416 research outputs found

    Molecular Longitudinal Tracking of Mycobacterium abscessus spp. during Chronic Infection of the Human Lung

    Get PDF
    <div><p>The <i>Mycobacterium abscessus</i> complex is an emerging cause of chronic pulmonary infection in patients with underlying lung disease. The <i>M. abscessus</i> complex is regarded as an environmental pathogen but its molecular adaptation to the human lung during long-term infection is poorly understood. Here we carried out a longitudinal molecular epidemiological analysis of 178 <i>M. abscessus</i> spp. isolates obtained from 10 cystic fibrosis (CF) and 2 non CF patients over a 13 year period. Multi-locus sequence and molecular typing analysis revealed that 11 of 12 patients were persistently colonized with the same genotype during the course of the infection while replacement of a <i>M. abscessus sensu stricto</i> strain with a <i>Mycobacterium massiliense</i> strain was observed for a single patient. Of note, several patients including a pair of siblings were colonized with closely-related strains consistent with intra-familial transmission or a common infection reservoir. In general, a switch from smooth to rough colony morphology was observed during the course of long-term infection, which in some cases correlated with an increasing severity of clinical symptoms. To examine evolution during long-term infection of the CF lung we compared the genome sequences of 6 sequential isolates of <i>Mycobacterium bolletii</i> obtained from a single patient over an 11 year period, revealing a heterogeneous clonal infecting population with mutations in regulators controlling the expression of virulence factors and complex lipids. Taken together, these data provide new insights into the epidemiology of <i>M. abscessus</i> spp. during long-term infection of the CF lung, and the molecular transition from saprophytic organism to human pathogen.</p></div

    Mycobacteria clumping increase their capacity to damage macrophages

    Get PDF
    The rough morphotypes of non-tuberculous mycobacteria have been associated with the most severe illnesses in humans. This idea is consistent with the fact that Mycobacterium tuberculosis presents a stable rough morphotype. Unlike smooth morphotypes, the bacilli of rough morphotypes grow close together, leaving no spaces among them and forming large aggregates (clumps). Currently, the initial interaction of macrophages with clumps remains unclear. Thus, we infected J774 macrophages with bacterial suspensions of rough morphotypes of M. abscessus containing clumps and suspensions of smooth morphotypes, primarily containing isolated bacilli. Using confocal laser scanning microscopy and electron microscopy, we observed clumps of at least five rough-morphotype bacilli inside the phagocytic vesicles of macrophages at 3 h post-infection. These clumps grew within the phagocytic vesicles, killing 100% of the macrophages at 72 h post-infection, whereas the proliferation of macrophages infected with smooth morphotypes remained unaltered at 96 h post-infection. Thus, macrophages phagocytose large clumps, exceeding the bactericidal capacities of these cells. Furthermore, proinflammatory cytokines and granuloma-like structures were only produced by macrophages infected with rough morphotypes. Thus, the present study provides a foundation for further studies that consider mycobacterial clumps as virulence factors

    Conditional Gene Expression in Mycobacterium abscessus

    Get PDF
    Mycobacterium abscessus is an emerging human pathogen responsible for lung infections, skin and soft-tissue infections and disseminated infections in immunocompromised patients. It may exist either as a smooth (S) or rough (R) morphotype, the latter being associated with increased pathogenicity in various models. Genetic tools for homologous recombination and conditional gene expression are desperately needed to allow the study of M. abscessus virulence. However, descriptions of knock-out (KO) mutants in M. abscessus are rare, with only one KO mutant from an S strain described so far. Moreover, of the three major tools developed for homologous recombination in mycobacteria, only the one based on expression of phage recombinases is working. Several conditional gene expression tools have recently been engineered for Mycobacterium tuberculosis and Mycobacterium smegmatis, but none have been tested yet in M. abscessus. Based on previous experience with genetic tools allowing homologous recombination and their failure in M. abscessus, we evaluated the potential interest of a conditional gene expression approach using a system derived from the two repressors system, TetR/PipOFF. After several steps necessary to adapt TetR/PipOFF for M. abscessus, we have shown the efficiency of this system for conditional expression of an essential mycobacterial gene, fadD32. Inhibition of fadD32 was demonstrated for both the S and R isotypes, with marginally better efficiency for the R isotype. Conditional gene expression using the dedicated TetR/PipOFF system vectors developed here is effective in S and R M. abscessus, and may constitute an interesting approach for future genetic studies in this pathogen

    Understanding the impact of antibiotic therapies on the respiratory tract resistome: A novel pooled-template metagenomic sequencing strategy

