To date, the expression profile of α-Klotho in human tissues is unknown, and its existence in some human tissue types is subject to much controversy.This is the first study to characterize systemwide tissue expression of transmembrane α-Klotho in humans. We have employed next-generation targeted proteomic analysis using parallel reaction monitoring in parallel with conventional antibody-based methods to determine the expression and spatial distribution of human α-Klotho expression in health.The distribution of α-Klotho in human tissues from various organ systems, including arterial, epithelial, endocrine, reproductive, and neuronal tissues, was first identified by immunohistochemistry. Kidney tissues showed strong α-Klotho expression, whereas liver did not reveal a detectable signal. These results were next confirmed by Western blotting of both whole tissues and primary cells. To validate our antibody-based results, α-Klotho-expressing tissues were subjected to parallel reaction monitoring mass spectrometry  identifying peptides specific for the full-length, transmembrane α-Klotho isoform.The data presented confirm α-Klotho expression in the kidney tubule and in the artery and provide evidence of α-Klotho expression across organ systems and cell types that has not previously been described in humans. The identification of the novel anti-aging protein α-Klotho in 1997 , aging hIn humans, emerging data indicate that aging is also modifiable and subject to regulation by complex genomic, proteomic, and environmental interactions . PrematuSα-Klotho) that contains only the SS domain and KL1, with the terminal 15 residues replaced by SQLTKPISSLTKPYH , single-pass transmembrane protein that contains a signal sequence, two homologous domains (denoted KL1 and KL2), a transmembrane domain, and a short cytoplasmic tail. The second isoform 62 kDa, 549 AA) arises from alternative splicing and is a secreted soluble protein (hereafter referred to as SSLTKPYH A (5). Al49 AA ariSSLTKPYH  and fibrSSLTKPYH , promotiSSLTKPYH , and regSα-Klotho. Given its implications for human health, data on the expression of α-Klotho in human tissues are urgently needed.α-Klotho protein expression has been found in mice, rats, and humans predominantly in the renal distal convoluted tubular cells  and to aTo overcome the above limitations, we employed state-of-the-art targeted proteomic analysis using parallel reaction monitoring (PRM) in parallel with conventional antibody-based methods to determine the distribution and precise nature of human α-Klotho expression in health.Cell lysates were obtained from commercially available primary cell cultures (ScienCell Research Laboratories), including: Human Epidermal Keratinocyte Lysate-adult , Human Prostate Epithelial Cell Lysate , Human Mammary Epithelial Cell Lysate , Human Renal Proximal Tubular Epithelial Cell Lysate , and Human Neuron Lysate . Human hepatocytes  were kindly gifted by Dr. Graeme Alexander, University of Cambridge. Human aortic smooth muscle cells (HA-SMCs) were provided by Caltag Medsystems .Human tissues for immunohistochemistry (IHC) and tissue lysate preparation were obtained with local ethical approval and informed written consent from four sources: 1) surgical specimens from the human tissue bank, Department of Pathology, University Hospital Coventry, and Warwickshire NHS Trust, UK ; 2) artery and kidney tissue from Warwick Medical School, UK ; 3) aorta from the Department of Medicine, University of Cambridge ; and 4) neuronal tissue, kindly donated by the New York Brain Bank at Columbia University, New York. Human parathyroid whole cell lysate was obtained from Abcam .Supplemental Data for details of the IHC protocol.The rabbit polyclonal α-Klotho antibody  and rabbit polyclonal isotype antibody  were used at a concentration of 1:100 to 1:250 for IHC staining and control experiments on formalin-fixed and paraffin-embedded, glass-mounted sections. Please see Assessment of α-Klotho distribution along the nephron was done morphologically. Determination was limited to proximal tubular and distal tubular  nephron segments. Consecutive section staining with hematoxylin and eosin was also used.g for 10 minutes at 4°C, and protein concentration was assayed by Lowry assay. As a control, we used recombinant human (rh) full-length α-Klotho protein (rh-α-Klotho) .Human tissues were homogenized in liquid nitrogen and resuspended in standard RIPA buffer supplemented with protease inhibitors cocktail (Sigma) and 1% Triton X-100. The lysates were clarified by microcentrifugation at 10 000 × Aliquots of cell lysates containing 20 μg protein were separated by SDS-PAGE and Western blotted with antihuman α-Klotho  at a concentration of 1:1000. Ten micrograms of rh-α-Klotho were used as control . The expected protein size for the full-length α-Klotho protein was 116 kDa.All protein samples (50 μg) were fractionated on precast SDS-PAGE gels  and stained with Coomassie blue stain. All samples were run with either an empty lane in between or on separate gels to avoid sample-to-sample contamination.Sα-Klotho  in 50 mm ammonium hydrogen carbonate (0.005 μg/μL). This process yielded 25 μL, of which 5 μL (1 μL for rh-α-Klotho) was subjected to liquid chromatography-electrospray ionization tandem mass spectrometry (MS/MS) and PRM analysis in an Orbitrap nano-ESI Q-Exactive mass spectrometer (Thermo Scientific), coupled to a nanoLC (Dionex Ultimate 3000 UHPLC).To restrict analyses to the full-length protein, only the gel band between 75 and 150 kDa was resected for analysis, thus excluding α-Klotho A. Gel fr2O, 0.1% formic acid; buffer B, 100% ACN, 0.1% formic acid; 60-min gradient; 0–5 min: 5% buffer B, 5–45 min: 5 to >56% buffer B, 45.1 to 50 min: 56% to >95% buffer B, 50.1 to 60 min, 5% buffer B). Peptides were transferred to the gaseous phase with positive ion electrospray ionization at 1.8 kV. In data-dependent acquisition, the top 10 precursors were acquired between 400 and 1600 m/z with a 2Th (Thomson) selection window, dynamic exclusion of 30 seconds, normalized collision energy of 25, and resolution of 70 000. For PRM, precursors were targeted in a 2Th selection window around the m/z of interest. Precursors were fragmented in high-energy collisional dissociation mode with normalized collision energy dependent on the target peptide. The first mass analysis was performed at a 70 000 resolution, an automatic gain control target of 3e  in μL-pickup injection mode at 4 μL/min flow rate for 10 minutes. Samples were then loaded on a Rapid Separation Liquid Chromatography, 75 μm × 50 cm nanoViper C18 3 μm 100 column  retrofitted to an EASY-Spray source with a flow rate of 350 nL/min  using the following settings: carbamidomethyl as fixed modification, methionine oxidation as variable modification; 25 ppm peptide tolerance, 0.8 Da MS/MS tolerance; maximum of two missed cleavages, peptide selection charge of +2, +3, or +4, and selection of a decoy database.Consistent with previous reports , we confExpression of α-Klotho was identified in smooth muscle cells throughout the arterial tree , C–H. InWe next examined α-Klotho protein expression in epithelial tissues. α-Klotho was highly expressed throughout the epidermis A and skiIn the male reproductive system, α-Klotho was expressed in Sertoli cells , E and FExamination of endocrine tissue revealed α-Klotho expression in follicular epithelial cells of the thyroid A, in theWe next examined α-Klotho expression in neural tissues. Expression was found in neuronal cells of the cerebral cortex D. In theTo confirm the presence and molecular weight of α-Klotho for those tissues where it was identified by IHC experiments, we next performed Western blotting on both human tissue lysates and (where available) human primary cells. For these experiments, we used full-length rh-α-Klotho protein as positive control. Western blotting demonstrated the presence of full-length (116 kDa) α-Klotho in lysates from human renal PTEC, HA-SMCs, neuronal cells, keratinocytes, and mammary epithelial cells C. ConsisSα-Klotho from α-Klotho. To further mitigate against the potential for contamination with Sα-Klotho, samples were first fractionated by one-dimensional gel electrophoresis. Only gel fragments between 75 and 150 kDa were excised for analysis, thus excluding the Sα-Klotho isoform (62 kDa). Full-length transmembrane rh-α-Klotho protein served as control and allowed generation of a reference PRM signature.The data described above provide strong support for the presence of full-length α-Klotho in the tissues shown. However, because these data are antibody-dependent, we next sought to corroborate these findings using PRM, a targeted proteomics approach. PRM is able to robustly and reproducibly identify a peptide signature for a protein of interest with precision and specificity and, importantly, can yield isoform-specific data. This methodology is therefore able to distinguish Sα-Klotho. The most robust signatures were yielded by GLFYVDFLSQDK (α-Klotho and Sα-Klotho) and QGAWENPYTALAFAEYAR, NNFLLPYFTEDEK, VYYMQNYINEALK, and LWITMNEPYTR . Given that GLFYVDFLSQDK is also present in Sα-Klotho, it does not distinguish between isoforms; in contrast, QGAWENPYTALAFAEYAR, NNFLLPYFTEDEK, VYYMQNYINEALK, and LWITMNEPYTR are not present in Sα-Klotho and can only be identified if full-length α-Klotho is present. We therefore restricted subsequent PRM analyses to GLFYVDFLSQDK and the four full-length-specific peptides described above. First, spectra for 16 candidate peptides unique to α-Klotho were generated by subjecting purified rh-α-Klotho to liquid chromatography-electrospray ionization MS/MS . We next sought the presence of these peptide spectra in positive control samples (human kidney and PTEC lysates) by PRM, aiming to identify the most reliable spectra and seeking in particular peptide spectra present in α-Klotho but absent in We next sought the presence of these peptide signatures in a variety of human tissue and cell lysates. α-Klotho extracellular domain peptide GLFYVDFLSQDK was present in whole kidney lysate, kidney cortex, kidney medulla, proximal tubular epithelial cells, parathyroid, pancreas, keratinocytes, mammary epithelial cells, prostate epithelial cells, neuronal cells, cerebral cortex, cerebellum, and artery  . The speSα-Klotho were identified in whole kidney lysate, kidney cortex, kidney medulla, proximal tubular epithelial cells, parathyroid, pancreas, keratinocytes, neuronal cells, cerebral cortex, cerebellum, and artery , indicating the presence of full-length α-Klotho in these tissues .Peptides present only in the full-length α-Klotho and absent from  tissues . RepreseWe report the identification of full-length α-Klotho protein in a wide variety of human tissues including the arterial tree, epithelia, endocrine, and neuronal tissues. Previously, α-Klotho protein expression in humans had only been described in the kidney  and paraThe wide tissue distribution of α-Klotho is consistent with its known role in aging. Rodent models of α-Klotho deficiency demonstrate reduced life span with a wide range of tissue phenotypes including gonadal failure, arteriosclerosis, emphysema, impaired cognition, hearing loss, vascular calcification, cardiac hypertrophy, osteopenia, and atrophy of skin, adipose tissue, thymus, and skeletal muscle . In humaOur data show full-length α-Klotho in elastic and muscular artery, consistent with our previous report . Other iExpression of α-Klotho in human skin has not previously been reported. α-Klotho deficiency results in skin atrophy, a hallmark of the aging process. The mechanisms by which α-Klotho maintains healthy skin are not known. We also identified α-Klotho in other epithelia including the intestine. Although it is possible that α-Klotho may similarly maintain intestinal epithelial health, it may equally be involved in phosphate and calcium transport and homeostasis. Indeed, α-Klotho may regulate expression of intestinal TRPV5 and TRPV6 as observed in kidney . EvidencWe found strong expression of α-Klotho in thyroid follicular cells, suggesting that it may be involved in the regulation of thyroid hormone production. Given that clinical and subclinical hypothyroidism increases in prevalence with increasing age, α-Klotho may be involved in regulating synthesis of thyroid hormone . This maWe identified α-Klotho at various sites throughout the central nervous system (CNS). Whereas previous reports have demonstrated α-Klotho in the rodent brain choroid plexus, Purkinje cells of the cerebellum , and stuAlthough we identified α-Klotho in a wide variety of organs and tissues, it could not be identified in hepatocytes. This is consistent with a recent report by Chen et al , showingOur study has several important strengths. First, our report is the first to systematically evaluate α-Klotho expression in a wide range of human tissue samples and cell types. Second, we have confirmed our previous observation of α-Klotho in the vascular tree. Third, in parallel to IHC and Western blotting, we have employed state-of-the-art PRM to provide antibody-independent data confirming the presence of α-Klotho within samples. All mass spectrometry data are publically accessible. Furthermore, we were able to identify peptide signatures specific for isoform 1 of α-Klotho , allowinOur findings should be interpreted against the limitations of our study. First, it has proven very challenging to liberate the hydrophobic full-length α-Klotho protein sufficiently to allow detection. Although evidence of presence is robust, the absence of peptides derived exclusively from full-length α-Klotho (isoform 1) should be interpreted with caution, for example in the case of prostate and mammary epithelial cell lines . SecondlIn summary, this is the first study to systematically characterize tissue expression of full-length α-Klotho in humans. Given the influence of α-Klotho on longevity and the impact of its deficiency on health and aging across multiple organ systems, our data provide an important first step toward elucidating the role of α-Klotho in health and disease.


Natural products play a significant role in cancer chemotherapy. They are likely to provide many lead structures, which can be used as templates for the construction of novel drugs with enhanced antitumor activity. Traditional research approaches studied structure-activity relationship of natural products and obtained key structural properties, such as chemical bond or group, with the purpose of ascertaining their effect on a single cell line or a single tissue type. Here, for the first time, we develop a machine learning method to comprehensively predict natural products responses against a panel of cancer cell lines based on both the gene expression and the chemical properties of natural products. The results on two datasets, training set and independent test set, show that this proposed method yields significantly better prediction accuracy. In addition, we also demonstrate the predictive power of our proposed method by modeling the cancer cell sensitivity to two natural products, Curcumin and Resveratrol, which indicate that our method can effectively predict the response of cancer cell lines to these two natural products. Taken together, the method will facilitate the identification of natural products as cancer therapies and the development of precision medicine by linking the features of patient genomes to natural product sensitivity. In recent years, many natural products were purified and shown to have cancer chemopreventive activity in laboratory, as exemplified by Camptothecin, Vinblastine, Embelin and Paclitaxel . These aTraditional exploring approaches of SAR focus on producing a range of analogues based on the basic skeleton of lead structure by synthetic chemists and searching empirically for their structural properties predictive of the antitumor activity . These SIn the present study, we developed a machine learning approach to predict the cell lines response to natural products, based on gene expression of cancer cell lines (genomic information) and the chemical descriptors of the considered natural products  for the first time. Empirical studies show that our method can obtain good performance when predicting sensitivity for hundreds of cancer cell lines to natural products in test set and case study analyses and indicate that both the structural properties and gene expression signatures are important determinants of antitumor activity of natural products. Taken together, this study outlines a first approach to predict drug response for natural products and generate novel natural product candidates for further studies.http://pubchem.ncbi.nlm.nih.gov), we identified 17 drugs as natural products or their derivatives from these 138 drugs (279–565 cell lines per drug (mean = 495 cell lines per drug) representing 8,420 cancer cell line-drug interactions. The publically available drug sensitivity  data for all the 17 natural products was downloaded from GDSC (http://www.cancerrxgene.org).In order to develop robust predictors of response to natural products, we collected and annotated a published large-scale preclinical dataset, namely, the Genomics of Drug Sensitivity in Cancer (GDSC) . This la38 drugs . These nAmong these 17 natural products, 13 of them were randomly chosen for models’ building, which represents 6,450 cancer cell line-natural product interactions , we obtained two antitumor natural products (Curcumin and Resveratrol), which have been proven effective in inhibiting proliferation and inducing apoptosis of various kinds of cancer cell lines. For Curcumin, it was screened on 16 cancer cell lines derived from 5 cancer types; and for Resveratrol, it encompasses drug sensitivity data for 13 cancer cell lines derived from 6 cancer types. After removing cell lines for which we could not find the corresponding gene expression information in GDSC, we finally obtained 7 and 8 cancer cell line-natural product interactions for Curcumin and Resveratrol, respectively (An independent test set (case studies) was extracted from the literature to further assess the performance of our proposed method. By searching anticancer herbs database of systems pharmacology (CancerHSP)  and natuectively .The GDSC gene expression microarray data were derived directly from the work of http://pubchem.ncbi.nlm.nih.gov). Initially, we obtained 1,444 1-D and 2-D descriptors of natural products directly from PaDEL. The chemical features with the same value across all natural products were further eliminated. Finally, we obtained 1,114 chemical features in this study.The chemical features of the natural products were generated with PaDEL software  from theIn this study, the prediction model was built using the software WEKA  with thein vitro drug sensitivity data derived from cell lines, with the addition of chemical properties, to predict cell lines’ response to natural products. The conceptual framework for prediction of cancer cell sensitivity to natural products is shown in 50 values) to a given drug with K-Means algorithm in WEKA , SVM (Support Vector Machine), Random Forest and Rotation Forest  were evaluated based on 10-fold cross validation (training set) and test set using Rotation Forest models. As can be seen in K = 3 compared with those in the case K = 2 when features number is set as 50. The similar situation occurred when the features number is set as 100 or 500 , and only cell lines in Sensitive and Resistant groups were used in the subsequent analyses.To find the optimal number of cancer cell lines clusters in In feature selection step, a 10-fold cross validation on the training set was conducted to get the optimal gene numbers. Examination on predicted AUC with respect to numbers of selected feature numbers showed a consistent trend of increasing first and decreasing afterwards with the increase of selected feature numbers except SVM model . As a reP-value zero was obtained in this permutation test. So the genomic features (genes) we chosen were more likely to be related to tumorigenesis.There were 468 genes (genomic features) in the top 1,000 features, of which 59 genes are cancer related genes (oncogenes or tumor suppressor genes), where oncogenes were obtained from database Cancer Gene Census , and tumThe top 1,000 features also contained 532 chemical descriptors of natural products. The systematic machine learning-based integration of various data sources, including chemical structure and genomic information, can provide better discriminative power than those using only individual data sources. This may presents a simple and promising strategy to predict antitumor activity of unknown natural products using pharmacology data and machine learning approaches.The detailed 468 genes and 532 descriptors used in the top 1,000 features are shown in In this study, in order to identify the best machine learning technique suitable for predicting cancer cell sensitivity to natural products, we comprehensively evaluated the performances of SVM (LibSVM), Decision Tree (J48), Random Forest, and Rotation Forest classifiers. All these algorithms were implemented using the Weka package with the default parameter configuration. Rotation Forest has been proven to be a relatively stable machine learning method in our previous work , which aThe detailed classifiers assessment results of 10-fold cross validation (training set) and test set are shown in http://www.ncbi.nih.gov/pubmed), we obtained 2 natural products screened on 29 cancer cell lines: Curcumin  and test set, and further validated by modeling two additional natural products . The experimental results show that our method can effectively predict the response of cancer cell lines to natural products.Although our final best model is based on both the gene expression signatures of cancer cells lines and the chemical properties, novel features that better describe natural product sensitivity can be easily incorporated into our prediction system to further improve the prediction performance of natural product response. In our future work, we will add other genomic features such as mutation information into the prediction model. Besides these genomic information, epigenetic and protein level information also play very important role in natural product response mechanism, and thus should be incorporated in our prediction system. In addition, it should be noted that in the current study we focused on “natural product sensitivity in cancer.” In the future, we will consider extending our model to non-natural product sensitivity prediction. Last, we will offer an online web interface through which our approach can be implemented to computationally predict natural product sensitivity.10.7717/peerj.1425/supp-1Figure S1K = 3 (blue) we obtained a higher AUC than in the case K = 2 when features number is set as 100. Cluster3, the case K = 3; Cluster2, the case K = 2; CV, cross validation; Camp, Camptothecin; Epot, Epothilone B; Pacl, Paclitaxel; Shik, Shikonin; AUC, Area under the curve.Bar chart showing in the case Click here for additional data file.10.7717/peerj.1425/supp-2Figure S2K = 3 (blue) we obtained a higher AUC than in the case K = 2 when features number is set as 500. Cluster3, the case K = 3; Cluster2, the case K = 2; CV, cross validation; Camp, Camptothecin; Epot, Epothilone B; Pacl, Paclitaxel; Shik, Shikonin; AUC, Area under the curve.Bar chart showing in the case Click here for additional data file.10.7717/peerj.1425/supp-3Table S1Click here for additional data file.10.7717/peerj.1425/supp-4Table S2Click here for additional data file.10.7717/peerj.1425/supp-5Table S3Click here for additional data file.10.7717/peerj.1425/supp-6Table S4Click here for additional data file.10.7717/peerj.1425/supp-7Table S5Samples, cancer cell lines in literature. Overlap, cell lines that we predicted. Correctly, cell lines that we predicted correctly.Click here for additional data file.


CITED4 is one member of a family of transcriptional cofactors, several of which are deregulated in a variety of tumors, including colorectal cancer (CRC). We modulated CITED4 expression, in vitro, and analyzed the associated phenotypic and gene expression changes.CITED4-overexpressing and shRNA-mediated knockdown cell lines and control cell lines were established in the CRC cell line SW480. The cells were analyzed for changes in proliferation, apoptosis/cell cycle, migration, invasion, colony formation and adhesion. mRNA expression changes were determined by microarray and pathway analysis, and several deregulated genes were validated by qRT-PCR and Western blotting. Based on results obtained from these studies, the status of the actin cytoskeleton was evaluated by phalloidin/vinculin staining.Phenotypically, the CITED4-overexpressing cell line showed only moderate changes in adhesion. Microarray analysis identified several deregulated genes, including several G protein-coupled receptors. Phenotypic analysis of the CITED4 shRNA knockdown cell line demonstrated decreased cell proliferation and G2 cell cycle blockage. Microarray analysis identified many deregulated genes, and pathway analysis discovered genes linked to actin-associated adherens junctions/tight junctions . Phenotypically, no morphological changes of the actin cytoskeleton were seen.Upregulation of CITED4 in SW480 resulted in no obvious phenotype. CITED4 shRNA-mediated knockdown led to decreased cellular proliferation and modulation of a large number of genes, including the c-MET tyrosine kinase and several actin-associated adherens junctions/tight junction genes.The online version of this article (doi:10.1007/s00432-015-2011-5) contains supplementary material, which is available to authorized users. CITED4  is one member of the CITED family of proteins known to participate in the regulatory activity of several genes, including TGFB, ER, TFAP2 and HIF1A. CITED1 (msg1), CITED2  and CITED4 (mrg2) were originally identified as a family of homologous proteins possessing a conserved transactivating (CR2) domain that binds and transactivates the transcriptional regulator/histone acetyl transferase CBP/P300. A mouse transcript for CITED4 was originally identified, shown to be expressed in a wide variety of tissues, and to bind CBP/P300 . Cultures were grown in RPMI-1640 medium (Sigma) containing 10 % fetal bovine serum (FBS) (Biochrom) and 100 u penicillin/100 µg streptomycin (Sigma). All cell lines were passaged every 3–4 days. The cell lines in this study were regularly tested for contamination and authenticity.For the overexpression construct, the open reading frame of human CITED4  was amplified by PCR and then cloned into the pCDNA4 HISMAX vector . Thereafter, a green fluorescent protein , a kind gift of Dr. Daniel E. Stange, Dresden, Germany) was cloned into the EcoRI-XhoI site of the CITED4 construct as well as into the empty vector, and both constructs (IRES-GFP and CITED4-IRES-GFP) were sequenced. For the knockdown experiments, five short hairpin (sh) RNA constructs to CITED4, as well as a control hairpin, GFP and empty (pLKO.1) vector were obtained from the human RNAi Consortium library (Sigma-Aldrich) . The medium was changed after the first day, and the virus-containing medium was harvested 2 days later. The virus particles were concentrated by ultracentrifugation  in a Beckman L70 ultracentrifuge  and taken up in 100 µl PBS. The lentiviral titer was determined by GFP transduction in HEK293T cells as well as by puromycin-selected limited dilution and colony formation.Replication-incompetent lentivirus  was created using a second-generation lentiviral packaging system. The CITED4 shRNA/control plasmid constructs (7 µg) were co-transfected together with the helper plasmids pMD2.G (2.1 µg) and PSPAX2 (5.7 µg) into 2 × 10The CITED4-overexpressing cell line and control cell line were prepared by transfection of SW480 with the IRES-GFP and CITED4-IRES-GFP constructs using Jetprime transfection reagent  according to the manufacturer’s recommendations. Selection was performed starting at day 3 using 300 µg/ml zeocin . For the creation of CITED4 shRNA permanent cell line/control cell lines, SW480 cells were transduced with a tenfold multiplicity of infection (MOI) of the respective lentivirus (see above). The virus was removed by medium change at day 1, and selection was performed starting at day 3 using 2 µg/ml puromycin.4 cells of each cell line were plated in triplicate into 12-well plates and grown for a period of 8 days. The wells were harvested at day 2, 4, 6 and 8 and the cells counted using a Vi-Cell XR cell counter . For colony formation assays, 1 × 103 cells of each respective cell line were plated in triplicate into six-well plates using 2 ml medium/well. The medium was changed every 3 days. After 17 days, the medium was removed, and the cells were washed in PBS and stained 7 min with 2 % methylene blue/50 % ethanol. Thereafter, the excess stain was removed by H2O washings. The colonies (>50 cells) were manually quantified using the ImageJ program.Proliferation of both the CITED4-overexpressing/shRNA cell line was analyzed. To this end, 5 × 10.5 cells from each line were passaged into 12-well plates containing 1 ml RPMI-1640 plus 10 % FCS and penicillin/streptomycin. The cells were harvested at day 1, 3 and 5 for total RNA preparation . Several separate experiments were performed, and equal amounts of total RNA from each specific time points for each cell line were pooled. RNA integrity (RIN > 8.5) was confirmed using an Agilent Bioanalyzer. Microarray hybridization of each of the samples was performed on Agilent 4 × 44 k arrays according to the manufacturer’s instructions using a two-color system with Universal Human Reference RNA  as a control for each sample. The hybridized microarrays were scanned using an Agilent Microarray Scanner (GS2565CA), and raw data were generated from the scanned images using Agilent Feature Extraction Software. Further processing and normalization (filter vsn) were performed using Chipyard, an in-house developed microarray analysis tool. Venn analysis was performed using the Chipster Microarray Analysis Program  under the accession numbers E-MTAB-2486 and E-MTAB-2487.For microarray analysis of both the SW480 CITED4-overexpressing cell line (CITED4-ires-GFP)/control cell line (ires-GFP) and the CITED4 shRNA permanent cell line/control cell lines, 1 × 10http://r2.amc.nl). Functional annotation of the expression data from the microarray analysis obtained in the current study was performed using the DAVID bioinformatics resource . Genomic DNA removal and first-strand synthesis  were performed using the Quantitect Reverse Transcription Kit  according to the manufacturer’s instructions. The 20 µl first-strand syntheses were diluted 1/3 in water and 2 µl was used for quantitative real-time PCR together with 10 pmol of the appropriate primer pairs (see Supplementary Table 1) and 6 µl Absolute SYBR Green Rox Mix . The reaction mixtures were amplified/quantified using the 7900HT Real-Time PCR System thermocycler  using the following cycling conditions 50 °C—2 min, 95 °C—15 min, 40 cycles of 95 °C—15 s, 60 °C—1 min for amplification followed by 95 °C—15 s, 60 °C—15 s for annealing curve determination. Standard curves were performed for each of the amplifications. The genes of interest were normalized against the housekeeping genes DCTN2 and PGK1.All qRT-PCR experiments were analyzed in accordance with the MIQE guidelines  with protease inhibitors , incubated on ice for 45 min and pelleted thereafter by centrifugation. The supernatant was quantitated using BCA protein detection reagent . Twenty micrograms of the protein lysate was separated on 4–12 % polyacrylamide gels  using 1× MES-SDS as a running buffer. The separated proteins were transferred electrophoretically (2 h at 240 mAmp) onto a PVDF membrane  using an XCELLII transfer module (Invitrogen) in transfer buffer . Following the protein transfer, the blots were incubated in blocking buffer [5 % milk powder in TBS ] for 1 h. Thereafter, the blot was incubated with the primary antibody at the appropriate dilution (see Supplementary Table 2) in blocking buffer for 1 h or overnight. Then, the antibody was removed and the blot washed 3 × 10 min in TBST (TBS with 0.02 % Tween 20). Thereafter, the blot was incubated with the secondary antibody at the appropriate concentration (see Supplementary Table 2) in blocking buffer for 1 h. After three further washes in TBST, the protein of interest was identified using enhanced chemiluminescence  coupled with X-ray film exposure.5 cells of the CITED4 knockdown cell lines and the respective controls were passaged onto sterile Labtek Chamber slides  in 1 ml RPMI-1640 plus 10 % FCS and penicillin/streptomycin. The slides were prepared for fluorescence microscopy of actin  and vinculin (Alexa 488 staining) using an actin and focal adhesion staining kit (Millipore) according to the manufacturer’s instructions. The dilutions of phalloidin-TRIC, vinculin primary antibody and the secondary antibody can be found in Supplementary Table 2.2 × 10n − 1) or two-tailed Student’s t tests on 2–3 separate experiments with a total sample size of 6–9. The exact number of experiments and sample size are shown in the figure legends.Statistical evaluation of the phenotypic data/qRT-PCR assays were demonstrated by calculation of standard deviation for small samples (http://r2.amc.nl), was screened for data of CITED4 in CRC. Microarray expression data from 226 patients exhibiting different stages of CRC .Migration (scratch) assays of both the overexpressing and knockdown cell line were analyzed at 0, 6, 12, 24 and 30 h after scratching . No change in migratory activity was seen between the overexpression/and shRNA knockdown cell lines when compared to the control cell lines. Cellular invasion was also analyzed in the CITED4-overexpressing/shRNA knockdown cell lines using Matrigel-coated Transwell chambers at 48 h after plating . No increase in invasiveness was seen in the CITED4-overexpressing cell line , and a tendency toward increased invasiveness seen in the CITED4 shRNA permanent cell line was not statistically significant .Analysis of adhesiveness to several known basement membrane proteins using a commercially available adhesion array system demonstrated an increased adhesiveness of the CITED4-overexpressing cell line to collagen IV and vitronectin when compared to the control cell line . In contrast, no changes in adhesiveness to any of the basement membrane proteins were seen in the CITED4 shRNA knockdown cell line compared to controls .Expression analysis was performed in the CITED4-overexpressing, as well as the CITED4 shRNA knockdown cell lines, at day 1, 3 and 5 after plating using Agilent 4 × 44 K microarrays. Upon comparison of the overexpressing cell line with the control line, very few genes (120) were deregulated over the 5-day period, despite using a log2 fold change of one as a cutoff for deregulation (see Supplementary Table 3). In contrast, analysis of the CITED4 shRNA permanent cell line compared to the empty vector/control shRNA cell lines showed a larger number of deregulated genes   analysis on several genes derived from either the manual search of the overexpressing cell line or, for the knockdown cell line, the KEGG pathway genes list using RNA derived from multiple time course experiments. Although nearly all of the genes analyzed showed a tendency to correlate with the microarray data in general, only ca. 50 % of the genes analyzed showed a statistically significant deregulation, and a great majority were deregulated at more than one time point. Figure In order to see if the knockdown of CITED4 in the shRNA permanent cell line induced obvious morphological cell changes in either the actin cytoskeleton or in focal adhesion contact points, the CITED4 shRNA permanent cell line and controls were stained with phalloidin (F-actin) and vinculin  3 days after cell line plating . No obvious changes in either the actin filament cytoskeleton or in focal adhesion contact points were seen at this time point.Downregulation of c-MET might be causal for the inhibition of proliferation seen in the CITED4 shRNA permanent cell line. As such, transient transfections of the CITED4 shRNA permanent cell lines were performed using a c-MET overexpression construct in order to rescue the phenotype. Overexpression of c-MET resulted in a strong increase in c-MET mRNA and protein expression in the CITED4 shRNA permanent cell line as well as in the control cell lines, but the c-MET overexpression was not accompanied by increased proliferation in any of the cell lines (data not shown).The effect seen in the SW480 CITED4 shRNA permanent cell line (ND2) encouraged us to create two further CRC permanent cell lines by transduction of the same shRNA into LS174T and CL34 cells. Both permanent cell lines exhibited an effective knockdown of CITED4 mRNA. While no inhibition of proliferation was seen in LS174T, the inhibition of proliferation in CL34 was significant. However, as this effect was minor, we decided not to continue evaluation of these cell lines.Overexpression of CITED4 results in no significant changes in proliferation or colony formation, but shows a moderate upregulation of adhesiveness to the basement membrane proteins collagen IV and vitronectin, which, however, is not associated with a change in invasive phenotype. Decreased collagen IV expression is seen in CRC patients and is associated with poor prognosis ], colony formation, migration, invasion or adhesion were noted. Dowregulation of CITED4 in two further CRC cell lines showed significant, but only minor, inhibition of cell proliferation in one of the lines. This is best explained by the inhibition of proliferation seen in the CITED4 knockdown being dependent upon the genetic background in which it occurs.Knockdown of CITED4 in SW480 cells resulted in the deregulation of a large number of genes, several of which encode proteins seen together with actin-associated adherens/tight junctions  and/or cell proliferation . In the CITED4 knockdown cell line, downregulation of MET was initially thought to be causal for the inhibition of proliferation seen upon knockdown, for MET and its ligand, hepatocyte growth factor/scatter factor (HGF/SF) is known to influence proliferation in a number of different cell types  in SW480 cells. Ezrin, a known cytoskeletal linker which binds both to actin filaments and CD44  colorectal tumors. Knockdown of CITED1 in the APCmin/+ mouse model is associated with increased survival accompanied by a paradoxical WNT-associated increase in apoptosis . The CITED family members show not only fairly high sequence conservation, but also show similar binding partners, such as HIF-1alpha, CBP/P300 and TFAP2. All three members of the CITED4 family have been shown to be involved, to some degree, in tumorigenesis  associated G protein-coupled receptors. Knockdown of CITED4 in the same cell line inhibited cellular proliferation accompanied by a moderate (G2) cell cycle blockage. Expression analysis showed the deregulation of a large number of genes, including genes involved in the formation of actin-associated adherens/tight junctions, however, without an obvious disruption of the actin cytoskeleton.Supplementary Fig. 1. Kaplan-Meier analysis of the data from Jorissen et al .  (JPEG 86 kb)Supplementary Fig. 3. Migration (scratch) assays-CITED4 overexpressing- (left) and shRNA-knockdown (right) cell line at 30 h after scratching. Two experiments, n = 6.  (JPEG 80 kb)t test values CITED4 overexpressing/shRNA knockdown cell lines vs. controls. Two experiments, n = 6.  (JPEG 109 kb)Supplementary Fig. 4. Invasion assays. A CITED4 overexpressing permanent cell line and control. B CITED4 shRNA knockdown permanent cell line and controls. Invasion was measured 40 h after plating into transwells. *, t test values (p < 0.05) CITED4 overexpressing/shRNA knockdown cell lines vs control. Two experiments, n = 6  (JPEG 150 kb)Supplementary Fig. 5. Adhesion array analysis. A CITED4 overexpressing permanent cell line and control.  B CITED4 shRNA knockdown permanent cell line and controls. *, Significant Supplementary Fig. 6. F-actin  and vinculin immunofluorescence microscopy of the CITED4 shRNA permanent cell line and controls. A empty vector permanent cell line. B control shRNA permanent cell line. C CITED4 shRNA permanent cell line. Orange: actin  staining, green: vinculin staining, blue: DAPI. All pictures at 400x enlargement.  (JPEG 99 kb)Supplementary methods. (PDF 182 kb)Supplementary Table 1. Oligonucleotide primers for cloning and qRT-PCR analysis. (PDF 215 kb)Supplementary Table 2. Antibodies used for western blot analysis and fluorescence microscopy. (PDF 181 kb)Supplementary Table 3. Significantly deregulated genes-CITED4 overexpressing permanent cell line. Note: Several genes are represented by more than one oligonucleotide on the microarray.  (XLSX 95 kb)Supplementary Table 4. Significantly deregulated genes-CITED4 shRNA-knockdown permanent cell line. Note: Several genes have more than one oligonucleotide on the microarray. Therefore, the number of positions (clone numbers) in Supplementary Table 4 is larger than the number of genes illustrated in the Venn diagram Fig. .  (XLSX


Filoviridae, can cause severe hemorrhagic fever in humans. The ongoing Ebola virus (EBOV) disease epidemic in Western Africa claimed more than 11,300 lives and was associated with secondary cases outside Africa, demonstrating that filoviruses pose a global health threat. Bats constitute an important natural reservoir of filoviruses, including viruses of the recently identified Cuevavirus genus within the Filoviridae family. However, the interactions of filoviruses with bat cells are incompletely understood. Here, we investigated whether filoviruses employ different strategies to enter human and bat cells. For this, we examined host cell entry driven by glycoproteins (GP) from all filovirus species into cell lines of human and fruit bat origin. We show that all GPs were able to mediate entry into human and most fruit bat cell lines with roughly comparable efficiency. In contrast, the efficiency of entry into the cell line EidNi/41 derived from a straw-colored fruit bat varied markedly between the GPs of different filovirus species. Furthermore, inhibition studies demonstrated that filoviruses employ the same host cell factors for entry into human, non-human primate and fruit bat cell lines, including cysteine proteases, two pore channels and NPC1 (Niemann-Pick C1 molecule). Finally, processing of GP by furin and the presence of the mucin-like domain in GP were dispensable for entry into both human and bat cell lines. Collectively, these results show that filoviruses rely on the same host cell factors for entry into human and fruit bat cells, although the efficiency of the usage of these factors might differ between filovirus species.Ebola and marburgviruses, members of the family  Filovirus infection can cause a life threatening hemorrhagic fever  in non-human primates (NHP) and humans, with case-fatality rates of up to 90%. Before 2013, filovirus outbreaks in human populations were restricted to remote areas in Central Africa and were associated with less than 500 cases. The Ebola virus (EBOV) outbreak in Guinea in 2013 resulted for the first time in viral spread from rural to densely populated areas and had severe consequences: The Ebola virus disease (EVD) epidemic affected major cities in Guinea, Liberia and Sierra Leone and caused 11,312 deaths (as of 11 October 2015). Moreover, secondary infections occurred in countries not hit by the epidemic, including the USA and Spain. Thus, filoviruses constitute a global health threat.Filoviridae contains three genera, Ebolavirus, Marburgvirus and Cuevavirus. The only species within the Marburgvirus genus, Marburg marburgvirus , and the following ebolaviruses are pathogenic to humans: Ebola virus , Sudan virus  and Taï Forest virus, . Reston virus (RESTV), the only member of the fifth ebolavirus species, causes disease in NHP but has so far not been associated with disease in humans. Moreover, RESTV infection of pigs and bats has been documented , an, anRouseEidolon helvum, albeit with low efficacy , non-human primate (Vero) and four fruit bat cell lines with VSVpp harboring filovirus GPs or VSV-G as control. First, we normalized the VSVpp for comparable transduction of HEK-293T cells and then used the particles for transduction of primate and bat cell lines . For Verefficacy . In sum,497RRTRR501 > 497AGTAA501) and cleavage was abolished [We next addressed whether the furin cleavage site and MLD, which are dispensable for entry into humans cells , 57–60, bolished , and EBObolished –60 althoWe next investigated whether GP-mediated entry into fruit bat cells depends on the same host cell factors as described for human cells. To this end, we incubated human (HEK-293T) and fruit bat cell lines with inhibitors interrupting discrete steps of the entry process before pseudotypes harboring either of the filovirus GPs were added. We chose the fruit bat cell lines EpoNi/22.1 and EidNi/41 for these studies since they either showed a comparable susceptibility for transduction by all tested GPs or displayed varying susceptibility depending on which particular filovirus GP was used, respectively. For EidNi/41 cells, we only included VSVpp harboring SUDV-, TAFV-, RESTV-, MARV- and LLOV-GP, since transduction of pseudotypes decorated with EBOV1976-, EBOV2014- or BDBV-GP was only slightly above the limit of detection . On the In comparison to cells treated only with diluent , we found that ammonium chloride, bafilomycin A1, E-64d, MDL28170, tetrandrine and U18666A inhibited entry driven by all filovirus GPs tested, while mannan and camostat mesylate had no appreciable inhibitory effect . InhibitFiloviruses, in particular ebola- and marburgviruses, pose a severe threat to humans and wildlife , not onlEpomops buettikoferi, EpoNi/22.1) were comparably susceptible to transduction mediated by all filovirus GPs tested. In contrast, cells from the Hammer-headed fruit bat  and the Egyptian fruit bat , displayed a reduced susceptibility for TAFV-GP- and BDBV-GP-driven pseudotype entry, respectively. The results obtained for transduction of RoNi/7, HypNi/1.1 and EpoNi/22.1 by EBOV-, SUDV-, RESTV- and MARV-GP are in keeping with and expand previous studies [Eidolon helvum, EidNi/41). VSVpp decorated with either of the two EBOV-GPs or BDBV-GP failed to appreciably transduce these cells, while all other GPs mediated moderate  to robust  transduction. During the revision of this manuscript Ng and colleagues reported inefficient EBOV-GP-driven entry into cells from the straw-colored fruit bat as compared to cells from other bats [We employed rhabdoviral pseudotypes harboring GPs representing all filovirus species  to study GP-mediated entry. Our results point towards pronounced differences in susceptibility of fruit bat cell lines to entry driven by filovirus GPs: Cells from the kidney of a Buettikofer's epauletted fruit bat , in line with a previous study reporting superior transduction efficiency by LLOV-GP  when bat cell lines were examined . In addiA MLD and a furin-cleavage site are present in all filovirus GPs. Nevertheless, these elements are dispensable for entry into human cells lines and, in case of the furin cleavage site, for viral spread and pathogenesis in the host , 57–60. We employed inhibitors of cellular factors or processes known to be required for filovirus GP-driven entry into human cells to investigate whether filoviruses hijack the same factors/process to enter bat cells. We observed a profound decrease in GP-mediated transduction when endosomal acidification (inhibited by ammonium chloride or bafilomycin A1), calcium-dependent two-pore channels (inhibited by tetrandrine), cysteine proteases such as cathepsins and calpain (inhibited by E-64d and MDL28170) were blocked. Similarly, U18666A treatment, which induces an NPC1 knock-out phenotype, reduced entry into all cell lines tested, in line with the published finding that LLOV-GP requires NPC1 for entry into bat cell lines . In geneThe mannose-polymer mannan and the serine protease inhibitor camostat mesylate did not inhibit filovirus GP-driven entry into any of the cell lines tested. Mannan was previously shown to block augmentation of GP-driven entry by DC-SIGN and other mannose-binding lectins , 75, whiIn sum, the present study suggests that filoviruses do not need to adapt their entry mechanism to successfully jump between bats, NHPs and humans. However, differences in the efficiency with which filovirus species infect certain reservoir hosts might exist and might be due to polymorphisms in NPC1  and/or a


Cancer metastasis caused by circulating tumor cells (CTCs) accounts for 90% cancer-related death worldwide. Blocking the circulation of CTCs in bloodstream and their hetero-adhesion to vascular endothelia of the distant metastatic organs may prevent cancer metastasis. Nanomaterial-based intervention with adhesion between CTCs and endothelia has not been reported. Driven by the novel idea that multivalent conjugation of EpCAM and Slex antibodies to dendrimer surface may enhance the capacity and specificity of the nanomaterial conjugates for capturing and down-regulating colorectal CTCs, we conjugated the dendrimer nanomaterial with the EpCAM and Slex antibodies, and examined the capacity of the dual antibody-coated nanomaterial for their roles in interrupting CTCs-related cancer metastasis.The antibody-coated nanomaterial was synthesized and characterized. The conjugates specifically bound and captured colon cancer cells SW620. The conjugate inhibited the cells’ viability and their adhesion to fibronectin (Fn)-coated substrate or human umbilical vein endothelial cells (HUVECs) in a concentration-dependent manner. In comparison with SW480 and LoVo cell lines, the activity and adhesion of SW620 to Fn-coated substrate and HUVECs were more specifically inhibited by the dual antibody conjugate because of the higher levels of EpCAM and Slex on SW620 cell surface. The hetero-adhesion between SW620 and Fn-coated substrate, or HUVECs was inhibited by about 60-70%. The dual conjugate showed the inhibition capacity more significant than its corresponding single antibody conjugates.The present study provides the new evidence that coating nanomaterials with more than one antibody against CTCs may effectively interfere with the interaction between SW620 and HUVECs.The online version of this article (doi:10.1186/s12951-015-0072-x) contains supplementary material, which is available to authorized users. Cancer is the second killer that leads people to death worldwide ,2. It waper se. The serious adverse effects resulted in the damaged normal tissues and the decreased immunity  provided by Shandong Weihai Chenyuan New Silicone Materials, Co. Ltd were firstly surface-modified and sequentially conjugated with two fluorescence or non fluorescence-labeled antibodies. Briefly, G6 PAMAM dendrimers with amine ends of 256  were dissolved in 2 mL DMSO, and respectively reacted with 410 mg succinic anhydride (SA)  under vigorous stirring overnight. The obtained CC G6 dendrimers were dialyzed against DDI water to remove the unreacted molecules as well as organic solvents before lyophilization.The dual antibody-conjugated dendrimer conjugate was synthesized by employing the 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) catalytic method and designated as G6-5A-5S based on the reaction molar ratio of 1 dendrimers to 5 antiEpCAM to 5 antiSlex. AntiSlex (MW150KDa), IgG/IgM-FITC, antiEpCAM-PE were provided by BD company, and antiEpCAM (MW150 KDa) was purchased from Sigma-Aldrich and Abcam (Hong Kong) Ltd. CC G6 dendrimers  dissolved in 2 mL phosphate-buffered saline (PBS) were activated using EDC  and N-hydroxysuccinimide (NHS)  at room temperature for 1 h. The activated dendrimers were reacted with the combined antiEpCAM  and antiSlex  under vigorous stirring overnight. The single antibody-coated dendrimer conjugates were similarly synthesized and denoted as G6-5A and G6-5S. There were approximately two aEpCAM molecules in one G6-5A conjugate and six aSlex in one G6-5S conjugate according to the UV analysis. The fluorescence-labeled dual antibody conjugate PE-5A-G6-5S-FITC was also synthesized and designated following the similar procedure just by using antiEpCAM-PE and antiSlex-FITC (antiSlex and IgG/IgM-FITC were used together) antibodies, instead. The activated CC G6 dendrimers were reacted with antiSlex-FITC for 12 h, then with antiEpCAM-PE for another 12 h. All the reactions were conducted under vigorous stirring overnight in dark. Finally, all the conjugates were purified via dialysis  against DDI water overnight before lyophilization. Dendrimers with large surface functional groups were almost able to assemble with all of the antibodies as one entity at the above designed molar ratios. Transmembrane dialysis was used to remove the intermediates and small molecules.220 nm  and the merged fluorescence intensity at FITC λex 488 nm, λem 500–535 nm and PE λex 568 nm, λem 560–660 nm (Olympus FluoView 1000). The morphological property and particle size of G6-5A-5S conjugate were determined by FSEM measurement.The presence of antibody or fluorescence-labeled antibody onto the dendrimer surface was respectively confirmed by the UV absorption value at λ−1 of collagenase in PBS and cultured by us with M199 medium supplemented with 20% fetal bovine serum (FBS), 100 μg mL−1 endothelial cell growth supplement (ECGS), 50 μg mL−1 heparin sodium and 1% P/S in a culture flask coated with 0.2% gelatin after some necessary pretreatment [2 at 37°C for the subsequent experiments. HUVECs were used for no more than six passages.Human colorectal carcinoma cell lines including SW480, SW620 and LoVo were purchased from the Type Culture Collection of the Chinese Academy of Sciences, Shanghai, China and kept in a minimal number of passages, then cultured in RPMI 1640 medium supplemented with 10% heat-inactivated fetal calf serum (FCS) and 1% penicillin/ streptomycin (P/S). HUVECs were obtained from the fresh human umbilical cords of new-born babies with 1 mg mLreatment . All theA Becton Dickinson (BD) multiparametric fluorescence-activated cell sorting (FACS) Aria III with laser excitation set at 488 was used for flow cytometric analysis. According to the forward versus side scatter histograms, gating strategy was used to set P1 gate for determining the target colon cancer cell lines. Fluorescence signals derived from PI (or PE) and DiOC6(3) (or FITC) were respectively detected through 585 and 530 nm bandpass filters. Side angle scattered light (SSC) versus PI histogram displayed the cell cycle distribution, SSC versus PE (or FITC) histogram showed the expression levels of biomarkers, SSC versus DiOC6(3) histogram revealed the cellular MMP, and PE versus FITC dot plots showed the captured cell numbers by the synthesized conjugate. All the data were acquired based on the collected 10,000 cells satisfying the light scatter criteria and analyzed using the BD FACS Diva software provided with the system.−1) to the adherent and suspensory colon cancer cell lines, the optimal incubation time was in advance determined according to the analysis of time-response cell capture assay shown in Additional file sine qua non for the quick and efficient cell capture.To explore the binding and capture capability of fluorescence-labeled dual antibody conjugate PE-5A-G6-5S-FITC at various concentrations  (1% PBSA) for 30 min. After 1 h of co-incubation with PE-5A-G6-5S-FITC conjugate at various concentrations  in a humidified atmosphere of 5% CO2 at 37°C, cell lines were washed with PBS to remove the unbound conjugate, and fixed with stationary liquid (Vmethanol:Vacetone = 7:3) for 1 min, then stained with 10 μg mL−1 of nuclei stain dihydrochloride (DAPI) solution for 15 min. Finally, cell lines were covered with serum-free medium for images taken by an Olympus FluoView 1000 laser confocal microscope respectively in the channel of DAPI, Alex Fluor 488 and 568.Cell lines at the density of 106/mL were suspended in each tube. Cell lines were treated with 1% PBSA, then with 20 μg mL−1 of PE-5A-G6-5S-FITC conjugate for 1 h at 37°C water bath. Cell lines without the treatment of conjugate were incubated with immunoglobulins labeled with PE or FITC in the similar way as isotype controls. After washing and centrifugation, the unbound conjugates or antibodies were abandoned. Cell lines suspended with PBS buffer were directly analyzed on a BD FACS Aria III analyzer with laser excitation set at 488 nm or further stained with Hoechst 33258 (labeling the nucleus) for analysis with a fluorescence inverted microscope .To evaluate the efficiency of PE-5A-G6-5S-FITC conjugate at capturing the colon cancer cell lines, SW620 and LoVo cell lines at the density of 103-1 × 104 cells/mL were cultivated on the 96-well plates with 1640 medium. When grew in the confluence of 70%-80%, cell lines were individually exposed to the conjugates at various concentrations  for 48 h. Then, 100 μL of serum-free medium containing 1 mg mL−1 MTT solution was added to incubate for another 4 h. Finally, the supernatant was aspirated and 150 μL of DMSO was added to each well to dissolve the water-insoluble blue formazan. The viability of each cell line induced by the conjugates was determined based on the optical absorption value at the wavelength of 570 nm (A570 nm) and expressed as A570 nm of the treated group divided by that of the control group.To investigate how the single and dual antibody conjugates  affected the cell proliferation, MTT analysis was conducted as we previously described. The effect of completely-carboxylated G6 dendrimers on cell activity was also tested. Cell lines at the density of 5 × 10−1) for 48 h. Then cell lines were trypsinised and washed with ice-cold PBS for three times. After fixed with 70% ice-cold ethanol overnight at −20°C, cell lines were washed and stained with PI solution at 37°C for 15 min. Finally, data acquisition and analysis were performed on a BD FACS Aria III flow cytometer and DNA integration software mflt32, respectively.To further discuss the effects of the antibody conjugates  on the cell population distribution in every phases , PI staining experiment was performed at 37°C as the kit instructions. Cell lines were cultivated in 6-well plates overnight, and incubated with various concentrations of G6-5A-5S conjugate  for 48 h, cell lines were trypsinized and collected after centrifugation. 500 μL of DiOC6(3) (2 nM) working solution was individually added into each tube, and kept at 37°C water bath for 20 min. The cellular MMP (Δψm) was finally assessed according to the fluorescence intensity of DiOC6(3) examined by the flow cytometry (BD FACS Aria III).Depolarization of cellular MMP usually predicts the starting of cell apoptosis. In this assay, DiOC6(3) (a lipotropy cationic fluorescent dye) staining was used to determine the change of MMP in colon cancer cell lines. Increment of fluorescence intensity with the accumulation of DiOC6(3) in mitochondria was accompanied with the descent of MMP. After exposure to the antibody conjugates  at various concentrations  were added onto each well for 1 h of incubation. The post-processing was similar to that described in MTT assay. 100 μL of serum-free medium containing 1 mg mL−1 MTT solution was used for another 4 h. After the supernatant was aspirated, 100 μL of DMSO was added to each well to dissolve the water-insoluble blue formazan. The optical density was read on an ELISA reader at a wavelength of 570 nm to determine the abilities of the conjugates to interfere with the adhesion. The relative adhesion (%) was finally evaluated by the A570 nm in the treated group compared to that in the control group.Adhesion of CTCs to extracellular matrix (ECM) was a critical step in the process of cancer metastasis. Colon cancer cell lines were usually used as CTC models. In cell adhesion assays, CC G6 dendrimers were used as the control. Fn-coated substrates were prepared as the ECM to test the capability of single and dual antibody conjugates  in interfering the adhesion of colon cancer cell lines to ECM. First of all, 10 ng mL−1 cytokine IL-1β for 4 h followed by individually incubated with the mixture of rhodamine 123-labeled colon cancer cell lines and single or dual antibody conjugates  at various concentrations  for 1 h. After removal of the non-adhered cells with PBS washing, ten visual fields were randomly selected and taken images by a fluorescence inverted microscope . The capability of the conjugates in inhibiting the adherence of cancer cell lines to HUVECs was determined by counting the numbers of fluorescence-labeled cells that adhered to HUVECs in sample groups relative to those in the control group.Adhesion of CTCs to vascular endothelium was another crucial starting point of cancer metastasis. HUVECs instead of vascular endothelial cells were used for adhesion assay in vitro. After grew in the confluence of 100% on the 24-well plates, HUVECs were pre-treated with 1 ng mL* and ** represented the comparison between sample and control, while # and ## represented the comparison between any two samples. A probability value of <0.05 was considered significant (* and #), and <0.01 was considered extremely significant (** and ##).Every experiment was performed independently and repeated at least three times. Data were expressed as the means ± standard deviations (SD). Statistical analysis was done by Student’s t-test and one-way analysis of variance (One-ANOVA). Multiple comparisons of the means were made through One-ANOVA analysis and demonstrated by the least significance difference (LSD) test (IBM SPSS Statistics 19.0). The symbol of


Motivation: The recognition and normalization of cell line names in text is an important task in biomedical text mining research, facilitating for instance the identification of synthetically lethal genes from the literature. While several tools have previously been developed to address cell line recognition, it is unclear whether available systems can perform sufficiently well in realistic and broad-coverage applications such as extracting synthetically lethal genes from the cancer literature. In this study, we revisit the cell line name recognition task, evaluating both available systems and newly introduced methods on various resources to obtain a reliable tagger not tied to any specific subdomain. In support of this task, we introduce two text collections manually annotated for cell line names: the broad-coverage corpus Gellus and CLL, a focused target domain corpus.Results: We find that the best performance is achieved using NERsuite, a machine learning system based on Conditional Random Fields, trained on the Gellus corpus and supported with a dictionary of cell line names. The system achieves an F-score of 88.46% on the test set of Gellus and 85.98% on the independently annotated CLL corpus. It was further applied at large scale to 24 302 102 unannotated articles, resulting in the identification of 5 181 342 cell line mentions, normalized to 11 755 unique cell line database identifiers.Availability and implementation: The manually annotated datasets, the cell line dictionary, derived corpora, NERsuite models and the results of the large-scale run on unannotated texts are available under open licenses at http://turkunlp.github.io/Cell-line-recognition/.Contact:sukaew@utu.fi Biomedical text mining methods are increasingly capable of accounting for the diversity of information found in this domain. While proteins and their interactions received much attention in BioNLP research in the last decade , recent functional interactions between gene products. Two genes in a functional interaction can, for instance, be associated to a specific disease or condition, or a particular phenotype. An example of such a case is a pair of synthetically lethal (SL) genes, for which a mutation in one of the two does not cause loss of viability, but the simultaneous inhibition of both genes leads to cell death , CCLE . We then describe publicly available cell line corpora, as well as those that are newly annotated in this work (Section 2.2). Further, we outline the available NER tools that recognize cell line names (Section 2.3). Finally, we implement a normalization procedure to map ambiguous textual symbols of cell lines to their unique identifiers in external resources (Section 2.4).cell line as a genetically stable and homogenous population of cultured cells that shares a common propagation history via experimental and selection processes. Cell lines can thus establish uniform and stable populations that maintain their characteristics over long periods of time, even indefinitely. Consequently, non-specific mentions such as ‘HUVECs’ are not considered cell lines in our work, while mentions such as ‘HUVEC-C’ are.We firstly scope our task of cell line mention recognition by defining what is considered a cell line. Following the definition of Cell Line Ontology (CLO) , accessions (e.g. CVCL_2050), synonyms (e.g. GOS3), as well as non-ambiguous identifiers to external resources such as CCLE or CLDB (e.g. cl5278) from Cellosaurus. Additionally, we augmented this data with mutation information obtained from CCLE and Cosmic, which are specific for human cancer cell lines, our application domain of interest for the envisioned use-case on SL gene pairs.We gathered a dictionary of cell line names derived from the Cellosaurus resource (version 6.5). We extracted all cell line names   by annotating cell line names in 1212 documents drawn from PubMed abstracts and PMC full text extracts. The documents were annotated to identify the names of specific cell lines or established categories of cell lines (Section 2.1). Only the spans of the actual names were marked, not including premodifiers or head nouns such as ‘cells’. Half of the corpus texts were drawn from the AnEM corpus . To avoid bias toward a limited number of well-known and well-described cell lines, we first sampled 3000 cell names at random from the dictionary. For each sampled name, we then selected at random a PubMed citation or a PubMed Central Open Access (PMC-OA) full-text article that contained that specific name, using strict, case-sensitive matching criteria and ensuring that no single document was chosen twice for distinct names. Approximately 15% of the names were matched in the literature, resulting in an initial dataset of 454 documents with exactly one tagged candidate cell line name each.Cell line corpus (CLL) was used for the open-domain evaluation described in Section 3.4.We then manually evaluated a randomly selected subset of 201 documents, marking whether the candidate name did in fact represent a cell line. For candidates that were not cell lines, the correct entity type (e.g. gene/protein or organism) was marked. We simultaneously annotated all other cell lines names occurring in the same sentence as the candidate mention. This http://nersuite.nlplab.org/) system on our newly annotated training corpus Gellus (Section 2.2.4). We compare the performance of these methods on various corpora using their held-out test sets.To recognize cell line names in text, we consider both dictionary-based tagging and selected publicly available NER tools that are applicable to the task. These tools include ABNER , GENIA tTo accommodate for minor differences in the extent of annotated spans in the different corpora, the evaluation criteria applied in this study and reported throughout the manuscript accept any overlap between a cell line mention tagged by a system and a gold standard annotation as a match.exact matching, aiming for high precision, and approximate matching, for high recall. The approximate criterion considers strings to match regardless of case and additionally only requires alpha-numerical characters to match; all other characters, such as space and hyphen are ignored. Thus, for example gos3 matches both GOS 3 and Gos-3 under the approximate matching criterion.We perform pattern matching using the cell line dictionary (Section 2.2.1) against all three corpora as a baseline for comparison with ML-based methods. Dictionary-based tagging is performed using two matching-specificity criteria: The supervised ML-based tagger ABNER , is implABNER provides a graphical user interface with a variety of features including automatic tokenization, batch mode annotation and a Java API which allows training ABNER on new corpora . We use The integrated GENIA tagger system provides various levels of text analysis: part-of-speech (POS) tagging, text chunking and NER .For POS tagging, the system is specifically tuned for analyzing English biomedical text, as it is trained on a combination of corpora from both biomedical and newspapers domains. For NER, the tagger is trained on the JNLPBA corpus, and it can thus recognize cell lines along with the other four JNLPBA entity types.We use auto-tokenization, and apply the tagger with default settings. Note that the GENIA tagger does not provide tools for training on new corpora.To recognize various types of biomedical entities including cell lines, Gimli implements linguistic analysis with supervised ML  (NERsuite is a generic named entity recognition toolkit based on the CRFsuite (fsuite/)  implemenFor NERsuite, we trained new models on all corpora, selecting the regularization and label bias parameters using a grid search of parameter values and evaluating performance on the development set. To assess the benefits of features derived from dictionary matching, we trained for each corpus one model with and one without the compiled cell line dictionary (Section 2.2.1), applying strict string matching against the dictionary for feature generation. For final evaluation, the system was trained on the combination of training and development sets.A crucial step following the recognition of cell line mentions in text, is their normalization or grounding, i.e. the disambiguation of occasionally ambiguous abbreviations and synonyms to unique, well-defined concepts in authoritative cell line databases such as COSMIC and CCLE.exact matching approach to map tagged cell lines to Cellosaurus names and synonyms. If none of the names or synonyms are matched, we subsequently follow an approximate matching criterion where the mentions along with the Cellosaurus names and synonyms are case-lowered and punctuation-stripped prior to character matching.Once the symbols are recognized from text (Section 2.3), we further link the detected mentions to Cellosaurus identifiers using both exact and approximate matching criteria (Section 2.3.1). In detail, we applied both criteria in a stepwise manner. First, we use the cl in Section 3.2. We then perform evaluation with training and evaluation on additional corpora, CellFindercl and Gellus (Section 3.3), with evaluation also on the open-domain corpus CLL (Section 3.4). Finally, we apply the best-performing tool to the entire available literature and analyze the results in the framework of our application to identify SL gene pairs (Section 3.6).In this section, we first provide the results of our qualitative evaluation of all available corpora (Section 3.1). For a comparative evaluation, we present the results of all tools trained on JNLPBAcell line annotations and annotation statistics (e.g. most frequently annotated strings) to assess qualitative differences among the corpora. We observed a number of systematic differences in the annotation, the most apparent relating to specificity constraints, the distinction between names and other mentions, and the extent of annotated spans. This section briefly presents the primary findings of this evaluation.We studied the corpus annotation guidelines, individual cell line annotations in the corpora, we used approximate matching criteria (Section 2.3.1) to match each annotated string against the cell line dictionary. The results show that the JNLPBAcl corpus contains the smallest portion of specific mentions (45%), compared to CellFindercl (67%) and Gellus (79%).The specificity of annotated mentions, i.e. the degree to which a mention identifies a specific entity as opposed to a general category of entities, is closely related to the feasibility of normalizing mentions to external database resources. Mentions such as ‘MCF-7’ and ‘CHO’ that can be unambiguously linked to particular cell lines in external resources are considered specific cell line name mentions. These are the primary target of our study. By contrast, mentions such as ‘T cell line’ and ‘human monocytic cell line’ cannot be unambiguously linked to unique identifiers, and are thus insufficiently specific to qualify as cell line names in our task definition (Section 2.1). To quantify the specificity of cl annotation includes head nouns and various premodifiers. While most of the CellFinder cell line annotations are minimal, the corpus annotation contains a small mix of longer spans, likely resulting in part from the annotation merging. Note that for CellFindercl, instances of annotation overlap were resolved by eliminating nested annotations (Section 2.2.3).The newly introduced Gellus corpus only marks the minimal span of specific established individual cell lines or cell line categories, while the JNLPBAcell line annotations and unique strings, JNLPBAcl contains the largest number of annotations as well as unique strings, followed by the Gellus and CellFindercl corpora. Both JNLPBAcl and CellFindercl are specific to particular domains, while Gellus is a mixture of randomly selected articles and articles specifically selected for relevance to cancer genetics.cl (Section 2.2.2), CellFindercl (Section 2.2.3) and Gellus (Section 2.2.4). The results are summarized in We evaluated the performance of the cell line taggers on the held-out test sets of the three corpora, JNLPBAcl data set. As shown in F-scores (66–70%), ranking from highest to lowest in the order Gimli, NERsuite+dict, Genia Tagger, ABNER and NERsuite. Though the tools achieve similar F-scores, they differ more in terms of the precision/recall balance. Gimli obtains the highest precision, while NERsuite+dict achieves the best recall. All taggers considerably outperform dictionary matching on the JNLPBAcl corpus.As ABNER, GENIA Tagger and Gimli are trained on the JNLPBA corpus, we focus initially on the tagging results on the JNLPBAcl and Gellus than the dictionary method, which achieves comparatively high performance with exact matching. Remarkably high recall (>90%) is observed on the Gellus corpus with the approximate matching dictionary approach, but this inevitably comes with significantly lower precision (<15%) as a trade-off. These results support the observation of the qualitative analysis (Section 3.1) that the annotation scope of JNLPBA differs notably from that of the other two corpora in including also non-specific mentions.Next we consider the cross-corpus performance of the tools. All taggers trained on JNLPBA perform worse on CellFinderF-score is attained on JNLPBAcl (<4 p.p.), and very notable increases are observed on the CellFindercl (>16 p.p.) and Gellus (>20 p.p.) corpora. The difference in the increase in performance is also likely to be related to the proportion of annotated cell lines that can be linked to the dictionary, as discussed previously (Section 3.1).Focusing on NERsuite, dictionary features improve the performance of the tool on all tested corpora. A moderate improvement in cl and Gellus, to train NERsuite.As noted above, the performance of the ML-based taggers drops dramatically when they are evaluated across corpora. In this section, we further explore the influence of the corpus annotation scheme on tagging performance using two additional corpora, CellFindercl or Gellus corpora is similar to training the tagger with the JNLPBAcl corpus in that NERsuite achieves a relatively high F-score when trained and tested on datasets drawn from the same corpus. As noted in Section 3.2, the dictionary features greatly improve the performance of NERsuite on both the CellFindercl and Gellus corpora, achieving state-of-the-art results (>85% F-score).We first consider the intra- and cross-corpus performance of NERsuite with and without dictionary features. The results are summarized in cl or Gellus corpora and tested on the other corpus. A similar result, lower F-score, is observed when the NERsuite is evaluated across corpora, regardless whether it is trained on CellFindercl or Gellus. However, the system achieves a notably higher F-score on cross-corpus evaluation if it is supported with dictionary-based features. In particular, the tagger with dictionary features performs well also on CellFindercl if it is trained on Gellus, and vice versa. The performance of NERsuite+dict is slightly higher if it is trained on the Gellus corpus. Nonetheless, the performance of NERsuite without dictionary feature trained on either CellFindercl or Gellus on JNLPBAcl remains limited due to low recall.In addition, we also analyze the performance of NERsuite and NERsuite+dict trained on either CellFindercl and Gellus corpora. Additionally, the results indicate that taggers trained on data not limited to a specific domain generalize better to other corpora than taggers trained on domain-specific corpora. Finally, the performance of the tagger can be notably increased by incorporating relevant dictionary features. It should be noted that the choice of using NERsuite as retrainable system is due to its relative ease in incorporating the dictionary features. Comparable performance can be expected from other CRF-based retrainable systems with dictionary-derived features.In summary, the NERsuite tagger generalizes well if both the training and test corpora have similar specificity constraints in their annotation, such as the CellFinderOur original definition of relevant cell line mentions specifically included the need for recognized mentions to be linkable to external database identifiers so that the somatic mutations and SL interactions can be identified (Section 2.1). The CLL corpus was created specifically to assess the normalization opportunities of our approach.cell line annotations to random articles from PubMed, using the dictionary as an external reference of cell line names and following the cell line definition from CLO. The initial automatic annotation of 201 candidate cell line mentions was evaluated to have marked 147 cell line and 54 non-cell line mentions. The entity types of tagged non-cell lines include i) gene/gene product (37.03%), ii) chemical compound (14.81%) iii) organism or a part of organism name (12.96%) and other types (35.19%). Mentions of cell lines in the same sentence which were not pre-tagged were also annotated, resulting in an additional 194 cell line annotations. Altogether, there were 341 cell line name mentions in 148 sentences. From this dataset, we discarded all non cell line mentions creating CLL corpus suitable for evaluating taggers in recognizing established cell lines.As described in Section 2.2.5, we automatically introduced We apply NERsuite trained on different corpora both with and without dictionary features to assess the performance of the tagger on normalizable cell line names. The results, shown in As shown earlier, tagging cell lines can be carried out with relatively high accuracy, however, there still remain marginal mistakes of the tagger. To shed light on the remaining challenge, we perform an error analysis by training NERsuite supported with dictionary limiting to only Gellus train data and evaluate both false positive/negative predictions on the development set. We find that most of the false negatives (10 out of 12 unique cell line mentions) are cancer cell lines which are not included in our dictionary. Thus, we can expect an increase of the overall performance if an inclusive dictionary of cell line names is used. The false positive predictions are mainly caused by the overlapping symbols from other types of entities such as diseases (e.g AGS or gastric adenocarcinoma), genes/proteins (e.g. HK2 or hexokinase 2) and animal models (e.g. LLC or Lewis lung carcinoma model). It seems to be more difficult to improve the performance of the tagger by removing the false positives as they appear in contexts similar to cell lines.After thorough intrinsic evaluation of all tools and training corpora/settings, we applied the best performing NERsuite model to detect cell line names in the entire publicly available literature, including 23 343 329 PubMed citations and 958 773 PMC-OA full-text articles. The processing of this large-scale set of unannotated documents will provide valuable information about the scalability of our methods and their applicability to real-world use-cases and provide a first publicly accessible literature-scale resource of normalized cell line name mentions.We used the best model of NERsuite trained on Gellus+dict to recognize cell lines. NERsuite trained on the Gellus corpus recognized 5 181 342 mentions of cell line names in 1 003502 of a total of 24 302 102 documents. NERsuite alone took about 330 CPU core hours to tag cell line names in the entire dataset, averaging 42.5 PubMed abstracts and 1.5 PMCOA full-text documents per second. The run was parallelized on document level on a modern cluster computer.We further normalize the recognized cell lines on this large-scale data set (Section 2.4). The results are very promising, with 91.89% tagged cell lines matching a symbol in the dictionary. Among these, 6703 tagged mentions are linked to cancer cell lines, providing a ready data set for future work on identifying SL gene pairs. As a result, we can see that the tool can also perform well on a large scale, open-domain task, such as recognizing and normalizing the cell lines in the whole literature, without compromising its performance.We have presented a study of cell line mention recognition motivated by the needs of a project to identify synthetically lethal gene interactions in the literature. We analyzed publicly available machine learning-based taggers in comparison with dictionary-based tagging to identify the best-performing approach, with particular focus on mentions of specific cell line names.cell line annotation. We also created two additional manually annotated cell line corpora, Gellus and CLL. Gellus provides broad-coverage training data for ML-based taggers, and the CLL corpus, which marks sentences likely to contain normalizable cell line names, allows for extrinsic evaluation on an independently derived reference dataset.We prepared resources that are essential to the development and evaluation of cell line name recognition methods. For dictionary matching, we assembled cell line-related information from Cellosaurus and other resources. For the ML-based taggers, we prepared derived versions of the publicly available JNLPBA and CellFinder corpora filtered to cl were found to perform well on the test set of the same corpus but to generalize poorly to other corpora. The results of cross-corpus analysis using NERsuite showed that the Gellus and CellFindercl corpora can be used to train broadly compatible taggers, with training on Gellus providing the best results overall.We assessed tagger performance in both intra-corpus and cross-corpus settings, using approximate matching criteria to accommodate for differences in annotated mention spans. We additionally used the retrainable NERsuite system to evaluate the capability of dictionary features to improve tagger performance. Tools trained on JNLPBAF-score, far surpassing dictionary-based approaches. The tagger also achieved state-of-the-art performance when evaluated on the CLL corpus, demonstrating its suitability to recognizing cell lines that can be related to unique identifiers.The best-performing cell line name tagger, NERsuite trained on the Gellus corpus and supported by dictionary features, achieved a state-of-the-art best result of 88% Finally, we estimated the performance of the system for SL application, tagging and normalizing cell line names on the entire publicly available literature. The system was found to scale well and evaluation of its outputs against a cell line name dictionary indicated good generalization performance to large-scale data.To conclude, we have introduced new resources and a new system for the task of identifying specific cell line name mentions from text, achieving state-of-the-art performance. As part of an on-going project, the system applied to the entire literature recognized established cancer cell lines allowing future work in identifying synthetically lethal gene pairs from the literature. After cell line name recognition, we can form synthetic lethality relationship between cancer cell lines and human candidate genes. Depending on the data availability, we plan to continue our work on extracting the association between the cell line and candidate gene from text using rule-based and machine learning approaches. The textual support association can strengthen the synthetic lethality link between mutated gene and SL-candidate gene.