64 research outputs found
The Risk of Hepatotoxicity with Fluoroquinolones: A National Case-Control Safety Study
Purpose. Fluoroquinolones are generally considered safe and well-tolerated. However, suspected fluoroquinolone induced hepatotoxicity has been increasingly reported, but data are lacking. Thus, the objective of this study was to assess the risk of hepatotoxicity in patients using fluoroquinolones compared to non-users.
Methods. National Veterans Affairs (VA) hospital admissions were assessed between January 1, 2002 and December 31, 2008. Our case-control study matched patients with a primary diagnosis of hepatotoxicity (cases) to those with myocardial infarction (controls) on admission date (matched up to 1:6). Conditional logistic regression was used to compute adjusted odds ratios (OR) and 95% confidence intervals (CI) of hepatotoxicity associated with fluoroquinolone exposure.
Results. Our study included 7,862 cases and 45,512 matched controls. The majority of study patients were white (63.4%), males (97.7%), with a mean age of 61 years. After adjusting for confounders, fluoroquinolone use was significantly associated with a 20% increased risk of hepatotoxicity development (OR 1.20, 95% CI 1.04-1.38) compared to non-users. A statistically significant increased risk of hepatotoxicity was associated with ciprofloxacin use individually (OR 1.29, 95% CI 1.05-1.58), but not with levofloxacin or moxifloxacin use.
Conclusion. The use of fluoroquinolones was associated with an increased risk of hepatotoxicity relative to non-users in our national VA study population
Proteome-Wide Analysis of Functional Divergence in Bacteria: Exploring a Host of Ecological Adaptations
Functional divergence is the process by which new genes and functions originate through the modification of existing ones.
Both genetic and environmental factors influence the evolution of new functions, including gene duplication or changes in
the ecological requirements of an organism. Novel functions emerge at the expense of ancestral ones and are generally
accompanied by changes in the selective forces at constrained protein regions. We present software capable of analyzing
whole proteomes, identifying putative amino acid replacements leading to functional change in each protein and
performing statistical tests on all tabulated data. We apply this method to 750 complete bacterial proteomes to identify
high-level patterns of functional divergence and link these patterns to ecological adaptations. Proteome-wide analyses of
functional divergence in bacteria with different ecologies reveal a separation between proteins involved in information
processing (Ribosome biogenesis etc.) and those which are dependent on the environment (energy metabolism, defense
etc.). We show that the evolution of pathogenic and symbiotic bacteria is constrained by their association with the host, and
also identify unusual events of functional divergence even in well-studied bacteria such as Escherichia coli. We present a
description of the roles of phylogeny and ecology in functional divergence at the level of entire proteomes in bacteria.This study was supported by a grant from the Spanish Ministerio de Ciencia e Inovacion (BFU2009-12022) and a grant of the Research Frontiers Program (10/RFP/GEN2685) from Science Foundation Ireland. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Caffrey, BE.; Williams, TA.; Jiang, X.; Toft, C.; Hokamp, K.; Fares Riaño, MA. (2012). Proteome-Wide Analysis of Functional Divergence in Bacteria: Exploring a Host of Ecological Adaptations. PLoS ONE. 7:35659-35659. https://doi.org/10.1371/journal.pone.003565935659356597Conant, G. C., & Wolfe, K. H. (2008). Turning a hobby into a job: How duplicated genes find new functions. Nature Reviews Genetics, 9(12), 938-950. doi:10.1038/nrg2482Lynch, M. (2000). The Evolutionary Fate and Consequences of Duplicate Genes. Science, 290(5494), 1151-1155. doi:10.1126/science.290.5494.1151Pinto, G., Mahler, D. L., Harmon, L. J., & Losos, J. B. (2008). Testing the island effect in adaptive radiation: rates and patterns of morphological diversification in Caribbean and mainland