    Get PDF
    Determining the effects of antimicrobial therapies on airway microbiology at a population-level is essential. Such analysis allows, for example, surveillance of antibiotic-induced changes in pathogen prevalence, the emergence and spread of antibiotic resistance, and the transmission of multi-resistant organisms. However, current analytical strategies for understanding these processes are limited. Culture- and PCR-based assays for specific microbes require the a priori selection of targets, while antibiotic sensitivity testing typically provides no insight into either the molecular basis of resistance, or the carriage of resistance determinants by the wider commensal microbiota. Shotgun metagenomic sequencing provides an alternative approach that allows the microbial composition of clinical samples to be described in detail, including the prevalence of resistance genes and virulence traits. While highly informative, the application of metagenomics to large patient cohorts can be prohibitively expensive. Using sputum samples from a randomised placebo-controlled trial of erythromycin in adults with bronchiectasis, we describe a novel, cost-effective strategy for screening patient cohorts for changes in resistance gene prevalence. By combining metagenomic screening of pooled DNA extracts with validatory quantitative PCR-based analysis of candidate markers in individual samples, we identify population-level changes in the relative abundance of specific macrolide resistance genes. This approach has the potential to provide an important adjunct to current analytical strategies, particularly within the context of antimicrobial clinical trials

    Evidence of the Red-Queen hypothesis from accelerated rates of evolution of genes involved in biotic interactions in Pneumocystis

