Exploring the role of PMEPA1 in gastric cancer.

Although there are several treatments available for gastric cancer (GC), the prognosis of the disease is still poor due to many factors, such as late diagnosis and tumor heterogeneity. To identify potential therapeutic targets, bioinformatics techniques and clinical sample validation were employed and prostate transmembrane protein androgen induced 1 (PMEPA1) was selected for further study. In the present study, we found that elevated PMEPA1 expression correlates with a worse prognosis and weaker anti-tumor immunity in GC patients. Moreover, our study showed that PMEPA1 not only influences cell proliferation, clone formation, invasion, and migration in vitro, but also plays an important role in GC progression in vivo. Mechanically, PMEPA1 exerts its oncogenic effects through activating the Wnt/ß-catenin signaling pathway. Therefore, PMEPA1 is a potential target for treating GC effectively.

PICH Activates Cyclin A1 Transcription to Drive S-Phase Progression and Chemoresistance in Gastric Cancer.

The chemotherapeutic agent 5-fluorouracil (5-FU) remains the backbone of postoperative adjuvant treatment for gastric cancer. However, fewer than half of patients with gastric cancer benefit from 5-FU-based chemotherapies owing to chemoresistance and limited clinical biomarkers. Here, we identified the SNF2 protein Polo-like kinase 1-interacting checkpoint helicase (PICH) as a predictor of 5-FU chemosensitivity and characterized a transcriptional function of PICH distinct from its role in chromosome separation. PICH formed a transcriptional complex with RNA polymerase II (Pol II) and ATF4 at the CCNA1 promoter in an ATPase-dependent manner. Binding of the PICH complex promoted cyclin A1 transcription and accelerated S-phase progression. Overexpressed PICH impaired 5-FU chemosensitivity in human organoids and patient-derived xenografts. Furthermore, elevated PICH expression was negatively correlated with survival in postoperative patients receiving 5-FU chemotherapy. Together, these findings reveal an ATPase-dependent transcriptional function of PICH that promotes cyclin A1 transcription to drive 5-FU chemoresistance, providing a potential predictive biomarker of 5-FU chemosensitivity for postoperative patients with gastric cancer and prompting further investigation into the transcriptional activity of PICH. SIGNIFICANCE: PICH binds Pol II and ATF4 in an ATPase-dependent manner to form a transcriptional complex that promotes cyclin A1 expression, accelerates S-phase progression, and impairs 5-FU chemosensitivity in gastric cancer.

Therapeutic strategies targeting uPAR potentiate anti-PD-1 efficacy in diffuse-type gastric cancer.

The diffuse-type gastric cancer (DGC) is a subtype of gastric cancer (GC) associated with low HER2 positivity rate and insensitivity to chemotherapy and immune checkpoint inhibitors. Here, we identify urokinase-type plasminogen activator receptor (uPAR) as a potential therapeutic target for DGC. We have developed a novel anti-uPAR monoclonal antibody, which targets the domains II and III of uPAR and blocks the binding of urokinase-type plasminogen activator to uPAR. We show that the combination of anti-uPAR and anti-Programmed cell death protein 1 (PD-1) remarkably inhibits tumor growth and prolongs survival via multiple mechanisms, using cell line-derived xenograft and patient-derived xenograft mouse models. Furthermore, uPAR chimeric antigen receptor-expressing T cells based on the novel anti-uPAR effectively kill DGC patient-derived organoids and exhibit impressive survival benefit in the established mouse models, especially when combined with PD-1 blockade therapy. Our study provides a new possibility of DGC treatment by targeting uPAR in a unique manner.

Metagenomic sequencing revealed the potential of banknotes as a repository of microbial genes.

BACKGROUND: Genetic resources are important natural assets. Discovery of new enzyme gene sequences has been an ongoing effort in biotechnology industry. In the genomic age, genomes of microorganisms from various environments have been deciphered. Increasingly, it has become more and more difficult to find novel enzyme genes. In this work, we attempted to use the easily accessible banknotes to search for novel microbial gene sequences. RESULTS: We used high-throughput genomic sequencing technology to comprehensively characterize the diversity of microorganisms on the US dollars and Chinese Renminbis (RMBs). In addition to finding a vast diversity of microbes, we found a significant number of novel gene sequences, including an unreported superoxide dismutase (SOD) gene, whose catalytic activity was further verified by experiments. CONCLUSIONS: We demonstrated that banknotes could be a good and convenient genetic resource for finding economically valuable biologicals.

