Modeling the Role of the Microbiome in Evolution

There is undeniable evidence showing that bacteria have strongly influenced the evolution and biological functions of multicellular organisms. It has been hypothesized that many host-microbial interactions have emerged so as to increase the adaptive fitness of the holobiont (the host plus its microbiota). Although this association has been corroborated for many specific cases, general mechanisms explaining the role of the microbiota in the evolution of the host are yet to be understood. Here we present an evolutionary model in which a network representing the host adapts in order to perform a predefined function. During its adaptation, the host network (HN) can interact with other networks representing its microbiota. We show that this interaction greatly accelerates and improves the adaptability of the HN without decreasing the adaptation of the microbial networks. Furthermore, the adaptation of the HN to perform several functions is possible only when it interacts with many different bacterial networks in a specialized way (each bacterial network participating in the adaptation of one function). Disrupting these interactions often leads to non-adaptive states, reminiscent of dysbiosis, where none of the networks the holobiont consists of can perform their respective functions. By considering the holobiont as a unit of selection and focusing on the adaptation of the host to predefined but arbitrary functions, our model predicts the need for specialized diversity in the microbiota. This structural and dynamical complexity in the holobiont facilitates its adaptation, whereas a homogeneous (non-specialized) microbiota is inconsequential or even detrimental to the holobiont's evolution. To our knowledge, this is the first model in which symbiotic interactions, diversity, specialization and dysbiosis in an ecosystem emerge as a result of coevolution. It also helps us understand the emergence of complex organisms, as they adapt more easily to perform multiple tasks than non-complex ones

A 500-year tale of co-evolution, adaptation, and virulence: Helicobacter pylori in the Americas

Helicobacter pylori is a common component of the human stomach microbiota, possibly dating back to the speciation of Homo sapiens. A history of pathogen evolution in allopatry has led to the development of genetically distinct H. pylori subpopulations, associated with different human populations, and more recent admixture among H. pylori subpopulations can provide information about human migrations. However, little is known about the degree to which some H. pylori genes are conserved in the face of admixture, potentially indicating host adaptation, or how virulence genes spread among different populations. We analyzed H. pylori genomes from 14 countries in the Americas, strains from the Iberian Peninsula, and public genomes from Europe, Africa, and Asia, to investigate how admixture varies across different regions and gene families. Whole-genome analyses of 723 H. pylori strains from around the world showed evidence of frequent admixture in the American strains with a complex mosaic of contributions from H. pylori populations originating in the Americas as well as other continents. Despite the complex admixture, distinctive genomic fingerprints were identified for each region, revealing novel American H. pylori subpopulations. A pan-genome Fst analysis showed that variation in virulence genes had the strongest fixation in America, compared with non-American populations, and that much of the variation constituted non-synonymous substitutions in functional domains. Network analyses suggest that these virulence genes have followed unique evolutionary paths in the American populations, spreading into different genetic backgrounds, potentially contributing to the high risk of gastric cancer in the region.Fil: Muñoz Ramirez, Zilia Y.. INSTITUTO POLITÉCNICO NACIONAL (IPN);Fil: Pascoe, Ben. University of Bath; Reino UnidoFil: Mendez Tenorio, Alfonso. INSTITUTO POLITÉCNICO NACIONAL (IPN);Fil: Mourkas, Evangelos. University of Bath; Reino UnidoFil: Sandoval Motta, Santiago. Consejo Nacional de Ciencia y Tecnología; MéxicoFil: Perez Perez, Guillermo. New York University Langone Medical Center; Estados UnidosFil: Morgan, Douglas R.. University of Alabama at Birmingahm; Estados UnidosFil: Dominguez, Ricardo Leonel. Western Honduras Gastric Cancer Prevention Initiative Hospital de Occidente Santa Rosa de Copan; HondurasFil: Ortiz Princz, Diana. No especifíca;Fil: Cavazza, Maria Eugenia. No especifíca;Fil: Rocha, Gifone. Universidade Federal de Minas Gerais; BrasilFil: Queiroz, Dulcienne. Universidade Federal de Minas Gerais; BrasilFil: Catalano, Mariana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Zerbetto de Palma, Gerardo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Química y Físico-Química Biológicas "Prof. Alejandro C. Paladini". Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Química y Físico-Química Biológicas; ArgentinaFil: Goldman, Cinthia Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; ArgentinaFil: Venegas, Alejandro. Universidad Diego Portales; ChileFil: Alarcon, Teresa. Universidad Autónoma de Madrid; EspañaFil: Oleastro, Monica. Universidade Nova de Lisboa; PortugalFil: Vale, Filipa F.. Universidade Nova de Lisboa; PortugalFil: Goodman, Karen J.. University of Alberta; CanadáFil: Torres, Roberto C.. Instituto Mexicano del Seguro Social; MéxicoFil: Berthenet, Elvire. Swansea University Medical School; Reino UnidoFil: Hitchings, Matthew D.. Swansea University Medical School; Reino UnidoFil: Blaser, Martin J.. Rutgers University; Estados UnidosFil: Sheppard, Samuel K.. University of Bath; Reino UnidoFil: Thorell, Kaisa. University of Gothenburg; SueciaFil: Torres, Javier. Instituto Mexicano del Seguro Social; Méxic

