应用RNA原位核酸杂交检测乳腺癌石蜡切片中ER mRNA、PR mRNA表达

目的：制备地高辛标记的寡核苷酸探针,检测乳腺癌石蜡切片中雌激素受体（ER）mRNA、孕激素受体（PR）mRNA表达。方法：应用RNA原位核酸杂交（RISH）和免疫组化（IHC）检测技术,结果：（1）278例乳腺癌石蜡切片ERmRNA,PRmRNA阳性率分别为67．3％和61．5％,阴性率分别为32．7％和38．5％,ERmRNA,PRmRNA双阳性,双阴性分别为54．3％和25．6％,ERmRNA阳性PRmRNA阴性或ERmRNA阴性PRmRNA阳性率为20．1％。（2）RISH法的mRNA、PRmRNA阳性率分别为67．3％和61．5％均高于IHC法ER、PR的48．9％和41．2％（P均＜0．01）。（3）ERmRNA与ER、PRmRNA与PR的阳性和阴性符合率分别为7％和75．95。（4）杂产信号在乳腺癌细胞中的分布可分为,柱状上皮的胞质的上下方,胞质内均匀或近胞膜,核周偏位和核内外分布。（5）ERmRNA与ER、PRmRNA阳性率与组织学级别有关,Ⅱ级组中RV的阳性率为83．5％分别高于Ⅰ组中62．4％和Ⅲ组中54．5％（P＜0．01）,Ⅰ、Ⅱ级组中PRV的阳性率为66．7％,67．3％,均高于Ⅲ组中50．6％（P＜0．05）。结论。结论：地高辛标记的ER、PR寡核苷酸探针和RISH是一种较为理想的检测乳腺癌组织中ERmRNA和PRmRNA的分子检测技术。     

Freeze substitution Hi-C,a convenient and cost-effective method for capturing the natural 3D chromatin conformation from frozen samples

Chromatin interactions functionally affect genome architecture and gene regulation, but to date, only fresh samples must be used in High-through chromosome conformation capture(Hi-C) to keep natural chromatin conformation intact. This requirement has impeded the advancement of 3 D genome research by limiting sample collection and storage options for researchers and severely limiting the number of samples that can be processed in a short time. Here, we develop a freeze substitution Hi-C(FS-Hi-C) technique that overcomes the need for fresh samples. FS-Hi-C can be used with samples stored in liquid nitrogen(LN2):the water in a vitreous form in the sample cells is replaced with ethanol via automated freeze substitution.After confirming that the FS step preserves the natural chromosome conformation during sample thawing,we tested the performance of FS-Hi-C with Drosophila melanogaster and Gossypium hirsutum. Beyond allowing the use of frozen samples and confirming that FS-Hi-C delivers robust data for generating contact heat maps and delineating A/B compartments and topologically associating domains, we found that FS-HiC outperforms the in situ Hi-C in terms of library quality, reproducibility, and valid interactions. Thus, FS-HiC will probably extend the application of 3 D genome structure analysis to the vast number of experimental contexts in biological and medical research for which Hi-C methods have been unfeasible to date.     

Characterization of Severe Fever with Thrombocytopenia Syndrome in Rural Regions of Zhejiang,China

Severe fever with thrombocytopenia syndrome virus (SFTSV) infections have recently been found in rural regions of Zhejiang. A severe fever with thrombocytopenia syndrome (SFTS) surveillance and sero-epidemiological investigation was conducted in the districts with outbreaks. During the study period of 2011–2014, a total of 51 SFTSV infection cases were identified and the case fatality rate was 12% (6/51). Ninety two percent of the patients (47/51) were over 50 years of age, and 63% (32/51) of laboratory confirmed cases occurred from May to July. Nine percent (11/120) of the serum samples from local healthy people without symptoms were found to be positive for antibodies to the SFTS virus. SFTSV strains were isolated by culture using Vero, and the whole genomic sequences of two SFTSV strains (01 and Zhao) were sequenced and submitted to the GenBank. Homology analysis showed that the similarity of the target nucleocapsid gene from the SFTSV strains from different geographic areas was 94.2–100%. From the constructed phylogenetic tree, it was found that all the SFTSV strains diverged into two main clusters. Only the SFTSV strains from the Zhejiang (Daishan) region of China and the Yamaguchi, Miyazakj regions of Japan, were clustered into lineage II, consistent with both of these regions being isolated areas with similar geographic features. Two out of eight predicted linear B cell epitopes from the nucleocapsid protein showed mutations between the SFTSV strains of different clusters, but did not contribute to the binding ability of the specific SFTSV antibodies. This study confirmed that SFTSV has been circulating naturally and can cause a seasonal prevalence in Daishan, China. The results also suggest that the molecular characteristics of SFTSV are associated with the geographic region and all SFTSV strains can be divided into two genotypes.     

