Isolation and characterization of clones with hydrolase activity from a metagenomic library of thermal water

[Resumen]: Las enzimas termoestables presentan gran importancia debido a las características que les confieren mayor estabilidad y actividad ante condiciones extremas. Por otra banda, las enzimas con actividad lipolítica presentan una gran capacidad y alcance en la industria biotecnológica, pues están siendo usadas en un amplio abanico de industrias, como la alimentaria, la del papel, la de los detergentes, etc. De ahí que suscite gran interés la bioprospección de estas enzimas en metagenotecas procedentes de aguas termales, ya que su gran capacidad de acción en dichas condiciones facilita su posterior uso en la industria. En el trabajo que se expone a continuación, se partió de una metagenoteca de ADN del microbioma de aguas termales procedente de Muíño da Veiga (Ourense) en una cepa de Escherichia coli con los objetivos de aislar un clon con actividad lipolítica, caracterizar la enzima obtenida y secuenciar dicho ADN. Partiendo de 9 candidatos iniciales, de los cuales se comprobó previamente su actividad en tributirina, trabajamos con el que menos problemas nos presentaba a la hora de replicar los experimentos aquí descritos. Dicho candidato produjo halo de actividad frente a la tributirina en placas con medio LB, lo que confirmó como positivo en dicha actividad. Posteriormente procedimos a caracterizar nuestra enzima, realizando ensayos de actividad lipolítica en los cuales usamos laurato, octanoato y estearato como sustratos de la reacción. Solo mostró actividad con laurato, con el cual pudimos deducir que presenta un óptimo de actividad a la temperatura de 90ºC. Finalmente se realizó la secuenciación del ADN insertado y, a partir de los resultados obtenidos del alineamiento realizado con Blastn, pudimos deducir que se trata de un organismo perteneciente al mismo género que Conexibacter woesei, pero no a la misma especie.[Resumo]: As enzimas termoestables presentan unha gran importancia debido ás características que lles confiren maior estabilidade e actividade ante condicións extremas. Por outra banda, as enzimas con actividade lipolítica presentan unha gran capacidade e alcance na industria biotecnolóxica, pois úsanse nun amplo abanico de industrias, como a alimentaria, a de papel, a de deterexentes, etc. De aí que suscite gran interese a bioprospección destas enzimas en metaxenotecas procedentes de aguas termais, xa que a súa gran capacidade de acción en ditas condicións facilita o seu posterior uso na industria. No traballo que se expón a continuación, partiuse dunha metaxenoteca de ADN do microbioma de augas termais procedente de Muíño da Veiga (Ourense) nunha cepa de Escherichia coli cos obxectivos de illar un clon con actividade lipolítica, caracterizar a enzima obtida e secuenciar dito ADN. Partindo de 9 candidatos iniciais, dos que se comprobou previamente a súa actividade en tributirina, traballamos có que menos problemas nos presentaba á hora de replicar os experimentos aquí descritos. Dito candidato mostrou halo de actividade fronte á tributirina nas placas con medio LB, o que confirmou como positivo en dita actividade. Posteriormente procedemos a caracterizar a nosa enzima, realizando ensaios de actividade lipolítica nos que usamos laurato, octanoato e estearato como substratos da reacción. Só presentou actividade co laurato, co cal poidemos deducir que presenta un óptimo de actividade a unha temperatura de 90ºC. Finalmente, realizouse a secuenciación do ADN insertado e, a partir dos resultados obtidos no aliñamento realizado co Blastn, poidemos deducir que se trata dun organismo pertencente ao mesmo xénero que Conexibacter woesei, pero non á mesma especie.[Abstract]: Thermostable enzymes show an enormous importance due to the characteristics, that confer them more stability and also more activity against extreme conditions. Moreover, the enzymes with lipolytic activity have a high capacity and reach in the biotechnological industry, being used in a wide range of industries, such as the food-processing industry, the paper industry, the detergent industry and so on. Hence, it arises interest the bioprospecting of these enzymes in metagenomics libraries from thermal waters, since their great capacity of action in these conditions makes easier their later use in the industry. In this assignment, which is going to be exposed above, we departed from a metagenomics library of DNA from the microbiome of thermal waters from Muíño da Veiga (Ourense) transformed into a strain of Escherichia coli. Starting from 9 initial candidates, whose activity on tributyrin was previously checked, we worked with the one that gave us the least problems to replicate the experiments that are described here. This candidate produced an halo of activity against tributyrin in plaques with LB medium, which finally confirmed its positive activity. Later, we proceeded in order to characterize our enzyme, performing lipolityc activity assays. In this assays we used laurate, octanoate and estearate as substrates. It only showed activity on laurate with which we could deduce that it shows an optimum of activity at the temperature of 90ºC (194ºF). Finally, we performed the inserted DNA sequencing and, regarding to the results that we obtained from the alignment done with Blastn, we could deduce that it is an organism belonging to the same gender that Conexibacter woesei but a different species.Traballo fin de grao (UDC.CIE). Bioloxía. Curso 2017/201