The Helicobacter pylori Genome Project : insights into H. pylori population structure from analysis of a worldwide collection of complete genomes

Helicobacter pylori, a dominant member of the gastric microbiota, shares co-evolutionary history with humans. This has led to the development of genetically distinct H. pylori subpopulations associated with the geographic origin of the host and with differential gastric disease risk. Here, we provide insights into H. pylori population structure as a part of the Helicobacter pylori Genome Project (HpGP), a multi-disciplinary initiative aimed at elucidating H. pylori pathogenesis and identifying new therapeutic targets. We collected 1011 well-characterized clinical strains from 50 countries and generated high-quality genome sequences. We analysed core genome diversity and population structure of the HpGP dataset and 255 worldwide reference genomes to outline the ancestral contribution to Eurasian, African, and American populations. We found evidence of substantial contribution of population hpNorthAsia and subpopulation hspUral in Northern European H. pylori. The genomes of H. pylori isolated from northern and southern Indigenous Americans differed in that bacteria isolated in northern Indigenous communities were more similar to North Asian H. pylori while the southern had higher relatedness to hpEastAsia. Notably, we also found a highly clonal yet geographically dispersed North American subpopulation, which is negative for the cag pathogenicity island, and present in 7% of sequenced US genomes. We expect the HpGP dataset and the corresponding strains to become a major asset for H. pylori genomics

Adaptive Resistance in Bacteria Requires Epigenetic Inheritance, Genetic Noise, and Cost of Efflux Pumps

Adaptive resistance emerges when populations of bacteria are subjected to gradual increases of antibiotics. It is characterized by a rapid emergence of resistance and fast reversibility to the non-resistant phenotype when the antibiotic is removed from the medium. Recent work shows that adaptive resistance requires epigenetic inheritance and heterogeneity of gene expression patterns that are, in particular, associated with the production of porins and efflux pumps. However, the precise mechanisms by which inheritance and variability govern adaptive resistance, and what processes cause its reversibility remain unclear. Here, using an efflux pump regulatory network (EPRN) model, we show that the following three mechanisms are essential to obtain adaptive resistance in a bacterial population: 1) intrinsic variability in the expression of the EPRN transcription factors; 2) epigenetic inheritance of the transcription rate of EPRN associated genes; and 3) energetic cost of the efflux pumps activity that slows down cell growth. While the first two mechanisms acting together are responsible for the emergence and gradual increase of the resistance, the third one accounts for its reversibility. In contrast with the standard assumption, our model predicts that adaptive resistance cannot be explained by increased mutation rates. Our results identify the molecular mechanism of epigenetic inheritance as the main target for therapeutic treatments against the emergence of adaptive resistance. Finally, our theoretical framework unifies known and newly identified determinants such as the burden of efflux pumps that underlie bacterial adaptive resistance to antibiotics

Plan de negocio para un market place para productos de infantes de 0 a 4 años en Lima

El crecimiento económico del Perú en los últimos años, el crecimiento del comercio electrónico, la confianza y seguridad de pago mediante tarjetas de crédito y débito, son factores que incrementas las ventas y compras por internet. Por tanto, se decidió evaluar la viabilidad económica, comercial y operativa de una empresa intermediaria entre compañías proveedoras de productos para infantes y clientes compradores a través de un Marketplace denominado Ibaby; mediante una plataforma web, aplicativo móvil y un módulo de integración de los sistemas internos y externos. Es así como esta startup basada en el efecto red, permitirá que el cliente comprador pueda acceder a varios productos de calidad en un solo lugar, en una plataforma de uso fácil y rápido, donde tendrá atención personalizada en la compra y entrega del producto, además, se le brindará propuestas de ofertas y promociones exclusivas e individualizadas; y donde el cliente proveedor tendrá la posibilidad de exhibir sus productos en una plataforma especializada, en la que la cercanía al cliente potencial final le permitirá aumentar sus utilidades, también, podrá encontrar información abierta y transparente sobre sus ventas. Asimismo, el cliente comprador podrá participar en campañas de recolección de ropa y juguetes para su respectiva donación

Reversibility of the resistance phenotype.

<p>(A) This tracking plot shows that the expression of the activator increases while the antibiotic shocks are applied as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118464#pone.0118464.g002" target="_blank">Fig. 2</a>. Then, when the antibiotic is removed (indicated by the tilted black arrow), the expression of the activator decreases abruptly and eventually reaches its basal level. (B) Size of the population as a function of time for the same simulation as in A. After each antibiotic shock (small black arrows) the population size decreases exponentially and the recovery time becomes longer with each shock. After the antibiotic is removed (tilted black arrow) the population comes back again to its wild-type (WT) growth rate. To carry out the simulations in the antibiotic-free phase, every time the population reached the maximum size N = 5000, we took a random sample of 10% of the cells and made them grow without antibiotic, until the population reached again this maximum size, and so on. (C) Average transcription rate μ_β = ⟨β₀⟩ in the population as a function of time. Note that the average increases while the shocks are applied and then gradually comes back to small values when the antibiotic is removed. Error bars indicate the standard deviation. It can be observed that the standard deviation increases with the antibiotic stress. The panels below show the full distribution G (μ_β, σ_β) at three different times: before any antibiotic is introduced (circle); after several antibiotic shocks (star); after a long period of time without antibiotic (line). Time is measured generations, being one generation the time it takes for a cell with β₀ = 1 to reach θ_F starting from F = 0.</p

Efflux Pump Regulatory Network.

<p>Arrows indicate positive regulation. Blunt arrows indicate repression. A) Literature base reconstruction of the AcrAB-TolC efflux pump regulatory network of <i>Escherichia coli</i> as reported on [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118464#pone.0118464.ref009" target="_blank">9</a>]. B) Simplified version of the AcrAB-TolC efflux pump regulatory network (EPRN). The activator (Act) and repressor (Rep) are two Transcriptional Factors that belong to the same transcriptional unit (EPRN operon, indicated by the dashed line). When the repressor occupies its DNA binding site, the expression of the operon is restrained. Nonetheless, when the antibiotic (or inducer, <i>Ind</i>) enters the cell, it inactivates the repressor by binding to it, allowing the operon to be actively transcribed, promoting the production of pumps and decreasing the synthesis of porins (this last process is known to occur through an intermediary). Both food and inducer are expelled by the efflux pump system. In the population model, a reduction in food concentration implies an increase in the division time.</p

Genetic Assimilation occurs at longer time scales.

<p>(A) Resistance Index (RI) as a function of time for populations induced with <i>M</i> antibiotic shocks. The different curves correspond to different values of <i>M</i>, except by the black one which corresponds to a control population growing with no antibiotic. (B) Blow up showing the first 500 generations. For each curve, the corresponding arrow indicates the time at which the antibiotic is removed. In the case of the blue curve, the asterisk indicates the time at which the last antibiotic shock is applied, after which the antibiotic concentration is kept constant. (C) Blow up of the last part of the simulation showing the point at which the antibiotic is removed from the population corresponding to the blue curve. It can be observed that in this case the final stationary value of the RI is about five times higher than that of the control population. (D) Evolution of the average transcription rate μ_β and the average pump efficiency μ_ε for the population corresponding to the blue curve. Notice that as soon as the antibiotic concentration is kept constant, μ_β starts decreasing whereas μ_ε keeps rising until the antibiotic is completely removed. This shows that the evolutionary process does not reach a stationary state (or fixed point) in the presence of antibiotic.</p