Knowledge Levels and Training Needs of Disaster Medicine among Health Professionals,Medical Students,and Local Residents in Shanghai,China

Background

Disaster is a serious public health issue. Health professionals and community residents are main players in disaster responses but their knowledge levels of disaster medicine are not readily available. This study aimed to evaluate knowledge levels and training needs of disaster medicine among potential disaster responders and presented a necessity to popularize disaster medicine education.

Methods

A self-reporting questionnaire survey on knowledge level and training needs of disaster medicine was conducted in Shanghai, China, in 2012. A total of randomly selected 547 health professionals, 456 medical students, and 1,526 local residents provided intact information. The total response rate was 93.7%.

Results

Overall, 1.3% of these participants have received systematic disaster medicine training. News media (87.1%) was the most common channel to acquire disaster medicine knowledge. Although health professionals were more knowledgeable than community residents, their knowledge structure of disaster medicine was not intact. Medical teachers were more knowledgeable than medical practitioners and health administrators (p = 0.002). Clinicians performed better than public health physicians (p<0.001), whereas public health students performed better than clinical medical students (p<0.001). In community residents, education background significantly affected the knowledge level on disaster medicine (p<0.001). Training needs of disaster medicine were generally high among the surveyed. ‘Lecture’ and ‘practical training’ were preferred teaching methods. The selected key and interested contents on disaster medicine training were similar between health professionals and medical students, while the priorities chosen by local residents were quite different from health professionals and medical students (p<0.001).

Conclusions

Traditional clinical-oriented medical education might lead to a huge gap between the knowledge level on disaster medicine and the current needs of disaster preparedness. Continuing medical education and public education plans on disaster medicine via media should be practice-oriented, and selectively applied to different populations and take the knowledge levels and training needs into consideration.     

yeaI基因对奶牛源大肠杆菌NJ17生物学特性的影响

c-di-GMP是细菌中广泛存在的第二信使,可通过效应蛋白参与调控细菌的生物被膜形成、运动性和毒力等生物学特性。YeaI因含有能结合c-di-GMP分子的EGEVF基序,可能作为c-di-GMP效应蛋白发挥作用。[目的] 研究yeaI基因缺失对奶牛源大肠杆菌临床分离株NJ17生物学特性的影响。[方法] 构建NJ17的yeaI缺失株（NJ17ΔyeaI）及回复株cNJ17ΔyeaI,分析yeaI对NJ17生物学特性（如生长特性、生物被膜形成能力和对小鼠乳腺上皮细胞（EpH₄-Ev）的黏附）的影响。[结果] 成功构建NJ17的yeaI缺失株（NJ17ΔyeaI）及其回复株（cNJ17ΔyeaI）;与野生株NJ17相比,缺失株NJ17ΔyeaI生长特性及耐药性无显著变化,生物被膜形成能力显著下降,运动性显著升高（P<0.05）;透射电镜检测结果表明,yeaI缺失影响NJ17菌毛和鞭毛的形成;实时定量PCR（qPCR）结果显示,yeaI基因显著抑制NJ17鞭毛基因filG和motB的转录水平（P<0.05）;血清杀菌实验表明,yeaI缺失能显著增强其抵抗血清杀菌作用（P<0.05）;对EpH₄-Ev细胞黏附实验表明,yeaI缺失对NJ17黏附性无显著影响（P>0.05）。[结论] yeaI对奶牛源大肠杆菌NJ17的生物学特性具有重要的调控作用。     

铁还原菌降解石油烃的研究进展

铁还原菌是指能够利用细胞外Fe(III)作为末端电子受体,通过氧化有机物将Fe(III)还原为Fe(II)微生物的总称。铁还原作用广泛存在于土壤、河流、海洋、地表含水层以及高温高压的地下深部油藏。在厌氧或兼性厌氧条件下,Fe(III)还原耦合有机物的降解,对铁、碳元素的生物地球化学循环具有重要意义。本文介绍了铁还原菌的多样性和铁还原作用机理,综述了铁还原菌在石油烃降解方面的研究进展。此外,还总结了铁还原菌在生物修复中的潜在作用,并对未来的研究方向进行了展望。     

SESN2/sestrin2 suppresses sepsis by inducing mitophagy and inhibiting NLRP3 activation in macrophages

Proper regulation of mitophagy for mitochondrial homeostasis is important in various inflammatory diseases. However, the precise mechanisms by which mitophagy is activated to regulate inflammatory responses remain largely unknown. The NLRP3 (NLR family, pyrin domain containing 3) inflammasome serves as a platform that triggers the activation of CASP1 (caspase 1) and secretion of proinflammatory cytokines. Here, we demonstrate that SESN2 (sestrin 2), known as stress-inducible protein, suppresses prolonged NLRP3 inflammasome activation by clearance of damaged mitochondria through inducing mitophagy in macrophages. SESN2 plays a dual role in inducing mitophagy in response to inflammasome activation. First, SESN2 induces “mitochondrial priming” by marking mitochondria for recognition by the autophagic machinery. For mitochondrial preparing, SESN2 facilitates the perinuclear-clustering of mitochondria by mediating aggregation of SQSTM1 (sequestosome 1) and its binding to lysine 63 (Lys63)-linked ubiquitins on the mitochondrial surface. Second, SESN2 activates the specific autophagic machinery for degradation of primed mitochondria via an increase of ULK1 (unc-51 like kinase 1) protein levels. Moreover, increased SESN2 expression by extended LPS (lipopolysaccharide) stimulation is mediated by NOS2 (nitric oxide synthase 2, inducible)-mediated NO (nitric oxide) in macrophages. Thus, Sesn2-deficient mice displayed defective mitophagy, which resulted in hyperactivation of inflammasomes and increased mortality in 2 different sepsis models. Our findings define a unique regulatory mechanism of mitophagy activation for immunological homeostasis that protects the host from sepsis.     

