Project description:Mosquitoes (Diptera: Culicidae) are found widely throughout the world. Several species can transmit pathogens to humans and other vertebrates. Mosquitoes harbor great amounts of bacteria, fungi, and viruses. The bacterial composition of the microbiota of these invertebrates is associated with several factors, such as larval habitat, environment, and species. Yet little is known about bacterial interaction networks in mosquitoes. This study investigates the bacterial communities of eight species of Culicidae collected in Vale do Ribeira (Southeastern São Paulo State) and verifies the bacterial interaction network in these species. Sequences of the 16S rRNA region from 111 mosquito samples were analyzed. Bacterial interaction networks were generated from Spearman correlation values. Proteobacteria was the predominant phylum in all species. Wolbachia was the predominant genus in Haemagogus leucocelaenus. Aedes scapularis, Aedes serratus, Psorophora ferox, and Haemagogus capricornii were the species that showed a greater number of bacterial interactions. Bacterial positive interactions were found in all mosquito species, whereas negative correlations were observed in Hg. leucocelaenus, Ae. scapularis, Ae. serratus, Ps. ferox, and Hg. capricornii. All bacterial interactions with Asaia and Wolbachia were negative in Aedes mosquitoes.
Project description:PDZ domain-mediated interactions have greatly expanded during metazoan evolution, becoming important for controlling signal flow via the assembly of multiple signaling components. The evolutionary history of PDZ domain-mediated interactions has never been explored at the molecular level. It is of great interest to understand how PDZ domain-ligand interactions emerged and how they become rewired during evolution. Here, we constructed the first human PDZ domain-ligand interaction network (PDZNet) together with binding motif sequences and interaction strengths of ligands. PDZNet includes 1,213 interactions between 97 human PDZ proteins and 591 ligands that connect most PDZ protein-mediated interactions (98%) in a large single network via shared ligands. We examined the rewiring of PDZ domain-ligand interactions throughout eukaryotic evolution by tracing changes in the C-terminal binding motif sequences of the PDZ ligands. We found that interaction rewiring by sequence mutation frequently occurred throughout evolution, largely contributing to the growth of PDZNet. The rewiring of PDZ domain-ligand interactions provided an effective means of functional innovations in nervous system development. Our findings provide empirical evidence for a network evolution model that highlights the rewiring of interactions as a mechanism for the development of new protein functions. PDZNet will be a valuable resource to further characterize the organization of the PDZ domain-mediated signaling proteome.
Project description:A system-level framework of complex microbe-microbe and host-microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut.
Project description:Although ecological networks are usually considered a static representation of species' interactions, the interactions can change when the preferred partners are absent (rewiring). In mutualistic networks, rewiring with non-preferred partners can palliate extinction cascades, contributing to communities' stability. In spite of its significance, whether general patterns can shape the rewiring of ecological interactions remains poorly understood. Here, we show a phylogenetic constraint in the rewiring of mycorrhizal networks, so that rewired interactions (i.e., with non-preferred hosts) tend to involve close relatives of preferred hosts. Despite this constraint, rewiring increases the robustness of the fungal community to the simulated loss of their host species. We identify preferred and non-preferred hosts based on the probability that, when the two partners co-occur, they actually interact. Understanding general patterns in the rewiring of interactions can improve our predictions of community responses to interactions' loss, which influences how global changes will affect ecosystem stability.
Project description:Cellular differentiation requires dramatic changes in chromatin organization, transcriptional regulation, and protein production. To understand the regulatory connections between these processes, we generated proteomic, transcriptomic, and chromatin accessibility data during differentiation of mouse embryonic stem cells (ESCs) into postmitotic neurons and found extensive associations between different molecular layers within and across differentiation time points. We observed that SOX2, as a regulator of pluripotency and neuronal genes, redistributes from pluripotency enhancers to neuronal promoters during differentiation, likely driven by changes in its protein interaction network. We identified ATRX as a major SOX2 partner in neurons, whose co-localization correlated with an increase in active enhancer marks and increased expression of nearby genes, which we experimentally confirmed for three loci. Collectively, our data provide key insights into the regulatory transformation of SOX2 during neuronal differentiation, and we highlight the significance of multi-omic approaches in understanding gene regulation in complex systems.
Project description:Brain network connections rewire adaptively in response to neural activity. Adaptive rewiring may be understood as a process which, at its every step, is aimed at optimizing the efficiency of signal diffusion. In evolving model networks, this amounts to creating shortcut connections in regions with high diffusion and pruning where diffusion is low. Adaptive rewiring leads over time to topologies akin to brain anatomy: small worlds with rich club and modular or centralized structures. We continue our investigation of adaptive rewiring by focusing on three desiderata: specificity of evolving model network architectures, robustness of dynamically maintained architectures, and flexibility of network evolution to stochastically deviate from specificity and robustness. Our adaptive rewiring model simulations show that specificity and robustness characterize alternative modes of network operation, controlled by a single parameter, the rewiring interval. Small control parameter shifts across a critical transition zone allow switching between the two modes. Adaptive rewiring exhibits greater flexibility for skewed, lognormal connection weight distributions than for normally distributed ones. The results qualify adaptive rewiring as a key principle of self-organized complexity in network architectures, in particular of those that characterize the variety of functional architectures in the brain.
