SARS-associated Coronavirus Transmitted from Human to Pig

Severe acute respiratory syndrome–associatedcoronavirus (SARS-CoV) was isolated from a pig during a survey for possible routes of viral transmission after a SARS epidemic. Sequence and epidemiology analyses suggested that the pig was infected by a SARS-CoV of human origin

TRF1 and TRF2 use different mechanisms to find telomeric DNA but share a novel mechanism to search for protein partners at telomeres

Human telomeres are maintained by the shelterin protein complex in which TRF1 and TRF2 bind directly to duplex telomeric DNA. How these proteins find telomeric sequences among a genome of billions of base pairs and how they find protein partners to form the shelterin complex remains uncertain. Using single-molecule fluorescence imaging of quantum dot-labeled TRF1 and TRF2, we study how these proteins locate TTAGGG repeats on DNA tightropes. By virtue of its basic domain TRF2 performs an extensive 1D search on nontelomeric DNA, whereas TRF1's 1D search is limited. Unlike the stable and static associations observed for other proteins at specific binding sites, TRF proteins possess reduced binding stability marked by transient binding (? 9-17 s) and slow 1D diffusion on specific telomeric regions. These slow diffusion constants yield activation energy barriers to sliding ? 2.8-3.6 ?(B)T greater than those for nontelomeric DNA. We propose that the TRF proteins use 1D sliding to find protein partners and assemble the shelterin complex, which in turn stabilizes the interaction with specific telomeric DNA. This 'tag-team proofreading' represents a more general mechanism to ensure a specific set of proteins interact with each other on long repetitive specific DNA sequences without requiring external energy sources

Low circadian clock genes expression in cancers: A meta-analysis of its association with clinicopathological features and prognosis.

BACKGROUND:Per1, Per2, Per3, Cry1, Cry2, Bmal1, Npas2 and CLOCK genes are the eight core circadian clock genes. Low expression of these circadian clock genes plays an important role in the progression of cancers. However, its clinicopathological and prognostic value in patients with cancers remains controversial and inconclusive. We performed a meta-analysis of studies assessing the clinicopathological and prognostic significance of low expression of these genes in cancers. METHODS:Relevant studies were searched from the Cochrane Central Register of Controlled Trials, Embase, EBSCO, Ovid, PubMed, Science Direct, Wiley Online Library database, CNKI and Wan Fang database. The meta-analysis was performed by using STATA version 12 software. A random-effect model was employed to evaluate all pooled hazard ratios (HRs) and odd ratios (ORs). RESULTS:A total of 36 studies comprising 7476 cases met the inclusion criteria. Meta-analysis suggested that low expression of Per1 was associated with poor differentiation (Per1: OR=2.30, 95%CI: 1.36∼3.87, P=0.002) and deeper invasion depth (Per1: OR=2.12, 95%CI: 1.62∼2.77, Ρ<0.001); low Per2 expression was correlated with poor differentiation (Per2: OR=2.41, 95%CI: 1.53∼3.79, Ρ<0.001), worse TNM stage (Per2:OR=3.47, 95%CI: 1.88∼6.42, P<0.001) and further metastasis (Per2:OR=2.35, 95%CI: 1.35∼4.11, Ρ=0.003). Furthermore, the results revealed that low expressions of Per1 and Per2 were also correlated with poor overall survival of cancers (Per1: HR=1.35, 95%CI: 1.06∼1.72, P=0.014; Per2: HR=1.43, 95%CI: 1.10∼1.85, P=0.007). Subgroup analysis indicated that low Per1 and Per2 expressions were especially associated with poor prognosis of gastrointestinal caners (Per1: HR=1.33, 95%CI: 1.14∼1.55, Ρ<0.001, Ι2=4.2%; Per2: HR=1.62, 95%CI: 1.25∼2.18, P<0.001, I2=0.0%). CONCLUSIONS:Our study suggested that low Per1, Per2 and Npas2 expression played a distinct and crucial role in progression of cancers. Low expressions of Per1 and Per2 could serve as unfavorable indicators for cancers prognosis, especially for gastrointestinal cancers

Theileria, Hepatozoon and Taenia infection in great gerbils (Rhombomys opimus) in northwestern China

The great gerbil (Rhombomys opimus), widely distributed in Asia, is a natural reservoir for Yersinia pestis, Leishmania donovani and some species of helminths. In this study, 188 great gerbils were sampled in Alataw City and Manas County, northwestern China, and tested for the presence of Theileria, Hepatozoon and Taenia species by molecular methods. Theileria sp., named as “Candidatus Theileria xinjiangensis”, was detected in heart, liver, spleen, lung, and kidney of 6.9% rodents. Six genotypes of “Taenia sp. Rhombomys opimus”, which were close to Taenia laticollis (87.3–94.0% identities), were detected in cyst liquid of 5.3% rodents. “Hepatozoon ayorgbor-like” haemogregarines was detected in spleens of 1.6% rodents. To our best knowledge, Candidatus Theileria xinjiangensis, Hepatozoon ayorgbor-like and genotypes of “Taenia sp. Rhombomys opimus” were found for the first time in the great gerbil. These results extend our knowledge on the diversity and pathogenesis of Theileria, Hepatozoon and Taenia species

Quantification of Myxococcus xanthus Aggregation and Rippling Behaviors: Deep-Learning Transformation of Phase-Contrast into Fluorescence Microscopy Images

Myxococcus xanthus bacteria are a model system for understanding pattern formation and collective cell behaviors. When starving, cells aggregate into fruiting bodies to form metabolically inert spores. During predation, cells self-organize into traveling cell-density waves termed ripples. Both phase-contrast and fluorescence microscopy are used to observe these patterns but each has its limitations. Phase-contrast images have higher contrast, but the resulting image intensities lose their correlation with cell density. The intensities of fluorescence microscopy images, on the other hand, are well-correlated with cell density, enabling better segmentation of aggregates and better visualization of streaming patterns in between aggregates; however, fluorescence microscopy requires the engineering of cells to express fluorescent proteins and can be phototoxic to cells. To combine the advantages of both imaging methodologies, we develop a generative adversarial network that converts phase-contrast into synthesized fluorescent images. By including an additional histogram-equalized output to the state-of-the-art pix2pixHD algorithm, our model generates accurate images of aggregates and streams, enabling the estimation of aggregate positions and sizes, but with small shifts of their boundaries. Further training on ripple patterns enables accurate estimation of the rippling wavelength. Our methods are thus applicable for many other phenotypic behaviors and pattern formation studies