Humic Acid-Oxidizing, Nitrate-Reducing Bacteria in Agricultural Soils

This study demonstrates the prevalence, phylogenetic diversity, and physiology of nitrate-reducing microorganisms capable of utilizing reduced humic acids (HA) as electron donors in agricultural soils. Most probable number (MPN) enumeration of agricultural soils revealed large populations (104 to 106 cells g−1 soil) of microorganisms capable of reducing nitrate while oxidizing the reduced HA analog 2,6-anthrahydroquinone disulfonate (AH2DS) to its corresponding quinone. Nitrate-dependent HA-oxidizing organisms isolated from agricultural soils were phylogenetically diverse and included members of the Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria. Advective up-flow columns inoculated with corn plot soil and amended with reduced HA and nitrate supported both HA oxidation and enhanced nitrate reduction relative to no-donor or oxidized HA controls. The additional electron donating capacity of reduced HA could reasonably be attributed to the oxidation of reduced functional groups. Subsequent 16S rRNA gene-based high-density oligonucleotide microarray (PhyloChip) indicated that reduced HA columns supported the development of a bacterial community enriched with members of the Acidobacteria, Firmicutes, and Betaproteobacteria relative to the no-donor control and initial inoculum. This study identifies a previously unrecognized role for HA in stimulating denitrification processes in saturated soil systems. Furthermore, this study indicates that reduced humic acids impact soil geochemistry and the indigenous bacterial community composition

Comparison of two strategies for the start-up of a biological reactor for the treatment of hypersaline effluents from a table olive packaging industry

Biological treatment of hypersaline effluents with high organic matter concentrations is difficult to carry out and it can require a long start-up phase. This is the case of the treatment of fermentation brines from the table olive packaging (FTOP) industries. These effluents are characterized by conductivity values around 90 mS/cm, COD around 15,000 mg/L and total phenols concentration around 1000 mg/L. In this work, FTOP has been treated in two sequencing batch reactors (SBRs) operated in parallel. In each SBR a different start-up strategy has been carried out. In the SBR-2, biomass was previously acclimated to high salinity using simulated wastewater without phenolic compounds, meanwhile in the SBR-1, FTOP was added from the beginning of the start-up. Results indicated more operational problems in the SBR-2 consisting in a higher deflocculation that drove to high turbidity values in the effluent. Besides, at the end of the start-up, the SBR-1 reached higher COD removal efficiencies than SBR-2 (88% and 73%, respectively). In both reactors, an increase in gamma-Proteobacteria in the microbial population was observed for increasing conductivities. In addition, phenols were completely removed in both reactors at the end of the start-up, what implied very low toxicity values in the effluent.The authors of this work thank the financial support of CDTI (Centre for Industrial Technological Development) depending on the Spanish Ministry of Science and Innovation.Ferrer-Polonio, E.; Mendoza Roca, JA.; Iborra Clar, A.; Alonso Molina, JL.; Pastor Alcañiz, L. (2015). Comparison of two strategies for the start-up of a biological reactor for the treatment of hypersaline effluents from a table olive packaging industry. Chemical Engineering Journal. 273:595-602. doi:10.1016/j.cej.2015.03.062S59560227

RSAT variation-tools: An accessible and flexible framework to predict the impact of regulatory variants on transcription factor binding

International audienceGene regulatory regions contain short and degenerated DNA binding sites recognized by transcription factors (TFBS). When TFBS harbor SNPs, the DNA binding site may be affected, thereby altering the tran-scriptional regulation of the target genes. Such regulatory SNPs have been implicated as causal variants in Genome-Wide Association Study (GWAS) studies. In this study, we describe improved versions of the programs Variation-tools designed to predict regulatory variants, and present four case studies to illustrate their usage and applications. In brief, Variation-tools facilitate i) obtaining variation information, ii) interconversion of variation file formats, iii) retrieval of sequences surrounding variants, and iv) calculating the change on predicted transcription factor affinity scores between alleles, using motif scanning approaches. Notably, the tools support the analysis of haplotypes. The tools are included within the well-maintained suite Regulatory Sequence Analysis Tools (RSAT, http://rsat.eu), and accessible through a web interface that currently enables analysis of five metazoa and ten plant genomes. Variation-tools can also be used in command-line with any locally-installed Ensembl genome. Users can input personal collections of variants and motifs, providing flexibility in the analysis