    Get PDF
    [EN] Pneumocystis species are ascomycete fungi adapted to live inside the lungs of mammals. These ascomycetes show extensive stenoxenism, meaning that each species of Pneumocystis infects a single species of host. Here, we study the effect exerted by natural selection on gene evolution in the genomes of three Pneumocystis species. We show that genes involved in host interaction evolve under positive selection. In the first place, we found strong evidence of episodic diversifying selection in Major surface glycoproteins (Msg). These proteins are located on the surface of Pneumocystis and are used for host attachment and probably for immune system evasion. Consistent with their function as antigens, most sites under diversifying selection in Msg code for residues with large relative surface accessibility areas. We also found evidence of positive selection in part of the cell machinery used to export Msg to the cell surface. Specifically, we found that genes participating in glycosylphosphatidylinositol (GPI) biosynthesis show an increased rate of nonsynonymous substitutions (dN) versus synonymous substitutions (dS). GPI is a molecule synthesized in the endoplasmic reticulum that is used to anchor proteins to membranes. We interpret the aforementioned findings as evidence of selective pressure exerted by the host immune system on Pneumocystis species, shaping the evolution of Msg and several proteins involved in GPI biosynthesis. We suggest that genome evolution in Pneumocystis is well described by the Red-Queen hypothesis whereby genes relevant for biotic interactions show accelerated rates of evolution.L.D. wishes to thank Eugenia Flores and Ana Fayos for support provided. This project has received funding from the Marie Curie International Research Staff Exchange Scheme within the 7th European Community Framework Program under grant agreement No 612583-DEANN. Part of this work was done during an internship of L.D. as invited professor at the Universidad de Valencia. Support from CONACYT (grant 454938) is gratefully acknowledged. This work was supported by grants to A.M. from the Spanish Ministry of Science and Competitivity (projects SAF 2012-31187, SAF2013-49788-EXP, SAF2015-65878-R), Carlos III Institute of Health (projects PIE14/00045, AC 15/00022 and AC15/00042), Generalitat Valenciana (project PrometeoII/2014/065) and cofinanced by FEDER.Delaye, L.; Ruiz Ruiz, S.; Calderon, E.; Tarazona Campos, S.; Conesa, A.; Moya, A. (2018). Evidence of the Red-Queen hypothesis from accelerated rates of evolution of genes involved in biotic interactions in Pneumocystis. Genome Biology and Evolution. 10(6):1596-1606. https://doi.org/10.1093/gbe/evy116S15961606106Aliouat-Denis, C.-M., Chabé, M., Demanche, C., Aliouat, E. M., Viscogliosi, E., Guillot, J., … Dei-Cas, E. (2008). Pneumocystis species, co-evolution and pathogenic power. Infection, Genetics and Evolution, 8(5), 708-726. doi:10.1016/j.meegid.2008.05.001Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., … Sherlock, G. (2000). Gene Ontology: tool for the unification of biology. Nature Genetics, 25(1), 25-29. doi:10.1038/75556Brockhurst, M. A., Chapman, T., King, K. C., Mank, J. E., Paterson, S., & Hurst, G. D. D. (2014). Running with the Red Queen: the role of biotic conflicts in evolution. Proceedings of the Royal Society B: Biological Sciences, 281(1797), 20141382. doi:10.1098/rspb.2014.1382Brown, G. D., Denning, D. W., Gow, N. A. R., Levitz, S. M., Netea, M. G., & White, T. C. (2012). Hidden Killers: Human Fungal Infections. Science Translational Medicine, 4(165), 165rv13-165rv13. doi:10.1126/scitranslmed.3004404Cagan, A., Theunert, C., Laayouni, H., Santpere, G., Pybus, M., Casals, F., … Andrés, A. M. (2016). Natural Selection in the Great Apes. Molecular Biology and Evolution, 33(12), 3268-3283. doi:10.1093/molbev/msw215Catherinot, E., Lanternier, F., Bougnoux, M.-E., Lecuit, M., Couderc, L.-J., & Lortholary, O. (2010). Pneumocystis jirovecii Pneumonia. Infectious Disease Clinics of North America, 24(1), 107-138. doi:10.1016/j.idc.2009.10.010Chagas, C. (1909). Nova tripanozomiaze humana: estudos sobre a morfolojia e o ciclo evolutivo do Schizotrypanum cruzi n. gen., n. sp., ajente etiolojico de nova entidade morbida do homem. Memórias do Instituto Oswaldo Cruz, 1(2), 159-218. doi:10.1590/s0074-02761909000200008Cissé, O. H., Pagni, M., & Hauser, P. M. (2014). Comparative Genomics Suggests That the Human Pathogenic Fungus Pneumocystis jirovecii Acquired Obligate Biotrophy through Gene Loss. Genome Biology and Evolution, 6(8), 1938-1948. doi:10.1093/gbe/evu155Cushion, M. T., Smulian, A. G., Slaven, B. E., Sesterhenn, T., Arnold, J., Staben, C., … Meller, J. (2007). Transcriptome of Pneumocystis carinii during Fulminate Infection: Carbohydrate Metabolism and the Concept of a