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.     

Chromosomal Integrity after UV Irradiation Requires FANCD2-Mediated Repair of Double Strand Breaks

Fanconi Anemia (FA) is a rare autosomal recessive disorder characterized by hypersensitivity to inter-strand crosslinks (ICLs). FANCD2, a central factor of the FA pathway, is essential for the repair of double strand breaks (DSBs) generated during fork collapse at ICLs. While lesions different from ICLs can also trigger fork collapse, the contribution of FANCD2 to the resolution of replication-coupled DSBs generated independently from ICLs is unknown. Intriguingly, FANCD2 is readily activated after UV irradiation, a DNA-damaging agent that generates predominantly intra-strand crosslinks but not ICLs. Hence, UV irradiation is an ideal tool to explore the contribution of FANCD2 to the DNA damage response triggered by DNA lesions other than ICL repair. Here we show that, in contrast to ICL-causing agents, UV radiation compromises cell survival independently from FANCD2. In agreement, FANCD2 depletion does not increase the amount of DSBs generated during the replication of UV-damaged DNA and is dispensable for UV-induced checkpoint activation. Remarkably however, FANCD2 protects UV-dependent, replication-coupled DSBs from aberrant processing by non-homologous end joining, preventing the accumulation of micronuclei and chromatid aberrations including non-homologous chromatid exchanges. Hence, while dispensable for cell survival, FANCD2 selectively safeguards chromosomal stability after UV-triggered replication stress.     

Retinoid X Receptor Agonists Upregulate Genes Responsible for the Biosynthesis of All-Trans-Retinoic Acid in Human Epidermis

UAB30 is an RXR selective agonist that has been shown to have potential cancer chemopreventive properties. Due to high efficacy and low toxicity, it is currently being evaluated in human Phase I clinical trials by the National Cancer Institute. While UAB30 shows promise as a low toxicity chemopreventive drug, the mechanism of its action is not well understood. In this study, we investigated the effects of UAB30 on gene expression in human organotypic skin raft cultures and mouse epidermis. The results of this study indicate that treatment with UAB30 results in upregulation of genes responsible for the uptake and metabolism of all-trans-retinol to all-trans-retinoic acid (ATRA), the natural agonist of RAR nuclear receptors. Consistent with the increased expression of these genes, the steady-state levels of ATRA are elevated in human skin rafts. In ultraviolet B (UVB) irradiated mouse skin, the expression of ATRA target genes is found to be reduced. A reduced expression of ATRA sensitive genes is also observed in epidermis of mouse models of UVB-induced squamous cell carcinoma and basal cell carcinomas. However, treatment of mouse skin with UAB30 prior to UVB irradiation prevents the UVB-induced decrease in expression of some of the ATRA-responsive genes. Considering its positive effects on ATRA signaling in the epidermis and its low toxicity, UAB30 could be used as a chemoprophylactic agent in the treatment of non-melanoma skin cancer, particularly in organ transplant recipients and other high risk populations.     

Escherichia coli O104 in Feedlot Cattle Feces: Prevalence,Isolation and Characterization

Escherichia coli O104:H4, an hybrid pathotype of Shiga toxigenic and enteroaggregative E. coli, involved in a major foodborne outbreak in Germany in 2011, has not been detected in cattle feces. Serogroup O104 with H type other than H4 has been reported to cause human illnesses, but their prevalence and characteristics in cattle have not been reported. Our objectives were to determine the prevalence of E. coli O104 in feces of feedlot cattle, by culture and PCR detection methods, and characterize the isolated strains. Rectal fecal samples from a total of 757 cattle originating from 29 feedlots were collected at a Midwest commercial slaughter plant. Fecal samples, enriched in E. coli broth, were subjected to culture and PCR methods of detection. The culture method involved immunomagnetic separation with O104-specific beads and plating on a selective chromogenic medium, followed by serogroup confirmation of pooled colonies by PCR. If pooled colonies were positive for the wzx_O104 gene, then colonies were tested individually to identify wzx_O104-positive serogroup and associated genes of the hybrid strains. Extracted DNA from feces were also tested by a multiplex PCR to detect wzx_O104-positive serogroup and associated major genes of the O104 hybrid pathotype. Because wzx_O104 has been shown to be present in E. coli O8/O9/O9a, wzx_O104-positive isolates and extracted DNA from fecal samples were also tested by a PCR targeting wbdD_O8/O9/O9a, a gene specific for E. coli O8/O9/O9a serogroups. Model-adjusted prevalence estimates of E. coli O104 (positive for wzx_O104 and negative for wbdD_O8/O9/O9a) at the feedlot level were 5.7% and 21.2%, and at the sample level were 0.5% and 25.9% by culture and PCR, respectively. The McNemar’s test indicated that there was a significant difference (P < 0.01) between the proportions of samples that tested positive for wzx_O104 and samples that were positive for wzx_O104, but negative for wbdD_O8/O9/O9a by PCR and culture methods. A total of 143 isolates, positive for the wzx_O104, were obtained in pure culture from 146 positive fecal samples. Ninety-two of the 143 isolates (64.3%) also tested positive for the wbdD_O8/O9/O9a, indicating that only 51 (35.7%) isolates truly belonged to the O104 serogroup (positive for wzx_O104 and negative for wbdD_O8/O9/O9a). All 51 isolates tested negative for eae, and 16 tested positive for stx1 gene of the subtype 1c. Thirteen of the 16 stx1-positive O104 isolates were from one feedlot. The predominant serotype was O104:H7. Pulsed-field gel electrophoresis analysis indicated that stx1-positive O104:H7 isolates had 62.4% homology to the German outbreak strain and 67.9% to 77.5% homology to human diarrheagenic O104:H7 strains. The 13 isolates obtained from the same feedlot were of the same PFGE subtype with 100% Dice similarity. Although cattle do not harbor the O104:H4 pathotype, they do harbor and shed Shiga toxigenic O104 in the feces and the predominant serotype was O104:H7.     

