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Journal articles on the topic 'Immunology; Molecular dynamics'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Stavrakoudis, Athanassios. "Conformational Flexibility in Designing Peptides for Immunology: The Molecular Dynamics Approach." Current Computer Aided-Drug Design 6, no. 3 (September 1, 2010): 207–22. http://dx.doi.org/10.2174/157340910791760073.

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2

Kortkhonjia, Ekaterine, Relly Brandman, Joe Zhongxiang Zhou, Vincent A. Voelz, Ilya Chorny, Bruce Kabakoff, Thomas W. Patapoff, Ken A. Dill, and Trevor E. Swartz. "Probing antibody internal dynamics with fluorescence anisotropy and molecular dynamics simulations." mAbs 5, no. 2 (March 2013): 306–22. http://dx.doi.org/10.4161/mabs.23651.

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3

Grossman, Zvi, Charles L. Greenblatt, and Irun R. Cohen. "Parasite immunology and lymphocyte population dynamics." Journal of Theoretical Biology 121, no. 2 (July 1986): 129–39. http://dx.doi.org/10.1016/s0022-5193(86)80088-1.

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4

Mallik, Buddhadeb, and Dimitrios Morikis. "Applications of Molecular Dynamics Simulations in Immunology: A Useful Computational Method in Aiding Vaccine Design." Current Proteomics 3, no. 4 (December 1, 2006): 259–70. http://dx.doi.org/10.2174/157016406780655568.

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5

Lopes, António M., and José A. Tenreiro Machado. "Symmetry in Complex Systems." Symmetry 12, no. 6 (June 8, 2020): 982. http://dx.doi.org/10.3390/sym12060982.

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Complex systems with symmetry arise in many fields, at various length scales, including financial markets, social, transportation, telecommunication and power grid networks, world and country economies, ecosystems, molecular dynamics, immunology, living organisms, computational systems, and celestial and continuum mechanics [...]

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6

Dongmo Foumthuim, Cedrix J., Alessandra Corazza, Rodolfo Berni, Gennaro Esposito, and Federico Fogolari. "Dynamics and Thermodynamics of Transthyretin Association from Molecular Dynamics Simulations." BioMed Research International 2018 (June 5, 2018): 1–14. http://dx.doi.org/10.1155/2018/7480749.

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Molecular dynamics simulations are used in this work to probe the structural stability and the dynamics of engineered mutants of transthyretin (TTR), i.e., the double mutant F87M/L110M (MT-TTR) and the triple mutant F87M/L110M/S117E (3M-TTR), in relation to wild-type. Free energy analysis from end-point simulations and statistical effective energy functions are used to analyze trajectories, revealing that mutations do not have major impact on protein structure but rather on protein association, shifting the equilibria towards dissociated species. The result is confirmed by the analysis of 3M-TTR which shows dissociation within the first 10 ns of the simulation, indicating that contacts are lost at the dimer-dimer interface, whereas dimers (formed by monomers which pair to form two extended β-sheets) appear fairly stable. Overall the simulations provide a detailed view of the dynamics and thermodynamics of wild-type and mutant transthyretins and a rationale of the observed effects.

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7

Mills, David M., and John C. Cambier. "B lymphocyte activation during cognate interactions with CD4+ T lymphocytes: molecular dynamics and immunologic consequences." Seminars in Immunology 15, no. 6 (December 2003): 325–29. http://dx.doi.org/10.1016/j.smim.2003.09.004.

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8

Klinke, David J., and Qing Wang. "Understanding Immunology via Engineering Design: The Role of Mathematical Prototyping." Computational and Mathematical Methods in Medicine 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/676015.

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A major challenge in immunology is how to translate data into knowledge given the inherent complexity and dynamics of human physiology. Both the physiology and engineering communities have rich histories in applying computational approaches to translate data obtained from complex systems into knowledge of system behavior. However, there are some differences in how disciplines approach problems. By referring to mathematical models as mathematical prototypes, we aim to highlight aspects related to the process (i.e., prototyping) rather than the product (i.e., the model). The objective of this paper is to review how two related engineering concepts, specifically prototyping and “fitness for use,” can be applied to overcome the pressing challenge in translating data into improved knowledge of basic immunology that can be used to improve therapies for disease. These concepts are illustrated using two immunology-related examples. The prototypes presented focus on the beta cell mass at the onset of type 1 diabetes and the dynamics of dendritic cells in the lung. This paper is intended to illustrate some of the nuances associated with applying mathematical modeling to improve understanding of the dynamics of disease progression in humans.

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9

Yamashita, Takefumi. "Toward rational antibody design: recent advancements in molecular dynamics simulations." International Immunology 30, no. 4 (January 28, 2018): 133–40. http://dx.doi.org/10.1093/intimm/dxx077.

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10

Loeffler, Dirk, and Timm Schroeder. "Understanding cell fate control by continuous single-cell quantification." Blood 133, no. 13 (March 28, 2019): 1406–14. http://dx.doi.org/10.1182/blood-2018-09-835397.

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Abstract Cells and the molecular processes underlying their behavior are highly dynamic. Understanding these dynamic biological processes requires noninvasive continuous quantitative single-cell observations, instead of population-based average or single-cell snapshot analysis. Ideally, single-cell dynamics are measured long-term in vivo; however, despite progress in recent years, technical limitations still prevent such studies. On the other hand, in vitro studies have proven to be useful for answering long-standing questions. Although technically still demanding, long-term single-cell imaging and tracking in vitro have become valuable tools to elucidate dynamic molecular processes and mechanisms, especially in rare and heterogeneous populations. Here, we review how continuous quantitative single-cell imaging of hematopoietic cells has been used to solve decades-long controversies. Because aberrant cell fate decisions are at the heart of tissue degeneration and disease, we argue that studying their molecular dynamics using quantitative single-cell imaging will also improve our understanding of these processes and lead to new strategies for therapies.

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11

Hoffren, Anna-Marja, Liisa Holm, Leif Laaksonen, Tuula Teeri, and Olle Teleman. "Molecular dynamics simulations of hapten binding to structural models of 2-phenyloxazolone antibodies." ImmunoMethods 1, no. 2 (October 1992): 80–90. http://dx.doi.org/10.1016/s1058-6687(05)80031-2.

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12

Ikeguchi, Mitsunori. "Water transport in aquaporins: molecular dynamics simulations." Frontiers in Bioscience Volume, no. 14 (2009): 1283. http://dx.doi.org/10.2741/3308.