Compatible Parasite. PLoS ONE, 2(5), e423. doi:10.1371/journal.pone.0000423Daub, J. T., Moretti, S., Davydov, I. I., Excoffier, L., & Robinson-Rechavi, M. (2017). Detection of Pathways Affected by Positive Selection in Primate Lineages Ancestral to Humans. Molecular Biology and Evolution, 34(6), 1391-1402. doi:10.1093/molbev/msx083Deitsch, K. W., Lukehart, S. A., & Stringer, J. R. (2009). Common strategies for antigenic variation by bacterial, fungal and protozoan pathogens. Nature Reviews Microbiology, 7(7), 493-503. doi:10.1038/nrmicro2145Demanche, C., Berthelemy, M., Petit, T., Polack, B., Wakefield, A. E., Dei-Cas, E., & Guillot, J. (2001). Phylogeny of Pneumocystis carinii from 18 Primate Species Confirms Host Specificity and Suggests Coevolution. Journal of Clinical Microbiology, 39(6), 2126-2133. doi:10.1128/jcm.39.6.2126-2133.2001Derouiche, S., Deville, M., Taylor, M., Akbar, H., Guillot, J., Carreto-Binaghi, L., … Demanche, C. (2009). Pneumocystis diversity as a phylogeographic tool. Memórias do Instituto Oswaldo Cruz, 104(1), 112-117. doi:10.1590/s0074-02762009000100017Edgar, R. C. (2004). MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research, 32(5), 1792-1797. doi:10.1093/nar/gkh340Edman, J. C., Kovacs, J. A., Masur, H., Santi, D. V., Elwood, H. J., & Sogin, M. L. (1988). Ribosomal RNA sequence shows Pneumocystis carinii to be a member of the Fungi. Nature, 334(6182), 519-522. doi:10.1038/334519a0Finn, R. D., Coggill, P., Eberhardt, R. Y., Eddy, S. R., Mistry, J., Mitchell, A. L., … Bateman, A. (2015). The Pfam protein families database: towards a more sustainable future. Nucleic Acids Research, 44(D1), D279-D285. doi:10.1093/nar/gkv1344Fujita, M., & Kinoshita, T. (2009). Structural remodeling of GPI anchors during biosynthesis and after attachment to proteins. FEBS Letters, 584(9), 1670-1677. doi:10.1016/j.febslet.2009.10.079Gerton, J. L., DeRisi, J., Shroff, R., Lichten, M., Brown, P. O., & Petes, T. D. (2000). Global mapping of meiotic recombination hotspots and coldspots in the yeast Saccharomyces cerevisiae. Proceedings of the National Academy of Sciences, 97(21), 11383-11390. doi:10.1073/pnas.97.21.11383Gotz, S., Garcia-Gomez, J. M., Terol, J., Williams, T. D., Nagaraj, S. H., Nueda, M. J., … Conesa, A. (2008). High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Research, 36(10), 3420-3435. doi:10.1093/nar/gkn176Guindon, S., Dufayard, J.-F., Lefort, V., Anisimova, M., Hordijk, W., & Gascuel, O. (2010). New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0. Systematic Biology, 59(3), 307-321. doi:10.1093/sysbio/syq010Hall, J. P. J., Wang, H., & Barry, J. D. (2013). Mosaic VSGs and the Scale of Trypanosoma brucei Antigenic Variation. PLoS Pathogens, 9(7), e1003502. doi:10.1371/journal.ppat.1003502Hauser, P. M. (2014). Genomic Insights into the Fungal Pathogens of the Genus Pneumocystis: Obligate Biotrophs of Humans and Other Mammals. PLoS Pathogens, 10(11), e1004425. doi:10.1371/journal.ppat.1004425Huerta-Cepas, J., Serra, F., & Bork, P. (2016). ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data. Molecular Biology and Evolution, 33(6), 1635-1638. doi:10.1093/molbev/msw046Hughes, A. L. (2007). Looking for Darwin in all the wrong places: the misguided quest for positive selection at the nucleotide sequence level. Heredity, 99(4), 364-373. doi:10.1038/sj.hdy.6801031Jackson, A. P., Otto, T. D., Darby, A., Ramaprasad, A., Xia, D., Echaide, I. E., … Pain, A. (2014). The evolutionary dynamics of variant antigen genes in Babesia reveal a history of genomic innovation underlying host-parasite interaction. Nucleic Acids Research, 42(11), 7113-7131. doi:10.1093/nar/gku322Keely, S. P., Renauld, H., Wakefield, A. E., Cushion, M. T., Smulian, A. G., Fosker, N., … Hall, N. (2005). Gene Arrays atPneumocystis cariniiTelomeres. Genetics, 170(4), 1589-1600. doi:10.1534/genetics.105.040733Keely, S. P., & Stringer, J. R. (2009). Complexity of the MSG gene family of Pneumocystis carinii. BMC Genomics, 10(1), 367. doi:10.1186/1471-2164-10-367Kosakovsky Pond, S. L., Posada, D., Gravenor, M. B., Woelk, C. H., & Frost, S. D. W. (2006). GARD: a genetic algorithm for recombination detection. Bioinformatics, 22(24), 3096-3098. doi:10.1093/bioinformatics/btl474Kumar, S., Stecher, G., & Tamura, K. (2016). MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. Molecular Biology and Evolution, 33(7), 1870-1874. doi:10.1093/molbev/msw054Kutty, G., England, K. J., & Kovacs, J. A. (2013). Expression of Pneumocystis jirovecii Major Surface Glycoprotein in Saccharomyces cerevisiae. The Journal of Infectious Diseases, 208(1), 170-179. doi:10.1093/infdis/jit131Kutty, G., Maldarelli, F., Achaz, G., & Kovacs, J. A. (2008). Variation in the Major Surface Glycoprotein Genes inPneumocystis jirovecii. The Journal of Infectious Diseases, 198(5), 