Geography, phylogeny and host switch drive the coevolution of parasitic Gyrodactylus flatworms and their hosts

BACKGROUND

Gyrodactylus is a lineage of monogenean flatworm ectoparasites exhibiting many features that make them a suitable model to study the host-parasite coevolutionary dynamics. Previous coevolutionary studies of this lineage mainly relied on low-power datasets (a small number of samples and a single molecular marker) and (now) outdated algorithms.

METHODS

To investigate the coevolutionary relationship of gyrodactylids and their fish hosts in high resolution, we used complete mitogenomes (including two newly sequenced Gyrodactylus species), a large number of species in the single-gene dataset, and four different coevolutionary algorithms.

RESULTS

The overall coevolutionary fit between the parasites and hosts was consistently significant. Multiple indicators confirmed that gyrodactylids are generally highly host-specific parasites, but several species could parasitize either multiple (more than 5) or phylogenetically distant fish hosts. The molecular dating results indicated that gyrodactylids tend to evolve towards high host specificity. Speciation by host switch was identified as a more important speciation mode than co-speciation. Assuming that the ancestral host belonged to Cypriniformes, we inferred four major host switch events to non-Cypriniformes hosts (mostly Salmoniformes), all of which occurred deep in the evolutionary history. Despite their relative rarity, these events had strong macroevolutionary consequences for gyrodactylid diversity. For example, in our dataset, 57.28% of all studied gyrodactylids parasitized only non-Cypriniformes hosts, which implies that the evolutionary history of more than half of all included lineages could be traced back to these major host switch events. The geographical co-occurrence of fishes and gyrodactylids determined the host use by these gyrodactylids, and geography accounted for most of the phylogenetic signal in host use.

CONCLUSIONS

Our findings suggest that the coevolution of Gyrodactylus flatworms and their hosts is largely driven by geography, phylogeny, and host switches.

Exploring spatiotemporal dynamics of flower visitor association pattern on two Avicennia mangroves: a network approach

Plant-flower visitor interaction is one of the most important relationships regarding the co-existence of the floral and faunal communities. The implication of network approaches is an efficient way to understand the impact of community structure on ecosystem functionality. To understand the association pattern of flower visitors, we performed this study on Avicennia officinalis and Avicennia marina mangroves from the islands of Indian Sundarban over three consecutive years. We found that visiting time and sites (islands) influenced the abundance of visitors. The bipartite networks showed a significant generalized structure for both site-visitor and visiting time-visitor networks where the strength and specialization of visitor species showed a highly and moderately significant positive correlation between both networks respectively. All the site-wise visiting time-visitor networks and year-wise site-visitor networks were significantly modular in structure. For both the plants, most of the visitors showed a generalized association pattern among islands and also among visiting times. Additionally, the study of the foraging behavior of dominant visitors showed Apis dorsata and Apis mellifera as the potential visitors for these plants. Our results showed that flower visitor networks are spatiotemporally dynamic. The interactions of visitors with flowers at different times influence their contribution to the network for becoming a generalist or peripheral species in the context of their visiting time, which may subsequently change over islands. This approach will help to devise more precise plant species-specific conservation strategies by understanding the contribution of visitors through the spatiotemporal context.

Optimal Physician Shared-Patient Networks and the Diffusion of Medical Technologies

Social network analysis has created a productive framework for the analysis of the histories of patient-physician interactions and physician collaboration. Notable is the construction of networks based on the data of "referral paths" - sequences of patient-specific temporally linked physician visits - in this case, culled from a large set of Medicare claims data in the United States. Network constructions depend on a range of choices regarding the underlying data. In this paper we introduce the use of a five-factor experiment that produces 80 distinct projections of the bipartite patient-physician mixing matrix to a unipartite physician network derived from the referral path data, which is further analyzed at the level of the 2,219 hospitals in the final analytic sample. We summarize the networks of physicians within a given hospital using a range of directed and undirected network features (quantities that summarize structural properties of the network such as its size, density, and reciprocity). The different projections and their underlying factors are evaluated in terms of the heterogeneity of the network features across the hospitals. We also evaluate the projections relative to their ability to improve the predictive accuracy of a model estimating a hospital's adoption of implantable cardiac defibrillators, a novel cardiac intervention. Because it optimizes the knowledge learned about the overall and interactive effects of the factors, we anticipate that the factorial design setting for network analysis may be useful more generally as a methodological advance in network analysis.