Oral Administration of Polymyxin B Modulates the Activity of Lipooligosaccharide E. coli B against Lung Metastases in Murine Tumor Models

Introduction

Polymyxin B (PmB) belongs to the group of cyclic peptide antibiotics, which neutralize the activity of LPS by binding to lipid A. The aim of this study was to analyze the effect of PmB on the biological activity of lipooligosaccharide (LOS E. coli B,rough form of LPS) in vitro and in experimental metastasis models.

Results

Cultures of murine macrophage J774A.1 cells and murine bone marrow-derived dendritic cells (BM-DC) stimulated in vitro with LOS and supplemented with PmB demonstrated a decrease in inflammatory cytokine production (IL-6, IL-10, TNF-α) and down-regulation of CD40, CD80, CD86 and MHC class II molecule expression. Additionally, PmB suspended in drinking water was given to the C57BL/6 mice seven or five days prior to the intravenous injection of B16 or LLC cells and intraperitoneal application of LOS. This strategy of PmB administration was continued throughout the duration of the experiments (29 or 21 days). In B16 model, statistically significant decrease in the number of metastases in mice treated with PmB and LOS (p<0.01) was found on the 14th day of the experiments, whereas the most intensive changes in surface-antigen expression and ex vivo production of IL-6, IL-1β and TNF-α by peritoneal cells were observed 7 days earlier. By contrast, antigen expression and ex vivo production of IL-6, IL-10, IFN-γ by splenocytes remained relatively high and stable. Statistically significant decrease in LLC metastases number was observed after the application of LOS (p<0.01) and in the group of mice preconditioned by PmB and subsequently treated with LOS (LOS + PmB, p<0.01).

Conclusions

In conclusion, prolonged in vivo application of PmB was not able to neutralize the LOS-induced immune cell activity but its presence in the organism of treated mice was important in modulation of the LOS-mediated response against the development of metastases.     

Causes of Stranding and Mortality,and Final Disposition of Loggerhead Sea Turtles (Caretta caretta) Admitted to a Wildlife Rehabilitation Center in Gran Canaria Island,Spain (1998-2014): A Long-Term Retrospective Study

Aims

The aims of this study were to analyze the causes of stranding of 1,860 loggerhead turtles (Caretta caretta) admitted at the Tafira Wildlife Rehabilitation Center in Gran Canaria Island, Spain, from 1998 to 2014, and to analyze the outcomes of the rehabilitation process to allow meaningful auditing of its quality.

Methods

Primary causes of morbidity were classified into seven categories: entanglement in fishing gear and/or plastics, ingestion of hooks and monofilament lines, trauma, infectious disease, crude oil, other causes, and unknown/undetermined. Final dispositions were calculated as euthanasia (E_r), unassisted mortality (M_r), and release (R_r) rates. Time to death (T_d) for euthanized and dead turtles, and length of stay for released (T_r) turtles were evaluated.

Results

The most frequent causes of morbidity were entanglement in fishing gear and/or plastics (50.81%), unknown/undetermined (20.37%), and ingestion of hooks (11.88%). The final disposition of the 1,634 loggerhead turtles admitted alive were: E_r = 3.37%, M_r = 10.34%, and R_r = 86.29%. E_r was significantly higher in the trauma category (18.67%) compared to the other causes of admission. The highest M_r was observed for turtles admitted due to trauma (30.67%). The highest R_r was observed in the crude oil (93.87%) and entanglement (92.38%) categories. The median T_r ranged from 12 days (unknown) to 70 days (trauma).

Conclusions

This survey is the first large-scale epidemiological study on causes of stranding and mortality of Eastern Atlantic loggerheads and demonstrates that at least 71.72% of turtles stranded due to anthropogenic causes. The high R_r (86.29%) emphasizes the importance of marine rehabilitation centers for conservation purposes. The stratified analysis by causes of admission of the three final disposition rates, and the parameters T_d and T_r should be included in the outcome research of the rehabilitation process of sea turtles in order to allow comparative studies between marine rehabilitation centers around the world.