Barium bioaccumulation by bacterial biofilms and implications for Ba cycling and use of Ba proxies

Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467- 018-04069-z.Data availability. The datasets generated during the current study are available from the corresponding author.Ba proxies have been broadly used to reconstruct past oceanic export production. However, the precise mechanisms underlying barite precipitation in undersaturated seawater are not known. The link between bacterial production and particulate Ba in the ocean suggests that bacteria may play a role. Here we show that under experimental conditions marine bacterial biofilms, particularly extracellular polymeric substances (EPS), are capable of bioaccumulating Ba, providing adequate conditions for barite precipitation. An amorphous P-rich phase is formed at the initial stages of Ba bioaccumulation, which evolves into barite crystals. This supports that in high productivity regions where large amounts of organic matter are subjected to bacterial degradation, the abundant EPS would serve to bind the necessary Ba and form nucleation sites leading to barite precipitation. This also provides new insights into barite precipitation and opens an exciting field to explore the role of EPS in mineral precipitation in the ocean.This study was supported by the European Regional Development Fund (ERDF) cofinanced grant CGL2015-66830-R (MINECO Secretaría de Estado de Investigación, Desarrollo e Innovación, Spain), Research Groups BIO 103 and RNM-179 (Junta de Andalucía), and the University of Granada (Unidad Científica de Excelencia UCEPP2016-05)

Discretization of expression quantitative trait loci in association analysis between genotypes and expression data

Expression quantitative trait loci are used as a tool to identify genetic causes of natural variation in gene expression. Only in a few cases the expression of a gene is controlled by a variant on a single genetic marker. There is a plethora of different complexity levels of interaction effects within markers, within genes and between marker and genes. This complexity challenges biostatisticians and bioinformatitians every day and makes findings difficult to appear. As a way to simplify analysis and better control confounders, we tried a new approach for association analysis between genotypes and expression data. We pursued to understand whether discretization of expression data can be useful in genome-transcriptome association analyses. By discretizing the dependent variable, algorithms for learning classifiers from data as well as performing block selection were used to help understanding the relationship between the expression of a gene and genetic markers. We present the results of using this approach to detect new possible causes of expression variation of DRB5, a gene playing an important role within the immune system. Together with expression of gene DRB5 obtained from the classical microarray technology, we have also measured DRB5 expression by using the more recent next-generation sequencing technology. A supplementary website including a link to the software with the method implemented can be found at http: //bios.ugr.es/DRB5

A comparison of genomic profiles of complex diseases under different models

Background: Various approaches are being used to predict individual risk to polygenic diseases from data provided by genome-wide association studies. As there are substantial differences between the diseases investigated, the data sets used and the way they are tested, it is difficult to assess which models are more suitable for this task. Results: We compared different approaches for seven complex diseases provided by the Wellcome Trust Case Control Consortium (WTCCC) under a within-study validation approach. Risk models were inferred using a variety of learning machines and assumptions about the underlying genetic model, including a haplotype-based approach with different haplotype lengths and different thresholds in association levels to choose loci as part of the predictive model. In accordance with previous work, our results generally showed low accuracy considering disease heritability and population prevalence. However, the boosting algorithm returned a predictive area under the ROC curve (AUC) of 0.8805 for Type 1 diabetes (T1D) and 0.8087 for rheumatoid arthritis, both clearly over the AUC obtained by other approaches and over 0.75, which is the minimum required for a disease to be successfully tested on a sample at risk, which means that boosting is a promising approach. Its good performance seems to be related to its robustness to redundant data, as in the case of genome-wide data sets due to linkage disequilibrium. Conclusions: In view of our results, the boosting approach may be suitable for modeling individual predisposition to Type 1 diabetes and rheumatoid arthritis based on genome-wide data and should be considered for more in-depth research.This work was supported by the Spanish Secretary of Research, Development and Innovation [TIN2010-20900-C04-1]; the Spanish Health Institute Carlos III [PI13/02714]and [PI13/01527] and the Andalusian Research Program under project P08-TIC-03717 with the help of the European Regional Development Fund (ERDF). The authors are very grateful to the reviewers, as they believe that their comments have helped to substantially improve the quality of the paper

Multiple Sclerosis Risk Variant HLA-DRB1*1501 Associates with High Expression of DRB1 Gene in Different Human Populations

