Tongji University

About: Tongji University is a education organization based out in Shanghai, China. It is known for research contribution in the topics: Population & Adsorption. The organization has 76116 authors who have published 81176 publications receiving 1248911 citations. The organization is also known as: Tongji & Tóngjì Dàxué.

Recent development of polymer nanofibers for biomedical and biotechnological applications.

Abstract: Research in polymer nanofibers has undergone significant progress in the last one decade. One of the main driving forces for this progress is the increasing use of these polymer nanofibers for biomedical and biotechnological applications. This article presents a review on the latest research advancement made in the use of polymer nanofibers for applications such as tissue engineering, controlled drug release, wound dressings, medical implants, nanocomposites for dental restoration, molecular separation, biosensors, and preservation of bioactive agents.

XBP1 promotes triple-negative breast cancer by controlling the HIF1α pathway

Abstract: Cancer cells induce a set of adaptive response pathways to survive in the face of stressors due to inadequate vascularization. One such adaptive pathway is the unfolded protein (UPR) or endoplasmic reticulum (ER) stress response mediated in part by the ER-localized transmembrane sensor IRE1 (ref. 2) and its substrate XBP1 (ref. 3). Previous studies report UPR activation in various human tumours, but the role of XBP1 in cancer progression in mammary epithelial cells is largely unknown. Triple-negative breast cancer (TNBC)--a form of breast cancer in which tumour cells do not express the genes for oestrogen receptor, progesterone receptor and HER2 (also called ERBB2 or NEU)--is a highly aggressive malignancy with limited treatment options. Here we report that XBP1 is activated in TNBC and has a pivotal role in the tumorigenicity and progression of this human breast cancer subtype. In breast cancer cell line models, depletion of XBP1 inhibited tumour growth and tumour relapse and reduced the CD44(high)CD24(low) population. Hypoxia-inducing factor 1α (HIF1α) is known to be hyperactivated in TNBCs. Genome-wide mapping of the XBP1 transcriptional regulatory network revealed that XBP1 drives TNBC tumorigenicity by assembling a transcriptional complex with HIF1α that regulates the expression of HIF1α targets via the recruitment of RNA polymerase II. Analysis of independent cohorts of patients with TNBC revealed a specific XBP1 gene expression signature that was highly correlated with HIF1α and hypoxia-driven signatures and that strongly associated with poor prognosis. Our findings reveal a key function for the XBP1 branch of the UPR in TNBC and indicate that targeting this pathway may offer alternative treatment strategies for this aggressive subtype of breast cancer.

Cistrome: an integrative platform for transcriptional regulation studies.

Abstract: The increasing volume of ChIP-chip and ChIP-seq data being generated creates a challenge for standard, integrative and reproducible bioinformatics data analysis platforms. We developed a web-based application called Cistrome, based on the Galaxy open source framework. In addition to the standard Galaxy functions, Cistrome has 29 ChIP-chip- and ChIP-seq-specific tools in three major categories, from preliminary peak calling and correlation analyses to downstream genome feature association, gene expression analyses, and motif discovery. Cistrome is available at http://cistrome.org/ap/.

Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCov

Abstract: A novel coronavirus (2019-nCov) was identified in Wuhan, Hubei Province, China in December of 2019. This new coronavirus has resulted in thousands of cases of lethal disease in China, with additional patients being identified in a rapidly growing number internationally. 2019-nCov was reported to share the same receptor, Angiotensin-converting enzyme 2 (ACE2), with SARS-Cov. Here based on the public database and the state-of-the-art single-cell RNA-Seq technique, we analyzed the ACE2 RNA expression profile in the normal human lungs. The result indicates that the ACE2 virus receptor expression is concentrated in a small population of type II alveolar cells (AT2). Surprisingly, we found that this population of ACE2-expressing AT2 also highly expressed many other genes that positively regulating viral reproduction and transmission. A comparison between eight individual samples demonstrated that the Asian male one has an extremely large number of ACE2-expressing cells in the lung. This study provides a biological background for the epidemic investigation of the 2019-nCov infection disease, and could be informative for future anti-ACE2 therapeutic strategy development.

Structure-based prediction of protein–protein interactions on a genome-wide scale

Abstract: Protein–protein interactions, essential for understanding how a cell functions, are predicted using a new method that combines protein structure with other computationally and experimentally derived clues The analysis of protein-interaction networks is essential to an understanding of the regulatory processes in a living cell Many methods have been developed with a view to predicting protein–protein interactions (PPIs) at a genome-wide level, although the differences obtained using these approaches suggest that there are still factors unaccounted for Barry Honig and colleagues have developed a new way of predicting PPIs that is based on the proteins' three-dimensional structures and functional data Tests of several predictions of the new algorithm, known as PREPPI, confirm the accuracy of the results The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms1,2 Much of our present knowledge derives from high-throughput techniques such as the yeast two-hybrid assay and affinity purification3, as well as from manual curation of experiments on individual systems4 A variety of computational approaches based, for example, on sequence homology, gene co-expression and phylogenetic profiles, have also been developed for the genome-wide inference of protein–protein interactions (PPIs)5,6 Yet comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages7,8,9 Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence Moreover, an algorithm, termed PrePPI, which combines structural information with other functional clues, is comparable in accuracy to high-throughput experiments, yielding over 30,000 high-confidence interactions for yeast and over 300,000 for human Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of considerable biological interest The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins