BackgroundThe animal mitochondrial genome is generally considered to be under selection for both compactness and gene order conservation. As more mitochondrial genomes are sequenced, mitochondrial duplications and gene rearrangements have been frequently identified among diverse animal groups. Although several mechanisms of gene rearrangement have been proposed thus far, more observational evidence from major taxa is needed to validate specific mechanisms. In the current study, the complete mitochondrial DNA of sixteen bird species from the family Ardeidae was sequenced and the evolution of mitochondrial gene rearrangements was investigated. The mitochondrial genomes were then used to review the phylogenies of these ardeid birds.ResultsThe complete mitochondrial genome sequences of the sixteen ardeid birds exhibited four distinct mitochondrial gene orders in which two of them, named as “duplicate tRNAGlu–CR” and “duplicate tRNAThr–tRNAPro and CR”, were newly discovered. These gene rearrangements arose from an evolutionary process consistent with the tandem duplication - random loss model (TDRL). Additionally, duplications in these gene orders were near identical in nucleotide sequences within each individual, suggesting that they evolved in concert. Phylogenetic analyses of the sixteen ardeid species supported the idea that Ardea ibis, Ardea modesta and Ardea intermedia should be classified as genus Ardea, and Ixobrychus flavicollis as genus Ixobrychus, and indicated that within the subfamily Ardeinae, Nycticorax nycticorax is closely related to genus Egretta and that Ardeola bacchus and Butorides striatus are closely related to the genus Ardea.ConclusionsThe duplicate tRNAThr–CR gene order is found in most ardeid lineages, suggesting this gene order is the ancestral pattern within these birds and persisted in most lineages via concerted evolution. In two independent lineages, when the concerted evolution stopped in some subsections due to the accumulation of numerous substitutions and deletions, the duplicate tRNAThr–CR gene order was transformed into three other gene orders. The phylogenetic trees produced from concatenated rRNA and protein coding genes have high support values in most nodes, indicating that the mitochondrial genome sequences are promising markers for resolving the phylogenetic issues of ardeid birds when more taxa are added.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-573) contains supplementary material, which is available to authorized users.

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PRR11 is a newly identified oncogene in lung cancer, yet its role in others tumors remains unclear. Gastrointestinal tissue microarrays were used to evaluate PRR11 expression and its association with clinical outcome was analyzed in patients with hilar cholangiocarcinoma. Overexpression of PRR11 was observed in esophageal, gastric, pancreatic, colorectal, and hilar cholangiocarcinoma. Expression of PRR11 correlated with lymph node metastasis and CA199 level in two HC patient cohorts. After an R0 resection, a high level of PRR11 expression was found to be an independent indicator of recurrence (P = 0.001). In cell culture, PRR11 silencing resulted in decreased cellular proliferation, cell migration, tumor growth of QBC939 cells. Microarray analysis revealed that several genes involved in cell proliferation, cell adhesion, and cell migration were altered in PRR11-knockout cells, including: vimentin (VIM), Ubiquitin carboxyl-terminal hydrolase 1 (UCHL1), early growth response protein (EGR1), and System A amino acid transporter1 (SNAT1). Silencing PRR11 inhibited the expression of UCHL1, EGR1, and SNAT1 proteins, with immunoassays revealing a significant correlation among the levels of these four proteins. These results indicate that PRR11 is an independent prognostic indicator for patients with HC.

Here we showed that pAMPKα and PTEN were down-regulated and p-mTOR, p-S6, p-4EBP1, MMP7, and DCN1 were up-regulated in human gastric cancer tissue samples as compared to that in the noncancerous tissues. Metformin inhibited tumor growth in mice. Also it enhanced cisplatin- or rapamycin-induced reduction of tumor growth as compared with treatment of either drug alone. In addition to activation of AMPK and suppression of the mTOR pathway, a series of increased and decreased genes expression were induced by metformin, including PTEN, MMP7, and FN1. We suggest that metformin could potentially be used for the treatment of gastric cancer especially in combination with cisplatin or rapamycin.

Gas slippage occurs when the mean free path of the gas molecules is in the order of the characteristic pore size of a porous medium. This phenomenon leads to Klinkenberg's effect where the measured permeability of a gas (apparent permeability) is higher than that of the liquid (intrinsic permeability). A generalized lattice Boltzmann model is proposed for flow through porous media that includes Klinkenberg's effect, which is based on the model of Guo et al. [Phys. Rev. E 65, 046308 (2002)]. The second-order Beskok and Karniadakis-Civan's correlation [A. Beskok and G. Karniadakis, Microscale Thermophys. Eng. 3, 43 (1999) and F. Civan, Transp. Porous Med. 82, 375 (2010)] is adopted to calculate the apparent permeability based on intrinsic permeability and the Knudsen number. Fluid flow between two parallel plates filled with porous media is simulated to validate the model. Simulations performed in a heterogeneous porous medium with components of different porosity and permeability indicate that Klinkenberg's effect plays a significant role on fluid flow in low-permeability porous media, and it is more pronounced as the Knudsen number increases. Fluid flow in a shale matrix with and without fractures is also studied, and it is found that the fractures greatly enhance the fluid flow and Klinkenberg's effect leads to higher global permeability of the shale matrix.

