Raquel Dias

Soil pH Determines Microbial Diversity and Composition in the Park Grass Experiment

The Park Grass experiment (PGE) in the UK has been ongoing since 1856. Its purpose is to study the response of biological communities to the long-term treatments and associated changes in soil parameters, particularly soil pH. In this study, soil samples were collected across pH gradient (pH 3.6–7) and a range of fertilizers (nitrogen as ammonium sulfate, nitrogen as sodium nitrate, phosphorous) to evaluate the effects nutrients have on soil parameters and microbial community structure. Illumina 16S ribosomal RNA (rRNA) amplicon sequencing was used to determine the relative abundances and diversity of bacterial and archaeal taxa. Relationships between treatments, measured soil parameters, and microbial communities were evaluated. Clostridium, Bacteroides, Bradyrhizobium, Mycobacterium, Ruminococcus, Paenibacillus, and Rhodoplanes were the most abundant genera found at the PGE. The main soil parameter that determined microbial composition, diversity, and biomass in the PGE soil was pH. The most probable mechanism of the pH impact on microbial community may include mediation of nutrient availability in the soil. Addition of nitrogen to the PGE plots as ammonium sulfate decreases soil pH through increased nitrification, which causes buildup of soil carbon, and hence increases C/N ratio. Plant species richness and plant productivity did not reveal significant relationships with microbial diversity; however, plant species richness was positively correlated with soil microbial biomass. Plants responded to the nitrogen treatments with an increase in productivity and a decrease in the species richness.

Bacteroides dorei dominates gut microbiome prior to autoimmunity in Finnish children at high risk for type 1 diabetes

The incidence of the autoimmune disease, type 1 diabetes (T1D), has increased dramatically over the last half century in many developed countries and is particularly high in Finland and other Nordic countries. Along with genetic predisposition, environmental factors are thought to play a critical role in this increase. As with other autoimmune diseases, the gut microbiome is thought to play a potential role in controlling progression to T1D in children with high genetic risk, but we know little about how the gut microbiome develops in children with high genetic risk for T1D. In this study, the early development of the gut microbiomes of 76 children at high genetic risk for T1D was determined using high-throughput 16S rRNA gene sequencing. Stool samples from children born in the same hospital in Turku, Finland were collected at monthly intervals beginning at 4–6 months after birth until 2.2 years of age. Of those 76 children, 29 seroconverted to T1D-related autoimmunity (cases) including 22 who later developed T1D, the remaining 47 subjects remained healthy (controls). While several significant compositional differences in low abundant species prior to seroconversion were found, one highly abundant group composed of two closely related species, Bacteroides dorei and Bacteroides vulgatus, was significantly higher in cases compared to controls prior to seroconversion. Metagenomic sequencing of samples high in the abundance of the B. dorei/vulgatus group before seroconversion, as well as longer 16S rRNA sequencing identified this group as Bacteroides dorei. The abundance of B. dorei peaked at 7.6 months in cases, over 8 months prior to the appearance of the first islet autoantibody, suggesting that early changes in the microbiome may be useful for predicting T1D autoimmunity in genetically susceptible infants. The cause of increased B. dorei abundance in cases is not known but its timing appears to coincide with the introduction of solid food.

Genomic Targets and Features of BarA-UvrY (-SirA) Signal Transduction Systems.

The two-component signal transduction system BarA-UvrY of Escherichia coli and its orthologs globally regulate metabolism, motility, biofilm formation, stress resistance, virulence of pathogens and quorum sensing by activating the transcription of genes for regulatory sRNAs, e.g. CsrB and CsrC in E. coli. These sRNAs act by sequestering the RNA binding protein CsrA (RsmA) away from lower affinity mRNA targets. In this study, we used ChIP-exo to identify, at single nucleotide resolution, genomic sites for UvrY (SirA) binding in E. coli and Salmonella enterica. The csrB and csrC genes were the strongest targets of crosslinking, which required UvrY phosphorylation by the BarA sensor kinase. Crosslinking occurred at two sites, an inverted repeat sequence far upstream of the promoter and a site near the -35 sequence. DNAse I footprinting revealed specific binding of UvrY in vitro only to the upstream site, indicative of additional binding requirements and/or indirect binding to the downstream site. Additional genes, including cspA, encoding the cold-shock RNA-binding protein CspA, showed weaker crosslinking and modest or negligible regulation by UvrY. We conclude that the global effects of UvrY/SirA on gene expression are primarily mediated by activating csrB and csrC transcription. We also used in vivo crosslinking and other experimental approaches to reveal new features of csrB/csrC regulation by the DeaD and SrmB RNA helicases, IHF, ppGpp and DksA. Finally, the phylogenetic distribution of BarA-UvrY was analyzed and found to be uniquely characteristic of γ-Proteobacteria and strongly anti-correlated with fliW, which encodes a protein that binds to CsrA and antagonizes its activity in Bacillus subtilis. We propose that BarA-UvrY and orthologous TCS transcribe sRNA antagonists of CsrA throughout the γ-Proteobacteria, but rarely or never perform this function in other species.