Anolis
lizards. Proceedings of the Royal Society B: Biological Sciences, 275(1652), 2749-2757. doi:10.1098/rspb.2008.0686Lynch, M., & Katju, V. (2004). The altered evolutionary trajectories of gene duplicates. Trends in Genetics, 20(11), 544-549. doi:10.1016/j.tig.2004.09.001Innan, H., & Kondrashov, F. (2010). The evolution of gene duplications: classifying and distinguishing between models. Nature Reviews Genetics, 11(2), 97-108. doi:10.1038/nrg2689Moran, N. A. (2002). Microbial Minimalism. Cell, 108(5), 583-586. doi:10.1016/s0092-8674(02)00665-7Toft, C., Williams, T. A., & Fares, M. A. (2009). Genome-Wide Functional Divergence after the Symbiosis of Proteobacteria with Insects Unraveled through a Novel Computational Approach. PLoS Computational Biology, 5(4), e1000344. doi:10.1371/journal.pcbi.1000344Dykhuizen, D. E. (1998). Antonie van Leeuwenhoek, 73(1), 25-33. doi:10.1023/a:1000665216662Gans, J. (2005). Computational Improvements Reveal Great Bacterial Diversity and High Metal Toxicity in Soil. Science, 309(5739), 1387-1390. doi:10.1126/science.1112665Pikuta, E. V., Hoover, R. B., & Tang, J. (2007). Microbial Extremophiles at the Limits of Life. Critical Reviews in Microbiology, 33(3), 183-209. doi:10.1080/10408410701451948Pace, N. R. (1997). A Molecular View of Microbial Diversity and the Biosphere. Science, 276(5313), 734-740. doi:10.1126/science.276.5313.734Dyall, S. D. (2004). Ancient Invasions: From Endosymbionts to Organelles. Science, 304(5668), 253-257. doi:10.1126/science.1094884Zhang, J. (2003). Evolution by gene duplication: an update. Trends in Ecology & Evolution, 18(6), 292-298. doi:10.1016/s0169-5347(03)00033-8Lynch, M., & Conery, J. S. (2003). The Origins of Genome Complexity. Science, 302(5649), 1401-1404. doi:10.1126/science.1089370Ochman, H., Lawrence, J. G., & Groisman, E. A. (2000). Lateral gene transfer and the nature of bacterial innovation. Nature, 405(6784), 299-304. doi:10.1038/35012500McKenzie, G. J., Harris, R. S., Lee, P. L., & Rosenberg, S. M. (2000). The SOS response regulates adaptive mutation. Proceedings of the National Academy of Sciences, 97(12), 6646-6651. doi:10.1073/pnas.120161797Dagan, T., & Martin, W. (2006). Genome Biology, 7(10), 118. doi:10.1186/gb-2006-7-10-118Kimura, M. (1983). The Neutral Theory of Molecular Evolution. doi:10.1017/cbo9780511623486Yang, Z., & Bielawski, J. P. (2000). Statistical methods for detecting molecular adaptation. Trends in Ecology & Evolution, 15(12), 496-503. doi:10.1016/s0169-5347(00)01994-7Suzuki, Y., & Gojobori, T. (1999). A method for detecting positive selection at single amino acid sites. Molecular Biology and Evolution, 16(10), 1315-1328. doi:10.1093/oxfordjournals.molbev.a026042Yang, Z., & Nielsen, R. (2002). Codon-Substitution Models for Detecting Molecular Adaptation at Individual Sites Along Specific Lineages. Molecular Biology and Evolution, 19(6), 908-917. doi:10.1093/oxfordjournals.molbev.a004148Fares, M. A., Elena, S. F., Ortiz, J., Moya, A., & Barrio, E. (2002). A Sliding Window-Based Method to Detect Selective Constraints in Protein-Coding Genes and Its Application to RNA Viruses. Journal of Molecular Evolution, 55(5), 509-521. doi:10.1007/s00239-002-2346-9Suzuki, Y. (2004). New Methods for Detecting Positive Selection at Single Amino Acid Sites. Journal of Molecular Evolution, 59(1). doi:10.1007/s00239-004-2599-6Zhang, J. (2004). Frequent False Detection of Positive Selection by the Likelihood Method with Branch-Site Models. Molecular Biology and Evolution, 21(7), 1332-1339. doi:10.1093/molbev/msh117Suzuki, Y. (2004). Three-Dimensional Window Analysis for Detecting Positive Selection at Structural Regions of Proteins. Molecular Biology and Evolution, 21(12), 2352-2359. doi:10.1093/molbev/msh249Zhang, J. (2005). Evaluation of an Improved Branch-Site Likelihood Method for Detecting Positive Selection at the Molecular Level. Molecular Biology and Evolution, 22(12), 2472-2479. doi:10.1093/molbev/msi237Berglund, A.-C., Wallner, B., Elofsson, A., & Liberles, D. A. (2005). Tertiary Windowing to Detect Positive Diversifying Selection. Journal of Molecular Evolution, 60(4), 499-504. doi:10.1007/s00239-004-0223-4Gu, X. (1999). Statistical methods for testing functional divergence after gene duplication. Molecular Biology and Evolution, 16(12), 1664-1674. doi:10.1093/oxfordjournals.molbev.a026080Gu, X. (2001). Mathematical Modeling for Functional Divergence after Gene Duplication. Journal of Computational Biology, 8(3), 221-234. doi:10.1089/10665270152530827Gu, X. (2006). A Simple Statistical Method for Estimating Type-II (Cluster-Specific) Functional Divergence of Protein Sequences. Molecular Biology and Evolution, 23(10), 1937-1945. doi:10.1093/molbev/msl056Williams, T. A., Codoñer, F. M., Toft, C., & Fares, M. A. (2010). Two chaperonin systems in bacterial genomes with distinct ecological roles. Trends in Genetics, 26(2), 47-51. doi:10.1016/j.tig.2009.11.009Tatusov, R. L., Fedorova, N. D., Jackson, J. D., Jacobs, A. R., Kiryutin, B., Koonin, E. V., … Natale, D. A. (2003). BMC Bioinformatics, 4(1), 41. doi:10.1186/1471-2105-4-41Lake, J. A. (1999). GENOMICS:Mix and Match in the Tree of Life. Science, 283(5410), 2027-2028. doi:10.1126/science.283.5410.2027Mushegian, A. R., & Koonin, E. V. (1996). A minimal gene set for cellular life derived by comparison of complete bacterial genomes. Proceedings of the National Academy of Sciences, 93(19), 10268-10273. doi:10.1073/pnas.93.19.10268Azuma, Y., & Ota, M. (2009). An evaluation of minimal cellular functions to sustain a bacterial cell. BMC Systems Biology, 3(1). doi:10.1186/1752-0509-3-111Crick, F. H. C. (1968). The origin of the genetic code. Journal of Molecular Biology, 38(3), 367-379. doi:10.1016/0022-2836(68)90392-6Lund, P. A. (2009). Multiple chaperonins in bacteria – why so many? FEMS Microbiology Reviews, 33(4), 785-800. doi:10.1111/j.1574-6976.2009.00178.xKampinga, H. H., Dynlacht, J. R., & Dikomey, E. (2004). Mechanism of radiosensitization by hyperthermia (43°C) as derived from studies with DNA repair defective mutant cell lines. International Journal of Hyperthermia, 20(2), 131-139. doi:10.1080/02656730310001627713Laszlo, A. (1992). The effects of hyperthermia on mammalian cell structure and function. Cell Proliferation, 25(2), 59-87. doi:10.1111/j.1365-2184.1992.tb01482.xKregel, K. C. (2002). Invited Review: Heat shock proteins: modifying factors in physiological stress responses and acquired thermotolerance. Journal of Applied Physiology, 92(5), 2177-2186. doi:10.1152/japplphysiol.01267.2001Lepock, J. R. (1997). Protein Denaturation During Heat Shock. Advances in Molecular and Cell Biology, 223-259. doi:10.1016/s1569-2558(08)60079-xDegnan, P. H. (2005). Genome sequence of Blochmannia pennsylvanicus indicates parallel evolutionary trends among bacterial mutualists of insects. Genome Research, 15(8), 1023-1033. doi:10.1101/gr.3771305Gil, R., Sabater-Munoz, B., Latorre, A., Silva, F. J., & Moya, A. (2002). Extreme genome reduction in Buchnera spp.: Toward the minimal genome needed for symbiotic life. Proceedings of the National Academy of Sciences, 99(7), 4454-4458. doi:10.1073/pnas.062067299Perez-Brocal, V., Gil, R., Ramos, S., Lamelas, A., Postigo, M., Michelena, J. M., … Latorre, A. (2006). A Small Microbial Genome: The End of a Long Symbiotic Relationship? Science, 314(5797), 312-313. doi:10.1126/science.1130441Shigenobu, S., Watanabe, H., Hattori, M., Sakaki, Y., & Ishikawa, H. (2000). Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp. APS. Nature, 407(6800), 81-86. doi:10.1038/35024074Tamas, I. (2002). 50 Million Years of Genomic Stasis in Endosymbiotic Bacteria. Science, 296(5577), 2376-2379. doi:10.1126/science.1071278Van Ham, R. C. H. J., Kamerbeek, J., Palacios, C., Rausell, C., Abascal, F., Bastolla, U., … Moya, A. (2003). Reductive genome evolution in Buchnera aphidicola. Proceedings of the National Academy of Sciences, 100(2), 581-586. doi:10.1073/pnas.0235981100Nakabachi, A., Yamashita, A., Toh, H., Ishikawa, H., Dunbar, H. E., Moran, N. A., & Hattori, M. (2006). The 160-Kilobase Genome of the Bacterial Endosymbiont Carsonella. Science, 314(5797), 267-267. doi:10.1126/science.1134196Baron, C. (2010). Antivirulence drugs to target bacterial secretion systems. Current Opinion in Microbiology, 13(1), 100-105. doi:10.1016/j.mib.2009.12.003Douglas, A. E. (1998). Nutritional Interactions in Insect-Microbial Symbioses: Aphids and Their Symbiotic BacteriaBuchnera. Annual Review of Entomology, 43(1), 17-37. doi:10.1146/annurev.ento.43.1.17Sandström, J., Telang, A., & Moran, N. . (2000). Nutritional enhancement of host plants by aphids — a comparison of three aphid species on grasses. Journal of Insect Physiology, 46(1), 33-40. doi:10.1016/s0022-1910(99)00098-0Anderson, B. E., & Neuman, M. A. (1997). Bartonella spp. as emerging human pathogens. Clinical Microbiology Reviews, 10(2), 203-219. doi:10.1128/cmr.10.2.203Dramsi, S., & Cossart, P. (1998). INTRACELLULAR PATHOGENS AND THE ACTIN CYTOSKELETON. Annual Review of Cell and Developmental Biology, 14(1), 137-166. doi:10.1146/annurev.cellbio.14.1.137Dehio, C. (2001). Bartonella interactions with endothelial cells and erythrocytes. Trends in Microbiology, 9(6), 279-285. doi:10.1016/s0966-842x(01)02047-9Ihler, G. M. (1996). Bartonella bacilliformis: dangerous pathogen slowly emerging from deep background. FEMS Microbiology Letters, 144(1), 1-11. doi:10.1111/j.1574-6968.1996.tb08501.xFricke, W. F., Wright, M. S., Lindell, A. H., Harkins, D. M., Baker-Austin, C., Ravel, J., & Stepanauskas, R. (2008). Insights into the Environmental Resistance Gene Pool from the Genome Sequence of the Multidrug-Resistant Environmental Isolate Escherichia coli SMS-3-5. Journal of Bacteriology, 190(20), 6779-6794. doi:10.1128/jb.00661-08Ren, C.-P., Beatson, S. A., Parkhill, J., & Pallen, M. J. (2005). The Flag-2 Locus, an Ancestral Gene Cluster, Is Potentially Associated with a Novel Flagellar System from Escherichia coli. Journal of Bacteriology, 187(4), 1430-1440. doi:10.1128/jb.187.4.1430-1440.2005Manges, A. R., Johnson, J. R., Foxman, B., O’Bryan, T. T., Fullerton, K. E., & Riley, L. W. (2001). Widespread Distribution of Urinary Tract Infections Caused by a Multidrug-ResistantEscherichia coliClonal Group. New England Journal of Medicine, 345(14), 1007-1013. doi:10.1056/nejmoa011265Cascales, E., & Christie, P. J. (2003). The versatile bacterial type IV secretion systems. Nature Reviews Microbiology, 1(2), 137-149. doi:10.1038/nrmicro753Bailey, S., Ward, D., Middleton, R., Grossmann, J. G., & Zambryski, P. C. (2006). Agrobacterium tumefaciens VirB8 structure reveals potential protein-protein interaction sites. Proceedings of the National Academy of Sciences, 103(8), 2582-2587. doi:10.1073/pnas.0511216103Altenhoff, A. M., & Dessimoz, C. (2009). Phylogenetic and Functional Assessment of Orthologs Inference Projects and Methods. PLoS Computational Biology, 5(1), e1000262. doi:10.1371/journal.pcbi.1000262Roth, A. C., Gonnet, G. H., & Dessimoz, C. (2008). Algorithm of OMA for large-scale orthology inference. BMC Bioinformatics, 9(1). doi:10.1186/1471-2105-9-518Schneider, A., Dessimoz, C., & Gonnet, G. H. (2007). OMA