Antimicrobial potential of deep surface sediment associated bacteria from the Sea of Japan

The aim of this study was to survey microorganisms from the deep surface sediment samples collected from the Sea of Japan and to screen them for antimicrobial and antagonistic effects. Phylogenetic analysis revealed most isolates sharing 98–100 % sequence similarity to recognized species, including those recovered previously from marine or saline environments. Alteromonas, Halomonas, Marinobacter, Pseudoalteromonas, Salinicola, within the class Gammaproteobacteria, Sulfitobacter (Alphaproteobacteria), Bacillus, Paenibacillus and Paenisporosarcina (Firmicutes), Nocardiopsis and Streptomyces (Actinobacteria) occurred abundantly in all sediment samples. Antimicrobial screening revealed twenty three strains (13 %) capable to inhibit growth of one to eight test cultures and deep sediment isolates. Based on phylogenetic analysis mostly active strains belonged to the genera Bacillus, Brevibacillus, Nocardiopsis, Paenibacillus and Streptomyces. Antimicrobial substances (1–3) were isolated from strain Paenibacillus sp. Sl 79w showing a high inhibitory activity. On the basis of combined spectral analyses (IR, UV, ¹H and ¹³C NMR) the compounds 1, 2 and 3 with [M + H]⁺ at 409.1 and 409.2 m/z, and with [M + Na]⁺ at 822.5 m/z were found to have a carbon skeleton of isocoumarin and peptide antibiotics, respectively. Our findings demonstrated that the deep surface sediments of the Sea of Japan represent an untapped source of diverse microorganisms capable of antimicrobial metabolite production.     

The carbohydrate-binding module of Fragaria × ananassa expansin 2 (CBM-FaExp2) binds to cell wall polysaccharides and decreases cell wall enzyme activities “in vitro”

A putative carbohydrate binding module (CBM) from strawberry (Fragaria × ananassa Duch.) expansin 2 (CBM-FaExp2) was cloned and the encoding protein was over-expressed in Escherichia coli and purified in order to evaluate its capacity to bind different cell wall polysaccharides “in vitro”. The protein CBM-FaExp2 bound to microcrystalline cellulose, xylan and pectin with different affinities (K_ad = 33.6 ± 0.44 mL g^?1, K_ad = 11.37 ± 0.87 mL g^?1, K_ad = 10.4 ± 0.19 mL g^?1, respectively). According to “in vitro” enzyme assays, this CBM is able to decrease the activity of cell wall degrading enzymes such as polygalacturonase, endo-glucanase, pectinase and xylanase, probably because the binding of CBM-FaExp2 to the different substrates interferes with enzyme activity. The results suggest that expansins would bind not only cellulose but also a wide range of cell wall polymers.     

Suboptimal growth temperatures enhance the biological activity of cultured cyanobacterium Gloeocapsa sp.

The cytotoxic, antibacterial, and antifungal activities of cyanobacterium Gloeocapsa sp. strain Gacheva 2007/R-06/1 were investigated and the possibility for an enhancement of these activities by changing the culture conditions evaluated. Fatty acids of this cyanobacterium were found to be active against Streptococcus pyogenes. Exopolysaccharides inhibited the growth of both Gram-positive and Gram-negative bacteria and the fungus Candida albicans. Both exopolysaccharides and fatty acid mixtures also significantly decreased the viability of human cervical carcinoma cells, HeLa. Greater biological activities were exhibited by Gloeocapsa sp., cultured at suboptimal temperatures (15–26°C) than at optimal and supraoptimal ones. In comparison with higher light intensity, the low-light cultivation stimulated the cytotoxicity of the fatty acids. In general, low temperatures decreased the growth of Gloeocapsa sp., but promoted its biological activity. Prolonged cultivation also had a beneficial impact on the bioactivity. Compared to 4 days, the 17-day cultivation resulted in fourfold higher antibacterial and antifungal activities of exopolysaccharides and more than twice increases in their cytotoxicity. The study revealed that this cyanobacterial isolate is a new source of natural products with potential for pharmacological and medical applications.     

Environmental Suitability and Distribution of the Caucasian Rock Agama, Paralaudakia caucasia （Sauria： Agamidae） in Western and Central Asia

Predictive potential distribution modeling is crucial in outlining habitat usage and establishing conservation management priorities. In this paper we provide detailed data on the distribution of the Caucasian rock agama Para- laudakia caucasia, and use species distribution models （MAXENT） to evaluate environmental suitability and potential distribution at a broad spatial scale. Locality data on the distribution of P. caucasia have been gathered over nearly its entire range by various authors from field surveys. The distribution model ofP caucasia showed good performance （AUC = 0.887）, and predicted high suitability in regions mainly located in Tajikistan, north Pakistan, Afghanistan, southeast Turkmenistan, northeast Iran along the Elburz mountains, Transcaueasus （Azerbajan, Armenia, Georgia）, northeastern Turkey and northward along the Caspian Sea coast in Daghestan, Russia. The identification of suitable areas for this species will help to assess conservation status of the species, and to set up management programs.     

Phototoxic effects of fluorescent protein KillerRed on tumor cells in mice

KillerRed is known to be a unique red fluorescent protein displaying strong phototoxic properties. Its effectiveness has been shown previously for killing bacterial and cancer cells in vitro. Here, we investigated the photototoxicity of the protein on tumor xenografts in mice. HeLa Kyoto cell line stably expressing KillerRed in mitochondria and in fusion with histone H2B was used. Irradiation of the tumors with 593 nm laser led to photobleaching of KillerRed indicating photosensitization reaction and caused significant destruction of the cells and activation of apoptosis. The portion of the dystrophically changed cells increased from 9.9% to 63.7%, and the cells with apoptosis hallmarks from 6.3% to 14%. The results of this study suggest KillerRed as a potential genetically encoded photosensitizer for photodynamic therapy of cancer. (© 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

The Role of TLR2, TLR4 and CD14 Genetic Polymorphisms in Gastric Carcinogenesis: A Case-Control Study and Meta-Analysis

Background

In addition to Helicobacter pylori infection, host genetic factors contribute to gastric cancer (GC). Recognition of H. pylori is known to involve Toll-like receptors (TLR), which subsequently leads to activation of NF-κB. Thus, the overall aim of this study was to estimate for the first time the pooled effect size of polymorphisms in TLR2, TLR4 and CD14 on GC development through a meta-analysis.

Methods

A case-control study comprising 284 ethnic Chinese individuals (70 non-cardia GC cases and 214 functional dyspepsia controls) was conducted for the genotyping of TLR2 -196 to -174del, CD14 -260 C/T and TLR4 rs11536889 using PCR, RT-PCR and mass spectrometry. Case-control studies of TLR2, TLR4 and CD14 polymorphisms and GC were searched up to June 2012. Pooled odds ratios and 95% confidence intervals were obtained by means of the random effects model.

Results

In our ethnic Chinese case-control study, the TLR4 rs11536889 C allele increased the risk of GC (OR: 1.89, 95%CI: 1.23–2.92) while the CD14 -260 T allele was protective (OR: 0.62, 95%CI: 0.42–0.91). TLR2 -196 to -174 increased the risk of GC only in H. pylori-infected individuals (OR: 3.10, 95%CI: 1.27–7.60). In the meta-analysis, TLR4 Asp299Gly showed borderline results in the general analysis (pooled OR: 1.58, 95%CI: 0.98–2.60), nevertheless, stratified analysis by ethnicity showed that the mutant allele was a definitive risk factor for GC in Western populations (pooled OR: 1.87, 95%CI: 1.31–2.65). There was a potential association between the TLR2 -196 to -174 deletion allele and GC in Japanese (pooled OR: 1.18, 95%CI: 0.96–1.45). TLR4 Thr399Ile did not provide significant results.

Conclusions

TLR4 rs11536889 and CD14 -260 C/T are associated with non-cardia GC in Chinese. Based on our meta-analysis, the TLR signalling pathway is involved in gastric carcinogenesis, TLR4 Asp299Gly and TLR2 -196 to -174del showing associations with GC in an ethnic-specific manner.     

Proliferation of Murine Midbrain Neural Stem Cells Depends upon an Endogenous Sonic Hedgehog (Shh) Source

The Sonic Hedgehog (Shh) pathway is responsible for critical patterning events early in development and for regulating the delicate balance between proliferation and differentiation in the developing and adult vertebrate brain. Currently, our knowledge of the potential role of Shh in regulating neural stem cells (NSC) is largely derived from analyses of the mammalian forebrain, but for dorsal midbrain development it is mostly unknown. For a detailed understanding of the role of Shh pathway for midbrain development in vivo, we took advantage of mouse embryos with cell autonomously activated Hedgehog (Hh) signaling in a conditional Patched 1 (Ptc1) mutant mouse model. This animal model shows an extensive embryonic tectal hypertrophy as a result of Hh pathway activation. In order to reveal the cellular and molecular origin of this in vivo phenotype, we established a novel culture system to evaluate neurospheres (nsps) viability, proliferation and differentiation. By recreating the three-dimensional (3-D) microenvironment we highlight the pivotal role of endogenous Shh in maintaining the stem cell potential of tectal radial glial cells (RGC) and progenitors by modulating their Ptc1 expression. We demonstrate that during late embryogenesis Shh enhances proliferation of NSC, whereas blockage of endogenous Shh signaling using cyclopamine, a potent Hh pathway inhibitor, produces the opposite effect. We propose that canonical Shh signaling plays a central role in the control of NSC behavior in the developing dorsal midbrain by acting as a niche factor by partially mediating the response of NSC to epidermal growth factor (EGF) and fibroblast growth factor (FGF) signaling. We conclude that endogenous Shh signaling is a critical mechanism regulating the proliferation of stem cell lineages in the embryonic dorsal tissue.     

