Rapid generation of endogenously driven transcriptional reporters in cells through CRISPR/Cas9

CRISPR/Cas9 technologies have been employed for genome editing to achieve gene knockouts and knock-ins in somatic cells. Similarly, certain endogenous genes have been tagged with fluorescent proteins. Often, the detection of tagged proteins requires high expression and sophisticated tools such as confocal microscopy and flow cytometry. Therefore, a simple, sensitive and robust transcriptional reporter system driven by endogenous promoter for studies into transcriptional regulation is desirable. We report a CRISPR/Cas9-based methodology for rapidly integrating a firefly luciferase gene in somatic cells under the control of endogenous promoter, using the TGFβ-responsive gene PAI-1. Our strategy employed a polycistronic cassette containing a non-fused GFP protein to ensure the detection of transgene delivery and rapid isolation of positive clones. We demonstrate that firefly luciferase cDNA can be efficiently delivered downstream of the promoter of the TGFβ-responsive gene PAI-1. Using chemical and genetic regulators of TGFβ signalling, we show that it mimics the transcriptional regulation of endogenous PAI-1 expression. Our unique approach has the potential to expedite studies on transcription of any gene in the context of its native chromatin landscape in somatic cells, allowing for robust high-throughput chemical and genetic screens

Drug allergy

Drug allergy encompasses a spectrum of immunologically-mediated hypersensitivity reactions with varying mechanisms and clinical presentations. This type of adverse drug reaction (ADR) not only affects patient quality of life, but may also lead to delayed treatment, unnecessary investigations, and even mortality. Given the myriad of symptoms associated with the condition, diagnosis is often challenging. Therefore, referral to an allergist experienced in the identification, diagnosis and management of drug allergy is recommended if a drug-induced allergic reaction is suspected. Diagnosis relies on a careful history and physical examination. In some instances, skin testing, graded challenges and induction of drug tolerance procedures may be required

Dissecting the roles of mass and environment quenching in galaxy evolution with EAGLE

We exploit the pioneering cosmological hydrodynamical simulation, EAGLE, to study how the connection between halo mass (M_halo), stellar mass (M*) and star-formation rate (SFR) evolves across redshift. Using Principal Component Analysis we identify the key axes of correlation between these physical quantities, for the full galaxy sample and split by satellite/central and low/high halo mass. The first principal component of the z=0 EAGLE galaxy population is a positive correlation between M_halo, M* and SFR. This component is particularly dominant for central galaxies in low mass haloes. The second principal component, most significant in high mass haloes, is a negative correlation between M_halo and SFR, indicative of environmental quenching. For galaxies above M*~10^10M_solar, however, the SFR is seen to decouple from the M_halo-M* correlation; this result is found to be independent of environment, suggesting that mass quenching effects are also in operation. We find extremely good agreement between the EAGLE principal components and those of SDSS galaxies; this lends confidence to our conclusions. Extending our study to EAGLE galaxies in the range z=0-4, we find that, although the relative numbers of galaxies in the different subsamples change, their principal components do not change significantly with redshift. This indicates that the physical processes that govern the evolution of galaxies within their dark matter haloes act similarly throughout cosmic time. Finally, we present halo occupation distribution model fits to EAGLE galaxies and show that one flexible 6-parameter functional form is capable of fitting a wide range of different mass- and SFR-selected subsamples.Comment: 17 pages, 5 figures; accepted for publication in MNRA

A Model-Based Bayesian Estimation of the Rate of Evolution of VNTR Loci in Mycobacterium tuberculosis

Variable numbers of tandem repeats (VNTR) typing is widely used for studying the bacterial cause of tuberculosis. Knowledge of the rate of mutation of VNTR loci facilitates the study of the evolution and epidemiology of Mycobacterium tuberculosis. Previous studies have applied population genetic models to estimate the mutation rate, leading to estimates varying widely from around to per locus per year. Resolving this issue using more detailed models and statistical methods would lead to improved inference in the molecular epidemiology of tuberculosis. Here, we use a model-based approach that incorporates two alternative forms of a stepwise mutation process for VNTR evolution within an epidemiological model of disease transmission. Using this model in a Bayesian framework we estimate the mutation rate of VNTR in M. tuberculosis from four published data sets of VNTR profiles from Albania, Iran, Morocco and Venezuela. In the first variant, the mutation rate increases linearly with respect to repeat numbers (linear model); in the second, the mutation rate is constant across repeat numbers (constant model). We find that under the constant model, the mean mutation rate per locus is (95% CI: ,)and under the linear model, the mean mutation rate per locus per repeat unit is (95% CI: ,). These new estimates represent a high rate of mutation at VNTR loci compared to previous estimates. To compare the two models we use posterior predictive checks to ascertain which of the two models is better able to reproduce the observed data. From this procedure we find that the linear model performs better than the constant model. The general framework we use allows the possibility of extending the analysis to more complex models in the future

On Born approximation in black hole scattering

A massless field propagating on spherically symmetric black hole metrics such as the Schwarzschild, Reissner-Nordstr\"{o}m and Reissner-Nordstr\"{o}m-de Sitter backgrounds is considered. In particular, explicit formulae in terms of transcendental functions for the scattering of massless scalar particles off black holes are derived within a Born approximation. It is shown that the conditions on the existence of the Born integral forbid a straightforward extraction of the quasi normal modes using the Born approximation for the scattering amplitude. Such a method has been used in literature. We suggest a novel, well defined method, to extract the large imaginary part of quasinormal modes via the Coulomb-like phase shift. Furthermore, we compare the numerically evaluated exact scattering amplitude with the Born one to find that the approximation is not very useful for the scattering of massless scalar, electromagnetic as well as gravitational waves from black holes

Causes and Consequences of Genome Expansion in Fungi

Fungi display a large diversity in genome size and complexity, variation that is often considered to be adaptive. But because nonadaptive processes can also have important consequences on the features of genomes, we investigated the relationship of genetic drift and genome size in the phylum Ascomycota using multiple indicators of genetic drift. We detected a complex relationship between genetic drift and genome size in fungi: genetic drift is associated with genome expansion on broad evolutionary timescales, as hypothesized for other eukaryotes; but within subphyla over smaller timescales, the opposite trend is observed. Moreover, fungi and bacteria display similar patterns of genome degradation that are associated with initial effects of genetic drift. We conclude that changes in genome size within Ascomycota have occurred using two different routes: large-scale genome expansions are catalyzed by increasing drift as predicted by the mutation-hazard model of genome evolution and small-scale modifications in genome size are independent of drift

Chance and necessity in the genome evolution of endosymbiotic bacteria of insects

[EN] An open question in evolutionary biology is how does the selection¿drift balance determine the fates of biological interactions. We searched for signatures of selection and drift in genomes of five endosymbiotic bacterial groups known to evolve under strong genetic drift. Although most genes in endosymbiotic bacteria showed evidence of relaxed purifying selection, many genes in these bacteria exhibited stronger selective constraints than their orthologs in free-living bacterial relatives. Remarkably, most of these highly constrained genes had no role in the host¿symbiont interactions but were involved in either buffering the deleterious consequences of drift or other host-unrelated functions, suggesting that they have either acquired new roles or their role became more central in endosymbiotic bacteria. Experimental evolution of Escherichia coli under strong genetic drift revealed remarkable similarities in the mutational spectrum, genome reduction patterns and gene losses to endosymbiotic bacteria of insects. Interestingly, the transcriptome of the experimentally evolved lines showed a generalized deregulation of the genome that affected genes encoding proteins involved in mutational buffering, regulation and amino acid biosynthesis, patterns identical to those found in endosymbiotic bacteria. Our results indicate that drift has shaped endosymbiotic associations through a change in the functional landscape of bacterial genes and that the host had only a small role in such a shiftThis work was supported by Science Foundation Ireland (12/IP/1637) and grants from the Spanish Ministerio de Economia y Competitividad (MINECO-FEDER; BFU2012-36346 and BFU2015-66073-P) to MAF. DAP and CT were supported by Juan de la Cierva fellowships from MINECO (references: JCI-2011-11089 and JCA-2012-14056, respectively). DAP is supported by funds from the University of Nevada, Reno, NV, USA.Sabater-Muñoz, B.; Toft, C.; Alvarez-Ponce, D.; Fares Riaño, MA. (2017). Chance and necessity in the genome evolution of endosymbiotic bacteria of insects. The ISME Journal. 11(6):1291-1304. https://doi.org/10.1038/ismej.2017.18S12911304116Aguilar-Rodriguez J, Sabater-Munoz B, Montagud-Martinez R, Berlanga V, Alvarez-Ponce D, Wagner A et al. (2016). The molecular chaperone DnaK is a source of mutational robustness. Genome Biol Evol 8: 2979–2991.Alvarez-Ponce D, Sabater-Munoz B, Toft C, Ruiz-Gonzalez MX, Fares MA . (2016). Essentiality is a strong determinant of protein rates of evolution during mutation accumulation experiments in Escherichia coli. Genome Biol Evol 8: 2914–2927.Anders S, Huber W . (2010). Differential expression analysis for sequence count data. Genome Biol 11: R106.Archibald J . (2014) One Plus One Equals One: Symbiosis and the Evolution of Complex Life. Oxford University Press: Oxford, UK.Aussel L, Loiseau L, Hajj Chehade M, Pocachard B, Fontecave M, Pierrel F et al. (2014). ubiJ, a new gene required for aerobic growth and proliferation in macrophage, is involved in coenzyme Q biosynthesis in Escherichia coli and Salmonella enterica serovar Typhimurium. J Bacteriol 196: 70–79.Baumann P, Baumann L, Clark MA . (1996). Levels of Buchnera aphidicola chaperonin groEL during growth of the aphid Schizaphis graminum. Curr Microbiol 32: 7.Benjamini Y, Yekutieli Y . (2005). False discovery rate controlling confidence intervals for selected parameters. J Am Stat Assoc 100: 10.Bennett GM, Moran NA . (2015). Heritable symbiosis: the advantages and perils of an evolutionary rabbit hole. Proc Natl Acad Sci USA 112: 10169–10176.Bermingham J, Rabatel A, Calevro F, Vinuelas J, Febvay G, Charles H et al. (2009). Impact of host developmental age on the transcriptome of the symbiotic bacterium Buchnera aphidicola in the pea aphid (Acyrthosiphon pisum. Appl Environ Microbiol 75: 7294–7297.Bogumil D, Dagan T . (2010). Chaperonin-dependent accelerated substitution rates in prokaryotes. Genome Biol Evol 2: 602–608.Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S et al. (2009). AmiGO: online access to ontology and annotation data. Bioinformatics 25: 288–289.Chen Z, Wang Y, Li Y, Li Y, Fu N, Ye J et al. (2012). Esre: a novel essential non-coding RNA in Escherichia coli. FEBS Lett 586: 1195–1200.Clark JW, Hossain S, Burnside CA, Kambhampati S . (2001). Coevolution between a cockroach and its bacterial endosymbiont: a biogeographical perspective. Proc Biol Sci 268: 393–398.Dale C, Wang B, Moran N, Ochman H . (2003). Loss of DNA recombinational repair enzymes in the initial stages of genome degeneration. Mol Biol Evol 20: 1188–1194.Deatherage DE, Barrick JE . (2014). Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Methods Mol Biol 1151: 165–188.Douglas AE . (2003). The nutritional physiology of aphids. Adv Insect Physiol 31: 68.Fares MA, Barrio E, Sabater-Munoz B, Moya A . (2002a). The evolution of the heat-shock protein GroEL from Buchnera, the primary endosymbiont of aphids, is governed by positive selection. Mol Biol Evol 19: 1162–1170.Fares MA, Ruiz-Gonzalez MX, Moya A, Elena SF, Barrio E . (2002b). Endosymbiotic bacteria: groEL buffers against deleterious mutations. Nature 417: 398.Gancedo C, Flores CL, Gancedo JM . (2016). The expanding landscape of moonlighting proteins in yeasts. Microbiol Mol Biol Rev 80: 765–777.Gerardo NM, Altincicek B, Anselme C, Atamian H, Barribeau SM, de Vos M et al. (2010). Immunity and other defenses in pea aphids, Acyrthosiphon pisum. Genome Biol 11: R21.Gomez-Valero L, Latorre A, Silva FJ . (2004). The evolutionary fate of nonfunctional DNA in the bacterial endosymbiont Buchnera aphidicola. Mol Biol Evol 21: 2172–2181.Gomez-Valero L, Silva FJ, Christophe Simon J, Latorre A . (2007). Genome reduction of the aphid endosymbiont Buchnera aphidicola in a recent evolutionary time scale. Gene 389: 87–95.Gonzalez-Domenech CM, Belda E, Patino-Navarrete R, Moya A, Pereto J, Latorre A . (2012). Metabolic stasis in an ancient symbiosis: genome-scale metabolic networks from two Blattabacterium cuenoti strains, primary endosymbionts of cockroaches. BMC Microbiol 12 (Suppl 1): S5.Hansen AK, Moran NA . (2011). Aphid genome expression reveals host-symbiont cooperation in the production of amino acids. Proc Natl Acad Sci USA 108: 2849–2854.Hansen AK, Moran NA . (2014). The impact of microbial symbionts on host plant utilization by herbivorous insects. Mol Ecol 23: 1473–1496.Henderson B, Fares MA, Lund PA . (2013). Chaperonin 60: a paradoxical, evolutionarily conserved protein family with multiple moonlighting functions. Biol Rev Camb Philos Soc 88: 955–987.Humphreys NJ, Douglas AE . (1997). Partitioning of symbiotic bacteria between generations of an insect: a quantitative study of a Buchnera sp. in the pea aphid (Acyrthosiphon pisum reared at different temperatures. Appl Environ Microbiol 63: 3294–3296.International Aphid Genomics Consortium. (2010). Genome sequence of the pea aphid Acyrthosiphon pisum. PLoS Biol 8: e1000313.Kadibalban AS, Bogumil D, Landan G, Dagan T . (2016). DnaK-dependent accelerated evolutionary rate in prokaryotes. Genome Biol Evol 8: 1590–1599.Katoh K, Standley DM . (2013). MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 30: 772–780.Kelkar YD, Ochman H . (2013). Genome reduction promotes increase in protein functional complexity in bacteria. Genetics 193: 303–307.Koga R, Meng XY, Tsuchida T, Fukatsu T . (2012). Cellular mechanism for selective vertical transmission of an obligate insect symbiont at the bacteriocyte-embryo interface. Proc Natl Acad Sci USA 109: E1230–E1237.Kuo CH, Moran NA, Ochman H . (2009). The consequences of genetic drift for bacterial genome complexity. Genome Res 19: 1450–1454.Kuo CH, Ochman H . (2009). Deletional bias across the three domains of life. Genome Biol Evol 1: 145–152.Law R, Lewis DH . (1983). Biotic environments and the maintenance of sex-some evidence from mutualistic symbioses. Biol J Linnean Soc 20: 28.Liu XD, Xie L, Wei Y, Zhou X, Jia B, Liu J et al. (2014). Abiotic stress resistance, a novel moonlighting function of ribosomal protein RPL44 in the halophilic fungus Aspergillus glaucus. Appl Environ Microbiol 80: 4294–4300.Lohse M, Bolger AM, Nagel A, Fernie AR, Lunn JE, Stitt M et al. (2012). RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics. Nucleic Acids Res 40: W622–W627.Macdonald SJ, Lin GG, Russell CW, Thomas GH, Douglas AE . (2012). The central role of the host cell in symbiotic nitrogen metabolism. Proc Biol Sci 279: 2965–2973.McClure R, Balasubramanian D, Sun Y, Bobrovskyy M, Sumby P, Genco CA et al. (2013). Computational analysis of bacterial RNA-Seq data. Nucleic Acids Res 41: e140.McCutcheon JP, Moran NA . (2012). Extreme genome reduction in symbiotic bacteria. Nat Rev Microbiol 10: 13–26.McFall-Ngai M, Hadfield MG, Bosch TC, Carey HV, Domazet-Loso T, Douglas AE et al. (2013). Animals in a bacterial world, a new imperative for the life sciences. Proc Natl Acad Sci USA 110: 3229–3236.Mira A, Ochman H, Moran NA . (2001). Deletional bias and the evolution of bacterial genomes. Trends Genet 17: 589–596.Moran NA . (1996). Accelerated evolution and Muller's rachet in endosymbiotic bacteria. Proc Natl Acad Sci USA 93: 2873–2878.Moran NA, Dunbar HE, Wilcox JL . (2005). Regulation of transcription in a reduced bacterial genome: nutrient-provisioning genes of the obligate symbiont Buchnera aphidicola. J Bacteriol 187: 4229–4237.Moran NA, McCutcheon JP, Nakabachi A . (2008). Genomics and evolution of heritable bacterial symbionts. Annu Rev Genet 42: 165–190.Moran NA, McLaughlin HJ, Sorek R . (2009). The dynamics and time scale of ongoing genomic erosion in symbiotic bacteria. Science 323: 379–382.Nakabachi A, Ishida K, Hongoh Y, Ohkuma M, Miyagishima SY . (2014). Aphid gene of bacterial origin encodes a protein transported to an obligate endosymbiont. Curr Biol 24: R640–R641.Nilsson AI, Koskiniemi S, Eriksson S, Kugelberg E, Hinton JC, Andersson DI . (2005). Bacterial genome size reduction by experimental evolution. Proc Natl Acad Sci USA 102: 12112–12116.Patino-Navarrete R, Moya A, Latorre A, Pereto J . (2013). Comparative genomics of Blattabacterium cuenoti: the frozen legacy of an ancient endosymbiont genome. Genome Biol Evol 5: 351–361.Pettersson ME, Berg OG . (2007). Muller's ratchet in symbiont populations. Genetica 130: 199–211.Price DR, Feng H, Baker JD, Bavan S, Luetje CW, Wilson AC . (2014). Aphid amino acid transporter regulates glutamine supply to intracellular bacterial symbionts. Proc Natl Acad Sci USA 111: 320–325.Reyes-Prieto M, Vargas-Chavez C, Latorre A, Moya A . (2015). SymbioGenomesDB: a database for the integration and access to knowledge on host-symbiont relationships. Database 2015: bav109 (1–8).Robinson MD, McCarthy DJ, Smyth GK . (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26: 139–140.Sabater-Muñoz B, Prats-Escriche M, Montagud-Martinez R, Lopez-Cerdan A, Toft C, Aguilar-Rodriguez J et al. (2015). Fitness trade-offs determine the role of the molecular chaperonin groel in buffering mutations. Mol Biol Evol 32: 2681–2693.Schlicker A, Domingues FS, Rahnenfuhrer J, Lengauer T . (2006). A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics 7: 302.Shigenobu S, Watanabe H, Hattori M, Sakaki Y, Ishikawa H . (2000). Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp. APS. Nature 407: 81–86.Supek F, Bosnjak M, Skunca N, Smuc T . (2011). REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6: e21800.Tamas I, Klasson L, Canback B, Naslund AK, Eriksson AS, Wernegreen JJ et al. (2002). 50 million years of genomic stasis in endosymbiotic bacteria. Science 296: 2376–2379.Toft C, Fares MA . (2008). The evolution of the flagellar assembly pathway in endosymbiotic bacterial genomes. Mol Biol Evol 25: 2069–2076.van Ham RC, Kamerbeek J, Palacios C, Rausell C, Abascal F, Bastolla U et al. (2003). Reductive genome evolution in Buchnera aphidicola. Proc Natl Acad Sci USA 100: 581–586.Wernegreen JJ . (2002). Genome evolution in bacterial endosymbionts of insects. Nat Rev Genet 3: 850–861.Wernegreen JJ . (2011). Reduced selective constraint in endosymbionts: elevation in radical amino acid replacements occurs genome-wide. PLoS One 6: e28905.Williams TA, Fares MA . (2010). The effect of chaperonin buffering on protein evolution. Genome Biol Evol 2: 609–619.Yang Z . (2007). PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol 24: 1586–1591