Deciphering microeukaryotic-bacterial co-occurrence networks in coastal aquaculture ponds

Microeukaryotes and bacteria are key drivers of primary productivity and nutrient cycling in aquaculture ecosystems. Although their diversity and composition have been widely investigated in aquaculture systems, the co-occurrence bipartite network between microeukaryotes and bacteria remains poorly understood. This study used the bipartite network analysis of high-throughput sequencing datasets to detect the co-occurrence relationships between microeukaryotes and bacteria in water and sediment from coastal aquaculture ponds. Chlorophyta and fungi were dominant phyla in the microeukaryotic-bacterial bipartite networks in water and sediment, respectively. Chlorophyta also had overrepresented links with bacteria in water. Most microeukaryotes and bacteria were classified as generalists, and tended to have symmetric positive and negative links with bacteria in both water and sediment. However, some microeukaryotes with high density of links showed asymmetric links with bacteria in water. Modularity detection in the bipartite network indicated that four microeukaryotes and twelve uncultured bacteria might be potential keystone taxa among the module connections. Moreover, the microeukaryotic-bacterial bipartite network in sediment harbored significantly more nestedness than that in water. The loss of microeukaryotes and generalists will more likely lead to the collapse of positive co-occurrence relationships between microeukaryotes and bacteria in both water and sediment. This study unveils the topology, dominant taxa, keystone species, and robustness in the microeukaryotic-bacterial bipartite networks in coastal aquaculture ecosystems. These species herein can be applied for further management of ecological services, and such knowledge may also be very useful for the regulation of other eutrophic ecosystems.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-022-00159-6.

Modeling shield immunity to reduce COVID-19 transmission in long-term care facilities

PURPOSE

Nursing homes and long-term care facilities have experienced severe outbreaks and elevated mortality rates of COVID-19. When available, vaccination at-scale has helped drive a rapid reduction in severe cases. However, vaccination coverage remains incomplete among residents and staff, such that additional mitigation and prevention strategies are needed to reduce the ongoing risk of transmission. One such strategy is that of "shield immunity", in which immune individuals modulate their contact rates and shield uninfected individuals from potentially risky interactions.

METHODS

Here, we adapt shield immunity principles to a network context, by using computational models to evaluate how restructured interactions between staff and residents affect SARS-CoV-2 epidemic dynamics.

RESULTS

First, we identify a mitigation rewiring strategy that reassigns immune healthcare workers to infected residents, significantly reducing outbreak sizes given weekly testing and rewiring (48% reduction in the outbreak size). Second, we identify a preventative prewiring strategy in which susceptible healthcare workers are assigned to immunized residents. This preventative strategy reduces the risk and size of an outbreak via the inadvertent introduction of an infectious healthcare worker in a partially immunized population (44% reduction in the epidemic size). These mitigation levels derived from network-based interventions are similar to those derived from isolating infectious healthcare workers.

CONCLUSIONS

This modeling-based assessment of shield immunity provides further support for leveraging infection and immune status in network-based interventions to control and prevent the spread of COVID-19.

Fish-parasite interaction networks reveal latitudinal and taxonomic trends in the structure of host-parasite associations

In recent years, treating host–parasite associations as bipartite interaction networks has proven a powerful tool to identify structural patterns and their likely causes in communities of fish and their parasites. Network analysis allows for both community-level properties to be computed and investigated, and species-level roles to be determined. Here, using data from 31 host–parasite interaction networks from local fish communities around the world, we test for latitudinal trends at whole-network level, and taxonomic patterns at individual parasite species level. We found that while controlling for network size (number of species per network), network modularity, or the tendency for the network to be subdivided into groups of species that interact mostly with each other, decreased with increasing latitude. This suggests that tropical fish–parasite networks may be more stable than those from temperate regions in the event of community perturbations, such as species extinction. At the species level, after accounting for the effect of host specificity, we observed no difference in the centrality of parasite species within networks between parasites with different transmission modes. However, species in some taxa, namely branchiurans, acanthocephalans and larval trematodes, generally had higher centrality values than other parasite taxa. Because species with a central position often serve as module connectors, these 3 taxa may play a key role in whole-network cohesion. Our results highlight the usefulness of network analysis to reveal the aspects of fish–parasite community interactions that would otherwise remain hidden and advance our understanding of their evolution.

The large-scale spatial patterns of ecological networks between phytoplankton and zooplankton in coastal marine ecosystems

Although autotrophic phytoplankton and heterotrophic zooplankton both play important roles in the food web of marine ecosystem, their comprehensive interactions and spatial patterns at continental scale remain poorly studied. Here, we collected 251 seawater samples along 13,000 km of Chinese coastline, and microscopically investigated the latitudinal gradients of planktonic diversities. In total, 307 phytoplanktonic and 311 zooplanktonic species were visually identified. Using the newly developed Inter-Domain Ecological Networks (IDENs) approach, the phytoplankton-zooplankton interaction networks were constructed. We found that the phyto-zooplankton network structure was varied across three regions, more complex and numerous connections along the southern coast than in the north. In addition, some particular associations between zooplanktonic and phytoplanktonic groups were found to be localized in specific regions. Furthermore, the seawater temperature and salinity were the major driving force for shaping planktonic interaction networks. These results provide a deeper understanding of planktonic biogeography and phytoplankton-zooplankton interaction patterns.