Erratum: JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework

JASPAR (http://jaspar.genereg.net) is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) and TF flexible models (TFFMs) for TFs across multiple species in six taxonomic groups. In the 2018 release of JASPAR, the CORE collection has been expanded with 322 new PFMs (60 for vertebrates and 262 for plants) and 33 PFMs were updated (24 for vertebrates, 8 for plants and 1 for insects). These new profiles represent a 30% expansion compared to the 2016 release. In addition, we have introduced 316 TFFMs (95 for vertebrates, 218 for plants and 3 for insects). This release incorporates clusters of similar PFMs in each taxon and each TF class per taxon. The JASPAR 2018 CORE vertebrate collection of PFMs was used to predict TF-binding sites in the human genome. The predictions are made available to the scientific community through a UCSC Genome Browser track data hub. Finally, this update comes with a new web framework with an interactive and responsive user-interface, along with new features. All the underlying data can be retrieved programmatically using a RESTful API and through the JASPAR 2018 R/Bioconductor package

Insights into mammalian transcription control by systematic analysis of ChIP sequencing data

Abstract Background Transcription regulation is a major controller of gene expression dynamics during development and disease, where transcription factors (TFs) modulate expression of genes through direct or indirect DNA interaction. ChIP sequencing has become the most widely used technique to get a genome wide view of TF occupancy in a cell type of interest, mainly due to established standard protocols and a rapid decrease in the cost of sequencing. The number of available ChIP sequencing data sets in public domain is therefore ever increasing, including data generated by individual labs together with consortia such as the ENCODE project. Results A total of 1735 ChIP-sequencing datasets in mouse and human cell types and tissues were used to perform bioinformatic analyses to unravel diverse features of transcription control. 1- We used the Heat*seq webtool to investigate global relations across the ChIP-seq samples. 2- We demonstrated that factors have a specific genomic location preferences that are, for most factors, conserved across species. 3- Promoter proximal binding of factors was more conserved across cell types while the distal binding sites are more cell type specific. 4- We identified combinations of factors preferentially acting together in a cellular context. 5- Finally, by integrating the data with disease-associated gene loci from GWAS studies, we highlight the value of this data to associate novel regulators to disease. Conclusion In summary, we demonstrate how ChIP sequencing data integration and analysis is powerful to get new insights into mammalian transcription control and demonstrate the utility of various bioinformatic tools to generate novel testable hypothesis using this public resource

