposed based on Naïve Bayesian Classifier. In CFNBC, an original lncRNA-miRNA-disease tripartite network is constructed first by integrating known miRNA-lncRNA associations, miRNA-disease associations and lncRNA-disease associations, and then, an updated lncRNA-miRNA-disease tripartite network is further constructed through applying the item-based collaborative filtering algorithm on the original tripartite network. Finally, based on the updated tripartite network, a novel approach based on the Naïve Bayesian Classifier is proposed to predict potential associations between lncRNAs and diseases. The novelty of CFNBC lies in the construction of the updated lncRNA-miRNA-disease tripartite network and the introduction of the item-based collaborative filtering algorithm and Naïve Bayesian Classifier, which guarantee that CFNBC can be applied to predict potential lncRNA-disease associations efficiently without entirely relying on known miRNA-disease associations. Simulation results show that CFNBC can achieve a reliable AUC of 0.8576 in the Leave-One-Out Cross Validation (LOOCV), which is considerably better than previous state-of-the-art results. Moreover, case studies of glioma, colorectal cancer and gastric cancer demonstrate the excellent prediction performance of CFNBC as well.ConclusionsAccording to simulation results, due to the satisfactory prediction performance, CFNBC may be an excellent addition to biomedical researches in the future.

Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks

A kind of noncoding RNA with length more than 200 nucleotides named long noncoding RNA (lncRNA) has gained considerable attention in recent decades. Many studies have confirmed that human genome contains many thousands of lncRNAs. LncRNAs play significant roles in many important biological processes, including complex disease diagnosis, prognosis, prevention and treatment. For some important diseases such as cancer, lncRNAs have been novel candidate biomarkers. However, the role of lncRNAs in human diseases is still in its infancy, and only a small part of lncRNA-disease associations have been experimentally verified. Predicting lncRNA-disease association is an important way to understand the mechanism and function of lncRNA involved in diseases to enrich the annotations of lncRNA. Therefore, it is urgent to prioritize lncRNAs potentially associated with diseases. Biological system is a highly complex heterogenous network involved different molecules. Therefore, the algorithms based on network methods have been extensively applied in information fields which can provide a quantifiable characterization for the networks characterizing multifarious biological systems. A heterogeneous network topology possessing abundant interactions between biomedical entities is rarely utilized in similarity-based methods for predicting lncRNA-disease associations based on the array of varying features of lncRNAs and diseases. DeepWalk, encoding the relations of nodes in a continuous vector space, is an extension of language model and unsupervised learning from sequence-based word to network. In this article, we present a novel lncRNA-disease association prediction method based on DeepWalk, which enhances the existing association discovery methods through a topology-based similarity measure. We integrate the heterogeneous data to construct a Linked Tripartite Network which is a heterogeneous network containing three types od nodes which generated from bioinformatics linked datasets and use DeepWalk method to extract topological structure features of the nodes in the linked tripartite network for calculating similarities. Our proposed method can be separated into the following steps: Firstly, we integrate heterogeneous data to construct a Linked Tripartite Network: containing the topological interactions of known lncRNA-disease, lncRNA-microRNA and microRNA-disease. Secondly, the topological structure features of the nodes are extracted based on DeepWalk. Thirdly, similarity scores of disease-disease pairs and lncRNA-lncRNA pairs are computed based on the topology of this network. Finally, new lncRNA and disease associations are discovered by rule-based inference method with lncRNA-lncRNA similarities. Our proposed method shows superior predictive performance for prediction of lncRNA-disease associations based on topological similarity from heterogenous network. The AUC value is used to show the performance of our method. The similarity measurement using network topology based on DeepWalk provide a novel perspective which is different from the similarity derived from sequence or structure information.Availability: All the data and codes are freely availability at: https://github.com/Pengeace/lncRNA-disease-link.

