Streptococcus pneumoniae early response genes to human lung epithelial cells

<p>Abstract</p> <p>Background</p> <p><it>Streptococcus pneumoniae </it>infection starts from colonization of the host respiratory tract where interaction with host respiratory tract epithelial cells occurs. To investigate pneumococcal genes that are involved in the early stage of interaction with host epithelial cells, transcriptional responses of an encapsulated pathogenic pneumococcal strain TIGR4 upon exposure to human lung epithelial cells A549 for 0.5 h and 1 h time periods were investigated by using TIGR (JCVI) microarray technology. Gene expression changes were validated by quantitative real-time PCR (qRT-PCR) analysis.</p> <p>Findings</p> <p>We observed different transcriptional profiles at two incubation time periods in which most gene expressions were down-regulated at 0.5 h but up-regulated at 1 h. Many genes associated with ribonucleotide biosynthesis were down-regulated at both time points, whereas the genes associated with cell envelope, energy metabolism, transport and protein synthesis were mostly up-regulated at 1 h. Furthermore, these profiles were compared to the transcriptomes of a TIGR4-derived strain in response to human macrophages for the same time periods. We found one set of genes that exhibited similar expression changes upon exposure to both types of host cells, including cell envelope-associated <it>bgaA </it>(SP0648) and <it>nanA </it>(SP1693), and uncharacterized gene clusters such as SP1677–SP1680 and SP1688–SP1690.</p> <p>Conclusion</p> <p>These data indicate that at the early stage of interaction with host epithelial cells, a complex gene regulation and expression change occur in bacteria. Some of them might play an essential role during pathogen-host interactions and for the establishment of infection.</p

Transcriptomic Analysis of Mecp2 Mutant Mice Reveals Differentially Expressed Genes and Altered Mechanisms in Both Blood and Brain

Rett syndrome is a rare neuropsychiatric disorder with a wide symptomatology including impaired communication and movement, cardio-respiratory abnormalities, and seizures. The clinical presentation is typically associated to mutations in the gene coding for the methyl-CpG-binding protein 2 (MECP2), which is a transcription factor. The gene is ubiquitously present in all the cells of the organism with a peak of expression in neurons. For this reason, most of the studies in Rett models have been performed in brain. However, some of the symptoms of Rett are linked to the peripheral expression of MECP2, suggesting that the effects of the mutations affect gene expression levels in tissues other than the brain. We used RNA sequencing in Mecp2 mutant mice and matched controls, to identify common genes and pathways differentially regulated across different tissues. We performed our study in brain and peripheral blood, and we identified differentially expressed genes (DEGs) and pathways in each tissue. Then, we compared the genes and mechanisms identified in each preparation. We found that some genes and molecular pathways that are differentially expressed in brain are also differentially expressed in blood of Mecp2 mutant mice at a symptomatic—but not presymptomatic—stage. This is the case for the gene Ube2v1, linked to ubiquitination system, and Serpin1, involved in complement and coagulation cascades. Analysis of biological functions in the brain shows the enrichment of mechanisms correlated to circadian rhythms, while in the blood are enriched the mechanisms of response to stimulus—including immune response. Some mechanisms are enriched in both preparations, such as lipid metabolism and response to stress. These results suggest that analysis of peripheral blood can reveal ubiquitous altered molecular mechanisms of Rett and have applications in diagnosis and treatments’ assessments

Altered retinal microRNA expression profile in a mouse model of retinitis pigmentosa

MicroRNA expression profiling showed that the retina of mice carrying a rhodopsin mutation that leads to retinitis pigmentosa have notably different microRNA profiles from wildtype mice; further in silico analyses identified potential retinal targets for differentially regulated microRNAs

The extracellular Leucine-Rich Repeat superfamily; a comparative survey and analysis of evolutionary relationships and expression patterns

Correction to Dolan J, Walshe K, Alsbury S, Hokamp K, O'Keeffe S, Okafuji T, Miller SF, Tear G, Mitchell KJ: The extracellular leucine-rich repeat superfamily; a comparative survey and analysis of evolutionary relationships and expression patterns. BMC Genomics 2007, 8:320

RNA-seq Brings New Insights to the Intra-Macrophage Transcriptome of Salmonella Typhimurium.

Salmonella enterica serovar Typhimurium is arguably the world's best-understood bacterial pathogen. However, crucial details about the genetic programs used by the bacterium to survive and replicate in macrophages have remained obscure because of the challenge of studying gene expression of intracellular pathogens during infection. Here, we report the use of deep sequencing (RNA-seq) to reveal the transcriptional architecture and gene activity of Salmonella during infection of murine macrophages, providing new insights into the strategies used by the pathogen to survive in a bactericidal immune cell. We characterized 3583 transcriptional start sites that are active within macrophages, and highlight 11 of these as candidates for the delivery of heterologous antigens from Salmonella vaccine strains. A majority (88%) of the 280 S. Typhimurium sRNAs were expressed inside macrophages, and SPI13 and SPI2 were the most highly expressed pathogenicity islands. We identified 31 S. Typhimurium genes that were strongly up-regulated inside macrophages but expressed at very low levels during in vitro growth. The SalComMac online resource allows the visualisation of every transcript expressed during bacterial replication within mammalian cells. This primary transcriptome of intra-macrophage S.-Typhimurium describes the transcriptional start sites and the transcripts responsible for virulence traits, and catalogues the sRNAs that may play a role in the regulation of gene expression during infection

Enhanced flavour profiles through radicicol induced genomic variation in the lager yeasts, Saccharomyces pastorianus

The yeasts, Saccharomyces pastorianus, are hybrids of Saccharomyces cerevisiae and Saccharomyces eubayanus and have acquired traits from the combined parental genomes such as ability to ferment a range of sugars at low temperatures and to produce aromatic flavour compounds, allowing for the production of lager beers with crisp, clean flavours. The polyploid strains are sterile and have reached an evolutionary bottleneck for genetic variation. Here we describe an accelerated evolution approach to obtain lager yeasts with enhanced flavour profiles. As the relative expression of orthologous alleles is a significant contributor to the transcriptome during fermentation, we aimed to induce genetic variation by altering the S. cerevisiae to S. eubayanus chromosome ratio. Aneuploidy was induced through the temporary inhibition of the cell's stress response and strains with increased production of aromatic amino acids via the Shikimate pathway were selected by resistance to amino acid analogues. Genomic changes such as gross chromosomal rearrangements, chromosome loss and chromosome gain were detected in the characterised mutants, as were single-nucleotide polymorphisms in ARO4, encoding for DAHP synthase, the catalytic enzyme in the first step of the Shikimate pathway. Transcriptome analysis confirmed the upregulation of genes encoding enzymes in the Ehrlich pathway and the concomitant increase in the production of higher alcohols and esters such as 2-phenylethanol, 2-phenylethyl acetate, tryptophol, and tyrosol. We propose that the polyploid nature of S. pastorianus genomes is an advantageous trait supporting opportunities for genetic alteration in otherwise sterile strain

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

BOSC 2022: the first hybrid and 23rd annual Bioinformatics Open Source Conference

The 23 rd annual Bioinformatics Open Source Conference (BOSC 2022) was part of this year's conference on Intelligent Systems for Molecular Biology (ISMB). Launched in 2000 and held every year since, BOSC is the premier meeting covering open source bioinformatics and open science. ISMB 2022 was, for the first time, a hybrid conference, with the in-person component hosted in Madison, Wisconsin (USA). About 1000 people attended ISMB 2022 in person, with another 800 online. Approximately 200 people participated in BOSC sessions, which included 28 talks chosen from submitted abstracts, 46 posters, and a panel discussion, "Building and Sustaining Inclusive Open Science Communities". BOSC 2022 included joint keynotes with two other COSIs. Jason Williams gave a BOSC / Education COSI keynote entitled "Riding the bicycle: Including all scientists on a path to excellence". A joint session with Bio-Ontologies featured a keynote by Melissa Haendel, "The open data highway: turbo-boosting translational traffic with ontologies.

BOSC 2019, the 20th annual Bioinformatics Open Source Conference.

The Bioinformatics Open Source Conference is a volunteer-organized meeting that covers open source software development and open science in bioinformatics. Launched in 2000, BOSC has been held every year since. BOSC 2019, the 20th annual BOSC, took place as one of the Communities of Special Interest (COSIs) at the Intelligent Systems for Molecular Biology meeting (ISMB/ECCB 2019). The two-day meeting included a total of 46 talks and 55 posters, as well as eight Birds of a Feather interest groups. The keynote speaker was University of Cape Town professor Dr. Nicola Mulder, who spoke on "Building infrastructure for responsible open science in Africa". Immediately after BOSC 2019, about 50 people participated in the two-day CollaborationFest (CoFest for short), an open and free community-driven event at which participants work together to contribute to bioinformatics software, documentation, training materials, and use cases

Lower density and shorter duration of nasopharyngeal carriage by pneumococcal serotype 1 (St217) may explain its increased invasiveness over other serotypes

ABSTRACT Streptococcus pneumoniae is a frequent colonizer of the human nasopharynx and a major cause of life-threating invasive infections such as pneumonia, meningitis and sepsis. Over 1 million people die every year due to invasive pneumococcal disease (IPD), mainly in developing countries. Serotype 1 is a common cause of IPD; however, unlike other serotypes, it is rarely found in the carrier state in the nasopharynx, which is often considered a prerequisite for disease. The aim of this study was to understand this dichotomy. We used murine models of carriage and IPD to characterize the pathogenesis of African serotype 1 (sequence type 217) pneumococcal strains obtained from the Queen Elizabeth Central Hospital in Blantyre, Malawi. We found that ST217 pneumococcal strains were highly virulent in a mouse model of invasive pneumonia, but in contrast to the generally accepted assumption, can also successfully establish nasopharyngeal carriage. Interestingly, we found that cocolonizing serotypes may proliferate in the presence of serotype 1, suggesting that acquisition of serotype 1 carriage could increase the risk of developing IPD by other serotypes. RNA sequencing analysis confirmed that key virulence genes associated with inflammation and tissue invasiveness were upregulated in serotype 1. These data reveal important new insights into serotype 1 pathogenesis, with implications for carriage potential and risk of invasive disease through interactions with other cocolonizing serotypes, an often-overlooked factor in transmission and disease progression. IMPORTANCE The pneumococcus causes serious diseases such as pneumonia, sepsis, and meningitis and is a major cause of morbidity and mortality worldwide. Serotype 1 accounts for the majority of invasive pneumococcal disease cases in sub-Saharan Africa but is rarely found during nasopharyngeal carriage. Understanding the mechanisms leading to nasopharyngeal carriage and invasive disease by this serotype can help reduce its burden on health care systems worldwide. In this study, we also uncovered the potential impact of serotype 1 on disease progression of other coinfecting serotypes, which can have important implications for vaccine efficacy. Understanding the interactions between different serotypes during nasopharyngeal carriage may lead to improved intervention methods and therapies to reduce pneumococcal invasive disease levels