Identification of Prevotella, Anaerotruncus and Eubacterium Genera by Machine Learning Analysis of Metagenomic Profiles for Stratification of Patients Affected by Type I Diabetes

[Abstract] Previous works have reported different bacterial strains and genera as the cause of different clinical pathological conditions. In our approach, using the fecal metagenomic profiles of newborns, a machine learning-based model was generated capable of discerning between patients affected by type I diabetes and controls. Furthermore, a random forest algorithm achieved a 0.915 in AUROC. The automation of processes and support to clinical decision making under metagenomic variables of interest may result in lower experimental costs in the diagnosis of complex diseases of high prevalence worldwide.This work was supported by the “Collaborative Project in Genomic Data Integration (CICLOGEN)” PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe.” and the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23) and Competitive Reference Groups (Ref. ED431C 2018/49). The funding body did not have a role in the experimental design; data collection, analysis and interpretation; and writing of this manuscriptXunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/4

Machine Learning Analysis of the Human Infant Gut Microbiome Identifies Influential Species in Type 1 Diabetes

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Diabetes is a disease that is closely linked to genetics and epigenetics, yet mechanisms for clarifying the onset and/or progression of the disease have sometimes not been fully managed. In recent years and due to the large number of recent studies, it is known that changes in the balance of the microbiota can cause a high battery of diseases, including diabetes. Machine Learning (ML) techniques are able to identify complex, non-linear patterns of expression and relationships within the data set to extract intrinsic knowledge without any biological assumptions about the data. At the same time, mass sequencing techniques allow us to obtain the metagenomic profile of an individual, whether it is a body part, organ or tissue, and thus identify the composition of a given microbe. The great increase in the development of both technologies in their respective fields of study leads to the logical union of both to try to identify the bases of a complex disease such as diabetes. To this end, a Random Forest model has been developed at different taxonomic levels, obtaining results above 0.80 in AUC for families and above 0.98 at species level, following a strict experimental design to ensure that results are compared under equal conditions. It is identified how, in infants, the species Bacteroides uniformis, Bacteroides dorei and Bacteroides thetaiotaomicron are reduced in the microbiota of those with T1D, while, the populations of Prevotella copri increase slightly and that of Bacteroides vulgatus is much higher. Finally, thanks to the more specific metagenomic signature at species level, a model has been generated to predict those seroconverted patients not previously diagnosed with diabetes but who have expressed at least two of the autoantibodies analysed.This work was supported by the “Collaborative Project in Genomic Data Integration (CICLOGEN)” PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe”. and the General Directorate of Culture, Education and University Management of Xunta de Galicia, Spain (Ref. ED431D 2017/16), the “Galician Network for Colorectal Cancer Research, Spain” (Ref. ED431D 2017/23) and Competitive Reference Groups, Spain (Ref. ED431C 2018/49). The funding body did not have a role in the experimental design; data collection, analysis and interpretation; and writing of this manuscript. CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidades from Xunta de Galicia, Spain”, supported in an 80% through ERDF Funds, Spain, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaría Xeral de Universidades, Spain” (Grant ED431G 2019/01). The funding body did not have a role in the experimental design; data collection, analysis and interpretation; and writing of this manuscript. The calculations were performed on resources provided by the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) . Funding for open access charge: Universidade da Coruña/CISUGXunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431G 2019/0

Identification of Prevotella, Anaerotruncus and Eubacterium Genera by Machine Learning Analysis of Metagenomic Profiles for Stratification of Patients Affected by Type I Diabetes

Previous works have reported different bacterial strains and genera as the cause of different clinical pathological conditions. In our approach, using the fecal metagenomic profiles of newborns, a machine learning-based model was generated capable of discerning between patients affected by type I diabetes and controls. Furthermore, a random forest algorithm achieved a 0.915 in AUROC. The automation of processes and support to clinical decision making under metagenomic variables of interest may result in lower experimental costs in the diagnosis of complex diseases of high prevalence worldwide