Physics textbooks and its network structures

We can observe self-organised networks all around us. These networks are, in general, scale-invariant networks described by the Barabasi-Albert model. The self-organised networks show certain universalities. These networks, in simplified models, have scale-invariant distribution (power law distribution) and the characteristic parameter α of the distribution has value between 2 and 5. Textbooks are an essential part of the learning process; therefore, we analysed the curriculum in secondary school textbooks of physics from the viewpoint of semantic network structures. We converted the textbook into a tripartite network, where the nodes represented sentences, terms and formulae. We found the same distribution as for self-organised networks. Cluster analysis was applied on the resulting network and we found individual modules—clusters. We obtained nine clusters, three of which were significantly larger. These clusters presented kinematics of point mass, dynamics of point mass and gravitational field with electric field.
 Keywords: Physics textbook, scale-invariant distribution, semantic network.

Evidence for an association of gut microbial Clostridia with brain functional connectivity and gastrointestinal sensorimotor function in patients with irritable bowel syndrome, based on tripartite network analysis

Background and aimsEvidence from preclinical and clinical studies suggests that interactions among the brain, gut, and microbiota may affect the pathophysiology of irritable bowel syndrome (IBS). As disruptions in central and peripheral serotonergic signaling pathways have been found in patients with IBS, we explored the hypothesis that the abundance of serotonin-modulating microbes of the order Clostridiales is associated with functional connectivity of somatosensory brain regions and gastrointestinal (GI) sensorimotor function.MethodsWe performed a prospective study of 65 patients with IBS and 21 healthy individuals (controls) recruited from 2011 through 2013 at a secondary/tertiary care outpatient clinic in Sweden. Study participants underwent functional brain imaging, rectal balloon distension, a nutrient and lactulose challenge test, and assessment of oroanal transit time within a month. They also submitted stool samples, which were analyzed by 16S ribosomal RNA gene sequencing. A tripartite network analysis based on graph theory was used to investigate the interactions among bacteria in the order Clostridiales, connectivity of brain regions in the somatosensory network, and GI sensorimotor function.ResultsWe found associations between GI sensorimotor function and gut microbes in stool samples from controls, but not in samples from IBS patients. The largest differences between controls and patients with IBS were observed in the Lachnospiraceae incertae sedis, Clostridium XIVa, and Coprococcus subnetworks. We found connectivity of subcortical (thalamus, caudate, and putamen) and cortical (primary and secondary somatosensory cortices) regions to be involved in mediating interactions among these networks.ConclusionsIn a comparison of patients with IBS and controls, we observed disruptions in the interactions between the brain, gut, and gut microbial metabolites in patients with IBS—these involve mainly subcortical but also cortical regions of brain. These disruptions may contribute to altered perception of pain in patients with IBS and may be mediated by microbial modulation of the gut serotonergic system.

Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction.

The drug discovery process is conventionally regarded as resource intensive and complex. Therefore, research effort has been put into a process called drug repositioning with the use of computational methods. Similarity-based methods are common in predicting drug-target association or the interaction between drugs and targets based on various features the drugs and targets have. Heterogeneous network topology involving many biomedical entities interactions has yet to be used in drug-target association. Deep learning can disclose features of vertices in a large network, which can be incorporated with heterogeneous network topology in order to assist similarity-based solutions to provide more flexibility for drug-target prediction. Here we describe a similarity-based drug-target prediction method that utilizes a topology-based similarity measure and two inference methods based on the similarities. We used DeepWalk, a deep learning method, to calculate the vertex similarities based on Linked Tripartite Network (LTN), which is a heterogeneous network created from different biomedical-linked datasets. The similarities are further used to feed to the inference methods, drug-based similarity inference (DBSI) and target-based similarity inference (TBSI), to obtain the predicted drug-target associations. Our previous experiments have shown that by utilizing deep learning and heterogeneous network topology, the proposed method can provide more promising results than current topology-based similarity computation methods.

Empowering the impaired astrocytes in the tripartite synapses to improve accuracy of pattern recognition