Browser Exploring orthologous relations across 352 complete genomes. Bioinformatics, 23(16), 2180-2182. doi:10.1093/bioinformatics/btm295Tatusov, R. L. (2001). The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Research, 29(1), 22-28. doi:10.1093/nar/29.1.22Lima, T., Auchincloss, A. H., Coudert, E., Keller, G., Michoud, K., Rivoire, C., … Bairoch, A. (2009). HAMAP: a database of completely sequenced microbial proteome sets and manually curated microbial protein families in UniProtKB/Swiss-Prot. Nucleic Acids Research, 37(Database), D471-D478. doi:10.1093/nar/gkn661Gascuel, O. (1997). BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data. Molecular Biology and Evolution, 14(7), 685-695. doi:10.1093/oxfordjournals.molbev.a025808Benjamini, Y., & Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289-300. doi:10.1111/j.2517-6161.1995.tb02031.xDutheil, J., Gaillard, S., Bazin, E., Glémin, S., Ranwez, V., Galtier, N., & Belkhir, K. (2006). BMC Bioinformatics, 7(1), 188. doi:10.1186/1471-2105-7-188Gu, X., & Vander Velden, K. (2002). DIVERGE: phylogeny-based analysis for functional-structural divergence of a protein family. Bioinformatics, 18(3), 500-501. doi:10.1093/bioinformatics/18.3.500Stamatakis, A., Ludwig, T., & Meier, H. (2004). RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees. Bioinformatics, 21(4), 456-463. doi:10.1093/bioinformatics/bti191Yang, Z. (2007). PAML 4: Phylogenetic Analysis by Maximum Likelihood. Molecular Biology and Evolution, 24(8), 1586-1591. doi:10.1093/molbev/msm088Baron, C. (2006). VirB8: a conserved type IV secretion system assembly factor and drug targetThis paper is one of a selection of papers published in this Special Issue, entitled CSBMCB — Membrane Proteins in Health and Disease. Biochemistry and Cell Biology, 84(6), 890-899. doi:10.1139/o06-14
Genomic Insights Into The Ixodes scapularis Tick Vector Of Lyme Disease
Ticks transmit more pathogens to humans and animals than any other arthropod. We describe the 2.1 Gbp nuclear genome of the tick, Ixodes scapularis (Say), which vectors pathogens that cause Lyme disease, human granulocytic anaplasmosis, babesiosis and other diseases. The large genome reflects accumulation of repetitive DNA, new lineages of retrotransposons, and gene architecture patterns resembling ancient metazoans rather than pancrustaceans. Annotation of scaffolds representing B57% of the genome, reveals 20,486 protein-coding genes and expansions of gene families associated with tick–host interactions. We report insights from genome analyses into parasitic processes unique to ticks, including host ‘questing’, prolonged feeding, cuticle synthesis, blood meal concentration, novel methods of haemoglobin digestion, haem detoxification, vitellogenesis and prolonged off-host survival. We identify proteins associated with the agent of human granulocytic anaplasmosis, an emerging disease, and the encephalitis-causing Langat virus, and a population structure correlated to life-history traits and transmission of the Lyme disease agent
Genomic Insights Into The Ixodes scapularis Tick Vector Of Lyme Disease
Ticks transmit more pathogens to humans and animals than any other arthropod. We describe the 2.1 Gbp nuclear genome of the tick, Ixodes scapularis (Say), which vectors pathogens that cause Lyme disease, human granulocytic anaplasmosis, babesiosis and other diseases. The large genome reflects accumulation of repetitive DNA, new lineages of retrotransposons, and gene architecture patterns resembling ancient metazoans rather than pancrustaceans. Annotation of scaffolds representing B57% of the genome, reveals 20,486 protein-coding genes and expansions of gene families associated with tick–host interactions. We report insights from genome analyses into parasitic processes unique to ticks, including host ‘questing’, prolonged feeding, cuticle synthesis, blood meal concentration, novel methods of haemoglobin digestion, haem detoxification, vitellogenesis and prolonged off-host survival. We identify proteins associated with the agent of human granulocytic anaplasmosis, an emerging disease, and the encephalitis-causing Langat virus, and a population structure correlated to life-history traits and transmission of the Lyme disease agent