Autophagy induced by AXL receptor tyrosine kinase alleviates acute liver injury via inhibition of NLRP3 inflammasome activation in mice

Severe hepatic inflammation is a common cause of acute or chronic liver disease. Macrophages are one of the key mediators which regulate the progress of hepatic inflammation. Increasing evidence shows that the TAM (TYRO3, AXL and MERTK) family of RTKs (receptor tyrosine kinases), which is expressed in macrophages, alleviates inflammatory responses through a negative feedback loop. However, the functional contribution of each TAM family member to the progression of hepatic inflammation remains elusive. In this study, we explore the role of individual TAM family proteins during autophagy induction and evaluate their contribution to hepatic inflammation. Among the TAM family of RTKs, AXL (AXL receptor tyrosine kinase) only induces autophagy in macrophages after interaction with its ligand, GAS6 (growth arrest specific 6). Based on our results, autophosphorylation of 2 tyrosine residues (Tyr815 and Tyr860) in the cytoplasmic domain of AXL in mice is required for autophagy induction and AXL-mediated autophagy induction is dependent on MAPK (mitogen-activated protein kinase)14 activity. Furthermore, induction of AXL-mediated autophagy prevents CASP1 (caspase 1)-dependent IL1B (interleukin 1, β) and IL18 (interleukin 18) maturation by inhibiting NLRP3 (NLR family, pyrin domain containing 3) inflammasome activation. In agreement with these observations, axl^?/? mice show more severe symptoms than do wild-type (Axl^+/+) mice following acute hepatic injury induced by administration of lipopolysaccharide (LPS) or carbon tetrachloride (CCl₄). Hence, GAS6-AXL signaling-mediated autophagy induction in murine macrophages ameliorates hepatic inflammatory responses by inhibiting NLRP3 inflammasome activation.     

Epigenetic alteration of imprinted genes during neural differentiation of germline-derived pluripotent stem cells

Spermatogonial stem cells (SSCs), which are unipotent stem cells in the testes that give rise to sperm, can be converted into germline-derived pluripotent stem (gPS) by self-induction. The androgenetic imprinting pattern of SSCs is maintained even after their reprogramming into gPS cells. In this study, we used an in vitro neural differentiation model to investigate whether the imprinting patterns are maintained or altered during differentiation. The androgenetic patterns of H19, Snrpn, and Mest were maintained even after differentiation of gPS cells into NSCs (gPS-NSCs), whereas the fully unmethylated status of Ndn in SSCs was altered to somatic patterns in gPS cells and gPS-NSCs. Thus, our study demonstrates epigenetic alteration of genomic imprinting during the induction of pluripotency in SSCs and neural differentiation, suggesting that gPS-NSCs can be a useful model to study the roles of imprinted genes in brain development and human neurodevelopmental disorders.     

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.     

Effects of BmCPV Infection on Silkworm Bombyx mori Intestinal Bacteria

The gut microbiota has a crucial role in the growth, development and environmental adaptation in the host insect. The objective of our work was to investigate the microbiota of the healthy silkworm Bombyx mori gut and changes after the infection of B. mori cypovirus (BmCPV). Intestinal contents of the infected and healthy larvae of B. mori of fifth instar were collected at 24, 72 and 144 h post infection with BmCPV. The gut bacteria were analyzed by pyrosequencing of the 16S rRNA gene. 147(135) and 113(103) genera were found in the gut content of the healthy control female (male) larvae and BmCPV-infected female (male) larvae, respectively. In general, the microbial communities in the gut content of healthy larvae were dominated by Enterococcus, Delftia, Pelomonas, Ralstonia and Staphylococcus, however the abundance change of each genus was depended on the developmental stage and gender. Microbial diversity reached minimum at 144 h of fifth instar larvae. The abundance of Enterococcus in the females was substantially lower and the abundance of Delftia, Aurantimonas and Staphylococcus was substantially higher compared to the males. Bacterial diversity in the intestinal contents decreased after post infection with BmCPV, whereas the abundance of both Enterococcus and Staphylococcus which belongs to Gram-positive were increased. Therefore, our findings suggested that observed changes in relative abundance was related to the immune response of silkworm to BmCPV infection. Relevance analysis of plenty of the predominant genera showed the abundance of the Enterococcus genus was in negative correlation with the abundance of the most predominant genera. These results provided insight into the relationship between the gut microbiota and development of the BmCPV-infected silkworm.