Phylogenetic relations of the dinoflagellate Gymnodinium baicalense from Lake Baikal

Freshwater dinoflagellates still remain poorly studied by modern biological methods. This lack of knowledge prevents us from understanding the evolution and colonization patterns of these ecologically important protists. Gymnodinium baicalense is the most abundant, and possibly endemic, planktonic dinoflagellate from the ancient Lake Baikal. This dinoflagellate species blooms in the spring under the ice. This study analyzed the origin of this Baikalian dinoflagellate using three markers (two ribosomal and one mitochondrial DNA). It was found that this species is a true member of the order Gymnodiniales and has close relatives in the glacial melt waters of the Arctic Ocean. It seems that G. baicalense has diversified relatively recently from the arctic marine gymnodinioids. These results shed light on dinoflagellate biogeography and their colonizations in Lake Baikala biodiversity hotspot.     

Dose-Dependent Induction of Murine Th1/Th2 Responses to Sheep Red Blood Cells Occurs in Two Steps: Antigen Presentation during Second Encounter Is Decisive

The differentiation of CD4 T cells into Th1 and Th2 cells in vivo is difficult to analyze since it is influenced by many factors such as genetic background of the mice, nature of antigen, and adjuvant. In this study, we used a well-established model, which allows inducing Th1 or Th2 cells simply by low (LD, 10⁵) or high dose (HD, 10⁹) injection of sheep red blood cells (SRBC) into C57BL/6 mice. Signature cytokine mRNA expression was determined in specific splenic compartments after isolation by laser-microdissection. LD immunization with SRBC induced T cell proliferation in the splenic T cell zone but no Th1 differentiation. A second administration of SRBC into the skin rapidly generated Th1 cells. In contrast, HD immunization with SRBC induced both T cell proliferation and immediate Th2 differentiation. In addition, splenic marginal zone and B cell zone were activated indicating B cells as antigen presenting cells. Interestingly, disruption of the splenic architecture, in particular of the marginal zone, abolished Th2 differentiation and led to the generation of Th1 cells, confirming that antigen presentation by B cells directs Th2 polarization. Only in its absence Th1 cells develop. Therefore, B cells might be promising targets in order to therapeutically modulate the T cell response.     