Global Metabolomic Profiling of Acute Myocarditis Caused by Trypanosoma cruzi Infection

© 2014 Gironès et al. Chagas disease is caused by Trypanosoma cruzi infection, being cardiomyopathy the more frequent manifestation. New chemotherapeutic drugs are needed but there are no good biomarkers for monitoring treatment efficacy. There is growing evidence linking immune response and metabolism in inflammatory processes and specifically in Chagas disease. Thus, some metabolites are able to enhance and/or inhibit the immune response. Metabolite levels found in the host during an ongoing infection could provide valuable information on the pathogenesis and/or identify deregulated metabolic pathway that can be potential candidates for treatment and being potential specific biomarkers of the disease. To gain more insight into those aspects in Chagas disease, we performed an unprecedented metabolomic analysis in heart and plasma of mice infected with T. cruzi. Many metabolic pathways were profoundly affected by T. cruzi infection, such as glucose uptake, sorbitol pathway, fatty acid and phospholipid synthesis that were increased in heart tissue but decreased in plasma. Tricarboxylic acid cycle was decreased in heart tissue and plasma whereas reactive oxygen species production and uric acid formation were also deeply increased in infected hearts suggesting a stressful condition in the heart. While specific metabolites allantoin, kynurenine and p-cresol sulfate, resulting from nucleotide, tryptophan and phenylalanine/tyrosine metabolism, respectively, were increased in heart tissue and also in plasma. These results provide new valuable information on the pathogenesis of acute Chagas disease, unravel several new metabolic pathways susceptible of clinical management and identify metabolites useful as potential specific biomarkers for monitoring treatment and clinical severity in patients.This work was supported by ‘‘Ministerio de Ciencia e Innovación’’ (SAF2010-17833); ‘‘Fondo de Investigaciones Sanitarias’’ (PS09/00538 and PI12/00289); ‘‘Red de Investigación de Centros de Enfermedades Tropicales’’ (RICET RD12/0018/0004); European Union (HEALTH-FE-2008-22303, ChagasEpiNet);‘‘Universidad Autónoma de Madrid’’ and ‘‘Comunidad de Madrid’’ (CC08-UAM/SAL-4440/08); AECID Cooperation with Argentine (A/025417/09 and A/031735/10), Comunidad de Madrid (S-2010/BMD-2332) and ‘‘Fundación Ramón Areces’Peer Reviewe