Identifying potential association on gene-disease network via dual hypergraph regularized least squares

BACKGROUND

Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases.

RESULTS

In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations.

CONCLUSION

Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases.

Generating a heterosexual bipartite network embedded in social network

We describe an approach to generate a heterosexual network with a prescribed joint-degree distribution embedded in a prescribed large-scale social contact network. The structure of a sexual network plays an important role in how all sexually transmitted infections (STIs) spread. Generating an ensemble of networks that mimics the real-world is crucial to evaluating robust mitigation strategies for controlling STIs. Most of the current algorithms to generate sexual networks only use sexual activity data, such as the number of partners per month, to generate the sexual network. Real-world sexual networks also depend on biased mixing based on age, location, and social and work activities. We describe an approach to use a broad range of social activity data to generate possible heterosexual networks. We start with a large-scale simulation of thousands of people in a city as they go through their daily activities, including work, school, shopping, and activities at home. We extract a social network from these activities where the nodes are the people, and the edges indicate a social interaction, such as working in the same location. This social network captures the correlations between people of different ages, living in different locations, their economic status, and other demographic factors. We use the social contact network to define a bipartite heterosexual network that is embedded within an extended social network. The resulting sexual network captures the biased mixing inherent in the social network, and models based on this pairing of networks can be used to investigate novel intervention strategies based on the social contacts among infected people. We illustrate the approach in a model for the spread of chlamydia in the heterosexual network representing the young sexually active community in New Orleans.

Comprehensive host-pathogen protein-protein interaction network analysis

BACKGROUND

Infectious diseases are a cruel assassin with millions of victims around the world each year. Understanding infectious mechanism of viruses is indispensable for their inhibition. One of the best ways of unveiling this mechanism is to investigate the host-pathogen protein-protein interaction network. In this paper we try to disclose many properties of this network. We focus on human as host and integrate experimentally 32,859 interaction between human proteins and virus proteins from several databases. We investigate different properties of human proteins targeted by virus proteins and find that most of them have a considerable high centrality scores in human intra protein-protein interaction network. Investigating human proteins network properties which are targeted by different virus proteins can help us to design multipurpose drugs.

RESULTS

As host-pathogen protein-protein interaction network is a bipartite network and centrality measures for this type of networks are scarce, we proposed seven new centrality measures for analyzing bipartite networks. Applying them to different virus strains reveals unrandomness of attack strategies of virus proteins which could help us in drug design hence elevating the quality of life. They could also be used in detecting host essential proteins. Essential proteins are those whose functions are critical for survival of its host. One of the proposed centralities named diversity of predators, outperforms the other existing centralities in terms of detecting essential proteins and could be used as an optimal essential proteins' marker.

CONCLUSIONS

Different centralities were applied to analyze human protein-protein interaction network and to detect characteristics of human proteins targeted by virus proteins. Moreover, seven new centralities were proposed to analyze host-pathogen protein-protein interaction network and to detect pathogens' favorite host protein victims. Comparing different centralities in detecting essential proteins reveals that diversity of predator (one of the proposed centralities) is the best essential protein marker.

Cluster correlation based method for lncRNA-disease association prediction

BACKGROUND

In recent years, increasing evidences have indicated that long non-coding RNAs (lncRNAs) are deeply involved in a wide range of human biological pathways. The mutations and disorders of lncRNAs are closely associated with many human diseases. Therefore, it is of great importance to predict potential associations between lncRNAs and complex diseases for the diagnosis and cure of complex diseases. However, the functional mechanisms of the majority of lncRNAs are still remain unclear. As a result, it remains a great challenge to predict potential associations between lncRNAs and diseases.

RESULTS

Here, we proposed a new method to predict potential lncRNA-disease associations. First, we constructed a bipartite network based on known associations between diseases and lncRNAs/protein coding genes. Then the cluster association scores were calculated to evaluate the strength of the inner relationships between disease clusters and gene clusters. Finally, the gene-disease association scores are defined based on disease-gene cluster association scores and used to measure the strength for potential gene-disease associations.