ProDeGe: a computational protocol for fully automated decontamination of genomes

Single amplified genomes and genomes assembled from metagenomes have enabled the exploration of uncultured microorganisms at an unprecedented scale. However, both these types of products are plagued by contamination. Since these genomes are now being generated in a high-throughput manner and sequences from them are propagating into public databases to drive novel scientific discoveries, rigorous quality controls and decontamination protocols are urgently needed. Here, we present ProDeGe (Protocol for fully automated Decontamination of Genomes), the first computational protocol for fully automated decontamination of draft genomes. ProDeGe classifies sequences into two classes—clean and contaminant—using a combination of homology and feature-based methodologies. On average, 84% of sequence from the non-target organism is removed from the data set (specificity) and 84% of the sequence from the target organism is retained (sensitivity). The procedure operates successfully at a rate of ~0.30 CPU core hours per megabase of sequence and can be applied to any type of genome sequence.Recent technological advancements have enabled the large-scale sampling of genomes from uncultured microbial taxa, through the high-throughput sequencing of single amplified genomes (SAGs; Rinke et al., 2013; Swan et al., 2013) and assembly and binning of genomes from metagenomes (GMGs; Cuvelier et al., 2010; Sharon and Banfield, 2013). The importance of these products in assessing community structure and function has been established beyond doubt (Kalisky and Quake, 2011). Multiple Displacement Amplification (MDA) and sequencing of single cells has been immensely successful in capturing rare and novel phyla, generating valuable references for phylogenetic anchoring. However, efforts to conduct MDA and sequencing in a high-throughput manner have been heavily impaired by contamination from DNA introduced by the environmental sample, as well as introduced during the MDA or sequencing process (Woyke et al., 2011; Engel et al., 2014; Field et al., 2014). Similarly, metagenome binning and assembly often carries various errors and artifacts depending on the methods used (Nielsen et al., 2014). Even cultured isolate genomes have been shown to lack immunity to contamination with other species (Parks et al., 2014; Mukherjee et al., 2015). As sequencing of these genome product types rapidly increases, contaminant sequences are finding their way into public databases as reference sequences. It is therefore extremely important to define standardized and automated protocols for quality control and decontamination, which would go a long way towards establishing quality standards for all microbial genome product types.Current procedures for decontamination and quality control of genome sequences in single cells and metagenome bins are heavily manual and can consume hours/megabase when performed by expert biologists. Supervised decontamination typically involves homology-based inspection of ribosomal RNA sequences and protein coding genes, as well as visual analysis of k-mer frequency plots and guanine–cytosine content (Clingenpeel, 2015). Manual decontamination is also possible through the software SmashCell (Harrington et al., 2010), which contains a tool for visual identification of contaminants from a self-organizing map and corresponding U-matrix. Another existing software tool, DeconSeq (Schmieder and Edwards, 2011), automatically removes contaminant sequences, however, the contaminant databases are required input. The former lacks automation, whereas the latter requires prior knowledge of contaminants, rendering both applications impractical for high-throughput decontamination.Here, we introduce ProDeGe, the first fully automated computational protocol for decontamination of genomes. ProDeGe uses a combination of homology-based and sequence composition-based approaches to separate contaminant sequences from the target genome draft. It has been pre-calibrated to discard at least 84% of the contaminant sequence, which results in retention of a median 84% of the target sequence. The standalone software is freely available at http://prodege.jgi-psf.org//downloads/src and can be run on any system that has Perl, R (R Core Team, 2014), Prodigal (Hyatt et al., 2010) and NCBI Blast (Camacho et al., 2009) installed. A graphical viewer allowing further exploration of data sets and exporting of contigs accompanies the web application for ProDeGe at http://prodege.jgi-psf.org, which is open to the wider scientific community as a decontamination service (Supplementary Figure S1).The assembly and corresponding NCBI taxonomy of the data set to be decontaminated are required inputs to ProDeGe (Figure 1a). Contigs are annotated with genes following which, eukaryotic contamination is removed based on homology of genes at the nucleotide level using the eukaryotic subset of NCBI''s Nucleotide database as the reference. For detecting prokaryotic contamination, a curated database of reference contigs from the set of high-quality genomes within the Integrated Microbial Genomes (IMG; Markowitz et al., 2014) system is used as the reference. This ensures that errors in public reference databases due to poor quality of sequencing, assembly and annotation do not negatively impact the decontamination process. Contigs determined as belonging to the target organism based on nucleotide level homology to sequences in the above database are defined as ‘Clean'', whereas those aligned to other organisms are defined as ‘Contaminant''. Contigs whose origin cannot be determined based on alignment are classified as ‘Undecided''. Classified clean and contaminated contigs are used to calibrate the separation in the subsequent 5-mer based binning module, which classifies undecided contigs as ‘Clean'' or ‘Contaminant'' using principal components analysis (PCA) of 5-mer frequencies. This parameter can also be specified by the user. When data sets do not have taxonomy deeper than phylum level, or a single confident taxonomic bin cannot be detected using sequence alignment, solely 9-mer based binning is used due to more accurate overall classification. In the absence of a user-defined cutoff, a pre-calibrated cutoff for 80% or more specificity separates the clean contigs from contaminated sequences in the resulting PCA of the 9-mer frequency matrix. Details on ProDeGe''s custom database, evaluation of the performance of the system and exploration of the parameter space to calibrate