两种同域分布蹄蝠在开阔度不同的环境中回声定位声波的可塑性

在广西桂林研究了同域分布的大蹄蝠(Hipposideros armiger)和中蹄蝠(H.larvatus)在不同开阔度环境中回声定位声波信号的变化。用超声波仪录制自由悬挂和分别释放于人工"大棚"和"小棚"内飞行的蝙蝠的回声定位声波,使用超声分析软件分析声脉冲时程、主频率及声脉冲间隔,通过重复测量方差分析比较不同状态下的声波参数。结果表明:中蹄蝠声波的主频在悬挂状态下最高,小棚内飞行时次之,大棚内飞行最低;两种蹄蝠声波的脉冲时程和脉冲间隔在悬挂状态下最长,大棚内飞行次之,小棚内飞行最低。总之,这两种蹄蝠的回声定位声波能够随所处状态的变化而变化,可根据生境的复杂度调节声讯号,具有明显的声波可塑性。     

Human Pluripotent Stem Cell-Derived Tumor Model Uncovers the Embryonic Stem Cell Signature as a Key Driver in Atypical Teratoid/Rhabdoid Tumor

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Prediction of protein-protein interactions using protein signature profiling

Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain interactions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as "interacting". In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis elegans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area under the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs increased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on average 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.     

Mechanotransduction of vocal fold fibroblasts and mesenchymal stromal cells in the context of the vocal fold mechanome

The design of cell-based therapies for vocal fold tissue engineering requires an understanding of how cells adapt to the dynamic mechanical forces found in the larynx. Our objective was to compare mechanotransductive processes in therapeutic cell candidates (mesenchymal stromal cells from adipose tissue and bone marrow, AT-MSC and BM-MSC) to native cells (vocal fold fibroblasts-VFF) in the context of vibratory strain. A bioreactor was used to expose VFF, AT-MSC, and BM-MSC to axial tensile strain and vibration at human physiological levels. Microarray, an empirical Bayes statistical approach, and geneset enrichment analysis were used to identify significant mechanotransductive pathways associated with the three cell types and three mechanical conditions. Two databases (Gene Ontology, Kyoto Encyclopedia of Genes and Genomes) were used for enrichment analyses. VFF shared more mechanotransductive pathways with BM-MSC than with AT-MSC. Gene expression that appeared to distinguish the vibratory strain condition from polystyrene condition for these two cells types related to integrin activation, focal adhesions, and lamellipodia activity, suggesting that vibratory strain may be associated with cytoarchitectural rearrangement, cell reorientation, and extracellular matrix remodeling. In response to vibration and tensile stress, BM-MSC better mimicked VFF mechanotransduction than AT-MSC, providing support for the consideration of BM-MSC as a cell therapy for vocal fold tissue engineering. Future research is needed to better understand the sorts of physical adaptations that are afforded to vocal fold tissue as a result of focal adhesions, integrins, and lamellipodia, and how these adaptations could be exploited for tissue engineering.     

Identification of a five-gene signature with prognostic value in colorectal cancer

Colorectal cancer (CRC) ranks as one of the most commonly diagnosed malignancies worldwide. Although mortality rates have been decreasing, the prognosis of CRC patients is still highly dependent on the individual. Therefore, identifying and understanding novel biomarkers for CRC prognosis remains crucial. The gene expression profiles of five-gene expression omnibus (GEO) data sets of CRC were first downloaded. A total of 352 consistent differentially expressed genes (DEGs) were identified for CRC and paired with normal tissues. Functional analysis including gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment revealed that these DEGs were related to metabolic pathways, tight junctions, and the cell cycle. Ten hub DEGs were identified based on the search tool for the retrieval of interacting genes database and protein–protein interaction networks. By using univariate Cox proportional hazard regression analysis, we found 11 survival-related genes among these DEGs. We finally established a five-gene signature (kinesin family member 15, N-acetyltransferase 2, glutathione peroxidase 3, secretogranin II, and chloride channel accessory 1) with prognostic value in CRC by step multivariate Cox regression analysis. Based on this risk scoring system, patients in the high-risk group had significantly poorer survival results compared with those in the low-risk group (log-rank test, p < 0.0001). Finally, we validated our gene signature scoring system in two independent GEO cohorts (GSE17536 and GSE33113). We found all five of the signature genes to be DEGs in The Cancer Genome Atlas database. In conclusion, our findings suggest that our five DEG-based signature can provide a novel biomarker with useful applications in CRC prognosis.     