BackgroundThe therapeutic and prognostic value of the glycolytic enzymes hexokinase, phosphofructokinase, and pyruvate kinase (PK) has been implicated in a variety of cancers, while their roles in treatment of and prognosis for hilar cholangiocarcinoma (HC) remain unclear. In this study, we determined the expression of PKM2 in and its impact on biology and clinical outcome of human HC.MethodsThe regulation and function of PKM2 in HC pathogenesis was evaluated using human tissues, molecular and cell biology, and animal models, and its prognostic significance was determined according to its impact on patient survival.ResultsWe found that expression of hexokinase 1 and the M2 splice isoform of PK (PKM2) was upregulated in HC tissues and that this expression correlated with tumor recurrence and outcome. PKM2 expression was increased in HC cases with chronic cholangitis as demonstrated by isobaric tags for relative and absolute quantification. High PKM2 expression was highly correlated with high syndecan 2 (SDC2) expression and neural invasion. PKM2 downregulation led to a decrease in SDC2 expression. Treatment with metformin markedly suppressed PKM2 and SDC2 expression at both the transcriptional and posttranscriptional levels and inhibited HC cell proliferation and tumor growth.ConclusionsPKM2 regulates neural invasion of HC cells at least in part via regulation of SDC2. Inhibition of PKM2 and SDC2 expression contributes to the therapeutic effect of metformin on HC. Therefore, PKM2 is an independent prognostic factor and potential therapeutic target for human HC.Electronic supplementary materialThe online version of this article (doi:10.1186/s12943-015-0462-6) contains supplementary material, which is available to authorized users.

Background: Adenosquamous carcinoma (ASC) of the lung is a heterogeneous disease that is composed of both adenocarcinoma components (ACC) and squamous cell carcinoma components (SCCC). Their genomic profile, genetic origin, and clinical management remain controversial. Patients and methods: Resected ASC and metastatic tumor in regional lymph nodes (LNs) were collected. The ACC and SCCC were separated by microdissection of primary tumor. The 1021 cancer-related genes were evaluated by nextgeneration sequencing independently in ACC and SCCC and LNs. Shared and private alterations in the two components were investigated. In addition, genomic profiles of independent cohorts of adenocarcinomas and squamous cell carcinomas were examined for comparison. We have also carried out a retrospective study of ASCs with known EGFR mutation status from 11 hospitals in China for their clinical outcomes. Results: The most frequent alterations in 28 surgically resected ASCs include EGFR (79%), TP53 (68%), MAP3K1 (14%) mutations, EGFR amplifications (32%), and MDM2 amplifications (18%). Twenty-seven patients (96%) had shared variations between ACC and SCCC, and pure SCCC metastases were not found in metastatic LNs among these patients. Only one patient with geographically separated ACC and SCCC had no shared mutations. Inter-component heterogeneity was a common genetic event of ACC and SCCC. The genomic profile of ASC was similar to that of 170 adenocarcinomas, but different from that of 62 squamous cell carcinomas. The incidence of EGFR mutations in the retrospective analysis of 517 ASCs was 51.8%. Among the 129 EGFR-positive patients who received EGFR-TKIs, the objective response rate was 56.6% and the median progression-free survival was 10.1 months (95% confidence interval: 9.0e11.2). Conclusions: The ACC and SCCC share a monoclonal origin, a majority with genetically inter-component heterogeneity. ASC may represent a subtype of adenocarcinoma with EGFR mutation being the most common genomic anomaly and sharing similar efficacy to EGFR TKI.

We report the application of machine learning methods for predicting the effective diffusivity (De) of two-dimensional porous media from images of their structures. Pore structures are built using reconstruction methods and represented as images, and their effective diffusivity is computed by lattice Boltzmann (LBM) simulations. The datasets thus generated are used to train convolutional neural network (CNN) models and evaluate their performance. The trained model predicts the effective diffusivity of porous structures with computational cost orders of magnitude lower than LBM simulations. The optimized model performs well on porous media with realistic topology, large variation of porosity (0.28–0.98), and effective diffusivity spanning more than one order of magnitude (0.1 ≲ De < 1), e.g., >95% of predicted De have truncated relative error of <10% when the true De is larger than 0.2. The CNN model provides better prediction than the empirical Bruggeman equation, especially for porous structure with small diffusivity. The relative error of CNN predictions, however, is rather high for structures with De < 0.1. To address this issue, the porosity of porous structures is encoded directly into the neural network but the performance is enhanced marginally. Further improvement, i.e., 70% of the CNN predictions for structures with true De < 0.1 have relative error <30%, is achieved by removing trapped regions and dead-end pathways using a simple algorithm. These results suggest that deep learning augmented by field knowledge can be a powerful technique for predicting the transport properties of porous media. Directions for future research of machine learning in porous media are discussed based on detailed analysis of the performance of CNN models in the present work.