Biogenic influence on cloud microphysics over the global ocean

 0  4  40  2017-02-01 13:04:01 Report infringing document
Atmospheric Chemistry and Physics Discussions Discussion Paper Biogenic influence on cloud microphysics over the global ocean | This discussion paper is/has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP if available. Discussion Paper Atmos. Chem. Phys. Discuss., 12, 3655–3694, 2012 doi:10.5194/acpd-12-3655-2012 © Author(s) 2012. CC Attribution 3.0 License. | 2 3 Received: 5 January 2012 – Accepted: 24 January 2012 – Published: 2 February 2012 Correspondence to: A. Lana ( | 1 Discussion Paper 1 A. Lana , R. Simó , S. M. Vallina , and J. Dachs 1 Institut de Ciències del Mar (ICM), CSIC, Barcelona, Spain EAPS, MIT, Cambridge, Massachusetts, USA 3 Department of Environmental Chemistry, IDAEA, CSIC, Barcelona, Spain 2 | 3655 Discussion Paper Published by Copernicus Publications on behalf of the European Geosciences Union. ACPD 12, 3655–3694, 2012 Biogenic influence on cloud microphysics over the global ocean A. Lana et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion 5 Discussion Paper | 3656 ACPD 12, 3655–3694, 2012 Biogenic influence on cloud microphysics over the global ocean A. Lana et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close | 25 Aerosols have a great impact on the Earth’s radiative budget by direct and indirect interactions with solar radiation. Direct effects occur through the absorption and the scattering of sunlight back into space, thus decreasing the solar energy that reaches Discussion Paper 1 Introduction | 20 Discussion Paper 15 | 10 Aerosols have a large potential to influence climate through their effects on the microphysics and optical properties of clouds and, hence, on the Earth’s radiation budget. Aerosol-cloud interactions have been intensively studied in polluted air, but the possibility that the marine biosphere plays a role in regulating cloud brightness in the pristine oceanic atmosphere remains largely unexplored. We used 9 yr of global satellite data and ocean climatologies to derive parameterizations of (a) production fluxes of sulfur aerosols formed by the oxidation of the biogenic gas dimethylsulfide emitted from the sea surface; (b) production fluxes of secondary organic aerosols from biogenic organic volatiles; (c) emission fluxes of biogenic primary organic aerosols ejected by wind action on sea surface; and (d) emission fluxes of sea salt also lifted by the wind upon bubble bursting. Series of global weekly estimates of these fluxes were correlated to series of cloud droplet effective radius data derived from satellite (MODIS). Similar analyses were conducted in more detail at 6 locations spread among polluted and clean regions of the oceanic atmosphere. The outcome of the statistical analysis was that negative correlation was common at mid and high latitude for sulfur and organic secondary aerosols, indicating both might be important in seeding cloud droplet activation. Conversely, primary aerosols (organic and sea salt) showed more variable, non-significant or positive correlations, indicating that, despite contributing to large shares of the marine aerosol mass, they are not major drivers of the variability of cloud microphysics. Uncertainties and synergisms are discussed, and recommendations of research needs are given. Discussion Paper Abstract Full Screen / Esc Printer-friendly Version Interactive Discussion 3657 | Discussion Paper ACPD 12, 3655–3694, 2012 Biogenic influence on cloud microphysics over the global ocean A. Lana et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close | Discussion Paper 25 | 20 Discussion Paper 15 | 10 Discussion Paper 5 the Earth’s surface (Haywood and Boucher, 2000). Indirect effects occur through the large influence that aerosols have on the formation and optical properties of clouds. The concentration number, physical and chemical characteristics of aerosols modify cloud microphysics, namely the size and number of cloud droplets, and thereby influence cloud brightness (Twomey, 1977) and longevity (Albrecht, 1989), among other properties (Lohmann and Feichter, 2005). The most salient of these complex indirect effects is that clouds formed in the presence of larger amounts of small aerosols have larger albedo (Andreae and Rosenfeld, 2008). This influence is predicted to be more acute in air