Fatty acids differentiate consumers despite variation within prey fatty acid profiles

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The discovery of a novel eight-mRNA-lncRNA signature predicting survival of hepatocellular carcinoma patients

Increasing evidence indicates that the expressions of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) undergo a frequent and aberrant change in carcinogenesis and cancer development. But some research was carried out on mRNA-lncRNA signatures for prediction of hepatocellular carcinoma (HCC) prognosis. We aimed to establish an mRNA-lncRNA signature to improve the ability to predict HCC patients’ survival. The subjects from the cancer genome atlas (TCGA) data set were randomly divided into two parts: training data set (n = 246) and testing data set (n = 124). Using computational methods, we selected eight gene signatures (five mRNAs and three lncRNAs) to generate the risk score model, which were significantly correlated with overall survival of patients with HCC in both training and testing data set. The signature had the ability to classify the patients in training data set into a high-risk group and low-risk group with significantly different overall survival (hazard ratio = 4.157, 95% confidence interval = 2.648-6.526, P < 0.001). The prognostic value was further validated in testing data set and the entire data set. Further analysis revealed that this signature was independent of tumor stage. In addition, Gene Set Enrichment Analysis suggested that high risk score group was associated with cell proliferation and division related pathways. Finally, we developed a well-performed nomogram integrating the prognostic signature and other clinical information to predict 3- and 5-year overall survival. In conclusion, the prognostic mRNAs and lncRNAs identified in our study indicate their potential role in HCC biogenesis. The risk score model based on the mRNA-lncRNA may be an efficient classification tool to evaluate the prognosis of patients’ with HCC.     

A metabolism-relevant signature as a predictor for prognosis and therapeutic response in pancreatic cancer

Although several altered metabolic genes have been identified to be involved in the tumorigenesis and advance of pancreatic cancer (PC), their prognostic values remained unclear. The purpose of this study was to explore new targets and establish a metabolic signature to predict prognosis and chemotherapy response for optimal individualized treatment. The expression data of PC patients from two independent cohorts and metabolism-related genes from KEGG were utilized and analyzed for the establishment of the signature via lasso regression. Then, the differentially expressed candidate genes were further confirmed via online data mining platform and qRT-PCR of clinical specimens. Then, the analyses of gene set enrichment, mutation, and chemotherapeutic response were performed via R package. As results showed, 109 differentially expressed metabolic genes were screened out in PC. Then a metabolism-related five-gene signature comprising B3GNT3, BCAT1, KYNU, LDHA, and TYMS was constructed and showed excellent ability for predicting survival. A novel nomogram coordinating the metabolic signature and other independent prognostic parameters was developed and showed better predictive power in predicting survival. In addition, this metabolic signature was significantly involved in the activation of multiple oncological pathways and regulation of the tumor immune microenvironment. The patients with high risk scores had higher tumor mutation burdens and were prone to be more sensitive to chemotherapy. In summary, our work identified a new metabolic signature and established a superior prognostic nomogram which may supply more indications to explore novel strategies for diagnosis and treatment.     

Identification of common structural features of binding sites in galactose-specific proteins

Galactose-binding proteins characterize an important subgroup of sugar-binding proteins that are involved in a variety of biological processes. Structural studies have shown that the Gal-specific proteins encompass a diverse range of primary and tertiary structures. The binding sites for galactose also seem to vary in different protein-galactose complexes. No common binding site features that are shared by the Gal-specific proteins to achieve ligand specificity are so far known. With the assumption that common recognition principles will exist for common substrate recognition, the present study was undertaken to identify and characterize any unique galactose-binding site signature by analyzing the three-dimensional (3D) structures of 18 protein-galactose complexes. These proteins belong to 7 nonhomologous families; thus, there is no sequence or structural similarity across the families. Within each family, the binding site residues and their relative distances were well conserved, but there were no similarities across families. A novel, yet simple, approach was adopted to characterize the binding site residues by representing their relative spatial dispositions in polar coordinates. A combination of the deduced geometrical features with the structural characteristics, such as solvent accessibility and secondary structure type, furnished a potential galactose-binding site signature. The signature was evaluated by incorporation into the program COTRAN to search for potential galactose-binding sites in proteins that share the same fold as the known galactose-binding proteins. COTRAN is able to detect galactose-binding sites with a very high specificity and sensitivity. The deduced galactose-binding site signature is strongly validated and can be used to search for galactose-binding sites in proteins. PROSITE-type signature sequences have also been inferred for galectin and C-type animal lectin-like fold families of Gal-binding proteins.