741-749. doi:10.1086/590433Kutty, G., Shroff, R., & Kovacs, J. A. (2013). Characterization of Pneumocystis Major Surface Glycoprotein Gene (msg) Promoter Activity in Saccharomyces cerevisiae. Eukaryotic Cell, 12(10), 1349-1355. doi:10.1128/ec.00122-13Kyes, S. A., Kraemer, S. M., & Smith, J. D. (2007). Antigenic Variation in Plasmodium falciparum: Gene Organization and Regulation of the var Multigene Family. Eukaryotic Cell, 6(9), 1511-1520. doi:10.1128/ec.00173-07Li, L. (2003). OrthoMCL: Identification of Ortholog Groups for Eukaryotic Genomes. Genome Research, 13(9), 2178-2189. doi:10.1101/gr.1224503Liang, M., Raley, C., Zheng, X., Kutty, G., Gogineni, E., Sherman, B. T., … Huang, D. W. (2016). Distinguishing highly similar gene isoforms with a clustering-based bioinformatics analysis of PacBio single-molecule long reads. BioData Mining, 9(1). doi:10.1186/s13040-016-0090-8Ma, L., Chen, Z., Huang, D. W., Kutty, G., Ishihara, M., Wang, H., … Kovacs, J. A. (2016). Genome analysis of three Pneumocystis species reveals adaptation mechanisms to life exclusively in mammalian hosts. Nature Communications, 7(1). doi:10.1038/ncomms10740Mancera, E., Bourgon, R., Brozzi, A., Huber, W., & Steinmetz, L. M. (2008). High-resolution mapping of meiotic crossovers and non-crossovers in yeast. Nature, 454(7203), 479-485. doi:10.1038/nature07135Murrell, B., Wertheim, J. O., Moola, S., Weighill, T., Scheffler, K., & Kosakovsky Pond, S. L. (2012). Detecting Individual Sites Subject to Episodic Diversifying Selection. PLoS Genetics, 8(7), e1002764. doi:10.1371/journal.pgen.1002764Palmer, G. H., & Brayton, K. A. (2007). Gene conversion is a convergent strategy for pathogen antigenic variation. Trends in Parasitology, 23(9), 408-413. doi:10.1016/j.pt.2007.07.008Paradis, E., Claude, J., & Strimmer, K. (2004). APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics, 20(2), 289-290. doi:10.1093/bioinformatics/btg412Paterson, S., Vogwill, T., Buckling, A., Benmayor, R., Spiers, A. J., Thomson, N. R., … Brockhurst, M. A. (2010). Antagonistic coevolution accelerates molecular evolution. Nature, 464(7286), 275-278. doi:10.1038/nature08798Petersen, B., Petersen, T., Andersen, P., Nielsen, M., & Lundegaard, C. (2009). A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Structural Biology, 9(1), 51. doi:10.1186/1472-6807-9-51Petes, T. D. (2001). Meiotic recombination hot spots and cold spots. Nature Reviews Genetics, 2(5), 360-369. doi:10.1038/35072078Pittet, M., & Conzelmann, A. (2007). Biosynthesis and function of GPI proteins in the yeast Saccharomyces cerevisiae. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids, 1771(3), 405-420. doi:10.1016/j.bbalip.2006.05.015Pond, S. L. K., Frost, S. D. W., & Muse, S. V. (2004). HyPhy: hypothesis testing using phylogenies. Bioinformatics, 21(5), 676-679. doi:10.1093/bioinformatics/bti079Schmid-Siegert, E., Richard, S., Luraschi, A., Mühlethaler, K., Pagni, M., & Hauser, P. M. (2017). Mechanisms of Surface Antigenic Variation in the Human Pathogenic Fungus Pneumocystis jirovecii. mBio, 8(6). doi:10.1128/mbio.01470-17Serra, F., Arbiza, L., Dopazo, J., & Dopazo, H. (2011). Natural Selection on Functional Modules, a Genome-Wide Analysis. PLoS Computational Biology, 7(3), e1001093. doi:10.1371/journal.pcbi.1001093STRINGER, J. R. (2007). Antigenic Variation in Pneumocystis. The Journal of Eukaryotic Microbiology, 54(1), 8-13. doi:10.1111/j.1550-7408.2006.00225.xStringer, S. L., Stringer, J. R., Blase, M. A., Walzer, P. D., & Cushion, M. T. (1989). Pneumocystis carinii: Sequence from ribosomal RNA implies a close relationship with fungi. Experimental Parasitology, 68(4), 450-461. doi:10.1016/0014-4894(89)90130-6Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., … Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545-15550. doi:10.1073/pnas.0506580102Thomas, C. F., & Limper, A. H. (2007). Current insights into the biology and pathogenesis of Pneumocystis pneumonia. Nature Reviews Microbiology, 5(4), 298-308. doi:10.1038/nrmicro1621Vink, C., Rudenko, G., & Seifert, H. S. (2012). Microbial antigenic variation mediated by homologous DNA recombination. FEMS Microbiology Reviews, 36(5), 917-948. doi:10.1111/j.1574-6976.2011.00321.xVinuesa, P., & Contreras-Moreira, B. (2015). Robust Identification of Orthologues and Paralogues for Microbial Pan-Genomics Using GET_HOMOLOGUES: A Case Study of pIncA/C Plasmids. Bacterial Pangenomics, 203-232. doi:10.1007/978-1-4939-1720-4_14Weatherly, D. B., Peng, D., & Tarleton, R. L. (2016). Recombination-driven generation of the largest pathogen repository of antigen variants in the protozoan Trypanosoma cruzi. BMC Genomics, 17(1). doi:10.1186/s12864-016-3037-zYang, Z. (2007). PAML 4: Phylogenetic Analysis by Maximum Likelihood. Molecular Biology and Evolution, 24(8), 1586-1591. doi:10.1093/molbev/msm08
    corecore