masses with fewer aerosols, such as those over the oceans away from continental influence (Twomey, 1977; Andreae and Rosenfeld, 2008). Among the natural climate-regulation processes hypothesized to act through aerosol-cloud interactions, the most notorious was postulated as the CLAW hypothesis (Charlson et al., 1987). CLAW suggested that oceanic emissions of dimethylsulfide (DMS) to the atmosphere could constitute a climate buffer through the regulation of the amount of solar radiation that reaches the Earth surface. DMS is formed in the surface ocean as a by-product of food-web processes and plankton ecophysiology (Simó, 2001; Stefels et al., 2007). Being a volatile compound, DMS is emitted from the ocean to the atmosphere where it is oxidized, mainly by OH radicals, to form H2 SO4 , non-sea−2 salt SO4 and other hygroscopic products that may nucleate into particles and grow to act as cloud condensation nuclei (CCN) or seeds for cloud drop formation (Andreae and Rosenfeld, 2008). If planktonic production of DMS increases with increasing temperature or sunlight, and its emission eventually reduces solar radiation, DMS might be the core of a negative (self-regulating) feedback between the marine biosphere and climate (Charlson et al., 1987). The cross-discipline and cross-scale nature of the CLAW hypothesis has stimulated research in and across fields as apparently distant as plankton ecophysiology, air-sea gas exchange and aerosol-cloud interactions (Simó, 2001). Even though some key aspects of the hypothesis have met strong support, notably through regional evidences for coupling between phytoplankton blooms cloud microphysics and optics (Meskhidze and Nenes, 2006; Krüger and Graßl, 2011), and global Full Screen / Esc Printer-friendly Version Interactive Discussion ACPD 12, 3655–3694, 2012 Biogenic influence on cloud microphysics over the global ocean A. Lana et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close | Discussion Paper | 3658 Discussion Paper 25 | 20 Discussion Paper 15 | 10 Discussion Paper 5 evidence for the sensitivity of DMS production to underwater light intensity (Vallina and Simó, 2007; Lana et al., 2011b), the existence and significance of the proposed feedback loop as a climate buffer remains elusive (Levasseur, 2011) and has been strongly challenged (Quinn and Bates, 2011). Despite DMS has drawn much of the attention because of the CLAW hypothesis, there might be other secondary organic aerosol (SOA) precursors (as yet largely unidentified) that are produced by similar mechanisms and might therefore play analogous roles (Liss and Lovelock, 2007). Marine SOA precursors are natural volatile organic compounds produced by plankton and photochemical reactions all over the oceans. Their emissions are, however, poorly constrained (Dachs et al., 2005, 2012; Simó, 2011). Initially it was suggested that biogenic isoprene fluxes could account for a significant fraction of SOA (Palmer and Shaw, 2005; Meskhidze and Nenes, 2006), as occurs over densely vegetated land. Recently, a number of other SOA precursors have been identified, namely iodomethanes, amines, monoterpenes and non-methane hydrocarbons (Simó, 2011; and references therein). They cause increases in aerosol number and organic matter during periods of higher biological productivity (O’Dowd et al., 2004; Vaattovaara et al., 2006; Müller et al., 2009). With these emissions being poorly quantified, combinations of modelling and observations indicate that known emission fluxes of marine volatiles cannot account for organic aerosol concentrations measured over the oceans, and important fluxes of primary organic aerosols (POA) are to be invoked (e.g., Arnold et al., 2009; Rinaldi et al., 2010). In fact, current estimates of POA and SOA precursor fluxes fall short at predicting organic aerosol levels through atmospheric models (Heald et al., 2005), thus calling for the existence of hitherto unaccounted sources of organic carbon. It should be noted that emissions of hydrophobic semivolatile chemicals that accumulate in the surface microlayer and are released through volatilization or in association with sea-spray, such as alkanes and polycyclic aromatic hydrocarbons (Nizzetto et al., 2008; Dachs et al., 2012), have been overlooked as marine aerosol precursors. Full Screen / Esc Printer-friendly Version Interactive Discussion 3659 | Discussion Paper