Metagenomic Sequencing Revealed the Potential Pathogenic Threats of Banknotes.

Banknotes have long been suspected to be biologically "dirty" due to their frequent human contact, which may transmit human microbial pathogens. Still, it is an unsettled issue whether the microbes on banknotes pose a real threat to human health. In several previous studies, metagenomic sequencing was used to reveal the diversities of microbes on banknotes but live microorganism culture and functional verification were lacking. In this study, we collected banknotes of RMB in China as well as dollar bills in the United States and analyzed the microbial biodiversity and drug resistance genes carried by the identified microbes by metagenomic sequencing and in vitro culture methods. We identified eight major genera of drug-resistant bacteria through screening of 30 antibiotics, and the blood agar plate culture uncovered six pathogenic fungal species. Numerous phage and six dangerous viral sequences were also found. These results should substantiate our concern about the potential risk of banknotes to human health.

Development of 18S rRNA gene arrays for forensic detection of diatoms.

Diatom test is the most commonly used method to diagnose drowning in forensic laboratories. However, microscopic examination and identification of diatom frustules is time-consuming and requires taxonomic expertise. At present, the identification of drowning is still a challenge in forensic casework. In this study, we developed a novel diatom microarray based on the detection of specific 18S rRNA gene fragments of diatom species. The array covers 169 diatom species which were documented as commonly found in a wide range of fresh waters in China. Diatom arrays were prepared from species specific oligonucleotide probes targeting to variable regions of the 18S rRNA gene. We also developed an auxiliary sample preparation method for isolation of diatom DNA from tissues, which enabled detection of diatom species in real forensic samples as well as environmental waters. We applied the diatom arrays to analyze six drowned cases and eight environmental samples. The diatom arrays showed much better sensitivity and more consistent results than those of the conventional SEM methods. We discovered major discrepancies between results generated by the diatom arrays and the routinely used SEM based diatom tests. We verified the results of our diatom arrays by species specific PCR and Sanger sequencing and found that the currently used SEM diatom test method has a serious deficiency in sensitivity due to high loss rate of frustules in the sample preparation procedure. We anticipate that the application of diatom arrays will transform current forensic practice of diagnosing drowning deaths.

Defined host factors support HBV infection in non-hepatic 293T cells.

Hepatitis B virus (HBV) is a human hepatotropic virus. However, HBV infection also occurs at extrahepatic sites, but the relevant host factors required for HBV infection in non-hepatic cells are only partially understood. In this article, a non-hepatic cell culture model is constructed by exogenous expression of four host genes (NTCP, HNF4α, RXRα and PPARα) in human non-hepatic 293T cells. This cell culture model supports HBV entry, transcription and replication, as evidenced by the detection of HBV pgRNA, HBV cccDNA, HBsAg, HBeAg, HBcAg and HBVDNA. Our results suggest that the above cellular factors may play a key role in HBV infection of non-hepatic cells. This model will facilitate the identification of host genes that support extrahepatic HBV infection.

Rapid detection of low-level HeLa cell contamination in cell culture using nested PCR.

HeLa cells are a commonly used cell line in many biological research areas. They are not picky for culture medium and proliferate rapidly. HeLa cells are a notorious source of cell cross-contamination and have been found to be able to contaminate a wide range of cell lines in cell culture. In this study, we reported a simple and efficient method for detecting the presence of HeLa cell contamination in cell culture. HPV-18 was used as a biomarker. The cell culture supernatant was used directly as the template for nested PCR without extracting nucleic acid. By PCR amplification of the cell culture supernatant with the designed primers, we were able to detect the presence of HeLa cells in the culture. The sensitivity of this method can reach 1%, which is 10-fold higher than Short tandem repeat sequence (STR) profiling. This simple, rapid, and "noninvasive" quality checking method should find applications in routine cell culture practice.

[Research progress on the involvement of nuclear receptor in regulating autophagy].