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13

Sharma, Shantanu. "Probing protein aggregation using discrete molecular dynamics." Frontiers in Bioscience Volume, no. 13 (2008): 4795. http://dx.doi.org/10.2741/3039.

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14

Davis, Patricia L., Edward C. Holmes, Florence Larrous, Wim H. M. Van der Poel, Kirsten Tjørnehøj, Wladimir J. Alonso, and Hervé Bourhy. "Phylogeography, Population Dynamics, and Molecular Evolution of European Bat Lyssaviruses." Journal of Virology 79, no. 16 (August 15, 2005): 10487–97. http://dx.doi.org/10.1128/jvi.79.16.10487-10497.2005.

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ABSTRACT European bat lyssaviruses types 1 and 2 (EBLV-1 and EBLV-2) are widespread in Europe, although little is known of their evolutionary history. We undertook a comprehensive sequence analysis to infer the selection pressures, rates of nucleotide substitution, age of genetic diversity, geographical origin, and population growth rates of EBLV-1. Our study encompassed data from 12 countries collected over a time span of 35 years and focused on the glycoprotein (G) and nucleoprotein (N) genes. We show that although the two subtypes of EBLV-1—EBLV-1a and EBLV-1b—have both grown at a low exponential rate since their introduction into Europe, they have differing population structures and dispersal patterns. Furthermore, there were strong constraints against amino acid change in both EBLV-1 and EBLV-2, as reflected in a low ratio of nonsynonymous to synonymous substitutions per site, particularly in EBLV-1b. Our inferred rate of nucleotide substitution in EBLV-1, approximately 5 × 10−5 substitutions per site per year, was also one of the lowest recorded for RNA viruses and implied that the current genetic diversity in the virus arose 500 to 750 years ago. We propose that the slow evolution of EBLVs reflects their distinctive epidemiology in bats, where they occupy a relatively stable fitness peak.

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Ribarics, Reiner, Rudolf Karch, Nevena Ilieva, and Wolfgang Schreiner. "Geometric Analysis of Alloreactive HLAα-Helices." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/943186.

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Molecular dynamics (MD) is a valuable tool for the investigation of functional elements in biomolecules, providing information on dynamic properties and processes. Previous work by our group has characterized static geometric properties of the two MHCα-helices comprising the peptide binding region recognized by T cells. We build upon this work and used several spline models to approximate the overall shape of MHCα-helices. We applied this technique to a series of MD simulations of alloreactive MHC molecules that allowed us to capture the dynamics of MHCα-helices’ steric configurations. Here, we discuss the variability of spline models underlying the geometric analysis with varying polynomial degrees of the splines.

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16

Coller, Kelly E., Nicholas S. Heaton, Kristi L. Berger, Jacob D. Cooper, Jessica L. Saunders, and Glenn Randall. "Molecular Determinants and Dynamics of Hepatitis C Virus Secretion." PLoS Pathogens 8, no. 1 (January 5, 2012): e1002466. http://dx.doi.org/10.1371/journal.ppat.1002466.

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17

Belay, Tesfaye, Francis O. Eko, Godwin A. Ananaba, Samera Bowers, Terri Moore, Deborah Lyn, and Joseph U. Igietseme. "Chemokine and Chemokine Receptor Dynamics during Genital Chlamydial Infection." Infection and Immunity 70, no. 2 (February 2002): 844–50. http://dx.doi.org/10.1128/iai.70.2.844-850.2002.

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ABSTRACT Current design strategies for vaccines against certain microbial pathogens, including Chlamydia trachomatis, require the induction and targeting of specific immune effectors to the local sites of infection known as the mucosal effector sites. Chemokines and their receptors are important mediators of leukocyte trafficking and of the controlled recruitment of specific leukocyte clonotypes during host defense against infections and during inflammation. We analyzed the dynamics of chemokine and chemokine receptor expression in genital mucosae during genital chlamydial infection in a murine model to determine how these molecular entities influence the development of immunity and the clearance of infection. A time course study revealed an increase of up to threefold in the levels of expression of RANTES, monocyte chemotactic protein 1 (MCP-1), gamma-interferon-inducible protein 10 (IP-10), macrophage inflammatory protein 1α (MIP-1α), and intercellular adhesion molecule type 1 (ICAM-1) after genital infection with the C. trachomatis agent of mouse pneumonitis. Peak levels of expression of RANTES, MCP-1, and MIP-1α occurred by day 7 after primary infection, while those of IP-10 and ICAM-1 peaked by day 21. Expression levels of these molecules decreased by day 42 after primary infection, by which time all animals had resolved the infection, suggesting an infection-driven regulation of expression. A rapid upregulation of expression of these molecules was observed after secondary infection. The presence of cells bearing the chemokine receptors CCR5 and CXCR3, known to be preferentially expressed on Th1 and dendritic cells, was also synchronous with the kinetics of immune induction in the genital tract and clearance of infection. Results demonstrated that genital chlamydial infection is associated with a significant induction of chemokines and chemokine receptors that are involved in the recruitment of Th1 cells into the site of infection. Future studies will focus on how selective modulation of chemokines and their receptors can be used to optimize long-term immunity against Chlamydia.

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18

Archontis, Georgios, Phanourios Tamamis, Spiros S. Skourtis, Dimitrios Morikis, and John D. Lambris. "Conformational analysis of compstatin analogues with molecular dynamics simulations in explicit water." Molecular Immunology 44, no. 1-3 (January 2007): 150. http://dx.doi.org/10.1016/j.molimm.2006.07.013.

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19

Mackey, Michael C., Marta Tyran-Kamińska, and Romain Yvinec. "Molecular distributions in gene regulatory dynamics." Journal of Theoretical Biology 274, no. 1 (April 2011): 84–96. http://dx.doi.org/10.1016/j.jtbi.2011.01.020.

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20

Schroeder, Timm. "Long-term Live Single Cell Quantification of Transcription Factor Dynamics." Blood 128, no. 22 (December 2, 2016): SCI—4—SCI—4. http://dx.doi.org/10.1182/blood.v128.22.sci-4.sci-4.

