Dihydrodinophysistoxin-1 produced by Dinophysis norvegica in the Gulf of Maine, USA and its accumulation in shellfish

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Deeds, J. R., Stutts, W. L., Celiz, M. D., MacLeod, J., Hamilton, A. E., Lewis, B. J., Miller, D. W., Kanwit, K., Smith, J. L., Kulis, D. M., McCarron, P., Rauschenberg, C. D., Burnell, C. A., Archer, S. D., Borchert, J., & Lankford, S. K. Dihydrodinophysistoxin-1 produced by Dinophysis norvegica in the Gulf of Maine, USA and its accumulation in shellfish. Toxins, 12(9), (2020): E533, doi:10.3390/toxins12090533.Dihydrodinophysistoxin-1 (dihydro-DTX1, (M-H)−m/z 819.5), described previously from a marine sponge but never identified as to its biological source or described in shellfish, was detected in multiple species of commercial shellfish collected from the central coast of the Gulf of Maine, USA in 2016 and in 2018 during blooms of the dinoflagellate Dinophysis norvegica. Toxin screening by protein phosphatase inhibition (PPIA) first detected the presence of diarrhetic shellfish poisoning-like bioactivity; however, confirmatory analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS) failed to detect okadaic acid (OA, (M-H)−m/z 803.5), dinophysistoxin-1 (DTX1, (M-H)−m/z 817.5), or dinophysistoxin-2 (DTX2, (M-H)−m/z 803.5) in samples collected during the bloom. Bioactivity-guided fractionation followed by liquid chromatography-high resolution mass spectrometry (LC-HRMS) tentatively identified dihydro-DTX1 in the PPIA active fraction. LC-MS/MS measurements showed an absence of OA, DTX1, and DTX2, but confirmed the presence of dihydro-DTX1 in shellfish during blooms of D. norvegica in both years, with results correlating well with PPIA testing. Two laboratory cultures of D. norvegica isolated from the 2018 bloom were found to produce dihydro-DTX1 as the sole DSP toxin, confirming the source of this compound in shellfish. Estimated concentrations of dihydro-DTX1 were >0.16 ppm in multiple shellfish species (max. 1.1 ppm) during the blooms in 2016 and 2018. Assuming an equivalent potency and molar response to DTX1, the authority initiated precautionary shellfish harvesting closures in both years. To date, no illnesses have been associated with the presence of dihydro-DTX1 in shellfish in the Gulf of Maine region and studies are underway to determine the potency of this new toxin relative to the currently regulated DSP toxins in order to develop appropriate management guidance.Partial support for this research was received from the National Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science Competitive Research, Ecology and Oceanography of Harmful Algal Blooms Program under awards NA17NOS4780184 and NA19NOS4780182 to Juliette Smith (VIMS) and Jonathan Deeds (US FDA), and Prevention, Control, and Mitigation of Harmful Algal Blooms program award NA17NOS4780179 to Stephen Archer. This paper is ECOHAB publication number EC0956

Machines vs. Ensembles: Effective MAPK Signaling through Heterogeneous Sets of Protein Complexes

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author’s publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Despite the importance of intracellular signaling networks, there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ. One prominent view involves stable signaling machines with well-defined quaternary structures. The combinatorial complexity of signaling networks has led to an opposing perspective, namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes. Since many hypotheses regarding network function rely on how we conceptualize signaling complexes, resolving this issue is a central problem in systems biology. Unfortunately, direct experimental characterization of these complexes has proven technologically difficult, while combinatorial complexity has prevented traditional modeling methods from approaching this question. Here we employ rule-based modeling, a technique that overcomes these limitations, to construct a model of the yeast pheromone signaling network. We found that this model exhibits significant ensemble character while generating reliable responses that match experimental observations. To contrast the ensemble behavior, we constructed a model that employs hierarchical assembly pathways to produce scaffold-based signaling machines. We found that this machine model could not replicate the experimentally observed combinatorial inhibition that arises when the scaffold is overexpressed. This finding provides evidence against the hierarchical assembly of machines in the pheromone signaling network and suggests that machines and ensembles may serve distinct purposes in vivo. In some cases, e.g. core enzymatic activities like protein synthesis and degradation, machines assembled via hierarchical energy landscapes may provide functional stability for the cell. In other cases, such as signaling, ensembles may represent a form of weak linkage, facilitating variation and plasticity in network evolution. The capacity of ensembles to signal effectively will ultimately shape how we conceptualize the function, evolution and engineering of signaling networks