The multiple faces of self-assembled lipidic systems
Lipids, the building blocks of cells, common to every living organisms, have the propensity to self-assemble into well-defined structures over short and long-range spatial scales. The driving forces have their roots mainly in the hydrophobic effect and electrostatic interactions. Membranes in lamellar phase are ubiquitous in cellular compartments and can phase-separate upon mixing lipids in different liquid-crystalline states. Hexagonal phases and especially cubic phases can be synthesized and observed in vivo as well. Membrane often closes up into a vesicle whose shape is determined by the interplay of curvature, area difference elasticity and line tension energies, and can adopt the form of a sphere, a tube, a prolate, a starfish and many more. Complexes made of lipids and polyelectrolytes or inorganic materials exhibit a rich diversity of structural morphologies due to additional interactions which become increasingly hard to track without the aid of suitable computer models. From the plasma membrane of archaebacteria to gene delivery, self-assembled lipidic systems have left their mark in cell biology and nanobiotechnology; however, the underlying physics is yet to be fully unraveled
The Genome of Anopheles darlingi, the main neotropical malaria vector
Anopheles darlingi is the principal neotropical malaria vector, responsible for more than a million cases of malaria per year on the American continent. Anopheles darlingi diverged from the African and Asian malaria vectors ∼100 million years ago (mya) and successfully adapted to the New World environment. Here we present an annotated reference A. darlingi genome, sequenced from a wild population of males and females collected in the Brazilian Amazon. A total of 10 481 predicted protein-coding genes were annotated, 72% of which have their closest counterpart in Anopheles gambiae and 21% have highest similarity with other mosquito species. In spite of a long period of divergent evolution, conserved gene synteny was observed between A. darlingi and A. gambiae. More than 10 million single nucleotide polymorphisms and short indels with potential use as genetic markers were identified. Transposable elements correspond to 2.3% of the A. darlingi genome. Genes associated with hematophagy, immunity and insecticide resistance, directly involved in vectorhuman and vectorparasite interactions, were identified and discussed. This study represents the first effort to sequence the genome of a neotropical malaria vector, and opens a new window through which we can contemplate the evolutionary history of anopheline mosquitoes. It also provides valuable information that may lead to novel strategies to reduce malaria transmission on the South American continent. The A. darlingi genome is accessible at www.labinfo.lncc.br/index.php/anopheles- darlingi. © 2013 The Author(s)
Investigation of the molecular mechanisms of functional innovation
THESIS 10001The robustness to perturbations and evolvability of genomes are two major principles
that govern the emergence of genetic diversity across all forms of life. Functional
innovations that occur through the genetic diversity cryptically present themselves in
the populations. The mechanisms underlying the emergence of biological innovations
from this genetic diversity have been a major question in evolutionary biology
- …