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Abstract Hematopoiesis is highly complex and dynamic, and consist of large numbers of different cells expressing many molecules. Despite intensive research, many long-standing questions in hematopoiesis research remain disputed. One major reason is the fact that we usually only analyze populations of cells - rather than individual cells - at very few time points of an experiment. Tracking of individual cells would be an extremely powerful approach to improve our understanding of molecular cell fate control. We are therefore developing imaging systems to follow the fate of single cells over many generations. We program new software to help recording and displaying the divisional history, position, properties, interaction, etc. of all individual cells over many generations. In addition, novel microfluidics devices are designed and produced to allow improved observation and manipulation of cells. Our technologies allow continuous long-term quantification of protein expression or activity in living cells. Among other approaches, we generate knock in models expressing transcription factor to fluorescent protein fusions from endogenous gene loci. This enables non-invasive long-term live quantification of transcription factor protein dynamics in single stem and progenitor cells throughout their differentiation. The resulting novel kind of continuous quantitative single cell data is used for the generation and falsification of models describing the molecular control of hematopoietic cell fates. Disclosures No relevant conflicts of interest to declare.

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21

Cung, M. T., P. Demange, M. Marraud, I. Papadouli, C. Sakarellos, V. Tsikaris, and S. Tzartos. "Anti-AChR monoclonal antibody-peptide interactions studied by 2D-NMR spectroscopy and molecular dynamics analysis." Journal of Autoimmunity 4, no. 6 (December 1991): xi. http://dx.doi.org/10.1016/0896-8411(91)90078-q.

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22

Richard, Guy-Franck, Alix Kerrest, and Bernard Dujon. "Comparative Genomics and Molecular Dynamics of DNA Repeats in Eukaryotes." Microbiology and Molecular Biology Reviews 72, no. 4 (December 2008): 686–727. http://dx.doi.org/10.1128/mmbr.00011-08.

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SUMMARY Repeated elements can be widely abundant in eukaryotic genomes, composing more than 50% of the human genome, for example. It is possible to classify repeated sequences into two large families, “tandem repeats” and “dispersed repeats.” Each of these two families can be itself divided into subfamilies. Dispersed repeats contain transposons, tRNA genes, and gene paralogues, whereas tandem repeats contain gene tandems, ribosomal DNA repeat arrays, and satellite DNA, itself subdivided into satellites, minisatellites, and microsatellites. Remarkably, the molecular mechanisms that create and propagate dispersed and tandem repeats are specific to each class and usually do not overlap. In the present review, we have chosen in the first section to describe the nature and distribution of dispersed and tandem repeats in eukaryotic genomes in the light of complete (or nearly complete) available genome sequences. In the second part, we focus on the molecular mechanisms responsible for the fast evolution of two specific classes of tandem repeats: minisatellites and microsatellites. Given that a growing number of human neurological disorders involve the expansion of a particular class of microsatellites, called trinucleotide repeats, a large part of the recent experimental work on microsatellites has focused on these particular repeats, and thus we also review the current knowledge in this area. Finally, we propose a unified definition for mini- and microsatellites that takes into account their biological properties and try to point out new directions that should be explored in a near future on our road to understanding the genetics of repeated sequences.

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23

Zuo, Zhili, Oi Wah Liew, Gang Chen, Pek Ching Jenny Chong, Siew Hui Lee, Kaixian Chen, Hualiang Jiang, Chum Mok Puah, and Weiliang Zhu. "Mechanism of NS2B-Mediated Activation of NS3pro in Dengue Virus: Molecular Dynamics Simulations and Bioassays." Journal of Virology 83, no. 2 (October 29, 2008): 1060–70. http://dx.doi.org/10.1128/jvi.01325-08.

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ABSTRACT The NS2B cofactor is critical for proteolytic activation of the flavivirus NS3 protease. To elucidate the mechanism involved in NS2B-mediated activation of NS3 protease, molecular dynamic simulation, principal component analysis, molecular docking, mutagenesis, and bioassay studies were carried out on both the dengue virus NS3pro and NS2B-NS3pro systems. The results revealed that the NS2B-NS3pro complex is more rigid than NS3pro alone due to its robust hydrogen bond and hydrophobic interaction networks within the complex. These potent networks lead to remodeling of the secondary and tertiary structures of the protease that facilitates cleavage sequence recognition and binding of substrates. The cofactor is also essential for proper domain motion that contributes to substrate binding. Hence, the NS2B cofactor plays a dual role in enzyme activation by facilitating the refolding of the NS3pro domain as well as being directly involved in substrate binding/interactions. Kinetic analyses indicated for the first time that Glu92 and Asp50 in NS2B and Gln27, Gln35, and Arg54 in NS3pro may provide secondary interaction points for substrate binding. These new insights on the mechanistic contributions of the NS2B cofactor to NS3 activation may be utilized to refine current computer-based search strategies to raise the quality of candidate molecules identified as potent inhibitors against flaviviruses.

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24

Buzón, Maria J., Francisco M. Codoñer, Simon D. W. Frost, Christian Pou, Maria C. Puertas, Marta Massanella, Judith Dalmau, et al. "Deep Molecular Characterization of HIV-1 Dynamics under Suppressive HAART." PLoS Pathogens 7, no. 10 (October 27, 2011): e1002314. http://dx.doi.org/10.1371/journal.ppat.1002314.

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25

Orban, Tivadar, and Michael Kalafatis. "Human Coagulation Factor Va Interaction with Phospholipid Vesicles: A Molecular Dynamics Study." Blood 108, no. 11 (November 16, 2006): 1699. http://dx.doi.org/10.1182/blood.v108.11.1699.1699.