Recent finding has demonstrated that glial cells and especially astrocytes are responsible for some important roles in the central nervous system. Laboratory examination of the roles of astrocytes in information processing of neural system is associated with technical difficulties. Therefore, computational modeling provides a suitable approach in describing cognitive phenomena and neuroscience. In this paper, the role of strong and weak astrocytes in pattern recognition has considered and bio-stimulator was used to compensate the malfunction of the weak astrocyte. Therefore, we designed the tripartite synapses network, which astrocytes dominate the synaptic spaces. Then, the network has been trained on MNIST dataset. After that, the control feedback of astrocyte to pre- and postsynaptic neurons has decreased so that the astrocyte dominance on synaptic transitions decreased and impaired tripartite synapses network has been configured. In the next step, due to the destructive effects of impaired astrocyte, based on the dynamical model of the astrocyte biophysical model, a bio-inspired stimulator was designed. Accordingly, a new concept “Stimulating the impaired astrocytes to compensate their malfunction in pattern recognition” was introduced. This new mechanism was proposed to stimulate the impaired astrocytes in a population of tripartite synapses for restoration of the normal neural oscillations. Reported results confirmed that the increased error of “always firing” in pattern recognition caused by astrocytes dysfunction could be compensated by the bio-inspired stimulator.

Preservation of quantum nonbilocal correlations in noisy entanglement-swapping experiments using weak measurements

A tripartite quantum network is said to be bilocal if two independent sources produce a pair of bipartite entangled states. Quantum non-bilocal correlation emerges when the central party which possesses two particles from two different sources performs Bell-state measurement on them and nonlocality is generated between the other two uncorrelated systems in this entanglement-swapping protocol. The interaction of such systems with the environment reduces quantum non-bilocal correlations. Here we show that the diminishing effect modelled by the amplitude damping channel can be slowed down by employing the technique of weak measurements and reversals. It is demonstrated that for a large range of parameters the quantum non-bilocal correlations are preserved against decoherence by taking into account the average success rate of the post-selection governing weak measurements.

The ecology of movement and behaviour: a saturated tripartite network for describing animal contacts.

Ecologists regularly use animal contact networks to describe interactions underlying pathogen transmission, gene flow, and information transfer. However, empirical descriptions of contact often overlook some features of individual movement, and decisions about what kind of network to use in a particular setting are commonly ad hoc Here, we relate individual movement trajectories to contact networks through a tripartite network model of individual, space, and time nodes. Most networks used in animal contact studies (e.g. individual association networks, home range overlap networks, and spatial networks) are simplifications of this tripartite model. The tripartite structure can incorporate a broad suite of alternative ecological metrics like home range sizes and patch occupancy patterns into inferences about contact network metrics such as modularity and degree distribution. We demonstrate the model's utility with two simulation studies using alternative forms of ecological data to constrain the tripartite network's structure and inform expectations about the harder-to-measure metrics related to contact.

Correlation of tryptophan metabolites with connectivity of extended central reward network in healthy subjects.

ObjectiveA growing body of preclinical and clinical literature suggests that brain-gut-microbiota interactions play an important role in human health and disease, including hedonic food intake and obesity. We performed a tripartite network analysis based on graph theory to test the hypothesis that microbiota-derived fecal metabolites are associated with connectivity of key regions of the brain’s extended reward network and clinical measures related to obesity.MethodsDTI and resting state fMRI imaging was obtained from 63 healthy subjects with and without elevated body mass index (BMI) (29 males and 34 females). Subjects submitted fecal samples, completed questionnaires to assess anxiety and food addiction, and BMI was recorded.ResultsThe study results demonstrate associations between fecal microbiota-derived indole metabolites (indole, indoleacetic acid, and skatole) with measures of functional and anatomical connectivity of the amygdala, nucleus accumbens, and anterior insula, in addition to BMI, food addiction scores (YFAS) and anxiety symptom scores (HAD Anxiety).ConclusionsThe findings support the hypothesis that gut microbiota-derived indole metabolites may influence hedonic food intake and obesity by acting on the extended reward network, specifically the amygdala-nucleus accumbens circuit and the amygdala-anterior insula circuit. These cross sectional, data-driven results provide valuable information for future mechanistic studies.

A Novel Probability Model for LncRNA⁻Disease Association Prediction Based on the Naïve Bayesian Classifier.