The human leukocyte antigen (HLA) DRB1*1501 has been consistently associated with multiple sclerosis (MS) in nearly all populations tested. This points to a specific antigen presentation as the pathogenic mechanism though this does not fully explain the disease association. The identification of expression quantitative trait loci (eQTL) for genes in the HLA locus poses the question of the role of gene expression in MS susceptibility. We analyzed the eQTLs in the HLA region with respect to MS-associated HLA-variants obtained from genome-wide association studies (GWAS). We found that the Tag of DRB1*1501, rs3135388 A allele, correlated with high expression of DRB1, DRB5 and DQB1 genes in a Caucasian population. In quantitative terms, the MS-risk AA genotype carriers of rs3135388 were associated with 15.7-, 5.2- and 8.3-fold higher expression of DQB1, DRB5 and DRB1, respectively, than the non-risk GG carriers. The haplotype analysis of expression-associated variants in a Spanish MS cohort revealed that high expression of DRB1 and DQB1 alone did not contribute to the disease. However, in Caucasian, Asian and African American populations, the DRB1*1501 allele was always highly expressed. In other immune related diseases such as type 1 diabetes, inflammatory bowel disease, ulcerative colitis, asthma and IgA deficiency, the best GWAS-associated HLA SNPs were also eQTLs for different HLA Class II genes. Our data suggest that the DR/DQ expression levels, together with specific structural properties of alleles, seem to be the causal effect in MS and in other immunopathologies rather than specific antigen presentation alone

Aplicación del principio inductivo de Mevr en la construcción de clasificadores / Mª del Mar Abad Grau ; director Luis Daniel Hernández Molinero.

Tesis-Universidad de Murcia.Consulte la tesis en: BCA. GENERAL. ARCHIVO UNIVERSITARIO. T.M.-2314

Cambios en las estructuras organizativas y en el estilo de dirección , promovidos por las nuevas tecnologías de la información

En el marco de la empresa, las tecnologías de la información tienen sin duda una importante dimensión en las actividades habituales de organización, producción, control de gestión, marketing, planificación estratégica, etc., Sin embargo no todas las organizaciones ven en estas técnicas un motor de cambio en su estructura organizacional y en determinadas actitudes gerenciales. Con el presente articulo, pretendemos poner de manifiesto que contar con dichas tecnologías conlleva una serie de cambios y beneficios que reportan, a la organización o empresa que las utilice, mejoras en los procesos de coordinación, tanto internos como externos. Asimismo pretendemos resaltar el papel destacado de estas tecnologías como integradoras del "saber organizacional" y como promotoras de un "rediseño de las organizaciones"

Conditional logistic regression analysis of the MS associated variants.

<p>F_A, Frequency of affected; F_U, Frequency of unaffected; NA, not applicable; <i>P</i> cond, <i>P</i>-value of logistic regression analysis conditioned on the respective SNP; <i>OR</i>, odds ratio; <i>SNP</i>, single-nucleotide polymorphism. The data from African American are from McElroy et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029819#pone.0029819-McElroy2" target="_blank">[7]</a>.</p

LD plots of the GWAS- SNPs associated with different immune related disease.

<p>Data are from HapMap III CEU population. (<b>A</b>) Linkage disequilibrium by r². (<b>B</b>) Linkage disequilibrium by D′. Disease abbreviations: Type 1 diabetes (T1D), inflammatory bowel disease (IBD), ulcerative colitis (UC), systemic lupus erythematosus (SLE), asthma and IgA deficiency.</p

Association of the MS-risk variant rs9271100 with <i>DRB1</i> gene expression levels.

<p>In all plots, expression levels are represented for the three genotype groups. (<b>A</b>) Box plots of expression data from normalized results of ILMN_1715169 (<i>DRB1</i>) probe generated by Illumina Human-6 v2 Expression BeadChip (EMBL-EBI database (<a href="http://www.ebi.ac.uk/arrayexpress/" target="_blank">http://www.ebi.ac.uk/arrayexpress/</a>) ID projects E-MTAB-198). (<b>B</b>) Box plot of expression data from the NM_002124 (<i>DRB1</i>) transcript of 41 CEU individuals obtained from RNA-Seq <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029819#pone.0029819-Cheung1" target="_blank">[19]</a>. P-values are calculated by Kruskal Wallis Test.</p

Association of HapMap III SNPs from the HLA region with expression levels of the HLA <i>DRB1</i> gene.

<p>The figure shows the strength of association between SNPs and gene expression levels, plotted as −log <i>P</i>-values. Coordinates are in NCBI Build 36. Shown are the CEU, YRI, LWK, MEX, GHI, CHB and JPT HapMap populations <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029819#pone.0029819-International1" target="_blank">[26]</a>.</p