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Abstract The prothrombinase complex, the enzyme responsible for the timely conversion of prothrombin to thrombin, is composed of factor Xa (the enzyme), factor Va (the cofactor) assembled on the activated cell surface in the presence of divalent metal ions. In our quest to propose a model of the prothrombinase complex we first created a homology model in solution of factor Va (pdb code 1y61). Next we created a mixed phospholipid bilayer model composed of 1-palmitoyl, 2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC) and 1-palmitoyl, 2-oleoyl-sn-glycero-3-phosphatidylserine (POPS) in a 4:1 ratio. The lipid bilayer was equilibrated for 10 ns. The data showed that the average area per head group and the deuterium order parameters of the fatty acyl chains compare well with the previously reported nuclear magnetic resonance data. We next created a system composed of factor Va, water molecules, phospholipid bilayer composed of POPS/POPC and sodium ions. Factor Va was placed at the near interface of the equilibrated POPC/POPS phospholipid bilayer but making sure that the two entities were not interacting. Molecular dynamics simulation was then performed on the entire system. Distance analysis performed between the center of masses of the factor Va molecule and the lipid bilayer revealed that during the 4.5 ns simulation time, the factor Va molecule gets inserted into the interface of the hydrophobic core of the bilayer. The distance between the two centers of masses decreased during the 4.5 ns simulation time from 92 Å to 78 Å. At the end of the 4.5 ns simulation time the indole moieties of Trp2063 and Trp2064 were found to be in the vicinity of the ester and the fatty acyl chain moieties of the phospholipids. Factor Va was found to participate in hydrogen bonds formation with both the carboxylate and the phosphate groups of POPS. Following 4.5 ns simulation time the farthest amino acid residue away from the membrane is located at ~ 100 Å from the lipid bilayer plane. This result is in agreement with previous fluorescence energy transfer studies that concluded that a domain of membrane-bound factor Va is positioned at a minimum distance of 90 Å above the membrane surface. It is noteworthy that the amino acid sequence comprising Pro1663 to Val1672 of factor Va had a root mean square displacemenent (RMSD) 4.5 times higher as the average RMSD of the other residues, i.e., 9 Å. This sequence is highly hydrophobic in nature and it was previously shown to contain a membrane binding site on factor Va. However, the present placement of factor Va on the lipid bilayer does not allow the insertion of this hydrophobic patch into the lipid bilayer. We next tested the hypothesis whether the region encompassing amino acid residues Glu323 to Val331 gets exposed to solvent following the interaction of factor Va with the phospholipids. This region was shown to contain a binding site of factor Xa on factor Va. Solvent accessible surface area calculated for each amino acid residue of the Glu323 to Val331 sequence revealed that during the 4.5 ns simulation time the solvent accessible surface area does not increase. In conclusion, our work proposes for the first time a model of factor Va bound to a mixed POPC/POPS lipid bilayer and provides the necessary framework that accounts for the presence of phospholipids as a major regulatory component of a protein complex. This model can be extrapolated to the study of the dynamics of other membrane associated complexes involved in blood coagulation.

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26

Invergo, Brandon M., Ludovica Montanucci, and Jaume Bertranpetit. "Dynamic sensitivity and nonlinear interactions influence the system-level evolutionary patterns of phototransduction proteins." Proceedings of the Royal Society B: Biological Sciences 282, no. 1820 (December 7, 2015): 20152215. http://dx.doi.org/10.1098/rspb.2015.2215.

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Determining the influence of complex, molecular-system dynamics on the evolution of proteins is hindered by the significant challenge of quantifying the control exerted by the proteins on system output. We have employed a combination of systems biology and molecular evolution analyses in a first attempt to unravel this relationship. We employed a comprehensive mathematical model of mammalian phototransduction to predict the degree of influence that each protein in the system exerts on the high-level dynamic behaviour. We found that the genes encoding the most dynamically sensitive proteins exhibit relatively relaxed evolutionary constraint. We also investigated the evolutionary and epistatic influences of the many nonlinear interactions between proteins in the system and found several pairs to have coevolved, including those whose interactions are purely dynamical with respect to system output. This evidence points to a key role played by nonlinear system dynamics in influencing patterns of molecular evolution.

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27

Zhu, Jieqing, Jianghai Zhu, Ana Negri, Davide Provasi, Marta Filizola, Barry S. Coller, and Timothy A. Springer. "Closed headpiece of integrin αIIbβ3 and its complex with an αIIbβ3-specific antagonist that does not induce opening." Blood 116, no. 23 (December 2, 2010): 5050–59. http://dx.doi.org/10.1182/blood-2010-04-281154.

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Abstract The platelet integrin αIIbβ3 is essential for hemostasis and thrombosis through its binding of adhesive plasma proteins. We have determined crystal structures of the αIIbβ3 headpiece in the absence of ligand and after soaking in RUC-1, a novel small molecule antagonist. In the absence of ligand, the αIIbβ3 headpiece is in a closed conformation, distinct from the open conformation visualized in presence of Arg-Gly-Asp (RGD) antagonists. In contrast to RGD antagonists, RUC-1 binds only to the αIIb subunit. Molecular dynamics revealed nearly identical binding. Two species-specific residues, αIIb Y190 and αIIb D232, in the RUC-1 binding site were confirmed as important by mutagenesis. In sharp contrast to RGD-based antagonists, RUC-1 did not induce αIIbβ3 to adopt an open conformation, as determined by gel filtration and dynamic light scattering. These studies provide insights into the factors that regulate integrin headpiece opening, and demonstrate the molecular basis for a novel mechanism of integrin antagonism.

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28

Chela-Flores, Julian. "Towards the molecular bases of polymerase dynamics." Journal of Theoretical Biology 154, no. 4 (February 1992): 519–39. http://dx.doi.org/10.1016/s0022-5193(05)80167-5.

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29

Carrasco, Yolanda R. "Molecular cues involved in the regulation of B cell dynamics: Assistants of antigen hunting." Journal of Leukocyte Biology 107, no. 6 (April 15, 2020): 1107–13. http://dx.doi.org/10.1002/jlb.1mr0220-276r.

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30

DiNatale, Renzo G., A. Ari Hakimi, and Timothy A. Chan. "Genomics-based immuno-oncology: bridging the gap between immunology and tumor biology." Human Molecular Genetics 29, R2 (October 1, 2020): R214—R225. http://dx.doi.org/10.1093/hmg/ddaa203.

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Abstract The first hypotheses about how the immune system affects cancers were proposed in the early 20th century. These early concepts about cancer immunosurveillance were further developed in the decades that followed, but a detailed understanding of cancer immunity remained elusive. It was only recently, through the advent of high-throughput technologies, that scientists gained the ability to profile tumors with a resolution that allowed for granular assessment of both tumor cells and the tumor microenvironment. The advent of immune checkpoint inhibitors (ICIs), which have proven to be effective cancer therapies in many malignancies, has spawned great interest in developing biomarkers for efficacy, an endeavor that highlighted the value of dissecting tumor immunity using large-scale methods. Response to ICI therapy has been shown to be a highly complex process, where the dynamics of tumor and immune cells is key to success. The need to understand the biologic mechanisms at the tumor–immune interface has given rise to the field of cancer immunogenomics, a discipline that aims to bridge the gap between cancer genomics and classical immunology. We provide a broad overview of this emerging branch of translational science, summarizing common platforms used and recent discoveries in the field, which are having direct clinical implications. Our discussion will be centered around the genetic foundations governing tumor immunity and molecular determinants associated with clinical benefit from ICI therapy. We emphasize the importance of molecular diversity as a driver of anti-tumor immunity and discuss how these factors can be probed using genomic approaches.