ACPD 12, 3655–3694, 2012 Biogenic influence on cloud microphysics over the global ocean A. Lana et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close | Discussion Paper 25 | 20 Discussion Paper 15 | 10 Discussion Paper 5 Sea-spray is ejected into the atmosphere by the action of wind speed on the surface of the ocean. It is generated by bubble bursting and carries sea salt together with organic particles, both of which may act as CCN once in the atmosphere. These seaspray POA are composed of virus, bacteria, biogenic polymeric organic material and associated semivolatiles (Bauer et al., 2003; Bowers et al., 2009; Russell et al., 2010; Orellana et al., 2011). Being all of biological origin, it is likely that POA precursors are somewhat proportional to plankton biomass and its most commonly used indicator, chlorophyll-a concentration. Indeed, the scarce existing measurements of POA in small marine aerosols (e.g., O’Dowd et al., 2004; Leck and Bigg, 2007) suggest that they are more abundant in air masses downwind of chlorophyll-a rich waters, particularly if strong winds enhance the lift of sea spray. The biological POA source may be reinforced by the action of surfactants exuded by phytoplankton, which lower surface tension and may facilitate the ejection of small aerosols. Sea salt (SS) is also ejected off the sea surface as sea-spray. It has an important presence in the marine atmosphere, contributing 44 % of the global aerosol optical depth (O’Dowd and de Leeuw, 2007). Sea salt was overlooked in the original CLAW hypothesis, because it was thought to be composed of too few and too big particles to have a significant influence in cloud microphysics despite their high hygroscopicity (Le Quéré and Saltzman, 2009). Today, however, it is known that a non-negligible proportion of sea salt particles belong to the small size fraction that makes them effective as CCN (Andreae and Rosenfeld, 2008; de Leeuw et al., 2011); moreover, sea salt aerosols play a role in the atmospheric chemistry of gaseous aerosol precursors (von Glassow, 2007). When the CLAW hypothesis was published (Charlson et al., 1987), DMS was thought to be the main, if not the only, source of new CCN in the pristine ocean. This scenario has been complicated with the discovery of the aforementioned wide range of volatiles and particles with potential to influence cloud condensation (O’Dowd et al., 1997; Andreae and Rosenfeld, 2008). Further complication comes from the widespread occurrence of continental aerosols in the marine atmosphere, co-existing with marine Full Screen / Esc Printer-friendly Version Interactive Discussion | Discussion Paper 10 Discussion Paper 5 aerosols in internal and external mixtures (Andreae and Rosenfeld, 2008). Any attempt to evaluate the role of the marine biosphere in cloud formation and the radiative budget on a global scale must therefore be able to distinguish between biotic and abiotic, and between anthropogenic and continental sources of the marine aerosols, and describe their geographical, temporal, concentration and size distributions. In this study, we make use of satellite data and ocean climatologies to parameterize the variability in the flux rates of aerosol formation from ocean-leaving DMS and SOA precursors. We also parameterize the emission fluxes of POA and sea salt from the surface ocean. These aerosol sources are compared with the satellite-derived size of cloud droplets on weekly and monthly bases over a 9-yr period. Temporal correlations at both the global scale and representative locations are analyzed as a means to assess the potential role of each marine aerosol source in driving the variability of cloud microphysics. Regions where the sought marine aerosol-cloud interactions are heavily interfered by continental aerosols are identified. | 2 Data and methodology 2.1 Biogenic sulfur aerosol flux | 3660 Discussion Paper 25 12, 3655–3694, 2012 Biogenic influence on cloud microphysics over the global ocean A. Lana et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close | 20 The global ocean DMS concentration data used in this study is the L10 DMS climatology (Lana et al., 2011), which consists of global monthly maps of concentrations distributed in 1 × 1 latitude-longitude pixels. This climatology, an update of that from 1999 (Kettle et al., 1999), was constructed using exclusively the surface DMS concentration measurements (approx. 