Network And Meta-Omics Reveal The Key Degrader And Cooperation Patterns In An Efficient 1,4-Dioxane-Degrading Microbial Community

Background: 1,4-dioxane is an emerging wastewater contaminant with probable human carcinogenicity. Our current understanding of microbial interactions during 1,4-dioxane biodegradation process in mix cultures is limited. Here, we applied metagenomic, metatranscriptomic and co-occurrence network analyses to unraveling the microbial cooperation between degrader and non-degraders in an efficient 1,4-dioxane-degrading microbial community CH1.Results: The 1,4-dioxane degrading bacterium, Ancylobacter polymorphus ZM13, was isolated from CH1 and proved to be the key degrader because of the high relative abundance, highly expressed toluene monooxygenase genes tmoABCDEF and high betweenness centrality of networks. The strain ZM13 cooperated obviously with 6 bacterial genera in the network, among which Xanthobacter and Mesorhizobium were proved to be involved in the intermediate metabolism with responsible genes encoding alcohol dehydrogenase (adh), aldehyde dehydrogenase (aldh), glycolate oxidase (glcDEF), glyoxylate carboligase (gcl), malate synthase (glcB) and 2-isopropylmalate synthase (leuA) upregulated. Also, 1,4-dioxane facilitated the shift of biodiversity and function of CH1, and those cooperators of CH1 cooperated with ZM13 in the way of providing amino acids or fatty acids and relieving environmental stresses to promote biodegradation.Conclusions: This study revealed the biodiversity, community structure, microbial functions and interactions in a microbial community CH1 during the efficient 1,4-dioxane degradation and proved the degrader Ancylobacter polymorphus ZM13 that isolated from CH1 was the key degrading bacterium. These results provide new insights into our understanding of how the key degrading bacterium interacted with cooperators in a 1,4-dioxane-degrading community, and has important implications for predicting microbial cooperation and constructing highly efficient synthetic 1,4-dioxane-degrading communities.

What Keeps a Vibrant Population Together?

Managing diversity is a challenging problem for organizations and governments. Diversity in a population may be of two kinds—acquired and innate. The former refers to diversity acquired by pre-existing social or organizational environments, attracting employees or immigrants because of their wealth and opportunities. Innate diversity, on the other hand, refers to a collection of pre-existing communities having to interact with one another and to build an overarching social or organizational identity. While acquired diversity has a prior element of common identity, innate diversity needs to build a common identity from a number of disparate regional or local identities. Diversity in any large population may have different extents of acquired and innate elements. In this paper, innate and acquired diversity are modeled in terms of two factors, namely: insularity and homophily, respectively. Insularity is the tendency of agents to act cooperatively only with others from the same community, which is often the primary challenge of innate diversity; while homophily is the tendency of agents to prefer members from their own community to start new social or business connections, which is often the primary challenge in acquired diversity. The emergence of network structure is studied when insularity and homophily are varied. In order to promote cooperation in a diverse population, the role played by a subset of agents called “global” agents who are not affected by homophily and insularity considerations is also studied. Simulation results show several interesting emergent properties. While the global agents are shown to acquire high betweenness, they are by no means the wealthiest or the most powerful in the network. However, the presence of global agents is important for the regional agents whose own wealth prospects increase because of their interaction with global agents.

Abusive Supervision and Supervisor-Directed Deviance: A Social Network Approach

Supervisor-directed deviance is a well-established consequence of abusive supervision. However, prior accounts of the abuse–deviance relationship have overlooked the role played by power embedded in subordinates’ informal social context. To address this gap, we draw on power-dependence theory and use a social network approach to explain the link between abusive supervision and supervisor-directed deviance. In doing so, we propose a three-way interaction in which the abuse–deviance relationship is impacted by two components of informal power: subordinate social network centrality and subordinate influence. In particular, we propose that the relationship will be the strongest when subordinates have high betweenness centrality and high influence. We gathered full social network data, as well as self-report surveys from 272 primary school teachers and government contract workers in Northern China. Our results provide support for the notion that supervisor-directed deviance emerges most strongly as a consequence of abusive supervision for employees who wield informal power in their organization.

Tear Film Proteome of Healthy Domestic Cats

The aim of this study was to investigate the proteins found in tear film of healthy domestic cats. Schirmer tear test strips were used to collect tear samples of twelve healthy cats, which were mixed, centrifuged, and placed in a single 1.5 mL microtube that was frozen at −20°C, until analysis by two-dimensional polyacrylamide gel and mass spectrometry associated with high-performance liquid chromatography. The resulting spectra were analyzed and compared with the Swiss-Prot search tool. Forty peptides were detected in the analyzed protein fragments of 90 spots, with 16 proteins identified. Of these, the authors confirmed what has been already found in other studies: lactotransferrin, serum albumin, allergenic lipocalins, and neutrophil gelatinase-associated lipocalin. Others were considered novel in tear film samples of all species: cyclin-dependent protein kinase, serine/arginine repetitive matrix protein, apelin receptor, secretory protein related to C1q/TNF, Wee1, α-1,4 glucan phosphorylase, and WD repeat domain 1. The network was divided into 11 clusters, and a biological function was assigned. Most of the proteins have functions in the defense and maintenance of feline ocular surface homeostasis. Serum albumin is a bottleneck protein, with a high betweenness value. This paper is a pioneer in reporting, in-depth, the tear film proteome of domestic cats.

Comparative analysis of Pure Hubs and Pure Bottlenecks in Human Protein-protein Interaction Networks

Bottlenecks and hubs form a set of topologically important nodes in a network. In this communication, we have made a detailed investigation on hubs and bottlenecks in human protein-protein interaction networks. We find that, three distinct groups exist which we refer to as: a) pure hubs (PHs, nodes having high degree but low betweenness values), b) mix proteins (MXs, nodes having both high degree and high betweenness values) and c) pure bottlenecks (PBs, nodes having high betweenness values but low degree values). Our investigations have revealed that pure hubs, as compared with MXs and PBs, (i) are more disordered, (ii) have higher potential to bind to multiple partners, (iii) are enriched with essential proteins as well as enriched with a higher number of splice variants. The MX proteins, as compared with PHs and PBs, (i) show slower evolutionary patterns, (ii) are involved in multiple pathways, (iii) enriched with the products of genes associated with various diseases and (iv) are more often targeted by bacteria, viruses, protozoa, and fungi pathogens. PBs, as compared with the PHs and MXs, (i) are associated with cancer genes and (ii) are the targets or the nearest neighbors of the targets of most of the approved drugs. Furthermore, our study revealed that these three categories of proteins are involved in distinct functional roles; PHs are involved in housekeeping processes such as transcription and replication; MXs proteins are involved in core signaling pathways whereas PBs are involved in signal transduction processes. Our work, therefore, has identified the distinct characteristics features associated with pure hubs, mix proteins and pure bottlenecks and thus helps in prioritizing proteins based on their degree and betweenness centrality values.

Lagrangian betweenness: detecting fluid transport bottlenecks in oceanic flows

<p> </p><p>The study of connectivity patterns in networks has brought novel insights across diverse fields ranging from neurosciences to epidemic spreading or climate. In this context, betweenness centrality has demonstrated to be a very effective measure to identify nodes that act as focus of congestion, or bottlenecks, in the network. However, there is not a way to define betweenness outside the network framework. Here we introduce the “Lagrangian betweenness”, an analogous quantity which relies only on the information provided by trajectories sampled across a generic dynamical system in the form of Finite Time Lyapunov Exponents, a widely used metric in Dynamical Systems Theory and Lagrangian oceanography. Our theoretical framework reveals a link between regions of high betweenness and the hyperbolic behavior of trajectories in the system. For example, it identifies bottlenecks in fluid flows where particles are first brought together and then widely dispersed. This has many potential applications including marine ecology and pollutant dispersal. We first test our definition of betweenness in an idealized double-gyre flow system. We then apply it in the characterization of transport by real geophysical flows in the semi-enclosed Adriatic Sea and the Kerguelen region of the highly turbulent Antarctic Circumpolar Current. In both cases, patterns of Lagrangian betweenness identify hidden bottlenecks of tracer transport that are surprisingly persistent across different spatio-temporal scales. In the marine context, high Lagrangian betweenness regions represent the optimal compromise between the heterogeneity of water origins and destinations, suggesting that they may be associated with relevant diversity reservoirs and hot-spots in marine ecosystems. Our new metric could also provide a novel approach useful for the management of environmental resources, informing strategies for marine spatial planning, and for designing observational networks to control pollutants or early-warning signals of climatic risks. </p>

Network Theory Reveals Principles of Spliceosome Structure and Dynamics

Cryo-EM has revolutionized structural biology of the spliceosome and dozens of distinct spliceosome structures representing much of the splicing cycle have now been determined. However, comparison of these structures is challenging due to extreme compositional and conformational dynamics of the splicing machinery and the thousands of intermolecular interactions created or dismantled as splicing progresses. We have used network theory to quantitatively analyze the dynamic interactions of splicing factors throughout the splicing cycle by constructing structure-based networks from every protein-protein, protein-RNA, and RNA-RNA interaction found in eight different spliceosome structures. Our networks reveal that structural modules comprising the spliceosome are highly dynamic with factors oscillating between modules during each stage along with large changes in the algebraic connectivities of the networks. Overall, the spliceosome's connectivity is focused on the active site in part due to contributions from non-globular proteins and components of the NTC. Many key components of the spliceosome including Prp8 and the U2 snRNA exhibit large shifts in both eigenvector and betweenness centralities during splicing. Other factors show transiently high betweenness centralities only at certain stages thereby suggesting mechanisms for regulating splicing by briefly bridging otherwise poorly connected network nodes. These observations provide insights into the organizing principles of spliceosome architecture and provide a framework for comparative network analysis of similarly complex and dynamic macromolecular machines.