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31

Jenwitheesuk, Ekachai, and Ram Samudrala. "Virtual screening of HIV-1 protease inhibitors against human cytomegalovirus protease using docking and molecular dynamics." AIDS 19, no. 5 (March 2005): 529–31. http://dx.doi.org/10.1097/01.aids.0000162343.96674.4c.

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32

Li, Na, Simei Qiu, Ying Fang, Jianhua Wu, and Quhuan Li. "Comparison of Linear vs. Cyclic RGD Pentapeptide Interactions with Integrin αvβ3 by Molecular Dynamics Simulations." Biology 10, no. 7 (July 20, 2021): 688. http://dx.doi.org/10.3390/biology10070688.

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Integrin αvβ3 interacting with the short Arg-Gly-Asp (RGD) motif plays a critical role in the progression of several types of tumors. However, the effects of the RGD structure (cyclic or linear) with integrin αvβ3 at the atomic level remain poorly understood. Here, we performed association and dissociation dynamic simulations for integrin αvβ3 in complex with a linear or cyclic pentapeptide by steered molecular dynamics simulations. Compared with cyclic RGD, the linear RGD peptide triggers instability of the configurational changes, mainly resting with the RGD domain due to its flexibility. The main interaction energy between Mg2+ and cyclic RGD is much stronger than that of the linear RGD system by the well shield to lessen attacks by free water molecules. The force-dependent dissociation results show that it is easier for linear RGD peptides to leave the active site and much quicker than the cyclic RGD ligand, whereas it is harder to enter the appropriate active binding site in linear RGD. The Ser123-AspRGD bond may play a critical role in the allosteric pathway. Our findings provide insights into the dynamics of αvβ3 interactions with linear and cyclic RGD ligands and contribute to the application of RGD-based strategies in preclinical therapy.

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Chen, Hui-Chen, and Nancy C. Reich. "11 Nuclear transport dynamics of STAT6." Cytokine 43, no. 3 (September 2008): 239. http://dx.doi.org/10.1016/j.cyto.2008.07.051.

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34

Bagnarelli, P., A. Valenza, S. Menzo, A. Manzin, G. Scalise, P. E. Varaldo, and M. Clementi. "Dynamics of molecular parameters of human immunodeficiency virus type 1 activity in vivo." Journal of Virology 68, no. 4 (1994): 2495–502. http://dx.doi.org/10.1128/jvi.68.4.2495-2502.1994.

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35

Salem, Rana, Radwan Massoud, Rami Mahfouz, Ali Bazarbachi, and Jean El Cheikh. "Dynamics of Molecular Response in AML Patients with NPM1 and FLT3 Mutations Undergoing Allogeneic Stem Cell Transplant." Blood 128, no. 22 (December 2, 2016): 5238. http://dx.doi.org/10.1182/blood.v128.22.5238.5238.

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Abstract BACKGROUND: Concomitant NPM1 and FLT3 mutation occurs in 20% of AML patients. Molecular response and achievement of negative minimal residual disease (MRD) are strong predictors of long-term outcome. However, little is known about the dynamics of molecular response in NPM-1 and FLT-3 double positive mutations. OBJECTIVE: To assess the dynamics of molecular response to treatment in AML adult patients with concomitant FLT3 and NPM1 mutations. DESIGN: Retrospective single center study. SETTING: This study was approved by the institutional review board of American University of Beirut Medical Center. PATIENTS OR OTHER PARTICIPANTS: Twelve consecutive newly diagnosed (n=11) or relapsed (n=1) AML patientsreceived Idarubicin/cytarabineinduction and one or two consolidation(s) (Table 1). Ten patients received allogeneic stem cell transplant (allo-SCT) followed by post-transplantsorafenibmaintenance. Median follow-up was 11.5 (6-27) months. All transplanted patients remain alive and disease free. INTERVENTIONS: FLT3 mutation was tested on DNA using a qualitative method with a sensitivity of0.01%. NPM-1 mutation was tested on cDNA using a qualitative or a quantitative RT-PCR with a sensitivity of 0.01% and0.008 NCN respectively. Patients were tested at diagnosis, after induction, after each consolidation, before and at days 30, 60 and 100 afterallo-SCT. MAIN OUTCOMES MEASURES: Kinetics of NPM1 and FLT3 molecular response. RESULTS: After induction, FLT3 became negative in all tested patients (n=10). After first consolidation, FLT-3 remained or became positive in 10/11 tested patients whereas a molecular relapse was noted in one patient who developed a hematological relapse and rapidly died. No molecular positivity for FLT-3 was noted later on, whether after second consolidation or post-transplant. Conversely, NPM-1 mutation became negative in 2 out of 12 tested patients after induction, in 1 additional patient after first consolidation and in 6 additional patients afterallo-SCT, mostly after startingsorafenib. NPM-1 MRD value remained elevated in 3 out of 4 patients with quantitative assessment at diagnosis and post induction. CONCLUSION: FLT3 become negative early after induction while NPM1 negativity lags behind. Persistent NPM-1 MRD does not seem to predict post-transplant outcome and may indeed become negative after sorafenib. These results need confirmation in larger studies. Figure Dynamics of NPM1 and FLT3 Molecular Response Figure. Dynamics of NPM1 and FLT3 Molecular Response Disclosures No relevant conflicts of interest to declare.

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Moroni, Elisabetta. "Structure and sequence determinants of aggregation investigated with molecular dynamics." Frontiers in Bioscience Volume, no. 14 (2009): 523. http://dx.doi.org/10.2741/3260.

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Wei, Guanghong. "Self-assembly of amyloid-forming peptides by molecular dynamics simulations." Frontiers in Bioscience Volume, no. 13 (2008): 5681. http://dx.doi.org/10.2741/3109.

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Suda, Hitoshi, and Minoru Saito. "Molecular Dynamics Simulations for Actin Monomers in Solution." Journal of Theoretical Biology 171, no. 3 (December 1994): 347–49. http://dx.doi.org/10.1006/jtbi.1994.1237.

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39

Essadssi, Soukaina, Al Mehdi Krami, Lamiae Elkhattabi, Zouhair Elkarhat, Ghita Amalou, Houria Abdelghaffar, Hassan Rouba, and Abdelhamid Barakat. "Computational Analysis of nsSNPs of ADA Gene in Severe Combined Immunodeficiency Using Molecular Modeling and Dynamics Simulation." Journal of Immunology Research 2019 (November 3, 2019): 1–14. http://dx.doi.org/10.1155/2019/5902391.