Nuclear receptors are transcriptional regulators involved in almost all biological processes such as cell growth, differentiation, apoptosis, substance metabolism and tumor formation, and they can be regulated by small molecules that bind to them. Autophagy is a special way of programmed cell death and it is a highly conserved metabolic process. Once autophagy defects or excessive autophagy occur, the disease will develop. In recent years, numerous studies have shown that nuclear receptors are related to autophagy. Therefore, this paper mainly reviews the research progress on nuclear receptors involved in the regulation of autophagy, and focuses on the mechanism of several nuclear receptors involved in the regulation of autophagy, aiming at understanding the molecular basis of how nuclear receptors participate in regulating autophagy, as well as providing possible ideas and strategies for the treatment of corresponding diseases.

Methods for genetic transformation of filamentous fungi.

Filamentous fungi have been of great interest because of their excellent ability as cell factories to manufacture useful products for human beings. The development of genetic transformation techniques is a precondition that enables scientists to target and modify genes efficiently and may reveal the function of target genes. The method to deliver foreign nucleic acid into cells is the sticking point for fungal genome modification. Up to date, there are some general methods of genetic transformation for fungi, including protoplast-mediated transformation, Agrobacterium-mediated transformation, electroporation, biolistic method and shock-wave-mediated transformation. This article reviews basic protocols and principles of these transformation methods, as well as their advantages and disadvantages.

Prediction of structural features and application to outer membrane protein identification.

Protein three-dimensional (3D) structures provide insightful information in many fields of biology. One-dimensional properties derived from 3D structures such as secondary structure, residue solvent accessibility, residue depth and backbone torsion angles are helpful to protein function prediction, fold recognition and ab initio folding. Here, we predict various structural features with the assistance of neural network learning. Based on an independent test dataset, protein secondary structure prediction generates an overall Q3 accuracy of ~80%. Meanwhile, the prediction of relative solvent accessibility obtains the highest mean absolute error of 0.164, and prediction of residue depth achieves the lowest mean absolute error of 0.062. We further improve the outer membrane protein identification by including the predicted structural features in a scoring function using a simple profile-to-profile alignment. The results demonstrate that the accuracy of outer membrane protein identification can be improved by ~3% at a 1% false positive level when structural features are incorporated. Finally, our methods are available as two convenient and easy-to-use programs. One is PSSM-2-Features for predicting secondary structure, relative solvent accessibility, residue depth and backbone torsion angles, the other is PPA-OMP for identifying outer membrane proteins from proteomes.

GPCRserver: an accurate and novel G protein-coupled receptor predictor.

G protein coupled receptors (GPCRs), also known as seven-transmembrane domain receptors, pass through the cellular membrane seven times and play diverse biological roles in the cells such as signaling, transporting of molecules and cell-cell communication. In this work, we develop a web server, namely the GPCRserver, which is capable of identifying GPCRs from genomic sequences, and locating their transmembrane regions. The GPCRserver contains three modules: (1) the Trans-GPCR for the transmembrane region prediction by using sequence evolutionary profiles with the assistance of neural network training, (2) the SSEA-GPCR for identifying GPCRs from genomic data by using secondary structure element alignment, and (3) the PPA-GPCR for identifying GPCRs by using profile-to-profile alignment. Our predictor was strictly benchmarked and showed its favorable performance in the real application. The web server and stand-alone programs are publicly available at .

Prediction of outer membrane proteins by combining the position- and composition-based features of sequence profiles.

Locating the transmembrane regions of outer membrane proteins (OMPs) is highly important for deciphering their biological functions at both molecular and cellular levels. Here, we propose a novel method to predict the transmembrane regions of OMPs by employing the position- and composition-based features of sequence profiles. Furthermore, a simple probability-based prediction model, which is estimated by the secondary structures of structurally known OMPs, is also developed. Considering that these two methods are both effective and well complementary, we integrate them into a method called TransOMP, which is also capable of identifying OMPs. Furthermore, we develop an OMP identification measure I_CScore by considering transmembrane regions by TransOMP and secondary structural topology by SSEA-OMP. Our methods were benchmarked against state-of-the-art methods and assessed in the genome of Escherichia coli. Benchmark results confirmed that our methods were reliable and useful. Meanwhile, we constructed an OMP prediction web server, which can be used for OMP identification, transmembrane region location, and 3D model building.

MMP13, Birc2 (cIAP1), and Birc3 (cIAP2), amplified on chromosome 9, collaborate with p53 deficiency in mouse osteosarcoma progression.