47 000) available in the Global Surface Seawater DMS database (GSSDD), maintained at the NOAA-PMEL ( and fed with contributions of individual scientists from all over the world. Ocean-to-atmosphere emission fluxes were computed with the climatological surface DMS concentrations and the corresponding gas transfer coefficients, which were parameterized taking into account both the water and the air side resistances, as Discussion Paper 15 ACPD Full Screen / Esc Printer-friendly Version Interactive Discussion where DMSflux is the ocean-to-atmosphere emission flux and γ is a dimensionless | 3661 Discussion Paper (1) ACPD 12, 3655–3694, 2012 Biogenic influence on cloud microphysics over the global ocean A. Lana et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close | γDMSflux (µmolS m−2 d−1 ) = γ · DMSflux Discussion Paper 25 | 20 Discussion Paper 15 | 10 Discussion Paper 5 described in McGillis et al. (2000). The parameterization used for the water side DMS gas transfer coefficient was that suggested by Nightingale et al. (2000) corrected to the Schmidt number of DMS according to Saltzman et al. (1993). The air side transfer coefficient calculation was based on the neutral stability water vapour bulk transfer coefficients from Kondo (1975). The computation of the emission flux also considers the sea surface temperatures (SST) and the non-linear influence of wind speed on air-water mass transfer coefficients. Monthly global, 1 × 1 climatologies of SST and wind speed were obtained from the NCEP/NCAR reanalysis project ( for the period 1978–2008, as most of the DMS data available in the database are from that period. Because the water side gas transfer coefficient has a nonlinear dependence on wind speed, the use of monthly averaged wind speeds introduces a bias into the flux calculation. The flux was corrected for this effect assuming that instantaneous winds follow a Weibull distribution, using the approach of Simó and Dachs (2002). To compute a proxy of DMS oxidation fluxes in the atmosphere we followed the same approach as Vallina et al. (2007). The hydroxyl radical (OH) is the main atmospheric DMS oxidant (Savoie and Prospero, 1989; Chin et al., 2000; Barrie et al., 2001; Kloster et al., 2006). Daytime DMS oxidation initiated by OH produces, among other products, aerosol-forming methanesulphonic acid (MSA), sulfuric acid and its corresponding anion non-sea-salt sulfate (nss-SO−2 4 ). Therefore the amount of DMS-derived aerosols that can act as CCN depends not only on the DMSflux but also on OH concentrations. We used a monthly global distribution of OH concentration data in the marine boundary layer (MBL) obtained from the GEOS-CHEM model run by the Atmospheric Chemistry Group at Harvard University for the year 2001 (Fiore et al., 2003). The potential source for CCN-forming DMS oxidation can be parameterized as follows: Full Screen / Esc Printer-friendly Version Interactive Discussion γ = x/(kS + x) (2) where, 5 ACPD 12, 3655–3694, 2012 Biogenic influence on cloud microphysics over the global ocean A. Lana et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close | Discussion Paper | 3662 Discussion Paper 25 | 20 In the absence of OH (or very low OH) concentrations respect to the DMSflux, most (or at least part) of the DMSflux cannot be converted to CCNDMS (in this situations γ will be low). On the other hand, if OH concentrations are in excess all the DMSflux can be oxidized to CCNDMS (in these situations γ will be close to one). The form of the equation accounts for an asymptotic behavior; as the availability of OH for DMS oxidation (the variable x) increases, a higher fraction of the DMSflux can be converted to CCNDMS approaching asymptotically the upper limit of gamma (for which all DMSflux is converted to CCNDMS ). Therefore γDMSflux gives the amount of biogenic sulfur potentially available for CCN production. Following Vallina et al. (2007), we took the value of kS derived from the annual averages of OH, DMSflux and γ over the Southern Ocean. Note that Vallina et al. (2007) validated equilibrium value of MeCpG steps (,+14 deg.) [31,44]. In comparison, methylation has a significantly lower stability cost when happening at major groove positions, such as 211 and 21 base pair from dyad (mutations 9 and 12), where the roll of the nucleosome bound conformation (+10 deg.) is