Exploring dynamics and network analysis of spike glycoprotein of SARS-COV-2

The ongoing pandemic caused by coronavirus SARS-COV-2 continues to rage with devastating consequences on human health and global economy. The spike glycoprotein on the surface of coronavirus mediates its entry into host cells and is the target of all current antibody design efforts to neutralize the virus. The glycan shield of the spike helps the virus to evade the human immune response by providing a thick sugar-coated barrier against any antibody. To study the dynamic motion of glycans in the spike protein, we performed microsecond-long MD simulation in two different states that correspond to the receptor binding domain in open or closed conformations. Analysis of this microsecond-long simulation revealed a scissoring motion on the N-terminal domain of neighboring monomers in the spike trimer. Role of multiple glycans in shielding of spike protein in different regions were uncovered by a network analysis, where the high betweenness centrality of glycans at the apex revealed their importance and function in the glycan shield. Microdomains of glycans were identified featuring a high degree of intra-communication in these microdomains. An antibody overlap analysis revealed the glycan microdomains as well as individual glycans that inhibit access to the antibody epitopes on the spike protein. Overall, the results of this study provide detailed understanding of the spike glycan shield, which may be utilized for therapeutic efforts against this crisis.

Superspreading k-cores at the center of COVID-19 pandemic persistence

The spread of COVID-19 caused by the recently discovered SARS-CoV-2 virus has become a worldwide problem with devastating consequences. To slow down the spread of the pandemic, mass quarantines have been implemented globally, provoking further social and economic disruptions. Here, we address this problem by implementing a large-scale contact tracing network analysis to find the optimal quarantine protocol to dismantle the chain of transmission of coronavirus with minimal disruptions to society. We track billions of anonymized GPS human mobility datapoints from a compilation of hundreds of mobile apps deployed in Latin America to monitor the evolution of the contact network of disease transmission before and after the confinements. As a consequence of the lockdowns, people's mobility across the region decreases by ~53%, which results in a drastic disintegration of the transmission network by ~90%. However, this disintegration did not halt the spreading of the disease. Our analysis indicates that superspreading k-core structures persist in the transmission network to prolong the pandemic. Once the k-cores are identified, the optimal strategy to break the chain of transmission is to quarantine a minimal number of 'weak links' with high betweenness centrality connecting the large k-cores. Our results demonstrate the effectiveness of an optimal tracing strategy to halt the pandemic. As countries race to build and deploy contact tracing apps, our results could turn into a valuable resource to help deploy protocols with minimized disruptions.

Metabolic Physiological Networks: The Impact of Age

Metabolic homeostasis emerges from the interplay between several feedback systems that regulate the physiological variables related to energy expenditure and energy availability, maintaining them within a certain range. Although it is well known how each individual physiological system functions, there is little research focused on how the integration and adjustment of multiple systems results in the generation of metabolic health. The aim here was to generate an integrative model of metabolism, seen as a physiological network, and study how it changes across the human lifespan. We used data from a transverse, community-based study of an ethnically and educationally diverse sample of 2572 adults. Each participant answered an extensive questionnaire and underwent anthropometric measurements (height, weight, waist), fasting blood tests (glucose, HbA1c, basal insulin, cholesterol HDL, LDL, triglycerides, uric acid, urea, creatinine), along with vital signs (axillar temperature, systolic and diastolic blood pressure). The sample was divided into 6 groups of increasing age, beginning with less than 25 years and increasing by decades up to more than 65 years. In order to model metabolic homeostasis as a network, we used these 15 physiological variables as nodes and modeled the links between them, either as a continuous association of those variables, or as a dichotomic association of their corresponding pathological states. Weight and overweight emerged as the most influential nodes in both types of networks, while high betweenness parameters, such as triglycerides, uric acid and insulin, were shown to act as gatekeepers between the affected physiological systems. As age increases, the loss of metabolic homeostasis is revealed by changes in the network's topology that reflect changes in the system-wide interactions that, in turn, expose underlying health stages. Hence, specific structural properties of the network, such as weighted transitivity, can provide topology-based indicators of health that assess the whole state of the system.