Osteosarcoma is the primary malignant cancer of bone and particularly affects adolescents and young adults, causing debilitation and sometimes death. As a model for human osteosarcoma, we have been studying p53(+/-) mice, which develop osteosarcoma at high frequency. To discover genes that cooperate with p53 deficiency in osteosarcoma formation, we have integrated array comparative genomic hybridization, microarray expression analyses in mouse and human osteosarcomas, and functional assays. In this study, we found seven frequent regions of copy number gain and loss in the mouse p53(+/-) osteosarcomas but have focused on a recurrent amplification event on mouse chromosome 9A1. This amplicon is syntenic with a similar chromosome 11q22 amplicon identified in several human tumor types. Three genes on this amplicon, the matrix metalloproteinase gene MMP13 and the antiapoptotic genes Birc2 (cIAP1) and Birc3 (cIAP2), show elevated expression in mouse and human osteosarcomas. We developed a functional assay using clonal osteosarcoma cell lines transduced with lentiviral short hairpin RNA vectors to show that down-regulation of MMP13, Birc2, or Birc3 resulted in reduced tumor growth when transplanted into immunodeficient recipient mice. These experiments revealed that high MMP13 expression enhances osteosarcoma cell survival and that Birc2 and Birc3 also enhance cell survival but only in osteosarcoma cells with the chromosome 9A1 amplicon. We conclude that the antiapoptotic genes Birc2 and Birc3 are potential oncogenic drivers in the chromosome 9A1 amplicon.

Using random forest for reliable classification and cost-sensitive learning for medical diagnosis.

BACKGROUND: Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. RESULTS: In this paper, we present a modified random forest classifier which is incorporated into the conformal predictor scheme. A conformal predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. CONCLUSION: The method of using RF outlier measure to design a nonconformity measure benefits the resultant predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class.

Overexpression of Separase induces aneuploidy and mammary tumorigenesis.

Separase is an endopeptidase that separates sister chromatids by cleaving cohesin Rad21 during the metaphase-to-anaphase transition. Conditional expression of Separase in tetracycline-inducible diploid FSK3 mouse mammary epithelial cells with both p53 WT and mutant (Ser-233-234) alleles of unknown physiological significance develops aneuploidy within 5 days of Separase induction in vitro. Overexpression of Separase induces premature separation of chromatids, lagging chromosomes, and anaphase bridges. In an in vivo mouse mammary transplant model, induction of Separase expression in the transplanted FSK3 cells for 3-4 weeks results in the formation of aneuploid tumors in the mammary gland. Xenograft studies combined with histological and cytogenetic analysis reveal that Separase-induced tumors are clonal in their genomic complements and have a mesenchymal phenotype suggestive of an epithelial-mesenchymal transition. Induction of Separase resulted in trisomies for chromosomes 8, 15, and 17; monosomy for chromosome 10; and amplification of the distal region of chromosomes 8 and 11. Separase protein is found to be significantly overexpressed in human breast tumors compared with matched normal tissue. These results collectively suggest that Separase is an oncogene, whose overexpression alone in mammary epithelial cells is sufficient to induce aneuploidy and tumorigenesis in a p53 mutant background.

Cell cycle regulator gene CDC5L, a potential target for 6p12-p21 amplicon in osteosarcoma.

Osteosarcoma is a primary malignant tumor of bone arising from primitive bone-forming mesenchymal cells and accounts for approximately 60% of malignant bone tumors. Our comparative genomic hybridization (CGH) studies have identified frequent amplification at 6p12-p21, 12q13-q15, and 17p11.2 in osteosarcoma. Of these amplified regions, 6p12-p21 is particularly interesting because of its association with progression and poor prognosis in patients with osteosarcoma. In an attempt to identify aberrantly expressed gene(s) mapping to the 6p12-p21 amplicon, a region-specific array was generated using 108 overlapping BAC and P1 clones covering a 28.8-Mb region at 0.26-Mb intervals. Based on array CGH analysis, the 6p amplicon was refined to 7.9 Mb between the clones RP11-91E11 and RP1-244F2 and 10 amplified clones, with possible target genes, were identified. To study the expression pattern of the target genes from the hotspot amplicon and known candidate genes from 6p12-21, we did quantitative reverse transcription-PCR analysis of MAPK14, MAPK13, CDKN1A, PIM1, MDGA1, BTB9, DNAH8, CCND3, PTK7, CDC5L, and RUNX2 on osteosarcoma patient samples and seven cell lines. The combined array CGH and quantitative reverse transcription-PCR analysis identified amplification and overexpression of CDC5L, CCND3, and RUNX2. We screened these three genes for protein expression by Western blotting and immunohistochemistry and detected overexpression of CDC5L. Furthermore, we used an in vivo assay to show that CDC5L possesses potential oncogenic activity. These results indicate that CDC5L, a cell cycle regulator important for the G2-M transition, is the most likely candidate oncogene for the 6p12-p21 amplicon found in osteosarcoma.