more compatible with the equilibrium geometry of MeCpG steps. The nucleosome destabilizing effect of cytosine methylation increases with the number of methylated cytosines, following the same position dependence as the single methylations. The multiple-methylation case reveals that each major groove meth- PLOS Computational Biology | 3 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning ylation destabilizes the nucleosome by around 1 kJ/mol (close to the average estimate of 2 kJ/mol obtained for from individual methylation studies), while each minor groove methylation destabilizes it by up to 5 kJ/mol (average free energy as single mutation is around 6 kJ/mol). This energetic position-dependence is the reverse of what was observed in a recent FRET/SAXS study [30]. The differences can be attributed to the use of different ionic conditions and different sequences: a modified Widom-601 sequence of 157 bp, which already contains multiple CpG steps in mixed orientations, and which could assume different positioning due to the introduction of new CpG steps and by effect of the methylation. The analysis of our trajectories reveals a larger root mean square deviation (RMSD) and fluctuation (RMSF; see Figures S2– S3 in Text S1) for the methylated nucleosomes, but failed to detect any systematic change in DNA geometry or in intermolecular DNA-histone energy related to methylation (Fig. S1B, S1C, S4–S6 in Text S1). The hydrophobic effect should favor orientation of the methyl group out from the solvent but this effect alone is not likely to justify the positional dependent stability changes in Figure 2, as the differential solvation of the methyl groups in the bound and unbound states is only in the order of a fraction of a water molecule (Figure S5 in Text S1). We find however, a reasonable correlation between methylation-induced changes in hydrogen bond and stacking interactions of the bases and the change in nucleosome stability (see Figure S6 in Text S1). This finding suggests that methylation-induced nucleosome destabilization is related to the poorer ability of methylated DNA to fit into the required conformation for DNA in a nucleosome. Changes in the elastic deformation energy between methylated and un-methylated DNA correlate with nucleosomal differential binding free energies To further analyze the idea that methylation-induced nucleosome destabilization is connected to a worse fit of methylated DNA into the required nucleosome-bound conformation, we computed the elastic energy of the nucleosomal DNA using a harmonic deformation method [36,37,44]. This method provides a rough estimate of the energy required to deform a DNA fiber to adopt the super helical conformation in the nucleosome (full details in Suppl. Information Text S1). As shown in Figure 2, there is an evident correlation between the increase that methylation produces in the elastic deformation energy (DDE def.) and the free energy variation (DDG bind.) computed from MD/TI calculations. Clearly, methylation increases the stiffness of the CpG step [31], raising the energy cost required to wrap DNA around the histone octamers. This extra energy cost will be smaller in regions of high positive roll (naked DNA MeCpG steps have a higher roll than CpG steps [31]) than in regions of high negative roll. Thus, simple elastic considerations explain why methylation is better tolerated when the DNA faces the histones through the major groove (where positive roll is required) that when it faces histones through the minor groove (where negative roll is required). Nucleosome methylation can give rise to nucleosome repositioning We have established that methylation affects the wrapping of DNA in nucleosomes, but how does this translate into chromatin structure? As noted above, accumulation of minor groove methylations strongly destabilizes the nucleosome, and could trigger nucleosome unfolding, or notable changes in positioning or phasing of DNA around the histone core. While accumulation of methylations might be well tolerated if placed in favorable positions, accumulation in unfavorable positions would destabilize the nucleosome, which might trigger changes in chromatin structure. Chromatin could in fact react in two different ways in response to significant levels of methylation in unfavorable positions: i) the DNA could either detach from the histone core, leading to nucleosome eviction or nucleosome repositioning, or ii) the DNA could rotate around the histone core, changing its phase to place MeCpG steps in favorable positions. Both effects are anticipated to alter DNA accessibility and impact gene expression regulation. The sub-microsecond time scale of our MD trajectories of methylated DNAs bound to nucleosomes is not large enough to capture these effects, but clear trends are visible in cases of multiple mutations occurring in unfavorable positions, where unmethylated and methylated DNA sequences are out of phase by around 28 degrees (Figure S7 in Text S1). Due to this repositioning, large or small, DNA could move and the nucleosome structure could assume a more compact and distorted conformation, as detected by Lee and Lee [29], or a slightly open conformation as found in Jimenez-Useche et al. [30]. Using the harmonic deformation method, we additionally predicted the change in stability induced by cytosine methylation for millions of different nucleosomal DNA sequences. Consistently with our calculations, we used two extreme scenarios to prepare our DNA sequences (see Fig. 3): i) all positions where the minor grooves contact the histone core are occupied by CpG steps, and ii) all positions where the major grooves contact the histone core are occupied by CpG steps. We then computed the elastic energy required to wrap the DNA around the histone proteins in unmethylated and methylated states, and, as expected, observed that methylation disfavors DNA wrapping (Figure 3A). We have rescaled the elastic energy differences with a factor of 0.23 to match the DDG prediction in figure 2B. In agreement with the rest of our results, our analysis confirms that the effect of methylation is position-dependent. In fact, the overall difference between the two extreme methylation scenarios (all-in-minor vs all-in-major) is larger than 60 kJ/mol, the average difference being around 15 kJ/ mol. We have also computed the elastic energy differences for a million sequences with CpG/MeCpG steps positioned at all possible intermediate locations with respect to the position (figure 3B). The large differences between the extreme cases can induce rotations of DNA around the histone core, shifting its phase to allow the placement of the methylated CpG steps facing the histones through the major groove. It is illustrative to compare the magnitude of CpG methylation penalty with sequence dependent differences. Since there are roughly 1.5e88 possible 147 base pairs long sequence combinations (i.e., (4n+4(n/2))/2, n = 147), it is unfeasible to calculate all the possible sequence effects. However, using our elastic model we can provide a range of values based on a reasonably large number of samples. If we consider all possible nucleosomal sequences in the yeast genome (around 12 Mbp), the energy difference between the best and the worst sequence that could form a nucleosome is 0.7 kj/mol per base (a minimum of 1 kJ/mol and maximum of around 1.7 kJ/mol per base, the first best and the last worst sequences are displayed in Table S3 in Text S1). We repeated the same calculation for one million random sequences and we obtained equivalent results. Placing one CpG step every helical turn gives an average energetic difference between minor groove and major groove methylation of 15 kJ/ mol, which translates into ,0.5 kJ/mol per methyl group, 2 kJ/ mol per base for the largest effects. Considering that not all nucleosome base pair steps are likely to be CpG steps, we can conclude that the balance between the destabilization due to CpG methylation and sequence repositioning will depend on the PLOS Computational Biology | 4 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning Figure 3. Methylated and non-methylated DNA elastic deformation energies. (A) Distribution of deformation energies for 147 bplong random DNA sequences with CpG steps positioned every 10 base steps (one helical turn) in minor (red and dark red) and major (light and dark blue) grooves respectively. The energy values were rescaled by the slope of a best-fit straight line of figure 2, which is 0.23, to in the aquatic environment for long-term survival and for transmission to arthropod and mammalian hosts. References 1. Ray K, Marteyn B, Sansonetti PJ, Tang CM. Life on the inside: the intracellular lifestyle of cytosolic bacteria. Nature reviews. 