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Severe combined immunodeficiency (SCID) is the most severe form of primary immunodeficiency (PID), characterized by fatal opportunistic infections. The ADA gene encodes adenosine deaminase, an enzyme that catalyzes the irreversible deamination of adenosine and deoxyadenosine in the catabolic pathway of purine. Mutations of the ADA gene have been identified in patients with severe combined immunodeficiency. In this study, we performed a bioinformatics analysis of the human ADA gene to identify potentially harmful nonsynonymous SNPs and their effect on protein structure and stability. Using eleven prediction tools, we identified 15 nsSNPs (H15D, H15P, H17Q, H17Y, D19N, T26I, G140E, C153F, A183D, G216R, H258Y, C262Y, S291L, S291W, and K34OE) as harmful. The results of ConSurf’s analysis revealed that all these nsSNPs are localised in the highly conserved positions and affect the structure of the native proteins. In addition, our computational analysis showed that the H15D, G140E, G216R, and S291L mutations identified as being associated with severe combined immunodeficiency affect protein structure. Similarly, the results of the analyses of Rmsd, Rmsf, and Rg showed that all these factors influence protein stability, flexibility, and compaction with different levels of impact. This study is the first comprehensive computational analysis of nsSNPs of the ADA gene. However, functional analyses are needed to elucidate the biological mechanisms of these polymorphisms in severe combined immunodeficiency.

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Barisas, B. George, William F. Wade, Thomas M. Jovin, Donna Arndt-Jovin, and Deborah A. Roess. "Dynamics of molecules involved in antigen presentation: effects of fixation." Molecular Immunology 36, no. 11-12 (August 1999): 701–8. http://dx.doi.org/10.1016/s0161-5890(99)00091-7.

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Hu, Wei, Chenyi An, and Wei J. Chen. "Molecular Mechanoneurobiology: An Emerging Angle to Explore Neural Synaptic Functions." BioMed Research International 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/486827.

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Neural synapses are intercellular asymmetrical junctions that transmit biochemical and biophysical information between a neuron and a target cell. They are very tight, dynamic, and well organized by many synaptic adhesion molecules, signaling receptors, ion channels, and their associated cytoskeleton that bear forces. Mechanical forces have been an emerging factor in regulating axon guidance and growth, synapse formation and plasticity in physiological and pathological brain activity. Therefore, mechanical forces are undoubtedly exerted on those synaptic molecules and modulate their functions. Here we review current progress on how mechanical forces regulate receptor-ligand interactions, protein conformations, ion channels activation, and cytoskeleton dynamics and discuss how these regulations potentially affect synapse formation, stabilization, and plasticity.

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Pollitt, Eric J. G., Piotr T. Szkuta, Nicola Burns, and Simon J. Foster. "Staphylococcus aureus infection dynamics." PLOS Pathogens 14, no. 6 (June 14, 2018): e1007112. http://dx.doi.org/10.1371/journal.ppat.1007112.

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43

Gillinder, Kevin R., Jim R. Hughes, Michael R. Tallack, Graham Magor, Melissa Ilsley, James Davies, Douglas Higgs, and Andrew C. Perkins. "Dynamics and Mechanics Of KLF1 Regulation In Erythropoiesis." Blood 122, no. 21 (November 15, 2013): 2176. http://dx.doi.org/10.1182/blood.v122.21.2176.2176.

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Abstract Krûppel-like factor-1 (KLF1) is a C2H2 zinc finger transcription factor which is essential for broad erythroid gene expression and erythropoiesis in vivo. A number of studies have shown ∼700 genes are poorly expressed when KLF1 is absent [1-8]. This global loss of expression is responsible for failure of effective red blood cell production in KLF1 knockout mice [9,10], and partly responsible for congenital anemia in humans and mice with dominant mutations in KLF1 [11,12]. To determine whether KLF1-dependent genes are direct or indirect targets of KLF1, we have previously performed global ChIP-seq experiments identifying 945-1350 regions of KLF1 occupancy in the mouse genome [7]. About 15% of these regions fall within the promoters of KLF1 target genes but surprisingly, most are thousands of kilobases distant from any known gene. Many of these distant sites exhibit co-occupancy with other transcriptional regulators involved in erythropoiesis, including GATA1. Approximately half of the KLF1 occupied sites are found within regions of mono-methylation of lysine 4 on histone 3 (H3K4me1). These regions are devoid of histones tri-methylated at the same residue (H3K4me3). This methylation signature is commonly associated with regions of the genome that act as transcriptional enhancers [13,14] and many are also bound by the co-activator, p300. The nature and function of these distant sites, particularly those without enhancer marks, is interesting as they may shed light on novel mechanisms of action of KLF1 and associated transcription factors. The transcriptional machinery of the cell, including many transcription factors is found in large sub-nuclear compartments called transcriptional factories [15]. KLF1 has been found localized to a subset of these in erythroid cells. KLF1 is also required for long-range looping of the β-globin gene into these transcription factories [16]. Other erythroid genes involved in the production of a functional haemoglobin molecule such as α-globin and haem synthesis enzymes are often found in the same transcription factory. This strongly suggests KLF1 can employ this sub-nuclear machine to co-ordinate the transcriptional output from many genes and thereby direct erythroid cell differentiation. To explore the function of KLF1-bound loci, we have performed multiplexed chromosome conformation capture (3C) coupled with sequencing (Capture-seq) using a tamoxifen responsive, KLF1 inducible cell line to investigate the role of KLF1 in chromosomal looping. In addition, we have analysed primary transcriptional output of KLF1 target genes by nascent RNA-seq. As expected β-globin and a-globin transcription is rapidly induced, becoming detectable within 5 minutes. However, the transcriptional response of dematin and a set direct KLF1 target genes is much slower. Thus, the mechanism of KLF1 transcriptional activation differs between target gene loci. We find a dynamic role of KLF1-dependent chromosomal looping and transcriptional co-factor recruitment required to effect gene transcription during erythropoiesis. We will discuss models of differentiation transcription regulation by KLF1. References: 1. Drissen R, et al. (2005). Molecular and Cellular Biology 25: 5205–5214. 2. Funnell APW, et al. (2007). Molecular and Cellular Biology 27: 2777–2790. 3. Hodge D, et al. (2006). Blood 107: 3359–3370. 4. Pilon AM, et al. (2008). Molecular and Cellular Biology 28: 7394–7401. 5. Siatecka M, et al. (2010). PNAS 107: 15151–15156. 6. Siatecka M, Bieker JJ (2011). Blood 118: 2044–2054. 7. Tallack MR, et al. (2010). Genome Res 20: 1052–1063. 8. Tallack MR, Perkins AC (2010). IUBMB Life 62: 886–890. 9. Perkins AC, Sharpe AH, Orkin SH (1995). Nature 375: 318–322. 10. Nuez B, et al. (1995). Nature 375: 316–318. 11. Arnaud L, S et al. (2010). Am J Hum Genet 87: 721–727. 12. Borg J, et al. (2011). Haematologica 96: 635–638. 13. Zentner GE, et al. (2011). Genome Res 21: 1273–1283. 14. Pekowska A, et al. (2011). EMBO J 30: 4198–4210. 15. Osborne CS, et al. (2004). Nat Genet 36: 1065–1071. 16. Schoenfelder S, et al. (2010). Nat Genet 42: 53–61. Disclosures: Perkins: Novartis Oncology: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees.