An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play crucial roles in biological processes, complex disease diagnoses, prognoses, and treatments. However, experimentally validated associations between lncRNAs and diseases are still very limited. Recently, computational models have been developed to discover potential associations between lncRNAs and diseases by integrating multiple heterogeneous biological data; this has become a hot topic in biological research. In this article, we constructed a global tripartite network by integrating a variety of biological information including miRNA–disease, miRNA–lncRNA, and lncRNA–disease associations and interactions. Then, we constructed a global quadruple network by appending gene–lncRNA interaction, gene–disease association, and gene–miRNA interaction networks to the global tripartite network. Subsequently, based on these two global networks, a novel approach was proposed based on the naïve Bayesian classifier to predict potential lncRNA–disease associations (NBCLDA). Comparing with the state-of-the-art methods, our new method does not entirely rely on known lncRNA–disease associations, and can achieve a reliable performance with effective area under ROC curve (AUCs)in leave-one-out cross validation. Moreover, in order to further estimate the performance of NBCLDA, case studies of colorectal cancer, prostate cancer, and glioma were implemented in this paper, and the simulation results demonstrated that NBCLDA can be an excellent tool for biomedical research in the future.

Digital configuration of astrocyte stimulation as a new technique to strengthen the impaired astrocytes in the tripartite synapse network

Recent findings have proven that glial cells and particularly astrocytes are responsible for some important roles in the central nervous system. Due to laboratory technical difficulties in the examination of astrocyte’s functions in information processing of neural systems, computational modeling is a suitable approach. In this paper, the process of synchronization in neuronal fluctuations in view of the role of astrocytes was investigated. Then, due to the destructive effects of hyper-synchronization, based on the dynamical model of the astrocyte biophysical model, a new bio-inspired stimulator was introduced. Many patients suffer from astrocyte-related epilepsy; however, drugs that affect astrocytes are not available currently. Therefore, here a new concept of “stimulating the impaired astrocytes to compensate for their malfunction” was introduced. This new mechanism was proposed to stimulate the impaired astrocytes in a population of tripartite synapses for restoration of the normal neural oscillations. Finally, a closed-loop interaction between the proposed stimulating method and a population of tripartite synapses was implemented on a Zynq system-on-chip (SoC) platform from Xilinx, because the Zynq SoC is an appropriate control platform in the implementation of the proposed architecture. Stimulator hardware design was optimized to attain a very low power consumption platform in real-time application. Results of hardware and software simulations confirmed that hyper-synchronization of a neuronal population caused by astrocytes dysfunction could be compensated by the bio-inspired stimulator.

Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases

Copy number variations (CNVs) are genomic structural variations (deletions, duplications, or translocations) that represent the 4.8–9.5% of human genome variation in healthy individuals. In some cases, CNVs can also lead to disease, being the etiology of many known rare genetic/genomic disorders. Despite the last advances in genomic sequencing and diagnosis, the pathological effects of many rare genetic variations remain unresolved, largely due to the low number of patients available for these cases, making it difficult to identify consistent patterns of genotype–phenotype relationships. We aimed to improve the identification of statistically consistent genotype–phenotype relationships by integrating all the genetic and clinical data of thousands of patients with rare genomic disorders (obtained from the DECIPHER database) into a phenotype–patient–genotype tripartite network. Then we assessed how our network approach could help in the characterization and diagnosis of novel cases in clinical genetics. The systematic approach implemented in this work is able to better define the relationships between phenotypes and specific loci, by exploiting large-scale association networks of phenotypes and genotypes in thousands of rare disease patients. The application of the described methodology facilitated the diagnosis of novel clinical cases, ranking phenotypes by locus specificity and reporting putative new clinical features that may suggest additional clinical follow-ups. In this work, the proof of concept developed over a set of novel clinical cases demonstrates that this network-based methodology might help improve the precision of patient clinical records and the characterization of rare syndromes.

NcRNA-disease association prediction based on sequence information and tripartite network

BackgroundCurrent technology has demonstrated that mutation and deregulation of non-coding RNAs (ncRNAs) are associated with diverse human diseases and important biological processes. Therefore, developing a novel computational method for predicting potential ncRNA-disease associations could benefit pathologists in understanding the correlation between ncRNAs and disease diagnosis, treatment, and prevention. However, only a few studies have investigated these associations in pathogenesis.ResultsThis study utilizes a disease-target-ncRNA tripartite network, and computes prediction scores between each disease-ncRNA pair by integrating biological information derived from pairwise similarity based upon sequence expressions with weights obtained from a multi-layer resource allocation technique. Our proposed algorithm was evaluated based on a 5-fold-cross-validation with optimal kernel parameter tuning. In addition, we achieved an average AUC that varies from 0.75 without link cut to 0.57 with link cut methods, which outperforms a previous method using the same evaluation methodology. Furthermore, the algorithm predicted 23 ncRNA-disease associations supported by other independent biological experimental studies.ConclusionsTaken together, these results demonstrate the capability and accuracy of predicting further biological significant associations between ncRNAs and diseases and highlight the importance of adding biological sequence information to enhance predictions.