Soil Microbial Community Responses to a Decade of Warming as Revealed by Comparative Metagenomics

Soil microbial communities are extremely complex, being composed of thousands of low-abundance species (<0.1% of total). How such complex communities respond to natural or human-induced fluctuations, including major perturbations such as global climate change, remains poorly understood, severely limiting our predictive ability for soil ecosystem functioning and resilience. In this study, we compared 12 whole-community shotgun metagenomic data sets from a grassland soil in the Midwestern United States, half representing soil that had undergone infrared warming by 2°C for 10 years, which simulated the effects of climate change, and the other half representing the adjacent soil that received no warming and thus, served as controls. Our analyses revealed that the heated communities showed significant shifts in composition and predicted metabolism, and these shifts were community wide as opposed to being attributable to a few taxa. Key metabolic pathways related to carbon turnover, such as cellulose degradation (∼13%) and CO₂ production (∼10%), and to nitrogen cycling, including denitrification (∼12%), were enriched under warming, which was consistent with independent physicochemical measurements. These community shifts were interlinked, in part, with higher primary productivity of the aboveground plant communities stimulated by warming, revealing that most of the additional, plant-derived soil carbon was likely respired by microbial activity. Warming also enriched for a higher abundance of sporulation genes and genomes with higher G+C content. Collectively, our results indicate that microbial communities of temperate grassland soils play important roles in mediating feedback responses to climate change and advance the understanding of the molecular mechanisms of community adaptation to environmental perturbations.     

Fully automated protein complex prediction based on topological similarity and community structure

To understand the function of protein complexes and their association with biological processes, a lot of studies have been done towards analyzing the protein-protein interaction (PPI) networks. However, the advancement in high-throughput technology has resulted in a humongous amount of data for analysis. Moreover, high level of noise, sparseness, and skewness in degree distribution of PPI networks limits the performance of many clustering algorithms and further analysis of their interactions.In addressing and solving these problems we present a novel random walk based algorithm that converts the incomplete and binary PPI network into a protein-protein topological similarity matrix (PP-TS matrix). We believe that if two proteins share some high-order topological similarities they are likely to be interacting with each other. Using the obtained PP-TS matrix, we constructed and used weighted networks to further study and analyze the interaction among proteins. Specifically, we applied a fully automated community structure finding algorithm (Auto-HQcut) on the obtained weighted network to cluster protein complexes. We then analyzed the protein complexes for significance in biological processes. To help visualize and analyze these protein complexes we also developed an interface that displays the resulting complexes as well as the characteristics associated with each complex.Applying our approach to a yeast protein-protein interaction network, we found that the predicted protein-protein interaction pairs with high topological similarities have more significant biological relevance than the original protein-protein interactions pairs. When we compared our PPI network reconstruction algorithm with other existing algorithms using gene ontology and gene co-expression, our algorithm produced the highest similarity scores. Also, our predicted protein complexes showed higher accuracy measure compared to the other protein complex predictions.     

Polyphyllin VII Induces an Autophagic Cell Death by Activation of the JNK Pathway and Inhibition of PI3K/AKT/mTOR Pathway in HepG2 Cells

Polyphyllin VII (PP7), a pennogenyl saponin isolated from Rhizoma Paridis, exhibited strong anticancer activities in various cancer types. Previous studies found that PP7 induced apoptotic cell death in human hepatoblastoma cancer (HepG2) cells. In the present study, we investigated whether PP7 could induce autophagy and its role in PP7-induced cell death, and elucidated its mechanisms. PP7 induced a robust autophagy in HepG2 cells as demonstrated by the conversion of LC3B-I to LC3B-II, degradation of P62, formation of punctate LC3-positive structures, and autophagic vacuoles tested by western blot analysis or InCell 2000 confocal microscope. Inhibition of autophagy by treating cells with autophagy inhibitor (chloroquine) abolished the cell death caused by PP7, indicating that PP7 induced an autophagic cell death in HepG2 cells. C-Jun N-terminal kinase (JNK) was activated after treatment with PP7 and pretreatment with SP600125, a JNK inhibitor, reversed PP7-induced autophagy and cell death, suggesting that JNK plays a critical role in autophagy caused by PP7. Furthermore, our study demonstrated that PP7 increased the phosphorylation of AMPK and Bcl-2, and inhibited the phosphorylation of PI3K, AKT and mTOR, suggesting their roles in the PP7-induced autophagy. This is the first report that PP7 induces an autophagic cell death in HepG2 cells via inhibition of PI3K/AKT/mTOR, and activation of JNK pathway, which induces phosphorylation of Bcl-2 and dissociation of Beclin-1 from Beclin-1/Bcl-2 complex, leading to induction of autophagy.     

Individual genome assembly from complex community short-read metagenomic datasets

Assembling individual genomes from complex community metagenomic data remains a challenging issue for environmental studies. We evaluated the quality of genome assemblies from community short read data (Illumina 100 bp pair-ended sequences) using datasets recovered from freshwater and soil microbial communities as well as in silico simulations. Our analyses revealed that the genome of a single genotype (or species) can be accurately assembled from a complex metagenome when it shows at least about 20 × coverage. At lower coverage, however, the derived assemblies contained a substantial fraction of non-target sequences (chimeras), which explains, at least in part, the higher number of hypothetical genes recovered in metagenomic relative to genomic projects. We also provide examples of how to detect intrapopulation structure in metagenomic datasets and estimate the type and frequency of errors in assembled genes and contigs from datasets of varied species complexity.