Pten inactivation accelerates oncogenic K-ras-initiated tumorigenesis in a mouse model of lung cancer.

Phosphatase and tensin homologue deleted from chromosome 10 (Pten) is expressed aberrantly in non-small cell lung cancer cells, but the role of Pten in lung neoplasia has not been fully elucidated. In this study, we used a genetic approach to inactivate Pten in the bronchial epithelium of mice. Although, by itself, Pten inactivation had no discernible effect on bronchial epithelial histology, it accelerated lung tumorigenesis initiated by oncogenic K-ras, causing more rapid lethality than that induced by oncogenic K-ras alone (8 weeks versus 24 weeks of median duration of survival, respectively). Lung tumors arose in K-ras mutant, Pten-deficient mice that rapidly obstructed bronchial lumina and replaced alveolar spaces. Relative to K-ras mutant tumors, the K-ras mutant, Pten-deficient tumors exhibited more advanced histologic severity and more prominent inflammation and vascularity. Thus, Pten inactivation cooperated with oncogenic K-ras in promoting lung tumorigenesis.

Glioma pathogenesis-related protein 1 exerts tumor suppressor activities through proapoptotic reactive oxygen species-c-Jun-NH2 kinase signaling.

Glioma pathogenesis-related protein 1 (GLIPR1), a novel p53 target gene, is down-regulated by methylation in prostate cancer and has p53-dependent and -independent proapoptotic activities in tumor cells. These properties suggest an important tumor suppressor role for GLIPR1, yet direct genetic evidence of a tumor suppressor function for GLIPR1 is lacking and the molecular mechanism(s), through which GLIPR1 exerts its tumor suppressor functions, has not been shown. Here, we report that the expression of GLIPR1 is significantly reduced in human prostate tumor tissues compared with adjacent normal prostate tissues and in multiple human cancer cell lines. Overexpression of GLIPR1 in cancer cells leads to suppression of colony growth and induction of apoptosis. Mice with an inactivated Glipr1 gene had significantly shorter tumor-free survival times than either Glipr1(+/+) or Glipr1(+/-) mice in both p53(+/+) and p53(+/-) genetic backgrounds, owing to their development of a unique array of malignant tumors. Mechanistic analysis indicated that GLIPR1 up-regulation increases the production of reactive oxygen species (ROS) leading to apoptosis through activation of the c-Jun-NH(2) kinase (JNK) signaling cascade. Thus, our results identify GLIPR1 as a proapoptotic tumor suppressor acting through the ROS-JNK pathway and support the therapeutic potential for this protein.

A Comparison of Fuzzy Clustering Approaches for Quantification of Microarray Gene Expression.

Despite the widespread application of microarray imaging for biomedical imaging research, barriers still exist regarding its reliability for clinical use. A critical major problem lies in accurate spot segmentation and the quantification of gene expression level (mRNA) from the microarray images. A variety of commercial and research freeware packages are available, but most cannot handle array spots with complex shapes such as donuts and scratches. Clustering approaches such as k-means and mixture models were introduced to overcome this difficulty, which use the hard labeling of each pixel. In this paper, we apply fuzzy clustering approaches for spot segmentation, which provides soft labeling of the pixel. We compare several fuzzy clustering approaches for microarray analysis and provide a comprehensive study of these approaches for spot segmentation. We show that possiblistic c-means clustering (PCM) provides the best performance in terms of stability criterion when testing on both a variety of simulated and real microarray images. In addition, we compared three statistical criteria in measuring gene expression levels and show that a new asymptotically unbiased statistic is able to quantify the gene expression level more accurately.