Genome Sequence of Candidatus Nitrososphaera evergladensis from Group I.1b Enriched from Everglades Soil Reveals Novel Genomic Features of the Ammonia-Oxidizing Archaea

The activity of ammonia-oxidizing archaea (AOA) leads to the loss of nitrogen from soil, pollution of water sources and elevated emissions of greenhouse gas. To date, eight AOA genomes are available in the public databases, seven are from the group I.1a of the Thaumarchaeota and only one is from the group I.1b, isolated from hot springs. Many soils are dominated by AOA from the group I.1b, but the genomes of soil representatives of this group have not been sequenced and functionally characterized. The lack of knowledge of metabolic pathways of soil AOA presents a critical gap in understanding their role in biogeochemical cycles. Here, we describe the first complete genome of soil archaeon Candidatus Nitrososphaera evergladensis, which has been reconstructed from metagenomic sequencing of a highly enriched culture obtained from an agricultural soil. The AOA enrichment was sequenced with the high throughput next generation sequencing platforms from Pacific Biosciences and Ion Torrent. The de novo assembly of sequences resulted in one 2.95 Mb contig. Annotation of the reconstructed genome revealed many similarities of the basic metabolism with the rest of sequenced AOA. Ca. N. evergladensis belongs to the group I.1b and shares only 40% of whole-genome homology with the closest sequenced relative Ca. N. gargensis. Detailed analysis of the genome revealed coding sequences that were completely absent from the group I.1a. These unique sequences code for proteins involved in control of DNA integrity, transporters, two-component systems and versatile CRISPR defense system. Notably, genomes from the group I.1b have more gene duplications compared to the genomes from the group I.1a. We suggest that the presence of these unique genes and gene duplications may be associated with the environmental versatility of this group.

Identification of the Genes Required for the Culture of Liberibacter crescens, the Closest Cultured Relative of the Liberibacter Plant Pathogens.

Here Tn5 random transposon mutagenesis was used to identify the essential elements for culturing Liberibacter crescens BT-1 that can serve as antimicrobial targets for the closely related pathogens of citrus, Candidatus Liberibacter asiaticus (Las) and tomato and potato, Candidatus Liberibacter solanacearum (Lso). In order to gain insight on the virulence, metabolism, and culturability of the pathogens within the genus Liberibacter, a mini-Tn5 transposon derivative system consisting of a gene specifying resistance to kanamycin, flanked by a 19-base-pair terminal repeat sequence of Tn5, was used for the genome-wide mutagenesis of L. crescens BT-1 and created an insertion mutant library. By analyzing the location of insertions using Sanger and Illumina Mi-Seq sequencing, 314 genes are proposed as essential for the culture of L. crescens BT-1 on BM-7 medium. Of those genes, 76 are not present in the uncultured Liberibacter pathogens and, as a result, suggest molecules necessary for the culturing these pathogens. Those molecules include the aromatic amino acids, several vitamins, histidine, cysteine, lipopolysaccharides, and fatty acids. In addition, the 238 essential genes of L. crescens in common with L. asiaticus are potential targets for the development of therapeutics against the disease.

Different combinations of atomic interactions predict protein-small molecule and protein-DNA/RNA affinities with similar accuracy.

Interactions between proteins and other molecules play essential roles in all biological processes. Although it is widely held that a proteins ligand specificity is determined primarily by its three‐dimensional structure, the general principles by which structure determines ligand binding remain poorly understood. Here we use statistical analyses of a large number of protein−ligand complexes with associated binding‐affinity measurements to quantitatively characterize how combinations of atomic interactions contribute to ligand affinity. We find that there are significant differences in how atomic interactions determine ligand affinity for proteins that bind small chemical ligands, those that bind DNA/RNA and those that interact with other proteins. Although protein‐small molecule and protein‐DNA/RNA binding affinities can be accurately predicted from structural data, models predicting one type of interaction perform poorly on the others. Additionally, the particular combinations of atomic interactions required to predict binding affinity differed between small‐molecule and DNA/RNA data sets, consistent with the conclusion that the structural bases determining ligand affinity differ among interaction types. In contrast to what we observed for small‐molecule and DNA/RNA interactions, no statistical models were capable of predicting protein−protein affinity with >60% correlation. We demonstrate the potential usefulness of protein‐DNA/RNA binding prediction as a possible tool for high‐throughput virtual screening to guide laboratory investigations, suggesting that quantitative characterization of diverse molecular interactions may have practical applications as well as fundamentally advancing our understanding of how molecular structure translates into function. Proteins 2015; 83:2100–2114.

Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data

BackgroundOne goal of structural biology is to understand how a protein’s 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex into its constituent atom-atom interactions is typically sufficient to rapidly predict ligand affinity with high accuracy (>70% correlation between predicted and experimentally-determined affinity), a fact that is exploited to support structure-based drug design. We recently found that protein-DNA/RNA affinity can also be predicted with high accuracy using extensions of existing techniques, but protein-protein affinity could not be predicted with >60% correlation, even when the protein-protein complex was available.MethodsX-ray and NMR structures of protein-protein complexes, their associated binding affinities and experimental conditions were obtained from different binding affinity and structural databases. Statistical models were implemented using a generalized linear model framework, including the experimental conditions as new model features. We evaluated the potential for new features to improve affinity prediction models by calculating the Pearson correlation between predicted and experimental binding affinities on the training and test data after model fitting and after cross-validation. Differences in accuracy were assessed using two-sample t test and nonparametric Mann–Whitney U test.ResultsHere we evaluate a range of potential factors that may interfere with accurate protein-protein affinity prediction. We find that X-ray crystal resolution has the strongest single effect on protein-protein affinity prediction. Limiting our analyses to only high-resolution complexes (≤2.5xa0Å) increased the correlation between predicted and experimental affinity from 54 to 68% (pu2009=u20094.32x10−3). In addition, incorporating information on the experimental conditions under which affinities were measured (pH, temperature and binding assay) had significant effects on prediction accuracy. We also highlight a number of potential errors in large structure-affinity databases, which could affect both model training and accuracy assessment.ConclusionsThe results suggest that the accuracy of statistical models for protein-protein affinity prediction may be limited by the information present in databases used to train new models. Improving our capacity to integrate large-scale structural and functional information may be required to substantively advance our understanding of the general principles by which a protein’s structure determines its function.

Integrating DNA Methylation and Gene Expression Data in the Development of the Soybean-Bradyrhizobium N2-Fixing Symbiosis

Very little is known about the role of epigenetics in the differentiation of a bacterium from the free-living to the symbiotic state. Here genome-wide analysis of DNA methylation changes between these states is described using the model of symbiosis between soybean and its root nodule-forming, nitrogen-fixing symbiont, Bradyrhizobium diazoefficiens. PacBio resequencing of the B. diazoefficiens genome from both states revealed 43,061 sites recognized by five motifs with the potential to be methylated genome-wide. Of those sites, 3276 changed methylation states in 2921 genes or 35.5% of all genes in the genome. Over 10% of the methylation changes occurred within the symbiosis island that comprises 7.4% of the genome. The CCTTGAG motif was methylated only during symbiosis with 1361 adenosines methylated among the 1700 possible sites. Another 89 genes within the symbiotic island and 768 genes throughout the genome were found to have methylation and significant expression changes during symbiotic development. Of those, nine known symbiosis genes involved in all phases of symbiotic development including early infection events, nodule development, and nitrogenase production. These associations between methylation and expression changes in many B. diazoefficiens genes suggest an important role of the epigenome in bacterial differentiation to the symbiotic state.

MPI-blastn and NCBI-TaxCollector: Improving metagenomic analysis with high performance classification and wide taxonomic attachment

Metagenomic sequencing technologies are advancing rapidly and the size of output data from high-throughput genetic sequencing has increased substantially over the years. This brings us to a scenario where advanced computational optimizations are requested to perform a metagenomic analysis. In this paper, we describe a new parallel implementation of nucleotide BLAST (MPI-blastn) and a new tool for taxonomic attachment of Basic Local Alignment Search Tool (BLAST) results that supports the NCBI taxonomy (NCBI-TaxCollector). MPI-blastn obtained a high performance when compared to the mpiBLAST and ScalaBLAST. In our best case, MPI-blastn was able to run 408 times faster in 384 cores. Our evaluations demonstrated that NCBI-TaxCollector is able to perform taxonomic attachments 125 times faster and needs 120 times less RAM than the previous TaxCollector. Through our optimizations, a multiple sequence search that currently takes 37 hours can be performed in less than 6 min and a post processing with NCBI taxonomic data attachment, which takes 48 hours, now is able to run in 23 min.

Convergence of Domain Architecture, Structure, and Ligand Affinity in Animal and Plant RNA-Binding Proteins

Abstract Reconstruction of ancestral protein sequences using phylogenetic methods is a powerful technique for directly examining the evolution of molecular function. Although ancestral sequence reconstruction (ASR) is itself very efficient, downstream functional, and structural studies necessary to characterize when and how changes in molecular function occurred are often costly and time-consuming, currently limiting ASR studies to examining a relatively small number of discrete functional shifts. As a result, we have very little direct information about how molecular function evolves across large protein families. Here we develop an approach combining ASR with structure and function prediction to efficiently examine the evolution of ligand affinity across a large family of double-stranded RNA binding proteins (DRBs) spanning animals and plants. We find that the characteristic domain architecture of DRBs—consisting of 2–3 tandem double-stranded RNA binding motifs (dsrms)—arose independently in early animal and plant lineages. The affinity with which individual dsrms bind double-stranded RNA appears to have increased and decreased often across both animal and plant phylogenies, primarily through convergent structural mechanisms involving RNA-contact residues within the β1–β2 loop and a small region of α2. These studies provide some of the first direct information about how protein function evolves across large gene families and suggest that changes in molecular function may occur often and unassociated with major phylogenetic events, such as gene or domain duplications.