2009; 7(5):333–40. doi: 10.1038/nrmicro2112 PMID: 19369949 2. Kingry LC, Petersen JM. Comparative review of Francisella tularensis and Francisella novicida. Frontiers in cellular and infection microbiology. 2014; 4:35. doi: 10.3389/fcimb.2014.00035 PMID: 24660164 3. Sjodin A, Svensson K, Ohrman C, Ahlinder J, Lindgren P, Duodu S, et al. Genome characterisation of the genus Francisella reveals insight into similar evolutionary paths in pathogens of mammals and fish. BMC genomics. 2012; 13:268. doi: 10.1186/1471-2164-13-268 PMID: 22727144 4. Ellis J, Oyston PC, Green M, Titball RW. Tularemia. Clinical microbiology reviews. 2002; 15(4):631–46. PMID: 12364373 5. Gurcan S. Epidemiology of tularemia. Balkan medical journal. 2014; 31(1):3–10. doi: 10.5152/ balkanmedj.2014.13117 PMID: 25207161 6. Petersen JM, Mead PS, Schriefer ME. Francisella tularensis: an arthropod-borne pathogen. Veterinary research. 2009; 40(2):7. doi: 10.1051/vetres:2008045 PMID: 18950590 7. Lundstrom JO, Andersson AC, Backman S, Schafer ML, Forsman M, Thelaus J. Transstadial transmission of Francisella tularensis holarctica in mosquitoes, Sweden. Emerging infectious diseases. 2011; 17(5):794–9. doi: 10.3201/eid1705.100426 PMID: 21529386 8. van Hoek ML. Biofilms: an advancement in our understanding of Francisella species. Virulence. 2013; 4(8):833–46. doi: 10.4161/viru.27023 PMID: 24225421 9. Ryden P, Sjostedt A, Johansson A. Effects of climate change on tularaemia disease activity in Sweden. Global health action. 2009; 2. PLOS Pathogens | DOI:10.1371/journal.ppat.1005208 December 3, 2015 6/8 10. Barker JR, Chong A, Wehrly TD, Yu JJ, Rodriguez SA, Liu J, et al. The Francisella tularensis pathogenicity island encodes a secretion system that is required for phagosome escape and virulence. Molecular microbiology. 2009; 74(6):1459–70. PMID: 20054881 11. de Bruin OM, Duplantis BN, Ludu JS, Hare RF, Nix EB, Schmerk CL, et al. The biochemical properties of the Francisella pathogenicity island (FPI)-encoded proteins IglA, IglB, IglC, PdpB and DotU suggest roles in type VI secretion. Microbiology (Reading, England). 2011; 157(Pt 12):3483–91. 12. Filloux A, Hachani A, Bleves S. The bacterial type VI secretion machine: yet another player for protein transport across membranes. Microbiology (Reading, England). 2008; 154(Pt 6):1570–83. 13. Nguyen JQ, Gilley RP, Zogaj X, Rodriguez SA, Klose KE. Lipidation of the FPI protein IglE contributes to Francisella tularensis ssp. novicida intramacrophage replication and virulence. Pathogens and disease. 2014; 72(1):10–8. doi: 10.1111/2049-632X.12167 PMID: 24616435 14. Hood RD, Singh P, Hsu F, Guvener T, Carl MA, Trinidad RR, et al. A type VI secretion system of Pseudomonas aeruginosa targets a toxin to bacteria. Cell host & microbe. 2010; 7(1):25–37. 15. Mougous JD, Cuff ME, Raunser S, Shen A, Zhou M, Gifford CA, et al. A virulence locus of Pseudomonas aeruginosa encodes a protein secretion apparatus. Science (New York, NY. 2006; 312 (5779):1526–30. 16. Pukatzki S, Ma AT, Revel AT, Sturtevant D, Mekalanos JJ. Type VI secretion system translocates a phage tail spike-like protein into target cells where it cross-links actin. Proceedings of the National Academy of Sciences of the United States of America. 2007; 104(39):15508–13. PMID: 17873062 17. Santic M, Molmeret M, Klose KE, Jones S, Kwaik YA. The Francisella tularensis pathogenicity island protein IglC and its regulator MglA are essential for modulating phagosome biogenesis and subsequent bacterial escape into the cytoplasm. Cellular microbiology. 2005; 7(7):969–79. PMID: 15953029 18. de Bruin OM, Ludu JS, Nano FE. The Francisella pathogenicity island protein IglA localizes to the bacterial cytoplasm and is needed for intracellular growth. BMC microbiology. 2007; 7:1. PMID: 17233889 19. Llewellyn AC, Jones CL, Napier BA, Bina JE, Weiss DS. Macrophage replication screen identifies a novel Francisella hydroperoxide resistance protein involved in virulence. PloS one. 2011; 6(9):e24201. doi: 10.1371/journal.pone.0024201 PMID: 21915295 20. Clemens DL, Lee BY, Horwitz MA. Virulent and avirulent strains of Francisella tularensis prevent acidification and maturation of their phagosomes and escape into the cytoplasm in human macrophages. Infection and immunity. 2004; 72(6):3204–17. PMID: 15155622 21. Moreau GB, Mann BJ. Adherence and uptake of Francisella into host cells. Virulence. 