Project description:Human interaction networks inferred from country-wide telephone activity recordings were recently used to redraw political maps by projecting their topological partitions into geographical space. The results showed remarkable spatial cohesiveness of the network communities and a significant overlap between the redrawn and the administrative borders. Here we present a similar analysis based on one of the most popular online social networks represented by the ties between more than 5.8 million of its geo-located users. The worldwide coverage of their measured activity allowed us to analyze the large-scale regional subgraphs of entire continents and an extensive set of examples for single countries. We present results for North and South America, Europe and Asia. In our analysis we used the well-established method of modularity clustering after an aggregation of the individual links into a weighted graph connecting equal-area geographical pixels. Our results show fingerprints of both of the opposing forces of dividing local conflicts and of uniting cross-cultural trends of globalization.
Project description:Species phenotypic traits affect the interaction patterns and the organization of seed-dispersal interaction networks. Understanding the relationship between species characteristics and network structure help us understand the assembly of natural communities and how communities function. Here, we examine how species traits may affect the rules leading to patterns of interaction among plants and fruit-eating vertebrates. We study a species-rich seed-dispersal system using a model selection approach to examine whether the rules underlying network structure are driven by constraints in fruit resource exploitation, by preferential consumption of fruits by the frugivores, or by a combination of both. We performed analyses for the whole system and for bird and mammal assemblages separately, and identified the animal and plant characteristics shaping interaction rules. The structure of the analyzed interaction network was better explained by constraints in resource exploitation in the case of birds and by preferential consumption of fruits with specific traits for mammals. These contrasting results when looking at bird-plant and mammal-plant interactions suggest that the same type of interaction is organized by different processes depending on the assemblage we focus on. Size-related restrictions of the interacting species (both for mammals and birds) were the most important factors driving the interaction rules. Our results suggest that the structure of seed-dispersal interaction networks can be explained using species traits and interaction rules related to simple ecological mechanisms.
Project description:Recent years have seen tremendous advances in the scientific study of networks, as more and larger data sets of relationships among nodes have become available in many different fields. This has led to pathbreaking discoveries of near-universal network behavior over time, including the principle of preferential attachment and the emergence of scaling in complex networks. Missing from the set of network analysis methods to date is a measure that describes for each node how its relationship (or links) with other nodes changes from one period to the next. Conventional measures of network change for the most part show how the degrees of a node change; these are scalar comparisons. Our contribution is to use, for the first time, the cosine similarity to capture not just the change in degrees of a node but its relationship to other nodes. These are vector (or matrix)-based comparisons, rather than scalar, and we refer to them as "rewiring" coefficients. We apply this measure to three different networks over time to show the differences in the two types of measures. In general, bigger increases in our rewiring measure are associated with larger increases in network density, but this is not always the case.
Project description:There is a compelling evidence that midbrain dopamine (DA) neurons and their projections to the ventral striatum provide a mechanism for motivating reward-seeking behavior, and for utilizing information about unexpected reward prediction errors (RPEs) to guide behavior based on current, rather than historical, outcomes. When this mechanism is compromised in addictions, it may produce patterns of maladaptive behavior that remain obdurate in the face of contrary information and even adverse consequences. Nonetheless, DAergic contributions to performance on behavioral tasks that rely on the ability to flexibly update stimulus-reward relationships remains incompletly understood. In the current study, we used a discrimination and reversal paradigm to monitor subsecond DA release in mouse NAc core (NAc) using in vivo fast-scan cyclic voltammetry (FSCV). We observed post-choice elevations in phasic NAc DA release; however, increased DA transients were only evident during early reversal when mice made responses at the newly rewarded stimulus. Based on this finding, we used in vivo optogenetic (eNpHR) photosilencing and (Channelrhodopsin2 [ChR2]) photostimulation to assess the effects of manipulating VTA-DAergic fibers in the NAc on reversal performance. Photosilencing the VTA → NAc DAergic pathway during early reversal increased errors, while photostimulation did not demonstrably affect behavior. Taken together, these data provide additional evidence of the importance of NAc DA release as a neural substrate supporting adjustments in learned behavior after a switch in expected stimulus-reward contingencies. These findings have possible implications for furthering understanding the role of DA in persistent, maladaptive decision-making characterizing addictions.