Etude des éléments cis-régulateurs : identification et caractérisation

The regulation of gene transcription is largely based on the existence of non-codingDNA sequences in the genome. These DNA sequences, called "cis-regulatory elements",have the particularity of recruiting many proteins capable of regulating the level of genetranscription. Among these proteins, transcription factors are capable of directly bindingto DNA. Transcription factors cooperate with other regulatory proteins, called cofactors,to regulate transcription. Transcription regulatory proteins allow the binding andregulation of the RNA polymerase II enzyme that transcribes genes into messenger RNA.The fixation of transcription factors on the cis-regulatory elements allows the regulationof genes in space and time. To better understand the regulation of gene expression, it isnecessary to identify cis-regulatory elements in the genome in order to characterize andidentify the mechanisms of action of regulatory elements and the proteins that are linkedto them. The rapid development of high throughput sequencing methods has made itpossible to identify DNA/protein interactions on a large scale. The massive accumulationof sequencing data in public databases allows the integration of many experiments thatcapture the interactions between transcription factors and DNA through bioinformatics.The purpose of my PhD was to annotate and process in a uniform way the raw data fromsequencing experiments whose objective is to identify the binding regions of regulatoryproteins for humans and then for Arabidopsis Thaliana. We processed data from ChIPseq, ChIP-exo and DAP-seq to develop several catalogues of regulatory regions in humansand Arabidopsis Thaliana. All this data is available within the ReMap project. Wecompleted them with an analysis of all histone brands for Arabidopsis Thaliana. To carryout these analyses we have developed reproducible, scalable and portable workflowsworking on different architectures. This high throughput integrative analysis has allowedus to identify many new cis-regulatory elements. These data were also used to identifythe attachment sites recognized by the transcription factors and to consolidate theJASPAR database for humans and Arabidopsis Thaliana. Finally, this catalogue was usedin the development of a new method applying an entropy-based algorithm to differentiatebetween direct and indirect protein binding events in ChIP-seq results.Le processus de régulation de la transcription des gènes repose très largement surl’existence de séquences d’ADN non codantes dans le génome. Ces séquences d’ADN,appelées “éléments cis-régulateurs”, ont la particularité de recruter de nombreusesprotéines capables de réguler le niveau de transcription des gènes. Parmi ces protéines,les facteurs de transcription sont capables de se fixer directement sur l’ADN. Les facteursde transcription coopèrent avec d’autres protéines régulatrices, les cofacteurs, afin deréguler la transcription. Les protéines régulatrices de la transcription permettent lafixation et la régulation de l’enzyme d’ARN polymérase II qui transcrit les gènes en ARNmessager. Leurs fixations sur les éléments cis-régulateurs permettent une régulation desgènes dans l’espace et dans le temps. Pour mieux comprendre la régulation del’expression des gènes, il est nécessaire d’identifier les éléments cis-régulateurs dans legénome afin de caractériser et d’identifier les mécanismes d’action des élémentsrégulateurs et des protéines qui leur sont liés. Le développement rapide des méthodes deséquençage à haut débit a permis l’identification des interactions ADN/protéines à grandeéchelle. L'accumulation massive des données de séquençage dans les banques de donnéespubliques permet l'intégration de nombreuses expériences capturant les interactionsentre les facteurs de transcription et l’ADN par des moyens bioinformatiques. Le but demon doctorat a été d’annoter et traiter de façon uniforme les données brutes issuesd’expériences de séquençage ayant pour objectif d’identifier les régions de fixation desprotéines régulatrices pour l’Homme puis chez Arabidopsis Thaliana. Nous avons traitédes données de ChIP-seq, ChIP-exo et DAP-seq afin d'élaborer plusieurs catalogues derégions régulatrices chez l’homme et chez Arabidopsis Thaliana. Toutes ces données sontdisponibles au sein du projet ReMap. Pour Arabidopsis Thaliana, nous avons complété cesdonnées par une analyse de toutes les marques d’histones. Pour effectuer ces analyses,nous avons développé des workflows reproductibles, scalables et portables sur desarchitectures différentes. Cette analyse intégrative à haut débit nous a permis d’identifierde nombreux nouveaux éléments cis-régulateurs. Ces données ont également été utiliséespour identifier les sites de fixations reconnus par les facteurs de transcription etpour consolider la base de données JASPAR pour l’Homme et pour Arabidopsis Thaliana.Enfin, ce catalogue a été utilisé dans le développement d’une nouvelle méthodeappliquant un algorithme basé sur l’entropie. Cet algorithme permet de différencier lesévénements de fixations directes et indirectes par les protéines dans les résultats de ChIPseq