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Litvinov, Rustem I., Dzhigangir A. Faizullin, Yuriy F. Zuev, Artyom Zhmurov, Olga Kononova, Valeri Barsegov, and John W. Weisel. "Molecular Basis of Biomechanics of Hemostasis and Thrombosis: Structural Molecular Transitions Underlying Deformation of Fibrin Clots and Thrombi." Blood 120, no. 21 (November 16, 2012): 2217. http://dx.doi.org/10.1182/blood.v120.21.2217.2217.

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Abstract Abstract 2217 A new field of biomedical research, biomechanics of hemostasis and thrombosis, has been quickly developing over the past few years. The mechanical properties of fibrin are essential in vivo for the ability of clots to stop bleeding in flowing blood but also determine the likelihood of obstructive thrombi that cause heart attack and stroke. Despite such critical importance, the structural basis of clot mechanics is not well understood. The structural changes underlying deformation of fibrin polymer occur at different spatial scales from macroscopic to submolecular, including molecular unfolding, about which relatively little is known. In this work, fibrin mechanics was studied with respect to molecular structural changes during fibrin deformation. The results of atomic force microscopy-induced unfolding of fibrinogen monomers and oligomers were correlated with force-extension curves obtained using Molecular Dynamics simulations. The mechanical unraveling of fibrin(ogen) was shown to be determined by molecular transitions that couple reversible extension-contraction of the α-helical coiled-coil regions with unfolding of the terminal γ-nodules. The coiled-coils act as molecular springs to buffer external mechanical perturbations, transmitting and distributing force as the γ-nodules unfold. Unfolding of the γ-nodules, stabilized by strong inter-domain interactions with the neighboring β-nodules, was characterized by an average force of ∼90 pN and peak-to-peak distance of ∼25 nm. All-atom Molecular Dynamics simulations further showed a transition from α-helix to β-sheet at higher extensions. To reveal the force-induced α-helix to β-sheet transition in fibrin experimentally, we used Fourier Transform infrared spectroscopy of hydrated fibrin clots made from human blood plasma. When extended or compressed, fibrin showed a shift of absorbance intensity mainly in the amide I band but also in the amide II and III bands, demonstrating an increase of the β-sheets and a corresponding reduction of the α-helices. These structural conversions correlated directly with the strain or pressure and were partially reversible at the conditions applied. The spectra characteristic of the nascent inter-chain β-sheets were consistent with protein aggregation and fiber bundling during clot deformation observed using scanning electron microscopy. Additional information on the mechanically induced α-helix to β-sheet transition in fibrin was obtained from computational studies of the forced elongation of the entire fibrin molecule and its α-helical coiled-coil portions. We found that upon force application, the coiled-coils undergo ∼5–50 nm extension and 360-degree unwinding. The force-extension curves for the coiled-coils showed three distinct regimes: the linear elastic regime, the constant-force plastic regime, and the non-linear regime. In the linear regime, the coiled-coils unwind but not unfold. In the plastic regime, the triple α-helical segments rewind and re-unwind while undergoing a non-cooperative phase transition to form parallel β-sheets. We conclude that under extension and/or compression an α-helix to β-sheet conversion of the coiled-coils occurs in the fibrin clot as a part of forced protein unfolding. These regimes of forced elongation of fibrin provide important qualitative and quantitative characteristics of the molecular mechanisms underlying fibrin mechanical properties at the microscopic and macroscopic scales. Furthermore, these structural characteristics of the dynamic mechanical behavior of fibrin at the nanometer scale determine whether or not clots have the strength to stanch bleeding and if thrombi become obstructive or embolize. Finally, this knowledge of the functional significance of different domains of fibrin(ogen) suggests new approaches for modulation of these properties as potential therapeutic interventions. Disclosures: No relevant conflicts of interest to declare.

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45

Lee, Tai-Sung, Steven Potts, Hagop Kantarjian, Jorge Cortes, Francis Giles, and Maher Albitar. "Molecular Basis Explanation of Imatinib Resistance of Bcr-Abl Due to T315I and P-Loop Mutations from Molecular Dynamics Simulations." Blood 110, no. 11 (November 16, 2007): 2917. http://dx.doi.org/10.1182/blood.v110.11.2917.2917.

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Abstract Molecular dynamics (MD) simulations on the complex of imatinib with the wild-type, T315I, and other 10 P-loop mutants of the tyrosine kinase Bcr-Abl have been performed to study the imatinib resistance mechanism at the atomic level. MD simulations show that large scale computational simulations could offer insight information that a static structure or simple homology modeling methods cannot provide for studying the Bcr-Abl imatinib resistance problem, especially in the case of conformational changes due to remote mutations. By utilizing the Molecular Mechanics/Poisson-Boltzmann surface area (MM-PBSA) techniques and analyzing the interactions between imatinib and individual residues, imatinib resistance mechanisms not previously thought have been revealed. Non-directly contacted P-loop mutations either unfavorably change the direct electrostatic interactions with imatinib, or cause the conformational changes influencing the contact energies between imatinib and other non-P-loop residues. We demonstrate that imatinib resistance of T315I mainly comes from the breakdown of the interactions between imatinib and E286 and M290, contradictory to previously suggested that the missing hydrogen bonding is the main contribution. We also demonstrate that except for the mutations of the direct contact residues, such as L248 and Y253, the unfavorable electrostatic interaction between P-loop and imatinib is the main reason for resistance for the P-loop mutations. Furthermore, in Y255H, protonation of the histidin is essential for rendering this mutation resistant to Gleevec. Our results demonstrate that MD is a powerful way to verify and predict clinical response or resistance to imatinib and other potential drugs.