Analytic Markovian Rates for Generalized Protein Structure Evolution

A general understanding of the complex phenomenon of protein evolution requires the accurate description of the constraints that define the sub-space of proteins with mutations that do not appreciably reduce the fitness of the organism. Such constraints can have multiple origins, in this work we present a model for constrained evolutionary trajectories represented by a Markovian process throughout a set of protein-like structures artificially constructed to be topological intermediates between the structure of two natural occurring proteins. The number and type of intermediate steps defines how constrained the total evolutionary process is. By using a coarse-grained representation for the protein structures, we derive an analytic formulation of the transition rates between each of the intermediate structures. The results indicate that compact structures with a high number of hydrogen bonds are more probable and have a higher likelihood to arise during evolution. Knowledge of the transition rates allows for the study of complex evolutionary pathways represented by trajectories through a set of intermediate structures

Nicotine Overrides DNA Damage-Induced G1/S Restriction in Lung Cells

As an addictive substance, nicotine has been suggested to facilitate pro-survival activities (such as anchorage-independent growth or angiogenesis) and the establishment of drug resistance to anticancer therapy. Tobacco smoking consists of a variety of carcinogens [such as benzopyrene (BP) and nitrosamine derivatives] that are able to cause DNA double strand breaks. However, the effect of nicotine on DNA damage-induced checkpoint response induced by genotoxins remains unknown. In this study, we investigated the events occurred during G1 arrest induced by γ-radiation or BP in nicotine-treated murine or human lung epithelial cells. DNA synthesis was rapidly inhibited after exposure to γ-radiation or BP treatment, accompanied with the activation of DNA damage checkpoint. When these cells were co-treated with nicotine, the growth restriction was compromised, manifested by upregulation of cyclin D and A, and attenuation of Chk2 phosphorylation. Knockdown of cyclin D or Chk2 by the siRNAs blocked nicotine-mediated effect on DNA damage checkpoint activation. However, nicotine treatment appeared to play no role in nocodazole-induced mitotic checkpoint activation. Overall, our study presented a novel observation, in which nicotine is able to override DNA damage checkpoint activated by tobacco-related carcinogen BP or γ-irradiation. The results not only indicates the potentially important role of nicotine in facilitating the establishment of genetic instability to promote lung tumorigenesis, but also warrants a dismal prognosis for cancer patients who are smokers, heavily exposed second-hand smokers or nicotine users

Understanding network concepts in modules

<p>Abstract</p> <p>Background</p> <p>Network concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory.</p> <p>Results</p> <p>Here we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node 'conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in module networks.</p> <p>Conclusion</p> <p>Using protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from the following webpage: <url>http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworks</url></p

Cross-Over between Discrete and Continuous Protein Structure Space: Insights into Automatic Classification and Networks of Protein Structures

