DDRprot: a database of DNA damage response-related proteins

The DNA Damage Response (DDR) signalling network is an essential system that protects the genome’s integrity. The DDRprot database presented here is a resource that integrates manually curated information on the human DDR network and its sub-pathways. For each particular DDR protein, we present detailed information about its function. If involved in post-translational modifications (PTMs) with each other, we depict the position of the modified residue/s in the three-dimensional structures, when resolved structures are available for the proteins. All this information is linked to the original publication from where it was obtained. Phylogenetic information is also shown, including time of emergence and conservation across 47 selected species, family trees and sequence alignments of homologues. The DDRprot database can be queried by different criteria: pathways, species, evolutionary age or involvement in (PTM). Sequence searches using hidden Markov models can be also used.E.A.-L. was supported by the European Commission grant [FP7-REGPOT-2012-2013-1; A.A. was partially supported by the Spanish Ministry of Science and Innovation grant [PS09/02111].Peer reviewe

Changes in the structure of N-fixing communities from <i>nif</i>H-gene pyrosequencing across different soils and sampling times.

<p>Changes in the structure of <i>nif</i>H-gene communities and the influence of environmental parameters as revealed by RDA, considering (a) the most abundant and (b) rare sequences. The number in each axis shows the percentage of total variation explained. The length of the corresponding arrows indicated the relative importance of the geochemical factor in explaining the variation in microbial profiles. Soil samples were analyzed in four replicates at each sampling time. B, Buinen; D, Droevendaal; K, Kollumerwaard and G, Grebbedijk; Ap, April; Ju, June; Oc, October.</p

Changes in the structure of total bacterial and N-fixing communities from DGGE fingerprints across different soils and sampling times.

<p>Changes in the structure of bacterial (a) and N-fixing (b) communities and the influence of environmental parameters, as revealed by CCA. Only statistically significant environmental variables are shown. The number in each axis shows the percentage of total variation explained. The length of the corresponding arrows indicated the relative importance of the geochemical factor in explaining the variation in microbial profiles. Soil samples were analyzed in four replicates at each sampling time. B, Buinen; D, Droevendaal; K, Kollumerwaard and G, Grebbedijk; Ap, April; Ju, June; Oc, October.</p

Rarefaction analysis of the diversities of <i>nif</i>H gene.

<p>Analyses of four soils across three sampling times after resampling of the sequences to the same depth (1921 sequences). The OTUs were classified at 90% similarity cutoff based on amino acid sequences. B, Buinen; D, Droevendaal; K, Kollumerwaard and G, Grebbedijk; Ap, April; Ju, June; Oc, October.</p

Does Long-Term Irrigation with Untreated Wastewater Accelerate the Dissipation of Pharmaceuticals in Soil?

Long-term irrigation with untreated wastewater may increase soil microbial adaptation to pollution load and lead to enhanced natural attenuation. We hypothesized that long-term wastewater irrigation accelerates the dissipation of pharmaceuticals. To test our hypothesis we performed an incubation experiment with soils from the Mezquital Valley, Mexico that were irrigated for 0, 14, or 100 years. The results showed that the dissipation half-lives (<i>DT</i>₅₀) of diclofenac (<0.1–1.4 days), bezafibrate (<0.1–4.8 days), sulfamethoxazole (2–33 days), naproxen (6–19 days), carbamazepine (355–1,624 days), and ciprofloxacin were not affected by wastewater irrigation. Trimethoprim dissipation was even slower in soils irrigated for 100 years (<i>DT</i>₅₀: 45–72 days) than in nonirrigated soils (<i>DT</i>₅₀: 12–16 days), was negatively correlated with soil organic matter content and soil-water distribution coefficients, and was inhibited in sterilized soils. Applying a kinetic fate model indicated that long-term irrigation enhanced sequestration of cationic or uncharged trimethoprim and uncharged carbamazepine, but did not affect sequestration of fast-dissipating zwitterions or negatively charged pharmaceuticals. We conclude that microbial adaptation processes play a minor role for pharmaceutical dissipation in wastewater-irrigated soils, while organic matter accumulation in these soils can retard trimethoprim and carbamazepine dissipation

Dynamics of Soil Bacterial Communities in Response to Repeated Application of Manure Containing Sulfadiazine

<div><p>Large amounts of manure have been applied to arable soils as fertilizer worldwide. Manure is often contaminated with veterinary antibiotics which enter the soil together with antibiotic resistant bacteria. However, little information is available regarding the main responders of bacterial communities in soil affected by repeated inputs of antibiotics via manure. In this study, a microcosm experiment was performed with two concentrations of the antibiotic sulfadiazine (SDZ) which were applied together with manure at three different time points over a period of 133 days. Samples were taken 3 and 60 days after each manure application. The effects of SDZ on soil bacterial communities were explored by barcoded pyrosequencing of 16S rRNA gene fragments amplified from total community DNA. Samples with high concentration of SDZ were analyzed on day 193 only. Repeated inputs of SDZ, especially at a high concentration, caused pronounced changes in bacterial community compositions. By comparison with the initial soil, we could observe an increase of the disturbance and a decrease of the stability of soil bacterial communities as a result of SDZ manure application compared to the manure treatment without SDZ. The number of taxa significantly affected by the presence of SDZ increased with the times of manure application and was highest during the treatment with high SDZ-concentration. Numerous taxa, known to harbor also human pathogens, such as <i>Devosia</i>, <i>Shinella</i>, <i>Stenotrophomonas</i>, <i>Clostridium</i>, <i>Peptostreptococcus</i>, <i>Leifsonia</i>, <i>Gemmatimonas</i>, were enriched in the soil when SDZ was present while the abundance of bacteria which typically contribute to high soil quality belonging to the genera <i>Pseudomonas</i> and <i>Lysobacter</i>, <i>Hydrogenophaga</i>, and <i>Adhaeribacter</i> decreased in response to the repeated application of manure and SDZ.</p></div

Taxa with significantly different relative abundance between manured soils with or without SDZ treatments shortly after manure addition as revealed by barcoded pyrosequencing using the reverse primer (Bonferroni adjusted p value<0.05).

<p>Bold numbers indicate taxa with significantly higher relative abundance in SDZ treated or non treated soils. Soils were collected three days after each treatment.</p

Composition of archaeal and bacterial communities described as relative OTU abundance of particular bacterial and archaeal taxa (phyla and classes) in different sample types.

<p>Mean relative abundances (expressed as percentages) for each taxonomical group (<i>n = 5</i>) in given sample are listed. Taxonomic units with abundance higher than 0.05% at least in one sample are shown. Significant differences (results of Tukey′s HSD test) are indicated by different letters in rows (<i>P</i> < 0.05).</p><p>Composition of archaeal and bacterial communities described as relative OTU abundance of particular bacterial and archaeal taxa (phyla and classes) in different sample types.</p

Double dendrogram and heatmap, based on the Ward minimum variance clustering method for abundant genera investigated using <i>nif</i>H gene pyrosequencing.

<p>The heatmap indicates the relative abundance of the (a) most abundant and (b) most rare genera within each sample. B, Buinen; D, Droevendaal; K, Kollumerwaard and G, Grebbedijk; Ap, April; Ju, June; Oc, October.</p

Dissimilarity between soils treated with manure (S0) and manure spiked with SDZ (S10 indicated by circles; S100 indicated by triangles) at different sampling times.

<p>Red symbols: results based on the data set acquired by the forward primer; blue symbols: results based on the data set acquired by the reverse primer. Error bars indicate the first and third quartiles.</p