ProDeGe for a high accurate classification rate are provided in the Supplementary Material.Open in a separate windowFigure 1(a) Schematic overview of the ProDeGe engine. (b) Features of data sets used to validate ProDeGe: SAGs from the Arabidopsis endophyte sequencing project, MDM project, public data sets found in IMG but not sequenced at the JGI, as well as genomes from metagenomes. All the data and results can be found in Supplementary Table S3.The performance of ProDeGe was evaluated using 182 manually screened SAGs (Figure 1b,Supplementary Table S1) from two studies whose data sets are publicly available within the IMG system: genomes of 107 SAGs from an Arabidopsis endophyte sequencing project and 75 SAGs from the Microbial Dark Matter (MDM) project* (only 75/201 SAGs from the MDM project had 1:1 mapping between contigs in the unscreened and the manually screened versions, hence these were used; Rinke et al., 2013). Manual curation of these SAGs demonstrated that the use of ProDeGe prevented 5311 potentially contaminated contigs in these data sets from entering public databases. Figure 2a demonstrates the sensitivity vs specificity plot of ProDeGe results for the above data sets. Most of the data points in Figure 2a cluster in the top right of the box reflecting a median retention of 89% of the clean sequence (sensitivity) and a median rejection of 100% of the sequence of contaminant origin (specificity). In addition, on average, 84% of the bases of a data set are accurately classified. ProDeGe performs best when the target organism has sequenced homologs at the class level or deeper in its high-quality prokaryotic nucleotide reference database. If the target organism''s taxonomy is unknown or not deeper than domain level, or there are few contigs with taxonomic assignments, a target bin cannot be assessed and thus ProDeGe removes contaminant contigs using sequence composition only. The few samples in Figure 2a that demonstrate a higher rate of false positives (lower specificity) and/or reduced sensitivity typically occur when the data set contains few contaminant contigs or ProDeGe incorrectly assumes that the largest bin is the target bin. Some data sets contain a higher proportion of contamination than target sequence and ProDeGe''s performance can suffer under this condition. However, under all other conditions, ProDeGe demonstrates high speed, specificity and sensitivity (Figure 2). In addition, ProDeGe demonstrates better performance in overall classification when nucleotides are considered than when contigs are considered, illustrating that longer contigs are more accurately classified (Supplementary Table S1).Open in a separate windowFigure 2ProDeGe accuracy and performance scatterplots of 182 manually curated single amplified genomes (SAGs), where each symbol represents one SAG data set. (a) Accuracy shown by sensitivity (proportion of bases confirmed ‘Clean'') vs specificity (proportion of bases confirmed ‘Contaminant'') from the Endophyte and Microbial Dark Matter (MDM) data sets. Symbol size reflects input data set size in megabases. Most points cluster in the top right of the plot, showing ProDeGe''s high accuracy. Median and average overall results are shown in Supplementary Table S1. (b) ProDeGe completion time in central processing unit (CPU) core hours for the 182 SAGs. ProDeGe operates successfully at an average rate of 0.30 CPU core hours per megabase of sequence. Principal components analysis (PCA) of a 9-mer frequency matrix costs more computationally than PCA of a 5-mer frequency matrix used with blast-binning. The lack of known taxonomy for the MDM data sets prevents blast-binning, thus showing longer finishing times than the endophyte data sets, which have known taxonomy for use in blast-binning.All SAGs used in the evaluation of ProDeGe were assembled using SPAdes (Bankevich et al., 2012). In-house testing has shown that reads assembled with SPAdes from different strains or even slightly divergent species of the same genera may be combined into the same contig (Personal communications, KT and Robert Bowers). Ideally, the DNA in a well that gets sequenced belongs to a single cell. In the best case, contaminant sequences need to be at least from a different species to be recognized as such by the homology-based screening stage. In the absence of closely related sequenced organisms, contaminant sequences need to be at least from a different genus to be recognized as such by the composition-based screening stage (Supplementary Material). Thus, there is little risk of ProDeGe separating sequences from clonal populations or strains. We have found species- and genus-level contamination in MDA samples to be rare.To evaluate the quality of publicly available uncultured genomes, ProDeGe was used to screen 185 SAGs and 14 GMGs (Figure 1b). Compared with CheckM (Parks et al., 2014), a tool which calculates an estimate of genome sequence contamination using marker genes, ProDeGe generally marks a higher proportion of sequence as ‘Contaminant'' (Supplementary Table S2). This is because ProDeGe has been calibrated to perform at high specificity levels. The command line version of ProDeGe allows users to conduct their own calibration and specify a user-defined distance cutoff. Further, CheckM only outputs the proportion of contamination, but ProDeGe actually labels each contig as ‘Clean'' or ‘Contaminant'' during the process of automated removal.The web application for ProDeGe allows users to export clean and contaminant contigs, examine contig gene calls with their corresponding taxonomies, and discover contig clusters in the first three components of their k-dimensional space. Non-linear approaches for dimensionality reduction of k-mer vectors are gaining popularity (van der Maaten and Hinton, 2008), but we observed no systematic advantage of using t-Distributed Stochastic Neighbor Embedding over PCA (Supplementary Figure S2).ProDeGe is the first step towards establishing a standard for quality control of genomes from both cultured and uncultured microorganisms. It is valuable for preventing the dissemination of contaminated sequence data into public databases, avoiding resulting misleading analyses. The fully automated nature of the pipeline relieves scientists of hours of manual screening, producing reliably clean data sets and enabling the high-throughput screening of data sets for the first time. ProDeGe, therefore, represents a critical component in our toolkit during an era of next-generation DNA sequencing and cultivation-independent microbial genomics.