Structural classifications of proteins assume the existence of the fold, which is an intrinsic equivalence class of protein domains. Here, we test in which conditions such an equivalence class is compatible with objective similarity measures. We base our analysis on the transitive property of the equivalence relationship, requiring that similarity of A with B and B with C implies that A and C are also similar. Divergent gene evolution leads us to expect that the transitive property should approximately hold. However, if protein domains are a combination of recurrent short polypeptide fragments, as proposed by several authors, then similarity of partial fragments may violate the transitive property, favouring the continuous view of the protein structure space. We propose a measure to quantify the violations of the transitive property when a clustering algorithm joins elements into clusters, and we find out that such violations present a well defined and detectable cross-over point, from an approximately transitive regime at high structure similarity to a regime with large transitivity violations and large differences in length at low similarity. We argue that protein structure space is discrete and hierarchic classification is justified up to this cross-over point, whereas at lower similarities the structure space is continuous and it should be represented as a network. We have tested the qualitative behaviour of this measure, varying all the choices involved in the automatic classification procedure, i.e., domain decomposition, alignment algorithm, similarity score, and clustering algorithm, and we have found out that this behaviour is quite robust. The final classification depends on the chosen algorithms. We used the values of the clustering coefficient and the transitivity violations to select the optimal choices among those that we tested. Interestingly, this criterion also favours the agreement between automatic and expert classifications. As a domain set, we have selected a consensus set of 2,890 domains decomposed very similarly in SCOP and CATH. As an alignment algorithm, we used a global version of MAMMOTH developed in our group, which is both rapid and accurate. As a similarity measure, we used the size-normalized contact overlap, and as a clustering algorithm, we used average linkage. The resulting automatic classification at the cross-over point was more consistent than expert ones with respect to the structure similarity measure, with 86% of the clusters corresponding to subsets of either SCOP or CATH superfamilies and fewer than 5% containing domains in distinct folds according to both SCOP and CATH. Almost 15% of SCOP superfamilies and 10% of CATH superfamilies were split, consistent with the notion of fold change in protein evolution. These results were qualitatively robust for all choices that we tested, although we did not try to use alignment algorithms developed by other groups. Folds defined in SCOP and CATH would be completely joined in the regime of large transitivity violations where clustering is more arbitrary. Consistently, the agreement between SCOP and CATH at fold level was lower than their agreement with the automatic classification obtained using as a clustering algorithm, respectively, average linkage (for SCOP) or single linkage (for CATH). The networks representing significant evolutionary and structural relationships between clusters beyond the cross-over point may allow us to perform evolutionary, structural, or functional analyses beyond the limits of classification schemes. These networks and the underlying clusters are available at http://ub.cbm.uam.es/research/ProtNet.ph

Simulated Evolution of Protein-Protein Interaction Networks with Realistic Topology

We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein’s neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution

Single-molecule in vivo imaging of bacterial respiratory complexes indicates delocalized oxidative phosphorylation

Chemiosmotic energy coupling through oxidative phosphorylation (OXPHOS) is crucial to life, requiring coordinated enzymes whose membrane organization and dynamics are poorly understood. We quantitatively explore localization, stoichiometry, and dynamics of key OXPHOS complexes, functionally fluorescent protein-tagged, in Escherichia coli using low-angle fluorescence and superresolution microscopy, applying single-molecule analysis and novel nanoscale co-localization measurements. Mobile 100-200nm membrane domains containing tens to hundreds of complexes are indicated. Central to our results is that domains of different functional OXPHOS complexes do not co-localize, but ubiquinone diffusion in the membrane is rapid and long-range, consistent with a mobile carrier shuttling electrons between islands of different complexes. Our results categorically demonstrate that electron transport and proton circuitry in this model bacterium are spatially delocalized over the cell membrane, in stark contrast to mitochondrial bioenergetic supercomplexes. Different organisms use radically different strategies for OXPHOS membrane organization, likely depending on the stability of their environment

Business networks and localization effects for new Swedish technology-based firms’ innovation performance

This study examines the business networks and localization effects for new technology-based firms (NTBFs) in the context of innovation performance (the number of patents and product differentiation). In this regard, the study includes 28 variables. A survey was conducted in 2016 with 401 Swedish NTBFs that were small and young (the employment mean was 1.80 and the average age of each firm was 28.3\ua0months). The biggest category of NTBFs was knowledge-intensive high-technology services, followed by medium high-technology manufacturing, and high-technology manufacturing. Hypotheses on how business networks and localization are related to innovation performance were tested using principal component analysis, correlation analysis, and regression analysis. The results show that the primary significant factor for innovation performance regarding business networks and localization dimensions are professional network services, while industrial and regional areas also have a positive relationship on product differentiation. Our study also shows that innovation performance enhances firms’ abilities to access external financing through professional network services (e.g., venture capital companies)