Protein structure and evolutionary history determine sequence space topology

This is the publisher's version, also available electronically from http://genome.cshlp.org/content/15/3/385.Understanding the observed variability in the number of homologs of a gene is a very important unsolved problem that has broad implications for research into coevolution of structure and function, gene duplication, pseudogene formation, and possibly for emerging diseases. Here, we attempt to define and elucidate some possible causes behind the observed irregularity in sequence space. We present evidence that sequence variability and functional diversity of a gene or fold family is influenced by quantifiable characteristics of the protein structure. These characteristics reflect the structural potential for sequence plasticity, i.e., the ability to accept mutation without losing thermodynamic stability. We identify a structural feature of a protein domain—contact density—that serves as a determinant of entropy in sequence space, i.e., the ability of a protein to accept mutations without destroying the fold (also known as fold designability). We show that (log) of average gene family size exhibits statistical correlation (R2 > 0.9.) with contact density of its three-dimensional structure. We present evidence that the size of individual gene families are influenced not only by the designability of the structure, but also by evolutionary history, e.g., the amount of time the gene family was in existence. We further show that our observed statistical correlation between gene family size and contact density of the structure is valid on many levels of evolutionary divergence, i.e., not only for closely related sequence, but also for less-related fold and superfamily levels of homology

A simple physical model for scaling in protein-protein interaction networks

It has recently been demonstrated that many biological networks exhibit a scale-free topology where the probability of observing a node with a certain number of edges (k) follows a power law: i.e. p(k) ~ k^-g. This observation has been reproduced by evolutionary models. Here we consider the network of protein-protein interactions and demonstrate that two published independent measurements of these interactions produce graphs that are only weakly correlated with one another despite their strikingly similar topology. We then propose a physical model based on the fundamental principle that (de)solvation is a major physical factor in protein-protein interactions. This model reproduces not only the scale-free nature of such graphs but also a number of higher-order correlations in these networks. A key support of the model is provided by the discovery of a significant correlation between number of interactions made by a protein and the fraction of hydrophobic residues on its surface. The model presented in this paper represents the first physical model for experimentally determined protein-protein interactions that comprehensively reproduces the topological features of interaction networks. These results have profound implications for understanding not only protein-protein interactions but also other types of scale-free networks.Comment: 50 pages, 17 figure

Dihydrodinophysistoxin-1 Produced by Dinophysis norvegica in the Gulf of Maine, USA and Its Accumulation in Shellfish

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 \u3e0.16 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

High resolution protein folding with a transferable potential

A generalized computational method for folding proteins with a fully transferable potential and geometrically realistic all-atom model is presented and tested on seven different helix bundle proteins. The protocol, which includes graph-theoretical analysis of the ensemble of resulting folded conformations, was systematically applied and consistently produced structure predictions of approximately 3 Angstroms without any knowledge of the native state. To measure and understand the significance of the results, extensive control simulations were conducted. Graph theoretic analysis provides a means for systematically identifying the native fold and provides physical insight, conceptually linking the results to modern theoretical views of protein folding. In addition to presenting a method for prediction of structure and folding mechanism, our model suggests that a accurate all-atom amino acid representation coupled with a physically reasonable atomic interaction potential (that does not require optimization to the test set) and hydrogen bonding are essential features for a realistic protein model.Comment: submitted to PNAS 2005-03-1

A probabilistic model for gene content evolution with duplication, loss, and horizontal transfer

We introduce a Markov model for the evolution of a gene family along a phylogeny. The model includes parameters for the rates of horizontal gene transfer, gene duplication, and gene loss, in addition to branch lengths in the phylogeny. The likelihood for the changes in the size of a gene family across different organisms can be calculated in O(N+hM^2) time and O(N+M^2) space, where N is the number of organisms, $h$ is the height of the phylogeny, and M is the sum of family sizes. We apply the model to the evolution of gene content in Preoteobacteria using the gene families in the COG (Clusters of Orthologous Groups) database

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

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

Paralytic shellfish poisoning (PSP) toxin binders for optical biosensor technology: problems and possibilities for the future: a review

This review examines the developments in optical biosensor technology, which uses the phenomenon of surface plasmon resonance, for the detection of paralytic shellfish poisoning (PSP) toxins. Optical biosensor technology measures the competitive biomolecular interaction of a specific biological recognition element or binder with a target toxin immobilised onto a sensor chip surface against toxin in a sample. Different binders such as receptors and antibodies previously employed in functional and immunological assays have been assessed. Highlighted are the difficulties in detecting this range of low molecular weight toxins, with analogues differing at four chemical substitution sites, using a single binder. The complications that arise with the toxicity factors of each toxin relative to the parent compound, saxitoxin, for the measurement of total toxicity relative to the mouse bioassay are also considered. For antibodies, the cross-reactivity profile does not always correlate to toxic potency, but rather to the toxin structure to which it was produced. Restrictions and availability of the toxins makes alternative chemical strategies for the synthesis of protein conjugate derivatives for antibody production a difficult task. However, when two antibodies with different cross-reactivity profiles are employed, with a toxin chip surface generic to both antibodies, it was demonstrated that the cross-reactivity profile of each could be combined into a single-assay format. Difficulties with receptors for optical biosensor analysis of low molecular weight compounds are discussed, as are the potential of alternative non-antibody-based binders for future assay development in this area

Increased airway glucose increases airway bacterial load in hyperglycaemia.

Diabetes is associated with increased frequency of hospitalization due to bacterial lung infection. We hypothesize that increased airway glucose caused by hyperglycaemia leads to increased bacterial loads. In critical care patients, we observed that respiratory tract bacterial colonisation is significantly more likely when blood glucose is high. We engineered mutants in genes affecting glucose uptake and metabolism (oprB, gltK, gtrS and glk) in Pseudomonas aeruginosa, strain PAO1. These mutants displayed attenuated growth in minimal medium supplemented with glucose as the sole carbon source. The effect of glucose on growth in vivo was tested using streptozocin-induced, hyperglycaemic mice, which have significantly greater airway glucose. Bacterial burden in hyperglycaemic animals was greater than control animals when infected with wild type but not mutant PAO1. Metformin pre-treatment of hyperglycaemic animals reduced both airway glucose and bacterial load. These data support airway glucose as a critical determinant of increased bacterial load during diabetes

Feed-Forward Microprocessing and Splicing Activities at a MicroRNA–Containing Intron

The majority of mammalian microRNA (miRNA) genes reside within introns of protein-encoding and non-coding genes, yet the mechanisms coordinating primary transcript processing into both mature miRNA and spliced mRNA are poorly understood. Analysis of melanoma invasion suppressor miR-211 expressed from intron 6 of melastatin revealed that microprocessing of miR-211 promotes splicing of the exon 6–exon 7 junction of melastatin by a mechanism requiring the RNase III activity of Drosha. Additionally, mutations in the 5′ splice site (5′SS), but not in the 3′SS, branch point, or polypyrimidine tract of intron 6 reduced miR-211 biogenesis and Drosha recruitment to intron 6, indicating that 5′SS recognition by the spliceosome promotes microprocessing of miR-211. Globally, knockdown of U1 splicing factors reduced intronic miRNA expression. Our data demonstrate novel mutually-cooperative microprocessing and splicing activities at an intronic miRNA locus and suggest that the initiation of spliceosome assembly may promote microprocessing of intronic miRNAs