Bank–insurer–firm tripartite interconnectedness of credit risk exposures in a cross-shareholding network

This study assesses the interconnectedness of credit risk exposures in a tripartite network of cross-shareholdings among banks, insurers, and firms in Japan’s stock market during the fiscal years 2008–2015. We use consistent measures: credit risk exposure by PD (probability of default)/LGD (loss given default) approach in Basel III and RORA (return on risk-weighted assets). We conduct a credit risk analysis of the risk exposures in the cross-shareholdings. The result shows that by following the PD/LGD approach, the credit risk weights become approximately 1.5–5 times as large as by the transitional risk weight method. The mean exposure-weighted risk for the firm’s shareholdings is 1.67 times as large as the bank’s and the insurer’s. We analyze the network structure of the cross-shareholdings using network centrality measures. Our analysis can provide each entity with important implications on credit risk management in their cross-shareholdings.

Social Learning and Epistemic Community for Environmental Cooperation in Northeast Asia: The Case of the Tripartite Environment Ministers Meeting (TEMM) and Tripartite Environmental Education Network (TEEN)

Social Learning and Epistemic Community for Environmental Cooperation in Northeast Asia: The Case of the Tripartite Environment Ministers Meeting (TEMM) and Tripartite Environmental Education Network (TEEN)

Reactive Astrocytes in Glioblastoma Multiforme.

Despite the multidisciplinary integration in the therapeutic management of glioblastoma multiforme (GBM), the prognosis of GBM patients is poor. There is growing recognition that the cells in the tumor microenvironment play a vital role in regulating the progression of glioma. Astrocytes are an important component of the blood-brain barrier (BBB) as well as the tripartite synapse neural network to promote bidirectional communication with neurons under physiological conditions. Emerging evidence shows that tumor-associated reactive astrocytes interact with glioma cells and facilitate the progression, aggression, and survival of tumors by releasing different cytokines. Communication between reactive astrocytes and glioma cells is further promoted through ion channels and ion transporters, which augment the migratory capacity and invasiveness of tumor cells by modifying H+ and Ca2+ concentrations and stimulating volume changes in the cell. This in part contributes to the loss of epithelial polarization, initiating epithelial-mesenchymal transition. Therefore, this review will summarize the recent findings on the role of reactive astrocytes in the progression of GBM and in the development of treatment-resistant glioma. In addition, the involvement of ion channels and transporters in bridging the interactions between tumor cells and astrocytes and their potential as new therapeutic anti-tumor targets will be discussed.

Design of a tripartite network for the prediction of drug targets.

Drug-target networks have aided in many target prediction studies aiming at drug repurposing or the analysis of side effects. Conventional drug-target networks are bipartite. They contain two different types of nodes representing drugs and targets, respectively, and edges indicating pairwise drug-target interactions. In this work, we introduce a tripartite network consisting of drugs, other bioactive compounds, and targets from different sources. On the basis of analog relationships captured in the network and so-called neighbor targets of drugs, new drug targets can be inferred. The tripartite network was found to have a stable structure and simulated network growth was accompanied by a steady increase in assortativity, reflecting increasing correlation between degrees of connected nodes leading to even network connectivity. Local drug environments in the tripartite network typically contained neighbor targets and revealed interesting drug-compound-target relationships for further analysis. Candidate targets were prioritized. The tripartite network design extends standard drug-target networks and provides additional opportunities for drug target prediction.