Etude des éléments cis-régulateurs : identification et caractérisation

The regulation of gene transcription is largely based on the existence of non-codingDNA sequences in the genome. These DNA sequences, called "cis-regulatory elements",have the particularity of recruiting many proteins capable of regulating the level of genetranscription. Among these proteins, transcription factors are capable of directly bindingto DNA. Transcription factors cooperate with other regulatory proteins, called cofactors,to regulate transcription. Transcription regulatory proteins allow the binding andregulation of the RNA polymerase II enzyme that transcribes genes into messenger RNA.The fixation of transcription factors on the cis-regulatory elements allows the regulationof genes in space and time. To better understand the regulation of gene expression, it isnecessary to identify cis-regulatory elements in the genome in order to characterize andidentify the mechanisms of action of regulatory elements and the proteins that are linkedto them. The rapid development of high throughput sequencing methods has made itpossible to identify DNA/protein interactions on a large scale. The massive accumulationof sequencing data in public databases allows the integration of many experiments thatcapture the interactions between transcription factors and DNA through bioinformatics.The purpose of my PhD was to annotate and process in a uniform way the raw data fromsequencing experiments whose objective is to identify the binding regions of regulatoryproteins for humans and then for Arabidopsis Thaliana. We processed data from ChIPseq, ChIP-exo and DAP-seq to develop several catalogues of regulatory regions in humansand Arabidopsis Thaliana. All this data is available within the ReMap project. Wecompleted them with an analysis of all histone brands for Arabidopsis Thaliana. To carryout these analyses we have developed reproducible, scalable and portable workflowsworking on different architectures. This high throughput integrative analysis has allowedus to identify many new cis-regulatory elements. These data were also used to identifythe attachment sites recognized by the transcription factors and to consolidate theJASPAR database for humans and Arabidopsis Thaliana. Finally, this catalogue was usedin the development of a new method applying an entropy-based algorithm to differentiatebetween direct and indirect protein binding events in ChIP-seq results.Le processus de régulation de la transcription des gènes repose très largement surl’existence de séquences d’ADN non codantes dans le génome. Ces séquences d’ADN,appelées “éléments cis-régulateurs”, ont la particularité de recruter de nombreusesprotéines capables de réguler le niveau de transcription des gènes. Parmi ces protéines,les facteurs de transcription sont capables de se fixer directement sur l’ADN. Les facteursde transcription coopèrent avec d’autres protéines régulatrices, les cofacteurs, afin deréguler la transcription. Les protéines régulatrices de la transcription permettent lafixation et la régulation de l’enzyme d’ARN polymérase II qui transcrit les gènes en ARNmessager. Leurs fixations sur les éléments cis-régulateurs permettent une régulation desgènes dans l’espace et dans le temps. Pour mieux comprendre la régulation del’expression des gènes, il est nécessaire d’identifier les éléments cis-régulateurs dans legénome afin de caractériser et d’identifier les mécanismes d’action des élémentsrégulateurs et des protéines qui leur sont liés. Le développement rapide des méthodes deséquençage à haut débit a permis l’identification des interactions ADN/protéines à grandeéchelle. L'accumulation massive des données de séquençage dans les banques de donnéespubliques permet l'intégration de nombreuses expériences capturant les interactionsentre les facteurs de transcription et l’ADN par des moyens bioinformatiques. Le but demon doctorat a été d’annoter et traiter de façon uniforme les données brutes issuesd’expériences de séquençage ayant pour objectif d’identifier les régions de fixation desprotéines régulatrices pour l’Homme puis chez Arabidopsis Thaliana. Nous avons traitédes données de ChIP-seq, ChIP-exo et DAP-seq afin d'élaborer plusieurs catalogues derégions régulatrices chez l’homme et chez Arabidopsis Thaliana. Toutes ces données sontdisponibles au sein du projet ReMap. Pour Arabidopsis Thaliana, nous avons complété cesdonnées par une analyse de toutes les marques d’histones. Pour effectuer ces analyses,nous avons développé des workflows reproductibles, scalables et portables sur desarchitectures différentes. Cette analyse intégrative à haut débit nous a permis d’identifierde nombreux nouveaux éléments cis-régulateurs. Ces données ont également été utiliséespour identifier les sites de fixations reconnus par les facteurs de transcription etpour consolider la base de données JASPAR pour l’Homme et pour Arabidopsis Thaliana.Enfin, ce catalogue a été utilisé dans le développement d’une nouvelle méthodeappliquant un algorithme basé sur l’entropie. Cet algorithme permet de différencier lesévénements de fixations directes et indirectes par les protéines dans les résultats de ChIPseq

Caractérisation de la microflore dénitrifiante des sols agricoles en lien avec leur aptitude à émettre N2O au cours de la dénitrification

Annexes*INRA Unité de Microbiologie des Sols de Dijon Diffusion du document : INRA Unité de Microbiologie des Sols de Dijon Diplôme : Dr. d'Universit