Aim: To investigate whether resveratrol (RSV) can improve non‐alcoholic fatty liver disease (NAFLD) and to find the possible mechanism. Methods: Rats fed a high‐fat diet were treated with RSV. The liver histology was observed. Hyperinsulinemic euglycemic clamp was performed to assess insulin sensitivity. Fat accumulation was induced in HepG2 cells, and the cells were treated with RSV. AMP‐activated protein kinase (AMPK) phosphorylation levels were determined both in the animal study and cell study. Results: Rats fed a high‐fat diet developed abdominal obesity, NAFLD, and insulin resistance (IR), which were markedly improved by 10 weeks of RSV administration. RSV treatment prevented triacylglycerol (TG) accumulation in HepG2 cells that were incubated with high concentration of glucose and insulin. Both in vivo and in vitro studies showed that RSV treatment could promote the phosphorylation of AMPK, which in this study, suppressed 2 lipogenesis gene expressions, contributing to the improvement of NAFLD and IR. Conclusion: The results indicated that by reducing TG accumulation and improving IR, RSV could protect the liver from NAFLD. The activation of AMPK was involved in the mechanism. RSV has the therapeutic potential for preventing or treating NAFLD and IR‐related metabolic disorders.

Previous work on text mining has almost exclusively focused on a single stream. However, we often have available multiple text streams indexed by the same set of time points (called coordinated text streams), which offer new opportunities for text mining. For example, when a major event happens, all the news articles published by different agencies in different languages tend to cover the same event for a certain period, exhibiting a correlated bursty topic pattern in all the news article streams. In general, mining correlated bursty topic patterns from coordinated text streams can reveal interesting latent associations or events behind these streams. In this paper, we define and study this novel text mining problem. We propose a general probabilistic algorithm which can effectively discover correlated bursty patterns and their bursty periods across text streams even if the streams have completely different vocabularies (e.g., English vs Chinese). Evaluation of the proposed method on a news data set and a literature data set shows that it can effectively discover quite meaningful topic patterns from both data sets: the patterns discovered from the news data set accurately reveal the major common events covered in the two streams of news articles (in English and Chinese, respectively), while the patterns discovered from two database publication streams match well with the major research paradigm shifts in database research. Since the proposed method is general and does not require the streams to share vocabulary, it can be applied to any coordinated text streams to discover correlated topic patterns that burst in multiple streams in the same period.

Streptococcus suis is a previously neglected, newly emerging multidrug-resistant zoonotic pathogen. Mobile genetic elements (MGEs) play a key role in intra- and interspecies horizontal transfer of antimicrobial resistance (AMR) determinants. Although, previous studies showed the presence of several MGEs, a comprehensive analysis of AMR-associated mobilome as well as their interaction and evolution has not been performed. In this study, we presented the AMR-associated mobilome and their insertion hotspots in S. suis. Integrative conjugative elements (ICEs), prophages and tandem MGEs were located at different insertion sites, while 86% of the AMR-associated MGEs were inserted at rplL and rum loci. Comprehensive analysis of insertions at rplL and rum loci among four pathogenic Streptococcus species (Streptococcus agalactiae, Streptococcus pneumoniae, Streptococcus pyogenes, and S. suis) revealed the existence of different groups of MGEs, including Tn5252, ICESp1108, and TnGBS2 groups ICEs, Φm46.1 group prophage, ICE_ICE and ICE_prophage tandem MGEs. Comparative ICE genomics of ICESa2603 family revealed that module exchange and acquisition/deletion were the main mechanisms in MGEs' expansion and evolution. Furthermore, the observation of tandem MGEs reflected a novel mechanism for MGE diversity. Moreover, an in vitro competition assay showed no visible fitness cost was observed between different MGE-carrying isolates and a conjugation assay revealed the transferability of ICESa2603 family of ICEs. Our statistics further indicated that the prevalence and diversity of MGEs in S. suis is much greater than in other three species which prompted our hypothesis that S. suis is probably a MGEs reservoir for other streptococci. In conclusion, our results showed that acquisition of MGEs confers S. suis not only its capability as a multidrug resistance pathogen, but also represents a paradigm to study the modular evolution and matryoshkas of MGEs.

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BackgroundGene homology type classification is required for many types of genome analyses, including comparative genomics, phylogenetics, and protein function annotation. Consequently, a large variety of tools have been developed to perform homology classification across genomes of different species. However, when applied to large genomic data sets, these tools require high memory and CPU usage, typically available only in computational clusters.FindingsHere we present a new graph-based orthology analysis tool, SwiftOrtho, which is optimized for speed and memory usage when applied to large-scale data. SwiftOrtho uses long k-mers to speed up homology search, while using a reduced amino acid alphabet and spaced seeds to compensate for the loss of sensitivity due to long k-mers. In addition, it uses an affinity propagation algorithm to reduce the memory usage when clustering large-scale orthology relationships into orthologous groups. In our tests, SwiftOrtho was the only tool that completed orthology analysis of proteins from 1,760 bacterial genomes on a computer with only 4 GB RAM. Using various standard orthology data sets, we also show that SwiftOrtho has a high accuracy.ConclusionsSwiftOrtho enables the accurate comparative genomic analyses of thousands of genomes using low-memory computers. SwiftOrtho is available at https://github.com/Rinoahu/SwiftOrtho

The UL13 protein of the duck enteritis virus (DEV), predicted to encode a Ser/Thr protein kinase, belongs to the family of conserved herpesvirus protein kinases (CHPK), which plays an important role in herpesvirus proliferation. In this study, truncated UL13 was expressed as a fusion protein of approximately 44 kDa using a prokaryotic expression system, and this protein was used to generate a specific anti-UL13 antibody. This antibody detected UL13 starting at 4 h post infection in duck embryonic fibroblast cells and identified UL13 to be present in both the cytoplasm and the nucleus. UL13 RNA was found to be transcribed starting at 2 h post infection, and the synthesis of the UL13 mRNA was found to be sensitive to the protein synthesis inhibitor cycloheximide (CHX) and tolerant of the DNA polymerase inhibitor ganciclovir (GCV). Its nuclear location and status as an early gene suggested that DEV UL13 might play important roles in DEV replication, which was confirmed by comparing the proliferation of a UL13-knockout mutant virus, a revertant virus, and the parent virus in cell culture. The specific mechanisms of UL13 in viral replication need to be further studied.