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Bhattacharyya, Srirupa, and Siddhartha Sankar Ghosh. "Unfolding transmembrane TNFα dynamics in cancer therapeutics." Cytokine 137 (January 2021): 155303. http://dx.doi.org/10.1016/j.cyto.2020.155303.

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Cimica, Velasco, Janaki Iyer, and Nancy C. Reich. "Cellular dynamics of the STAT3 transcription factor." Cytokine 48, no. 1-2 (October 2009): 127. http://dx.doi.org/10.1016/j.cyto.2009.07.538.

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48

Varona, Pablo, and Mikhail I. Rabinovich. "Hierarchical dynamics of informational patterns and decision-making." Proceedings of the Royal Society B: Biological Sciences 283, no. 1832 (June 15, 2016): 20160475. http://dx.doi.org/10.1098/rspb.2016.0475.

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Traditional studies on the interaction of cognitive functions in healthy and disordered brains have used the analyses of the connectivity of several specialized brain networks—the functional connectome. However, emerging evidence suggests that both brain networks and functional spontaneous brain-wide network communication are intrinsically dynamic. In the light of studies investigating the cooperation between different cognitive functions, we consider here the dynamics of hierarchical networks in cognitive space. We show, using an example of behavioural decision-making based on sequential episodic memory, how the description of metastable pattern dynamics underlying basic cognitive processes helps to understand and predict complex processes like sequential episodic memory recall and competition among decision strategies. The mathematical images of the discussed phenomena in the phase space of the corresponding cognitive model are hierarchical heteroclinic networks. One of the most important features of such networks is the robustness of their dynamics. Different kinds of instabilities of these dynamics can be related to ‘dynamical signatures’ of creativity and different psychiatric disorders. The suggested approach can also be useful for the understanding of the dynamical processes that are the basis of consciousness.

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49

Guerrero, Estefania Garcia, Irene Diaz Moreno, Miguel Angel De la rosa, Jose Antonio Pérez Simón, and Antonio Diaz Quintana. "Molecular Dynamics As a Computational Tool To Analyze Lymphocyte Alloreactivity In Hematopoietic Transplant Recipients." Blood 122, no. 21 (November 15, 2013): 1049. http://dx.doi.org/10.1182/blood.v122.21.1049.1049.

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Abstract Introduction The generation of the immune response requires the recognition of peptides presented by the major histocompatibility complex (MHC) through the T cell receptor (TCR). In the hematopoietic transplantation context, T cells (LT) from the donor recognize foreign MHC or own MHC bound to foreign peptides (pMHC), generating an alloimmune response. Currently, the molecular mechanisms of LT alloimmune activation are unknown. In order to analyze the molecular interactions between peptides, MHC and TCR, we have implemented Molecular Dynamics techniques. We have compared immunologically reactive complexes (HLA-A2/TAX/TCR-A6; HLA-A2/HUD/TCR-A6) to non/weakly reactive complexes (HLA-A2/V7R/TCR-A6; HLA-A2/P6A/TCR-A6; HLA-A2/Y8A/TCR-A6). Methods Starting structures of two reactive complexes were downloaded from the PDB database and used to model mutations known to lead to different degrees of immune reactivity. Dynamics simulations were performed and analyzed using the program AMBER version 9. The simulation time was approximately 10 ns. Further analysis was carried out using the script ARO (Díaz-Moreno et al. 2009) in the VMD Tk console. Results A total of 17 MD trajectories have been reckoned, to simulate the behavior of isolated components of the different MHC-TCR complexes. Analysis of the fluctuations shows that pMHC binding barely restrains TCR motions, affecting mostly to CDR3 loops. Opposite, pMHC displayed substantial changes in its dynamics upon comparing its free versus ternary form (pMHC-TCR). Furthermore, taking as reference the positions of the MHC´s helices in free binary structures (MHC-peptide) and comparing them to the positions in ternary structures (pMHC-TCR), we can observe that in reactive complexes MHC exhibits a higher variability than in non-reactive complexes, suggesting that the MHC must tighten and acquire a position further away from its position in free binary form. We also analyzed the position of the peptide in the groove of MHC and we found that at position 5 (aa aromatic) the peptide is diverted although its position in the groove of MHC seems to be unrelated to the reactivity of the interaction TCR-pMHC. Conclusions The MHC shows strong changes in its molecular dynamics upon binding TCR, decreasing its mobility. The structure of MHC is slightly perturbed in reactive complexes, but not in non-reactive ones. Financial support Spanish MINECO (BFU2009-07190/BMC, BFU2012-31670/BMC) the Andalusian Government (BIO198) and Instituto de Salud Carlos III (PFIS - FI12/00189 and FIS PI11/02366) Disclosures: No relevant conflicts of interest to declare.

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Jelsbak, Lars, Helle Krogh Johansen, Anne-Louise Frost, Regitze Thøgersen, Line E. Thomsen, Oana Ciofu, Lei Yang, Janus A. J. Haagensen, Niels Høiby, and Søren Molin. "Molecular Epidemiology and Dynamics of Pseudomonas aeruginosa Populations in Lungs of Cystic Fibrosis Patients." Infection and Immunity 75, no. 5 (January 29, 2007): 2214–24. http://dx.doi.org/10.1128/iai.01282-06.

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ABSTRACT The ability to establish lifelong persistent infections is a fundamental aspect of the interactions between many pathogenic microorganisms and their mammalian hosts. One example is chronic lung infections by the opportunistic pathogen Pseudomonas aeruginosa in cystic fibrosis (CF) patients. This infection process is associated with extensive genetic adaptation and microevolution of the infecting bacteria. Through investigations of P. aeruginosa populations and infection dynamics in a group of CF patients followed at the Danish CF Clinic in Copenhagen, we have identified two distinct and dominant clones that have evolved into highly successful colonizers of CF patient airways. A significant component of the evolutionary success of these two clones has been their efficient transmissibility among the CF patients. The two clones have been present and transmitted among different CF patients for more than 2 decades. Our data also suggest that the P. aeruginosa population structure in the CF patient airways has been influenced by competition between different clones and that the two dominant clones have been particularly competitive within the lungs, which may add to their overall establishment success. In contrast, we show that adaptive traits commonly associated with establishment of chronic P. aeruginosa infections of CF patients, such as transition to the mucoid phenotype and production of virulence factors, play minor roles in the ability of the two dominant clones to spread among patients and cause long-term chronic infections. These findings suggest that hitherto-unrecognized evolutionary pathways may be involved in the development of successful and persistent P. aeruginosa colonizers of CF patient lungs.

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