CONCLUSIONS

Leave-One Out Cross Validation (LOOCV) and 5-fold cross validation tests were implemented to evaluate the performance of our method. As a result, our method achieved reliable performance in the LOOCV (AUCs of 0.8169 and 0.8410 based on Yang's dataset and Lnc2cancer 2.0 database, respectively), and 5-fold cross validation (AUCs of 0.7573 and 0.8198 based on Yang's dataset and Lnc2cancer 2.0 database, respectively), which were significantly higher than the other three comparative methods. Furthermore, our method is simple and efficient. Only the known gene-disease associations are exploited in a graph manner and further new gene-disease associations can be easily incorporated in our model. The results for melanoma and ovarian cancer have been verified by other researches. The case studies indicated that our method can provide informative clues for further investigation.

ACCBN: ant-Colony-clustering-based bipartite network method for predicting long non-coding RNA-protein interactions

BACKGROUND

Long non-coding RNA (lncRNA) studies play an important role in the development, invasion, and metastasis of the tumor. The analysis and screening of the differential expression of lncRNAs in cancer and corresponding paracancerous tissues provides new clues for finding new cancer diagnostic indicators and improving the treatment. Predicting lncRNA-protein interactions is very important in the analysis of lncRNAs. This article proposes an Ant-Colony-Clustering-Based Bipartite Network (ACCBN) method and predicts lncRNA-protein interactions. The ACCBN method combines ant colony clustering and bipartite network inference to predict lncRNA-protein interactions.

RESULTS

A five-fold cross-validation method was used in the experimental test. The results show that the values of the evaluation indicators of ACCBN on the test set are significantly better after comparing the predictive ability of ACCBN with RWR, ProCF, LPIHN, and LPBNI method.

CONCLUSIONS

With the continuous development of biology, besides the research on the cellular process, the research on the interaction function between proteins becomes a new key topic of biology. The studies on protein-protein interactions had important implications for bioinformatics, clinical medicine, and pharmacology. However, there are many kinds of proteins, and their functions of interactions are complicated. Moreover, the experimental methods require time to be confirmed because it is difficult to estimate. Therefore, a viable solution is to predict protein-protein interactions efficiently with computers. The ACCBN method has a good effect on the prediction of protein-protein interactions in terms of sensitivity, precision, accuracy, and F1-score.

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.

[Inferring Disease-miRNA Associations by Self-Weighting with Multiple Data Source]

Increasing evidence has suggested that microRNAs (miRNAs) may function as positive regulators at the post-transcriptional level. A search for associations between miRNAs and diseases is crucial for understanding the pathogenesis. Various publicly available databases have been constructed to store meaningful information on a large number of miRNA molecules. In this study, to resolve the limitation that individual sources of miRNA target data tend to be incomplete and noisy, we propose a network-based computational method called self-weighting for integrating multiple data sources. A bipartite phenotype-miRNA network (BPMN) incorporates known disease-miRNA interactions as well as the similarities between disease phenotypes and functional similarities of miRNAs. Random walk with restart algorithm was deployed on the bipartite network to predict novel disease-miRNA associations. In leave-one-out cross-validation experiments, our technique achieves an AUC of 0.801 when evaluating against known disease-related miRNAs from HMDD. Systematic prioritization of miRNAs for 11 common diseases obtained an average AUC of 0.765. Additionally, a case study on colon cancer uncovered a number of potential miRNA candidates as biomarkers of this disease.

An integrative system biology approach to unravel potential drug candidates for multiple age related disorders

Aging, though an inevitable part of life, is becoming a worldwide social and economic problem. Healthy aging is usually marked by low probability of age related disorders. Good therapeutic approaches are still in need to cure age related disorders. Occurrence of more than one ARD in an individual, expresses the need of discovery of such target proteins, which can affect multiple ARDs. Advanced scientific and medical research technologies throughout last three decades have arrived to the point where lots of key molecular determinants affect human disorders can be examined thoroughly. In this study, we designed and executed an approach to prioritize drugs that may target multiple age related disorders. Our methodology, focused on the analysis of biological pathways and protein protein interaction networks that may contribute to the pharmacology of age related disorders, included various steps such as retrieval and analysis of data, protein-protein interaction network analysis, and statistical and comparative analysis of topological coefficients, pathway, and functional enrichment analysis, and identification of drug-target proteins. We assume that the identified molecular determinants may be prioritized for further screening as novel drug targets to cure multiple ARDs. Based on the analysis, an online tool named as 'ARDnet' has been developed to construct and demonstrate ARD interactions at the level of PPI, ARDs and ARDs protein interaction, ARDs pathway interaction and drug-target interaction. The tool is freely made available at http://genomeinformatics.dtu.ac.in/ARDNet/Index.html.

A Bipartite Network-based Method for Prediction of Long Non-coding RNA-protein Interactions

As one large class of non-coding RNAs (ncRNAs), long ncRNAs (lncRNAs) have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI). LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV) test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR) and protein-based collaborative filtering (ProCF). Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins.