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This document is a correction notice for an article titled "The BRD4 inhibitor JQ1 augments the antitumor efficacy of abemaciclib in preclinical models of gastric carcinoma." The authors identified minor errors in the image-typesetting in Figure 3 of the original article. The image for the NCI-N87 cell line in Figure 3C was mistakenly included, and the corresponding statistical analysis for NCI-N87 in Figure 3D needs to be corrected. The corrected figure has been provided, and the correction does not affect the results or conclusions of the paper. The correction notice is authored by Mei Feng, Hao Xu, Wenyuan Zhou, and Yisheng Pan. [Extracted from the article]

Background: Breast cancer (BC) is a heterogeneous neoplasm characterized by several subtypes. One of the most aggressive with high metastasis rates presents overexpression of the human epidermal growth factor receptor 2 (HER2). A quantitative evaluation of HER2 levels is essential for a correct diagnosis, selection of the most appropriate therapeutic strategy and monitoring the response to therapy. Results: In this paper, we propose the synergistic use of SERS and Raman technologies for the identification of HER2 expressing cells and its accurate assessment. To this end, we selected SKBR3 and MDA-MB-468 breast cancer cell lines, which have the highest and lowest HER2 expression, respectively, and MCF10A, a non-tumorigenic cell line from normal breast epithelium for comparison. The combined approach provides a quantitative estimate of HER2 expression and visualization of its distribution on the membrane at single cell level, clearly identifying cancer cells. Moreover, it provides a more comprehensive picture of the investigated cells disclosing a metabolic signature represented by an elevated content of proteins and aromatic amino acids. We further support these data by silencing the HER2 gene in SKBR3 cells, using the RNA interference technology, generating stable clones further analysed with the same combined methodology. Significant changes in HER2 expression are detected at single cell level before and after HER2 silencing and the HER2 status correlates with variations of fatty acids and downstream signalling molecule contents in the context of the general metabolic rewiring occurring in cancer cells. Specifically, HER2 silencing does reduce the growth ability but not the lipid metabolism that, instead, increases, suggesting that higher fatty acids biosynthesis and metabolism can occur independently of the proliferating potential tied to HER2 overexpression. Conclusions: Our results clearly demonstrate the efficacy of the combined SERS and Raman approach to definitely pose a correct diagnosis, further supported by the data obtained by the HER2 gene silencing. Furthermore, they pave the way to a new approach to monitor the efficacy of pharmacologic treatments with the aim to tailor personalized therapies and optimize patients' outcome. [ABSTRACT FROM AUTHOR]

Primary cell lines are invaluable for exploring cancer biology and investigating novel treatments. Despite their numerous advantages, primary cultures are laborious to obtain and maintain in culture. Hence, established cell lines are still more common. This study aimed to evaluate a range of techniques for isolating primary breast cancer cultures, employing distinct enzymatic compositions, incubation durations, and mechanical approaches, including filtration. Out of several protocols, we opted for a highly effective method (Method 5) that gave rise to a primary cell culture (BC160). This method combines mechanical disaggregation and enzymatic digestion with hyaluronidase and collagenase. Moreover, the paper addresses common issues in isolating primary cultures, shedding light on the struggle against fibroblasts overgrowing cancer cell populations. To make primary cell lines a preferred model, it is essential to elaborate and categorise isolation methods, develop approaches to separate heterogeneous cultures and investigate factors influencing the establishment of primary cell lines. [ABSTRACT FROM AUTHOR]

Background: Myxofibrosarcoma is a rare malignant soft tissue sarcoma characterised by multiple local recurrence and can become of higher grade with each recurrence. Consequently, myxofibrosarcoma represents a burden for patients, a challenge for clinicians, and an interesting disease to study tumour progression. Currently, few myxofibrosarcoma preclinical models are available. Methods: In this paper, we present a spontaneously immortalised myxofibrosarcoma patient-derived cell line (MF-R 3). We performed phenotypic characterization through multiple biological assays and analyses: proliferation, clonogenic potential, anchorage-independent growth and colony formation, migration, invasion, AgNOR staining, and ultrastructural evaluation. Results: MF-R 3 cells match morphologic and phenotypic characteristics of the original tumour as 2D cultures, 3D aggregates, and on the chorioallantoic membrane of chick embryos. Overall results show a clear neoplastic potential of this cell line. Finally, we tested MF-R 3 sensitivity to anthracyclines in 2D and 3D conditions finding a good response to these drugs. Conclusions: In conclusion, we established a novel patient-derived myxofibrosarcoma cell line that, together with the few others available, could serve as an important model for studying the molecular pathogenesis of myxofibrosarcoma and for testing new drugs and therapeutic strategies in diverse experimental settings. [ABSTRACT FROM AUTHOR]

Background: Malignant ascites is a manifestation of end stage events in a variety of cancers and is associated with significant morbidity. Epigenetic modulators play a key role in cancer initiation and progression, among which histone deacetylases (HDACs) are considered as one of the most important regulators for various cancer development, such as liver cancer, ovarian cancer, and pancreatic cancer et al. Thus, in this paper, we sought to explore the therapeutic effect of HDAC inhibitor on malignant ascites.Methods: In this report, we tested the therapeutic effect of different isoform selective HDAC inhibitors (Class I HDACI MS275, Class IIa HDACI MC1568, pan-HDAC inhibitors SAHA) on malignant ascites in vitro and in vivo. We further used proteome analysis to find the potential mechanisms for malignant ascites therapy.Results: Among the different isoform-selective HDAC inhibitors, the class I selective HDACI, MS275, exhibited preferential inhibition on various ascites cells. MS275 could induce cell cycle arrest in G0/G1 phase and promote apoptosis on ascites cells. Through proteome analysis, we found MS275 could downregulate proteins related to cell cycle progression, such as CDK4, CDC20, CCND1; MS275 could upregulate pro-apoptosis proteins such as PAPR1, LMNB2 and AIFM1; in addition, MS275 could change the expression of tumorigenic proteins related to the specific malignant ascites bearing tumors, such as TSP1 and CDK4 for bladder cancer. We then confirmed that abemaciclib (CDK4/6 selective inhibitor) could inhibit the proliferation of ascites cells, and the combination of abemaciclib and MS275 had synergistic anti-tumor effect. Finally, we found that MS275 could in vivo inhibit malignant ascites progression (ascites volume: 2.9 ± 1.0 mL vs 7.5 ± 1.2 mL, p < 0.01), tumor growth, and prolong 66% of the life-span when compared with the untreated group.Conclusion: This present research revealed that the class I selective HDAC inhibitor, MS275, could effectively inhibit malignant ascites development and tumor growth via multiple pathways. These results indicated that HDACI could have great potential for clinical therapy of malignant ascites. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Background: The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using sequence-based features and traditional machine learning that can compete with deep learning models. Results: In this paper, we present CRISPRpred(SEQ), a method for sgRNA on-target activity prediction that leverages only traditional machine learning techniques and hand-crafted features extracted from sgRNA sequences. We compare the results of CRISPRpred(SEQ) with that of DeepCRISPR, the current state-of-the-art, which uses a deep learning pipeline. Despite using only traditional machine learning methods, we have been able to beat DeepCRISPR for the three out of four cell lines in the benchmark dataset convincingly (2.174%, 6.905% and 8.119% improvement for the three cell lines). Conclusion: CRISPRpred(SEQ) has been able to convincingly beat DeepCRISPR in 3 out of 4 cell lines. We believe that by exploring further, one can design better features only using the sgRNA sequences and can come up with a better method leveraging only traditional machine learning algorithms that can fully beat the deep learning models. [ABSTRACT FROM AUTHOR]

Background: Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles, and thus it is necessary to accurately measure isoform expressions as well as gene expressions. While previous studies have demonstrated the strong agreement between mRNA sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains. Results: In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. The goal is to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 404 custom-designed probes for measuring the expressions of 478 isoforms in 155 genes, and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array, and Microarray data of 46 of the 59 cell lines for the comparative analysis. Conclusion: In the comparisons of the platforms for measuring the expressions at both isoform and gene levels, we found that (1) the agreement on isoform expressions is lower than the agreement on gene expressions across the four platforms; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array than NanoString in isoform quantification; (4) different RNA-seq isoform quantification methods show varying estimation results, and among the methods, Net-RSTQ and eXpress are more consistent across the platforms; and (5) RNA-seq has the best overall consistency with the other platforms on gene expression quantification. [ABSTRACT FROM AUTHOR]

Background: Drug-induced liver injury (DILI) is a serious concern during drug development and the treatment of human disease. The ability to accurately predict DILI risk could yield significant improvements in drug attrition rates during drug development, in drug withdrawal rates, and in treatment outcomes. In this paper, we outline our approach to predicting DILI risk using gene-expression data from Build 02 of the Connectivity Map (CMap) as part of the 2018 Critical Assessment of Massive Data Analysis CMap Drug Safety Challenge. Results: First, we used seven classification algorithms independently to predict DILI based on gene-expression values for two cell lines. Similar to what other challenge participants observed, none of these algorithms predicted liver injury on a consistent basis with high accuracy. In an attempt to improve accuracy, we aggregated predictions for six of the algorithms (excluding one that had performed exceptionally poorly) using a soft-voting method. This approach also failed to generalize well to the test set. We investigated alternative approaches—including a multi-sample normalization method, dimensionality-reduction techniques, a class-weighting scheme, and expanding the number of hyperparameter combinations used as inputs to the soft-voting method. We met limited success with each of these solutions. Conclusions: We conclude that alternative methods and/or datasets will be necessary to effectively predict DILI in patients based on RNA expression levels in cell lines. Reviewers: This article was reviewed by Paweł P Labaj and Aleksandra Gruca (both nominated by David P Kreil). [ABSTRACT FROM AUTHOR]

A conference paper on whether a rat colon carcinoma cell line CC531s could induce apoptosis in liver sinusoidal endothelial cells (SLECs). Apoptosis was visualized by markers such as Hoechst and Propidium iodide and cells were recorded by time lapse video microscopy with and without an antagonistic antibody for FasL. The paper concludes that CC531s cells can induce apoptosis in LSECs and antagonistic antibody against FasL.

Background: A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology. Results: In this paper, we provide examples of state-of-the-art cellular biomarker characterization using highcontent cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including "context annotations" in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations. Conclusion: The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges. [ABSTRACT FROM AUTHOR]

Background: Cytoskeleton is a highly dynamic network that helps to maintain the rigidity of a cell, and the mechanical properties of a cell are closely related to many cellular functions. This paper presents a new method to probe and characterize cell mechanical properties through dielectrophoresis (DEP)-based cell stretching manipulation and actin cytoskeleton modeling.Methods: Leukemia NB4 cells were used as cell line, and changes in their biological properties were examined after chemotherapy treatment with doxorubicin (DOX). DEP-integrated microfluidic chip was utilized as a low-cost and efficient tool to study the deformability of cells. DEP forces used in cell stretching were first evaluated through computer simulation, and the results were compared with modeling equations and with the results of optical stretching (OT) experiments. Structural parameters were then extracted by fitting the experimental data into the actin cytoskeleton model, and the underlying mechanical properties of the cells were subsequently characterized.Results: The DEP forces generated under different voltage inputs were calculated and the results from different approaches demonstrate good approximations to the force estimation. Both DEP and OT stretching experiments confirmed that DOX-treated NB4 cells were stiffer than the untreated cells. The structural parameters extracted from the model and the confocal images indicated significant change in actin network after DOX treatment.Conclusion: The proposed DEP method combined with actin cytoskeleton modeling is a simple engineering tool to characterize the mechanical properties of cells. [ABSTRACT FROM AUTHOR]

Background: Microelectrode array (MEA) is a widely used technique to study for example the functional properties of neuronal networks derived from human embryonic stem cells (hESC-NN). With hESC-NN, we can investigate the earliest developmental stages of neuronal network formation in the human brain.Methods: In this paper, we propose an in silico model of maturating hESC-NNs based on a phenomenological model called INEX. We focus on simulations of the development of bursts in hESC-NNs, which are the main feature of neuronal activation patterns. The model was developed with data from developing hESC-NN recordings on MEAs which showed increase in the neuronal activity during the investigated six measurement time points in the experimental and simulated data.Results: Our simulations suggest that the maturation process of hESC-NN, resulting in the formation of bursts, can be explained by the development of synapses. Moreover, spike and burst rate both decreased at the last measurement time point suggesting a pruning of synapses as the weak ones are removed.Conclusions: To conclude, our model reflects the assumption that the interaction between excitatory and inhibitory neurons during the maturation of a neuronal network and the spontaneous emergence of bursts are due to increased connectivity caused by the forming of new synapses. [ABSTRACT FROM AUTHOR]

Background: Proper cell models for breast cancer primary tumors have long been the focal point in the cancer's research. The genomic comparison between cell lines and tumors can investigate the similarity and dissimilarity and help to select right cell model to mimic tumor tissues to properly evaluate the drug reaction in vitro. In this paper, a comprehensive comparison in copy number variation (CNV), mutation, mRNA expression and protein expression between 68 breast cancer cell lines and 1375 primary breast tumors is conducted and presented. Results: Using whole genome expression arrays, strong correlations were observed between cells and tumors. PAM50 gene expression differentiated them into four major breast cancer subtypes: Luminal A and B, HER2amp, and Basal-like in both cells and tumors partially. Genomic CNVs patterns were observed between tumors and cells across chromosomes in general. High C > T and C > G trans-version rates were observed in both cells and tumors, while the cells had slightly higher somatic mutation rates than tumors. Clustering analysis on protein expression data can reasonably recover the breast cancer subtypes in cell lines and tumors. Although the drug-targeted proteins ER/PR and interesting mTOR/GSK3/TS2/PDK1/ER_P118 cluster had shown the consistent patterns between cells and tumor, low protein-based correlations were observed between cells and tumors. The expression consistency of mRNA verse protein between cell line and tumors reaches 0.7076. These important drug targets in breast cancer, ESR1, PGR, HER2, EGFR and AR have a high similarity in mRNA and protein variation in both tumors and cell lines. GATA3 and RP56KB1 are two promising drug targets for breast cancer. A total score developed from the four correlations among four molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors. Conclusions: The integrated data from across these multiple platforms demonstrates the existence of the similarity and dissimilarity of molecular features between breast cancer tumors and cell lines. The cell lines only mirror some but not all of the molecular properties of primary tumors. The study results add more evidence in selecting cell line models for breast cancer research. [ABSTRACT FROM AUTHOR]

Background: Cell lines are very useful for both clinical and basic research. The establishment of ovarian, malignant tumor cell lines with aggressive histology is especially important. We describe the establishment and characterization of a new human clear cell carcinoma cell line of the ovary. Results: The cell line HCH-1 was established from an ovarian tumor from a 67-year-old woman. This cell line has grown well for 230 months and has been subcultured more than 50 times. Monolayer cultured cells are polygonal in shape, showing a pavement-like arrangement and a tendency to pile up without contact inhibition. It exhibits a human karyotype with a modal chromosomal number in the hypodiploid range. The cells could be transplanted into the subcutis of SCID mice and produced tumors resembling the original tumor. HCH-1 cells produced CA125 and CA19-9, also identified immunohistochemically in both the original tumor and the heterotransplanted tumors. The cells were sensitive to actinomycin D, carboplatin, cisplatin and mitomycin C, drugs commonly used in the treatment of gynecological cancers. Variant was not found in hotspot of the 50 most commonly reported oncogenes and tumor suppressor genes. Only 12 ovarian clear cell carcinoma cell lines and their characteristics have thus far been reported in the literature. HCH-1 is the first ovarian clear cell carcinoma cell line reported in which the chromosome number is in the hypodiploid range and only the second cell line in which CA125 and CA19-9 are expressed. Conclusions: Since it is impossible to establish a cell line from the malignant tumor of each patient, the cell line that we established, characterized and report in this paper may be very useful in basic research on ovarian cancer. We have much to learn about the pathogenesis of clear cell carcinoma and this extra line of enquiry may lead us to a better understanding of how to treat and cure this serious disease. [ABSTRACT FROM AUTHOR]

Background: Cell-to-cell interactions are complex processes that involve physical interactions, chemical binding, and biological signaling pathways. Identification of the functions of special signaling pathway in cell-to-cell interaction from the very first contact will help characterize the mechanism underlying the interaction and advance new drug discovery.Methods: This paper reported a case study of characterizing initial interaction between leukemia cancer cells and bone marrow stromal cells, through the use of an optical tweezers-based cell manipulation tool. Optical traps were used to assemble leukemia cells at different positions of the stromal cell layer and enable their interactions by applying a small trapping force to maintain the cell contact for a few minutes. Specific drug was used to inhibit the binding of molecules during receptor-ligand-mediated adhesion.Results and Conclusions: Our results showed that the amount of adhesion molecule could affect cell adhesion during the first few minutes contact. We also found that leukemia cancer cells could migrate on the stromal cell layer, which was dependent on the adhesion state and activation triggered by specific chemokine. The reported approaches provided a new opportunity to investigate cell-to-cell interaction through single cell adhesion manipulation. [ABSTRACT FROM AUTHOR]

Background: Current techniques used to obtain lung samples have significant limitations and do not provide reproducible biomarkers of inflammation. We have developed a novel technique that allows multiple sampling methods from the same area (or multiple areas) of the lung under direct bronchoscopic vision. It allows collection of mucosal lining fluid and bronchial brushing from the same site; biopsy samples may also be taken. The novel technique takes the same time as standard procedures and can be conducted safely. Methods: Eight healthy smokers aged 40-65 years were included in this study. An absorptive filter paper was applied to the bronchial mucosa under direct vision using standard bronchoscopic techniques. Further samples were obtained from the same site using bronchial brushings. Bronchoalveolar lavage (BAL) was obtained using standard techniques. Chemokine (C-C Motif) Ligand 20 (CCL20), CCL4, CCL5, Chemokine (C-X-C Motif) Ligand 1 (CXCL1), CXCL8, CXCL9, CXCL10, CXCL11, Interleukin 1 beta (IL-1β), IL-6, Vascular endothelial growth factor (VEGF), Matrix metalloproteinase 8 (MMP-8) and MMP-9 were measured in exudate and BAL. mRNA was collected from the bronchial brushings for gene expression analysis. Results: A greater than 10 fold concentration of all the biomarkers was detected in lung exudate in comparison to BAL. High yield of good quality RNA with RNA integrity numbers (RIN) between 7.6 and 9.3 were extracted from the bronchial brushings. The subset of genes measured were reproducible across the samples and corresponded to the inflammatory markers measured in exudate and BAL. Conclusions: The bronchoabsorption technique as described offers the ability to sample lung fluid direct from the site of interest without the dilution effects caused by BAL. Using this method we were able to successfully measure the concentrations of biomarkers present in the lungs as well as collect high yield mRNA samples for gene expression analysis from the same site. This technique demonstrates superior sensitivity to standard BAL for the measurement of biomarkers of inflammation. It could replace BAL as the method of choice for these measurements. This method provides a systems biology approach to studying the inflammatory markers of respiratory disease progression. [ABSTRACT FROM AUTHOR]

Background: There is an ever-expanding range of technologies that generate very large numbers of biomarkers for research and clinical applications. Choosing the most informative biomarkers from a high-dimensional data set, combined with identifying the most reliable and accurate classification algorithms to use with that biomarker set, can be a daunting task. Existing surveys of feature selection and classification algorithms typically focus on a single data type, such as gene expression microarrays, and rarely explore the model's performance across multiple biological data types. Results: This paper presents the results of a large scale empirical study whereby a large number of popular feature selection and classification algorithms are used to identify the tissue of origin for the NCI-60 cancer cell lines. A computational pipeline was implemented to maximize predictive accuracy of all models at all parameters on five different data types available for the NCI-60 cell lines. A validation experiment was conducted using external data in order to demonstrate robustness. Conclusions: As expected, the data type and number of biomarkers have a significant effect on the performance of the predictive models. Although no model or data type uniformly outperforms the others across the entire range of tested numbers of markers, several clear trends are visible. At low numbers of biomarkers gene and protein expression data types are able to differentiate between cancer cell lines significantly better than the other three data types, namely SNP, array comparative genome hybridization (aCGH), and microRNA data. Interestingly, as the number of selected biomarkers increases best performing classifiers based on SNP data match or slightly outperform those based on gene and protein expression, while those based on aCGH and microRNA data continue to perform the worst. It is observed that one class of feature selection and classifier are consistently top performers across data types and number of markers, suggesting that well performing feature-selection/ classifier pairings are likely to be robust in biological classification problems regardless of the data type used in the analysis. [ABSTRACT FROM AUTHOR]

Background: The Baculovirus Expression Vector System (BEVS) is a very popular expression vector system in gene engineering. An effective host cell line cultivation protocol can facilitate the baculovirus preparation and following experiments. However, the counting of the number of host cells in the protocol is usually performed by manual observation with microscopy, which is time consuming and labor intensive work, and prone to errors for one person or between different individuals. This study aims at giving a bright field insect cells counting protocol to help improve the efficient of BEVS. Method: To develop a reliable and accurate counting method for the host cells in the bright field, such as Sf9 insect cells, a novel method based on a nonlinear Transformed Sliding Band Filter (TSBF) was proposed. And 3 collaborators counted cells at the same time to produce the ground truth for evaluation. The performance of TSBF method was evaluated with the image datasets of Sf9 insect cells according to the different periods of cell cultivation on the cell density, error rate and growth curve. Results: The average error rate of our TSBF method is 2.21% on average, ranging from 0.89% to 3.97%, which exhibited an excellent performance with its high accuracy in lower error rate compared with traditional methods and manual counting. And the growth curve was much the manual method well. Conclusion: Results suggest the proposed TSBF method can detect insect cells with low error rate, and it is suitable for the counting task in BEVS to take the place of manual counting by humans. Growth curve results can reflect the cells' growth manner, which was generated by our proposed TSBF method in this paper can reflected the similar manner with it's from the manual method. All of these proven that the proposed insect cell counting method can clearly improve the efficiency of BEVS. [ABSTRACT FROM AUTHOR]

Successfully reprogramming somatic cells to a pluripotent state generates induced pluripotent stem (iPS) cells (or iPSCs), which have extensive self-renewal capacity like embryonic stem cells (ESCs). iPSCs can also generate daughter cells that can further undergo differentiation into various lineages or terminally differentiate to reach their final functional state. The discovery of how to produce iPSCs opened a new field of stem cell research with both intellectual and therapeutic benefits. The huge potential implications of disease-specific or patient-specific iPSCs have impelled scientists to solve problems hindering their applications in clinical medicine, especially the issues of convenience and safety. To determine the range of tissue types amenable to reprogramming as well as their particular characteristics, cells from three embryonic germ layers have been assessed, and the advantages that some tissue origins have over fibroblast origins concerning efficiency and accessibility have been elucidated. To provide safe iPSCs in an efficient and convenient way, the delivery systems and combinations of inducing factors as well as the chemicals used to generate iPSCs have also been significantly improved in addition to the efforts on finding better donor cells. Currently, iPSCs can be generated without c-Myc and Klf4 oncogenes, and non-viral delivery integration-free chemically mediated reprogramming methods have been successfully employed with relatively satisfactory efficiency. This paper will review recent advances in iPS technology by highlighting tissue origin and generation of iPSCs. The obstacles that need to be overcome for clinical applications of iPSCs are also discussed. [ABSTRACT FROM AUTHOR]

Background: High-throughput RNA sequencing (RNA-seq) offers unprecedented power to capture the real dynamics of gene expression. Experimental designs with extensive biological replication present a unique opportunity to exploit this feature and distinguish expression profiles with higher resolution. RNA-seq data analysis methods so far have been mostly applied to data sets with few replicates and their default settings try to provide the best performance under this constraint. These methods are based on two well-known count data distributions: the Poisson and the negative binomial. The way to properly calibrate them with large RNA-seq data sets is not trivial for the non-expert bioinformatics user. Results: Here we show that expression profiles produced by extensively-replicated RNA-seq experiments lead to a rich diversity of count data distributions beyond the Poisson and the negative binomial, such as Poisson-Inverse Gaussian or Pó lya-Aeppli, which can be captured by a more general family of count data distributions called the Poisson-Tweedie. The flexibility of the Poisson-Tweedie family enables a direct fitting of emerging features of large expression profiles, such as heavy-tails or zero-inflation, without the need to alter a single configuration parameter. We provide a software package for R called tweeDEseq implementing a new test for differential expression based on the Poisson-Tweedie family. Using simulations on synthetic and real RNA-seq data we show that tweeDEseq yields P-values that are equally or more accurate than competing methods under different configuration parameters. By surveying the tiny fraction of sex-specific gene expression changes in human lymphoblastoid cell lines, we also show that tweeDEseq accurately detects differentially expressed genes in a real large RNA-seq data set with improved performance and reproducibility over the previously compared methodologies. Finally, we compared the results with those obtained from microarrays in order to check for reproducibility. Conclusions: RNA-seq data with many replicates leads to a handful of count data distributions which can be accurately estimated with the statistical model illustrated in this paper. This method provides a better fit to the underlying biological variability; this may be critical when comparing groups of RNA-seq samples with markedly different count data distributions. The tweeDEseq package forms part of the Bioconductor project and it is available for download at http://www.bioconductor.org. [ABSTRACT FROM AUTHOR]

Background: The expression of chemokine receptors CCR7 has been studied in relation to tumor dissemination and poor prognosis in a limited number of cancers. No such studies have been done on CCR7 expression in non-Hodgkin's lymphoma (T-NHL). Our aim in this paper is to investigate the association between CCR7 expression and progression and prognosis of T-NHL. Methods: 1) Analysis of clinical data: The specimens were obtained from 41 patients with T-NHL and 19 patients with lymphoid hyperplasia. Their corresponding clinicopathologic data were also collected. The expression levels of CCR7, MMP-2, and MMP-9 were examined by immunohistochemical staining. 2) Human T-NHL cell lines Hut 78 (cutaneous T-cell lymphoma) and Jurkat (adult T-cell leukemia/lymphoma) were cultured. The invasiveness of the two cell lines were measured with a Transwell invasion assay, and then used to study the effects of chemokine receptors on T-NHL invasion and the underlying molecular mechanism. The transcript and expression of CCR7 were evaluated using RT-PCR and western blotting. Results: 1) The higher CCR7 and MMP-9 expression ratios were significantly associated with multiple lesions and higher stage III/IV. Moreover, a positive correlation was observed between CCR7 and MMP-9 expression. 2) The Hut 78 cell line was more invasive than the Jurkat cells in the Transwell invasion assay. The transcript and expression levels of CCR7 were significantly higher in Hut78 than that of Jurkat cell line. The T-NHL cell lines were co-cultured with chemokine CCL21 which increased the invasiveness of T-NHL cell. The positive association between CCL21 concentration and invasiveness was found. 3) The stronger transcript and expression of PI3K, Akt and p- Akt were also observed in Hut78 than in Jurkat cell line. Conclusions: High CCR7 expression in T-NHL cells is significantly associated with lymphatic and distant dissemination as well as with tumor cell migration and invasion in vitro. Its underlying mechanism probably involves the PI3K/Akt signal pathway. [ABSTRACT FROM AUTHOR]

Background: Machine learning models (classifiers) for classifying genes to biological processes each have their own unique characteristics in what genes can be classified and to what biological processes. No single learning model is qualitatively superior to any other model and overall precision for each model tends to be low. The classification results for each classifier can be complementary and synergistic suggesting the benefit of a combination of algorithms, but often the prediction probability outputs of various learning models are neither comparable nor compatible for combining. A means to compare outputs regardless of the model and data used and combine the results into an improved comprehensive model is needed. Results: Gene expression patterns from NCI's panel of 60 cell lines were used to train a Random Forest, a Support Vector Machine and a Neural Network model, plus two over-sampled models for classifying genes to biological processes. Each model produced unique characteristics in the classification results. We introduce the Precision Index measure (PIN) from the maximum posterior probability that allows assessing, comparing and combining multiple classifiers. The class specific precision measure (PIC) is introduced and used to select a subset of predictions across all classes and all classifiers with high precision. We developed a single classifier that combines the PINs from these five models in prediction and found that the PIN Combined Classifier (PINCom) significantly increased the number of correctly predicted genes over any single classifier. The PINCom applied to test genes that were not used in training also showed substantial improvement over any single model. Conclusions: This paper introduces novel and effective ways of assessing predictions by their precision and recall plus a method that combines several machine learning models and capitalizes on synergy and complementation in class selection, resulting in higher precision and recall. Different machine learning models yielded incongruent results each of which were successfully combined into one superior model using the PIN measure we developed. Validation of the boosted predictions for gene functions showed the genes to be accurately predicted. [ABSTRACT FROM AUTHOR]

Background: Biological networks offer us a new way to investigate the interactions among different components and address the biological system as a whole. In this paper, a reverse-phase protein microarray (RPPM) is used for the quantitative measurement of proteomic responses. Results: To discover the signaling pathway responsive to RPPM, a new structure learning algorithm of Bayesian networks is developed based on mutual Information, conditional independence, and graph immorality. Trusted biology networks are thus predicted by the new approach. As an application example, we investigate signaling networks of ataxia telangiectasis mutation (ATM). The study was carried out at different time points under different dosages for cell lines with and without gene transfection. To validate the performance of the proposed algorithm, comparison experiments were also implemented using three well-known networks. From the experiment results, our approach produces more reliable networks with a relatively small number of wrong connection especially in mid-size networks. By using the proposed method, we predicted different networks for ATM under different doses of radiation treatment, and those networks were compared with results from eight different protein protein interaction (PPI) databases. Conclusions: By using a new protein microarray technology in combination with a new computational framework, we demonstrate an application of the methodology to the study of biological networks of ATM cell lines under low dose ionization radiation. [ABSTRACT FROM AUTHOR]

Background: The incidence of false positives is a potential problem in single-cell PCR experiments. This paper describes an optimized protocol for single-cell qPCR measurements in primary pituitary cell cultures following patch-clamp recordings. Two different cell harvesting methods were assessed using both the GH4 prolactin producing cell line from rat, and primary cell culture from fish pituitaries. Results: Harvesting whole cells followed by cell lysis and qPCR performed satisfactory on the GH4 cell line. However, harvesting of whole cells from primary pituitary cultures regularly produced false positives, probably due to RNA leakage from cells ruptured during the dispersion of the pituitary cells. To reduce RNA contamination affecting the results, we optimized the conditions by harvesting only the cytosol through a patch pipette, subsequent to electrophysiological experiments. Two important factors proved crucial for reliable harvesting. First, silanizing the patch pipette glass prevented foreign extracellular RNA from attaching to charged residues on the glass surface. Second, substituting the commonly used perforating antibiotic amphotericin B with β-escin allowed efficient cytosol harvest without loosing the giga seal. Importantly, the two harvesting protocols revealed no difference in RNA isolation efficiency. Conclusion: Depending on the cell type and preparation, validation of the harvesting technique is extremely important as contaminations may give false positives. Here we present an optimized protocol allowing secure harvesting of RNA from single cells in primary pituitary cell culture following perforated whole cell patch clamp experiments. [ABSTRACT FROM AUTHOR]

Introduction The tandem mass spectrometer is a powerful tool with which to generate peptide (tandem) mass spectrum data for the analysis of complex biological protein mixtures in genomic-related disease cell lines. However, the majority of experimental tandem mass spectra cannot be interpreted by any database search engines. One of the main reasons this happens is that majority of experimental spectra are of quality too poor to be interpretable. Interpreting these "uninterpretable" spectra is a waste of time. Therefore, it is worthwhile to determine the quality of mass spectra before any interpretation. Objectives This paper proposes an approach to classifying tandem spectra into two groups: one with high quality and one with poor quality. Methods The proposed approach has two steps. First, each spectrum is mapped to a feature vector which describes the quality of the spectrum. Then, a weighted K-means clustering method is applied in order to classify the tandem mass spectra. Results and Conclusion Computational experiments illustrate that one cluster contains the majority of the high-quality spectra, while the other contains the majority of the poor-quality spectra. This result indicates that if we just search the spectra in the high-quality cluster, we can save the time for searching the majority of poor-quality spectra while losing a minimal amount of high-quality spectra. The software created for this work is available upon request. [ABSTRACT FROM AUTHOR]

Background: Bone morphogenetic proteins (BMPs) are pleiotropic members of the TGF-beta superfamily which regulate many biological processes during development and adult tissue homeostasis and are implicated in the pathogenesis of a number of human diseases. Their involvement in both normal and aberrant physiology creates a need for rapid, sensitive and methodologically simple assays to evaluate their activity from a variety of biological samples. Previously alkaline phosphatase based assays, ELISA and luciferase based bioassays have been developed to evaluate either individual or total BMP activity. In this paper, we describe a highly sensitive, rapid and specific cell based assay for the simultaneous quantification of total and isoform specific BMP activity from biological samples. Results: A C2C12 cell line stably transfected with a reporter plasmid consisting of the BMP response element (BRE) from the Id1 promoter fused to a luciferase reporter gene was generated. Exposure of this cell line to human recombinant BMP2, BMP4, BMP6, BMP7, BMP9 and BMP10 induced the expression of luciferase which was quantified using a luminometer. This assay was specific for BMP activity as the other TGF-β superfamily members TGF-β 1, Nodal and Mullerian Inhibiting Substance (MIS) did not induce the reporter. Pretreatment of samples with isoform specific BMP blocking antibodies coupled with isoform specific titration analysis allowed the simultaneous identification and quantification of BMP4, BMP6 and BMP9 in serum samples. Conclusion: The assay is rapid (<48 hours) and can be used to simultaneously measure isoform specific and total BMP activity in complex solutions. [ABSTRACT FROM AUTHOR]

Background: PTX is one of the most widely used drug in oncology due to its high efficacy against solid tumors and several hematological cancers. PTX is administered in a formulation containing 1:1 Cremophor EL (polyethoxylated castor oil) and ethanol, often responsible for toxic effects. Its encapsulation in colloidal delivery systems would gain an improved targeting to cancer cells, reducing the dose and frequency of administration. Methods: In this paper PTX was loaded in PLGA NS. The activity of PTX-NS was assessed in vitro against thyroid, breast and bladder cancer cell lines in cultures. Cell growth was evaluated by MTS assay, intracellular NS uptake was performed using coumarin-6 labelled NS and the amount of intracellular PTX was measured by HPLC. Results: NS loaded with 3% PTX (w/w) had a mean size < 250 nm and a polydispersity index of 0.4 after freeze-drying with 0.5% HP-Cyd as cryoprotector. PTX encapsulation efficiency was 30% and NS showed a prolonged drug release in vitro. An increase of the cytotoxic effect of PTX-NS was observed with respect to free PTX in all cell lines tested. Conclusion: These findings suggest that the greater biological effect of PTX-NS could be due to higher uptake of the drug inside the cells as shown by intracellular NS uptake and cell accumulation studies. [ABSTRACT FROM AUTHOR]

Background: The formation of two distinctive cell lineages in preimplantation mouse embryos is characterized by differential gene expression. The cells of the inner cell mass are pluripotent and express high levels of Oct4 mRNA, which is down-regulated in the surrounding trophectoderm. In contrast, the trophectoderm of female embryos contains Xist mRNA, which is absent from cells of the inner mass. Prior to blastocyst formation, all blastomeres of female embryos still express both of these RNAs. We, thus, postulated that simultaneous quantification of Oct4 and Xist transcripts in individual blastomeres at the 8-cell stage could be informative as to their subsequent fate. Testing this hypothesis, however, presented numerous technical challenges. We overcame these difficulties by combining PurAmp, a single-tube method for RNA preparation and quantification, with LATE-PCR, an advanced form of asymmetric PCR. Results: We constructed a duplex RT-LATE-PCR assay for real-time measurement of Oct4 and Xist templates and confirmed its specificity and quantitative accuracy with different methods. We then undertook analysis of sets of blastomeres isolated from embryos at the 8-cell stage. At this stage, all cells in the embryo are still pluripotent and morphologically equivalent. Our results demonstrate, however, that both Oct4 and Xist RNA levels vary in individual blastomeres comprising the same embryo, with some cells having particularly elevated levels of either transcript. Analysis of multiple embryos also shows that Xist and Oct4 expression levels are not correlated at the 8-cell stage, although transcription of both genes is up-regulated at this time in development. In addition, comparison of data from males and females allowed us to determine that the efficiency of the Oct4/Xist assay is unaffected by sex-related differences in gene expression. Conclusion: This paper describes the first example of multiplex RT-LATE-PCR and its utility, when combined with PurAmp sample preparation, for quantitative analysis of transcript levels in single4cells. With this technique, copy numbers of different RNAs can be accurately measured independently from their relative abundance in a cell, a goal that cannot be achieved using symmetric PCR. The technique illustrated in this work is relevant to a wide array of applications, such as stem cell and cancer cell analysis and preimplantation genetic diagnostics. [ABSTRACT FROM AUTHOR]

Background: We describe the application of an ELISA-based assay (the Peptidomatrix) that can be used to simultaneously identify and quantitate a number of proteins in biological samples. The biological sample (blood component, biopsy, culture or other) is first lysed to release all the proteins, without any additional separation. The denatured proteins in the sample are then digested in bulk with the desired proteolytic enzyme(s). The peptides in the digest are then assayed by appropriate antibodies, using a competition ELISA protocol. Results: As an example of its use, the present paper applies the Peptidomatrix to the assay of four membrane proteins MDR1 (P-glycoprotein or ABCB1), MRP1 (ABCC1), BCRP/MXR (ABCG2) and the alpha subunit of the Na, ḴATPase (ATP1A1), present in a number of cell lines and in human lymphocytes. We show that we can detect and quantitate these proteins, using a series of peptideantibody pairs, and that we can differentiate between cell lines or cell preparations that express the target proteins and those that do not. Conclusion: We have devised a simple, ELISA-based proteomics assay that enables the quantitation of designated proteins in a cell or tissue sample, and that can be used in any laboratory, with minimal specialized equipment. [ABSTRACT FROM AUTHOR]

Background: This paper describes a microarray study including data quality control, data analysis and the analysis of the mechanism of toxicity (MOT) induced by 1-methyl-4-phenylpyridinium (MPP+) in a rat adrenal pheochromocytoma cell line (PC12 cells) using bioinformatics tools. MPP+ depletes dopamine content and elicits cell death in PC12 cells. However, the mechanism of MPP+- induced neurotoxicity is still unclear. Results: In this study, Agilent rat oligo 22K microarrays were used to examine alterations in gene expression of PC12 cells after 500 µM MPP+ treatment. Relative gene expression of control and treated cells represented by spot intensities on the array chips was analyzed using bioinformatics tools. Raw data from each array were input into the NCTR ArrayTrack database, and normalized using a Lowess normalization method. Data quality was monitored in ArrayTrack. The means of the averaged log ratio of the paired samples were used to identify the fold changes of gene expression in PC12 cells after MPP+ treatment. Our data showed that 106 genes and ESTs (Expressed Sequence Tags) were changed 2-fold and above with MPP+ treatment; among these, 75 genes had gene symbols and 59 genes had known functions according to the Agilent gene Refguide and ArrayTrack-linked gene library. The mechanism of MPP+-induced toxicity in PC12 cells was analyzed based on their genes functions, biological process, pathways and previous published literatures. Conclusion: Multiple pathways were suggested to be involved in the mechanism of MPP+-induced toxicity, including oxidative stress, DNA and protein damage, cell cycling arrest, and apoptosis. [ABSTRACT FROM AUTHOR]

Background: In this paper, we present and validate a way to measure automatically the extent of cell migration based on automated examination of a series of digital photographs. It was designed specifically to identify the impact of Second Hand Smoke (SHS) on endothelial cell migration but has broader applications. The analysis has two stages: (1) preprocessing of image texture, and (2) migration analysis. Results: The output is a graphic overlay that indicates the front lines of cell migration superimposed on each original image, with automated reporting of the distance traversed vs. time. Expert preference compares to manual placement of leading edge shows complete equivalence of automated vs. manual leading edge definition for cell migration measurement. Conclusion: Our method is indistinguishable from careful manual determinations of cell front lines, with the advantages of full automation, objectivity, and speed. [ABSTRACT FROM AUTHOR]

Background: In yeast and Caenorhabditis elegans, Silent Information Regulator (SIR2) proteins have been shown to be involved in ageing regulation. In Leishmania, the LmSIR2rp was originally isolated from the excreted/secreted material of the Leishmania parasites. Among the function(s) of this protein in Leishmania biology, we have documented its implication in parasite survival, and in particular in Leishmania amastigotes. In this paper we question the role of the excreted/secreted form of the protein. In particular we wonder if the Leishmania Sir2 homologue is involved in some aspect of its biological function(s), in various components and pathways, which could promote the host cell survival. To test this hypothesis we have mimicked an intracellular release of the protein through constitutive expression in mouse L929 fibrosarcoma cells. Results: Our results demonstrate that the LmSIR2 protein was properly expressed by fibroblasts and that LmSIR2 is localized both in the cytoplasm and the nucleus of all the transformed cell clones. Unexpectedly, we found that cells expressing LmSIR2 presents reduced saturation celldensity ranging from 40% to 60% and expressed an acidic (pH6.0) β-galactosidase activity, which is known to be a senescence biomarker. As a consequence, we observed that LmSIR2 positive fibroblasts were more permissive towards Leihmania infection. Conclusions: LmSIR2 is able to substantially interfere with the host cell physiology. Thus, it is tempting to speculate that these modifications could help Leishmania to survive for a long period in a cell with reduced capacity to multiply or respond to immunologic stimuli. The potential implications of our finding during the in vivo infection process are discussed. [ABSTRACT FROM AUTHOR]

Background: p21WAF1, implicated in the cell cycle control of both normal and malignant cells, can be induced by p53-dependent and independent mechanisms. In some cells, MEKs/ERKs regulate p21WAF1 transcriptionally, while in others they also affect the post-transcriptional processes. In myogenic differentiation, p21WAF1 expression is also controlled by the myogenic transcription factor MyoD. We have previously demonstrated that the embryonal rhabdomyosarcoma cell line undergoes growth arrest and myogenic differentiation following treatments with TPA and the MEK inhibitor U0126, which respectively activate and inhibit the ERK pathway. In this paper we attempt to clarify the mechanism of ERK-mediated and ERK-independent growth arrest and myogenic differentiation of embryonal and alveolar rhabdomyosarcoma cell lines, particularly as regards the expression of the cell cycle inhibitor p21WAF1. Results: p21WAF1 expression and growth arrest are induced in both embryonal (RD) and alveolar (RH30) rhabdomyosarcoma cell lines following TPA or MEK/ERK inhibitor (U0126) treatments, whereas myogenic differentiation is induced in RD cells alone. Furthermore, the TPA-mediated post-transcriptional mechanism of p21WAF1-enhanced expression in RD cells is due to activation of the MEK/ERK pathway, as shown by transfections with constitutively active MEK1 or MEK2, which induces p21WAF1 expression, and with ERK1 and ERK2 siRNA, which prevents p21WAF1 expression. By contrast, U0126-mediated p21WAF1 expression is controlled transcriptionally by the p38 pathway. Similarly, myogenin and MyoD expression is induced both by U0126 and TPA and is prevented by p38 inhibition. Although MyoD and myogenin depletion by siRNA prevents U0126-mediated p21WAF1 expression, the over-expression of these two transcription factors is insufficient to induce p21WAF1. These data suggest that the transcriptional mechanism of p21WAF1 expression in RD cells is rescued when MEK/ERK inhibition relieves the functions of myogenic transcription factors. Notably, the forced expression of p21WAF1 in RD cells causes growth arrest and the reversion of anchorage-independent growth. Conclusion: Our data provide evidence of the key role played by the MEK/ERK pathway in the growth arrest of Rhabdomyosarcoma cells. The results of this study suggest that the targeting of MEK/ERKs to rescue p21WAF1 expression and myogenic transcription factor functions leads to the reversal of the Rhabdomyosarcoma phenotype. [ABSTRACT FROM AUTHOR]

Background: Down syndrome is the most frequent genetic disorder in humans. Rare cases involving partial trisomy of chromosome 21 allowed a small chromosomal region common to all carriers, called Down Syndrome Critical Region (DSCR), to be determined. The DSCR1 gene was identified in this region and is expressed preferentially in the brain, heart and skeletal muscle. Recent studies have shown that DSCR1 belongs to a family of proteins that binds and inhibits calcineurin, a serine-threonine phosphatase. The work reported on herein consisted of a study of the subcellular location of DSCR1 and DSCR1-mutated forms by fusion with a green fluorescent protein, using various cell lines, including human. Results: The protein's location was preferentially nuclear, independently of the isoform, cell line and insertion in the GFP's N- or C-terminal. A segment in the C-terminal, which is important in the location of the protein, was identified by deletion. On the other hand, site-directed mutational analyses have indicated the involvement of some serine and threonine residues in this event. Conclusion: In this paper, we discuss the identification of amino acids which can be important for subcellular location of DSCR1. The involvement of residues that are prone to phosphorylation suggests that the location and function of DSCR1 may be regulated by kinases and/or phosphatases. [ABSTRACT FROM AUTHOR]

Background: Ethylene is a widely distributed alkene product which is formed enzymatically (e.g., in plants) or by photochemical reactions (e.g., in the upper oceanic layers from dissolved organic carbon). This gaseous compound was recently found to induce in cells from the marine sponge Suberites domuncula, an increase in intracellular Ca2+ level ([Ca2+]i) and an upregulation of the expression of two genes, the potential ethylene-responsive gene, SDERR, and a Ca2+/calmodulin-dependent protein kinase. Results: Here we describe for the first time, that besides sponge cells, mammalian cell lines (mouse NIH-3T3 and human HeLa and SaOS-2 cells) respond to ethylene, generated by ethephon, with an immediate and strong, transient increase in [Ca2+]i level, as demonstrated using Fura-2 imaging method. A rise of [Ca2+]i level was also found following exposure to ethylene gas of cells kept under pressure (SaOS-2 cells). The upregulation of [Ca2+]i was associated with an increase in the level of the cell cycle-associated Ki-67 antigen. In addition, we show that the effect of ethephon addition to S. domuncula cells depends on the presence of calcium in the extracellular milieu. Conclusion: The results presented in this paper indicate that ethylene, previously known to act as a mediator (hormone) in plants only, deserves also attention as a potential signaling molecule in higher vertebrates. Further studies are necessary to clarify the specificity and physiological significance of the effects induced by ethylene in mammalian cells. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Background: Myocardial infarction (MI) is a major cause of heart failure. The carboxypeptidase cathepsin A is a novel target in the treatment of cardiac failure. We aim to show that recently developed inhibitors of the protease cathepsin A attenuate post-MI heart failure.Methods: Mice were subjected to permanent left anterior descending artery (LAD) ligation or sham operation. 24 h post-surgery, LAD-ligated animals were treated with daily doses of the cathepsin A inhibitor SAR1 or placebo. After 4 weeks, the three groups (sham, MI-placebo, MI-SAR1) were evaluated.Results: Compared to sham-operated animals, placebo-treated mice showed significantly impaired cardiac function and increased plasma BNP levels. Cathepsin A inhibition prevented the increase of plasma BNP levels and displayed a trend towards improved cardiac functionality. Proteomic profiling was performed for the three groups (sham, MI-placebo, MI-SAR1). More than 100 proteins were significantly altered in placebo-treated LAD ligation compared to the sham operation, including known markers of cardiac failure as well as extracellular/matricellular proteins. This ensemble constitutes a proteome fingerprint of myocardial infarction induced by LAD ligation in mice. Cathepsin A inhibitor treatment normalized the marked increase of the muscle stress marker CA3 as well as of Igγ 2b and fatty acid synthase. For numerous further proteins, cathepsin A inhibition partially dampened the LAD ligation-induced proteome alterations.Conclusions: Our proteomic and functional data suggest that cathepsin A inhibition has cardioprotective properties and support a beneficial effect of cathepsin A inhibition in the treatment of heart failure after myocardial infarction. [ABSTRACT FROM AUTHOR]

Background: Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to build and test their models. Previously, most research in these areas starts from the molecular fingerprints or physiochemical features of drugs, instead of their structures. Results: In this paper, a model called twin Convolutional Neural Network for drugs in SMILES format (tCNNS) is introduced for phenotypic screening. tCNNS uses a convolutional network to extract features for drugs from their simplified molecular input line entry specification (SMILES) format and uses another convolutional network to extract features for cancer cell lines from the genetic feature vectors respectively. After that, a fully connected network is used to predict the interaction between the drugs and the cancer cell lines. When the training set and the testing set are divided based on the interaction pairs between drugs and cell lines, tCNNS achieves 0.826, 0.831 for the mean and top quartile of the coefficient of determinant (R2) respectively and 0.909, 0.912 for the mean and top quartile of the Pearson correlation (Rp) respectively, which are significantly better than those of the previous works (Ammad-Ud-Din et al., J Chem Inf Model 54:2347–9, 2014), (Haider et al., PLoS ONE 10:0144490, 2015), (Menden et al., PLoS ONE 8:61318, 2013). However, when the training set and the testing set are divided exclusively based on drugs or cell lines, the performance of tCNNS decreases significantly and Rp and R2 drop to barely above 0. Conclusions: Our approach is able to predict the drug effects on cancer cell lines with high accuracy, and its performance remains stable with less but high-quality data, and with fewer features for the cancer cell lines. tCNNS can also solve the problem of outliers in other feature space. Besides achieving high scores in these statistical metrics, tCNNS also provides some insights into the phenotypic screening. However, the performance of tCNNS drops in the blind test. [ABSTRACT FROM AUTHOR]

Background: Shikonin, a natural naphthoquinone, is abundant in Chinese herb medicine Zicao (purple gromwell) and has a wide range of biological activities, especially for cancer. Shikonin and its analogues have been reported to induce cell-cycle arrest, but target information is still unclear. We hypothesized that shikonin, with a structure similar to that of quinone-type compounds, which are inhibitors of cell division cycle 25 (Cdc25) phosphatases, will have similar effects on Cdc25s. To test this hypothesis, the effects of shikonin on Cdc25s and cell-cycle progression were determined in this paper.Methods: The in vitro effects of shikonin and its analogues on Cdc25s were detected by fluorometric assay kit. The binding mode between shikonin and Cdc25B was modelled by molecular docking. The dephosphorylating level of cyclin-dependent kinase 1 (CDK1), a natural substrate of Cdc25B, was tested by Western blotting. The effect of shikonin on cell cycle progression was investigated by flow cytometry analysis. We also tested the anti-proliferation activity of shikonin on cancer cell lines by MTT assay. Moreover, in vivo anti-proliferation activity was tested in a mouse xenograft tumour model.Results: Shikonin and its analogues inhibited recombinant human Cdc25 A, B, and C phosphatase with IC50 values ranging from 2.14 ± 0.21 to 13.45 ± 1.45 μM irreversibly. The molecular modelling results showed that shikonin bound to the inhibitor binding pocket of Cdc25B with a favourable binding mode through hydrophobic interactions and hydrogen bonds. In addition, an accumulation of the tyrosine 15-phosphorylated form of CDK1 was induced by shikonin in a concentration-dependent manner in vitro and in vivo. We also confirmed that shikonin showed an anti-proliferation effect on three cancer cell lines with IC50 values ranging from 6.15 ± 0.46 to 9.56 ± 1.03 μM. Furthermore, shikonin showed a promising anti-proliferation effect on a K562 mouse xenograph tumour model.Conclusion: In this study, we provide evidence for how shikonin induces cell cycle arrest and functions as a Cdc25s inhibitor. It shows an anti-proliferation effect both in vitro and in vivo by mediating Cdc25s. [ABSTRACT FROM AUTHOR]

Background: The deadly costs of cancer and necessity for an accurate method of early cancer detection have demanded the identification of genetic and epigenetic factors associated with cancer. DNA methylation, an epigenetic event, plays an important role in cancer susceptibility. In this paper, we use DNA methylation and gene expression data integration and pathway analysis to further explore and understand the complex relationship between methylation and gene expression. Results: Through linear modeling and analysis of variance, we obtain genes that show a significant correlation between methylation and gene expression. We then examine the functions and relationships of these genes using bioinformatic tools and databases. In particular, using ConsensusPathDB, we analyze the networks of statistically significant genes to identify hub genes, genes with a large number of links to other genes. We identify eight major hub genes, all in strong association with cancer susceptibility. Through further analysis of the function, gene expression level, and methylation level of these hub genes, we conclude that they are novel potential biomarkers for breast cancer. Conclusions: Our findings have various implications for cancer screening, early detection methods, and potential novel treatments for cancer. Researchers can also use our results to develop more effective methods for cancer study. [ABSTRACT FROM AUTHOR]

Background: Cancer Tissue Heterogeneity is an important consideration in cancer research as it can give insights into the causes and progression of cancer. It is known to play a significant role in cancer cell survival, growth and metastasis. Determining the compositional breakup of a heterogeneous cancer tissue can also help address the therapeutic challenges posed by heterogeneity. This necessitates a low cost, scalable algorithm to address the challenge of accurate estimation of the composition of a heterogeneous cancer tissue. Methods: In this paper, we propose an algorithm to tackle this problem by utilizing the data of accurate, but high cost, single cell line cell-by-cell observation methods in low cost aggregate observation method for heterogeneous cancer cell mixtures to obtain their composition in a Bayesian framework. Results: The algorithm is analyzed and validated using synthetic data and experimental data. The experimental data is obtained from mixtures of three separate human cancer cell lines, HCT116 (Colorectal carcinoma), A2058 (Melanoma) and SW480 (Colorectal carcinoma). Conclusion: The algorithm provides a low cost framework to determine the composition of heterogeneous cancer tissue which is a crucial aspect in cancer research. [ABSTRACT FROM AUTHOR]

A conference paper about the deregulation of transcriptional repressor (CTCF) or CCCTC-binding factor and BORIS, a type of proteins transcripts, in breast cancer is presented. It discusses the method used for the evaluation of sub-cellular localization of BORIS and CTCF for establishing a correlation between cell lines and cancer tissues. It informs that BORIS is abnormally over expressed in aggressive cancer tissue.

Background: A mouse model of metastasis of human gastric cancer is one of the most important tools for studying the biological mechanisms underlying human gastric cancer metastasis. In this paper, we established a mouse model of metastatic human gastric cancer in nude mice that has a higher rate of tumor formation and metastasis than existing models.Methods: To generate the mouse model of metastatic human gastric cancer, fresh tumor tissues from patients that have undergone surgery for gastric cancer were subcutaneously implanted in the right and left groins of nude mice. When the implanted tissue grew to 1 cubic centimeter, the mice were killed, and the tumor tissues were examined and resected. The tumor tissues were implanted into nude mice and subjected to pathological examination, immunohistochemical staining, and real-time PCR for cytokeratin 8/18 (CK8/18), E-cadherin, vascular cell adhesion molecule-1 (VCAM-1) and intercellular adhesion molecule-1 (ICAM-1). The mice were also analyzed for metastasis in their peritoneum, abdominal cavity, and internal organs by histopathological examination. Tissues collected from these organs were examined for pathology.Results: After ten generations of implantation, all mice developed tumor growth at the implanted position, 94% of the mice developed metastasis to the retroperitoneum and viscera. The implanted and metastatic tumor maintained the same histological features across all generations, and metastasis was observed in the esophagus, stomach, spleen, liver, kidney, adrenal, intestine, and pancreas. These metastatic tumors revealed no detectable expression of CK8/18, E-cadherin, VCAM-1, and ICAM-1.Conclusions: This model will serve as valuable tool for understanding the metastatic process of human gastric cancer. [ABSTRACT FROM AUTHOR]

An abstract of the research paper titled,"Development of xenotropic murine leukemia virus-related virus (XMRV) producing B Cell lines from lymphomas from patients with Chronic Fatigue Syndrome ," is presented.

An abstract of the research paper titled,"Cell line tropism and replication of xenotropic murine leukemia virus-related virus (XMRV) ,"is presented.