Predicting Potential Drugs for Breast Cancer based on miRNA and Tissue Specificity

Network-based computational method, with the emphasis on biomolecular interactions and biological data integration, has succeeded in drug development and created new directions, such as drug repositioning and drug combination. Drug repositioning, that is finding new uses for existing drugs to treat more patients, offers time, cost and efficiency benefits in drug development, especially when in silico techniques are used. MicroRNAs (miRNAs) play important roles in multiple biological processes and have attracted much scientific attention recently. Moreover, cumulative studies demonstrate that the mature miRNAs as well as their precursors can be targeted by small molecular drugs. At the same time, human diseases result from the disordered interplay of tissue- and cell lineage-specific processes. However, few computational researches predict drug-disease potential relationships based on miRNA data and tissue specificity. Therefore, based on miRNA data and the tissue specificity of diseases, we propose a new method named as miTS to predict the potential treatments for diseases. Firstly, based on miRNAs data, target genes and information of FDA (Food and Drug Administration) approved drugs, we evaluate the relationships between miRNAs and drugs in the tissue-specific PPI (protein-protein) network. Then, we construct a tripartite network: drug-miRNA-disease Finally, we obtain the potential drug-disease associations based on the tripartite network. In this paper, we take breast cancer as case study and focus on the top-30 predicted drugs. 25 of them (83.3%) are found having known connections with breast cancer in CTD (Comparative Toxicogenomics Database) benchmark and the other 5 drugs are potential drugs for breast cancer. We further evaluate the 5 newly predicted drugs from clinical records, literature mining, KEGG pathways enrichment analysis and overlapping genes between enriched pathways. For each of the 5 new drugs, strongly supported evidences can be found in three or more aspects. In particular, Regorafenib (DB08896) has 15 overlapping KEGG pathways with breast cancer and their p-values are all very small. In addition, whether in the literature curation or clinical validation, Regorafenib has a strong correlation with breast cancer. All the facts show that Regorafenib is likely to be a truly effective drug, worthy of our further study. It further follows that our method miTS is effective and practical for predicting new drug indications, which will provide potential values for treatments of complex diseases.

A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network.

Motivation Increasing studies have demonstrated that many human complex diseases are associated with not only microRNAs, but also long-noncoding RNAs (lncRNAs). LncRNAs and microRNA play significant roles in various biological processes. Therefore, developing effective computational models for predicting novel associations between diseases and lncRNA-miRNA pairs (LMPairs) will be beneficial to not only the understanding of disease mechanisms at lncRNA-miRNA level and the detection of disease biomarkers for disease diagnosis, treatment, prognosis, and prevention, but also the understanding of interactions between diseases and LMPairs at disease level. Results It is well known that genes with similar functions are often associated with similar diseases. In this article, a novel model named PADLMP for predicting associations between diseases and LMPairs is proposed. In this model, a Disease-LncRNA-miRNA (DLM) tripartite network was designed firstly by integrating the lncRNA-disease association network and miRNA-disease association network; then we constructed the disease-LMPairs bipartite association network based on the DLM network and lncRNA-miRNA association network; finally, we predicted potential associations between diseases and LMPairs based on the newly constructed disease-LMPair network. Simulation results show that PADLMP can achieve AUCs of 0.9318, 0.9090 ± 0.0264, and 0.8950 ± 0.0027 in the LOOCV, 2-fold, and 5-fold cross validation framework, respectively, which demonstrate the reliable prediction performance of PADLMP.

The Gut Microbiota Facilitates Drifts in the Genetic Diversity and Infectivity of Bacterial Viruses

The intestinal microbiota and human health are intimately linked, but interactions between bacteria and bacteriophages in the context of the mammalian intestine remain largely unexplored. We used comparative population genomics to study a tripartite network consisting of a virulent bacteriophage, its bacterial host, and a phage-insensitive bacterial strain both invitro and within the murine gut. The bacteriophage adapted to infect the insensitive strain when the three partners co-existed in the gut of conventional mice, but not in dixenic mice or in planktonic cultures. The molecular changes associated with modifications in the bacteriophage host spectrum included single amino acid substitutions and an unusual homologous intragenomic recombination event within the genome of the bacteriophage. An intermediate bacterial host isolated from the murine microbiota mediated bacteriophage adaptation. Our data indicate that by offering access to new hosts, the microbiota shifts the genetic diversity of bacteriophages, thereby promoting long-term persistence of bacteriophage populations.