2013; 4(8):826– 32. doi: 10.4161/viru.25629 PMID: 23921460 22. Tamilselvam B, Daefler S. Francisella targets cholesterol-rich host cell membrane domains for entry into macrophages. J Immunol. 2008; 180(12):8262–71. PMID: 18523292 23. Chong A, Wehrly TD, Nair V, Fischer ER, Barker JR, Klose KE, et al. The early phagosomal stage of Francisella tularensis determines optimal phagosomal escape and Francisella pathogenicity island protein expression. Infection and immunity. 2008; 76(12):5488–99. doi: 10.1128/IAI.00682-08 PMID: 18852245 24. Celli J, Zahrt TC. Mechanisms of Francisella tularensis intracellular pathogenesis. Cold Spring Harbor perspectives in medicine. 2013; 3(4):a010314. doi: 10.1101/cshperspect.a010314 PMID: 23545572 25. Santic M, Asare R, Skrobonja I, Jones S, Abu Kwaik Y. Acquisition of the vacuolar ATPase proton pump and phagosome acidification are essential for escape of Francisella tularensis into the macrophage cytosol. Infection and immunity. 2008; 76(6):2671–7. doi: 10.1128/IAI.00185-08 PMID: 18390995 26. Jones CL, Napier BA, Sampson TR, Llewellyn AC, Schroeder MR, Weiss DS. Subversion of host recognition and defense systems by Francisella spp. Microbiol Mol Biol Rev. 2012; 76(2):383–404. doi: 10. 1128/MMBR.05027-11 PMID: 22688817 27. Pierini R, Juruj C, Perret M, Jones CL, Mangeot P, Weiss DS, et al. AIM2/ASC triggers caspase-8dependent apoptosis in Francisella-infected caspase-1-deficient macrophages. Cell death and differentiation. 2012; 19(10):1709–21. doi: 10.1038/cdd.2012.51 PMID: 22555457 28. Asare R, Kwaik YA. Exploitation of host cell biology and evasion of immunity by francisella tularensis. Frontiers in microbiology. 2011; 1:145. doi: 10.3389/fmicb.2010.00145 PMID: 21687747 29. Keim P, Johansson A, Wagner DM. Molecular epidemiology, evolution, and ecology of Francisella. Annals of the New York Academy of Sciences. 2007; 1105:30–66. PMID: 17435120 30. Asare R, Akimana C, Jones S, Abu Kwaik Y. Molecular bases of proliferation of Francisella tularensis in arthropod vectors. Environmental microbiology. 2010; 12(9):2587–612. doi: 10.1111/j.1462-2920. 2010.02230.x PMID: 20482589 PLOS Pathogens | DOI:10.1371/journal.ppat.1005208 December 3, 2015 7/8 31. Vonkavaara M, Telepnev MV, Ryden P, Sjostedt A, Stoven S. Drosophila melanogaster as a model for elucidating the pathogenicity of Francisella tularensis. Cellular microbiology. 2008; 10(6):1327–38. doi: 10.1111/j.1462-5822.2008.01129.x PMID: 18248629 32. Santic M, Akimana C, Asare R, Kouokam JC, Atay S, Kwaik YA. Intracellular fate of Francisella tularensis within arthropod-derived cells. Environmental microbiology. 2009; 11(6):1473–81. doi: 10.1111/j. 1462-2920.2009.01875.x PMID: 19220402 33. Read A, Vogl SJ, Hueffer K, Gallagher LA, Happ GM. Francisella genes required for replication in mosquito cells. Journal of medical entomology. 2008; 45(6):1108–16. PMID: 19058636 34. Ahlund MK, Ryden P, Sjostedt A, Stoven S. Directed screen of Francisella novicida virulence determinants using Drosophila melanogaster. Infection and immunity. 2010; 78(7):3118–28. doi: 10.1128/IAI. 00146-10 PMID: 20479082 35. Abd H, Johansson T, Golovliov I, Sandstrom G, Forsman M. Survival and growth of Francisella tularensis in Acanthamoeba castellanii. Applied and environmental microbiology. 2003; 69(1):600–6. PMID: 12514047 36. Santic M, Ozanic M, Semic V, Pavokovic G, Mrvcic V, Kwaik YA. Intra-Vacuolar Proliferation of F. Novicida within H. Vermiformis. Frontiers in microbiology. 2011; 2:78. doi: 10.3389/fmicb.2011.00078 PMID: 21747796 37. El-Etr SH, Margolis JJ, Monack D, Robison RA, Cohen M, Moore E, et al. Francisella tularensis type A strains cause the rapid encystment of Acanthamoeba castellanii and survive in amoebal cysts for three weeks postinfection. Applied and environmental microbiology. 2009; 75(23):7488–500. doi: 10.1128/ AEM.01829-09 PMID: 19820161 38. Lauriano CM, Barker JR, Yoon SS, Nano FE, Arulanandam BP, Hassett DJ, et al. MglA regulates transcription of virulence factors necessary for Francisella tularensis intraamoebae and intramacrophage survival. Proceedings of the National Academy of Sciences of the United States of America. 2004; 101 (12):4246–9. PMID: 15010524 PLOS Pathogens | DOI:10.1371/journal.ppat.1005208 December 3, 2015 8/8
123dok avatar

Ingressou : 2016-12-29

Documento similar

Biogenic influence on cloud microphysics over..