Advances in nanocatalysts design for biofuels production

The exploitation of nanocatalysts, at the boundary between homogeneous and heterogeneous catalysis, is tracking new efficient ways to produce renewable biofuels in environmentally friendly conditions. Their solid state makes them recyclable, and their nanomateric particle size enables high activities approaching those offered by homogeneous catalysts, as well as novel and unique catalytic behaviors not accessible to solids above the nanometer range. Furthermore, the use of magnetically active materials has led to the development of nanocatalysts easily recoverable through the application of magnetic fields. In this mini-review, latest achievements in the production of advanced biofuels using stable, highly active, cheap and reusable nanocatalysts are described. Specifically, biodiesel and high density fuels have been chosen as major topics of research for the design of catalytic nanomaterials

Computational Prediction and Experimental Verification of New MAP Kinase Docking Sites and Substrates Including Gli Transcription Factors

In order to fully understand protein kinase networks, new methods are needed to identify regulators and substrates of kinases, especially for weakly expressed proteins. Here we have developed a hybrid computational search algorithm that combines machine learning and expert knowledge to identify kinase docking sites, and used this algorithm to search the human genome for novel MAP kinase substrates and regulators focused on the JNK family of MAP kinases. Predictions were tested by peptide array followed by rigorous biochemical verification with in vitro binding and kinase assays on wild-type and mutant proteins. Using this procedure, we found new ‘D-site’ class docking sites in previously known JNK substrates (hnRNP-K, PPM1J/PP2Czeta), as well as new JNK-interacting proteins (MLL4, NEIL1). Finally, we identified new D-site-dependent MAPK substrates, including the hedgehog-regulated transcription factors Gli1 and Gli3, suggesting that a direct connection between MAP kinase and hedgehog signaling may occur at the level of these key regulators. These results demonstrate that a genome-wide search for MAP kinase docking sites can be used to find new docking sites and substrates