Cell lines form the cornerstone of cell-based experimentation studies into understanding the underlying mechanisms of normal and disease biology including cancer. However, it is commonly acknowledged that contamination of cell lines is a prevalent problem affecting biomedical science and available methods for cell line authentication suffer from limited access as well as being too daunting and time-consuming for many researchers. Therefore, a new and cost effective approach for authentication and quality control of cell lines is needed.ρ > 0.9). Our benchmarking studies revealed that CeL-ID method can identify a cell line with high accuracy and can be a valuable tool of cell line authentication in biomedical science. Finally, CeL-ID estimates the possible cross contamination using linear mixture model if no perfect match was detected.We have developed a new RNA-seq based approach named CeL-ID for cell line authentication. CeL-ID uses RNA-seq data to identify variants and compare with variant profiles of other cell lines. RNA-seq data for 934 CCLE cell lines downloaded from NCI GDC were used to generate cell line specific variant profiles and pair-wise correlations were calculated using frequencies and depth of coverage values of all the variants. Comparative analysis of variant profiles revealed that variant profiles differ significantly from cell line to cell line whereas identical, synonymous and derivative cell lines share high variant identity and are highly correlated  contains supplementary material, which is available to authorized users. Cell lines are an indispensable component of biomedical research and serve as excellent in vitro model systems in disease biology research including cancer. Cell lines are usually named by the researcher who developed them and till recently were lacking a standard nomenclature protocol –3. This Profiling of short tandem repeats (STRs) across several loci is the most common and standard test for cell line authentication as recommended by the Standards Development Organization Workgroup ASN-0002 of American Type Culture Collection (ATCC) , 2, 9–11With the advent and success of sequencing technologies, more and more researchers are using RNA sequencing to profile large amounts of transcript data to gain new biological insights. Moreover, RNA-seq data is also being used to identify single nucleotide variants in expressed transcripts . It may In this study, we present a RNA-seq based approach for Cell Line Identification (CeL-ID). We identify variants in each cell lines using RNA-seq data followed by pairwise variant profile comparison between cell lines using frequencies and depth of coverage (DP) values. Comparative analysis of variants revealed that variant profiles are unique to each cell line. Our benchmarking studies revealed that CeL-ID method can identify a cell line with high accuracy and can be a valuable tool for cell line authentication in biomedical research. In addition, using linear model regression technique, the approach can also reliably identify possible contaminator if requested. We choose to explore the utility of RNA-seq data in cell line authentication because it is the most commonly used technique among the seq-based methods and also relatively inexpensive, and we also demonstrated the minimum sequence reads requirement for each RNA-seq to maintain the authentication accuracy using a series of subsampling BAM files at 1million up to 50 million reads. With the popularity and accessibility of RNA-seq technology, a significant number of studies anyway involve the use of data from RNA-seq and hence the same can also be used to check the authenticity of the cell line.https://portal.gdc.cancer.gov/). The names of cell lines are used as is listed in NCI GDC archive and are listed in Additional file https://broadinstitute.github.io/picard]), followed by local re-alignment and re-calibration of base quality scores  and it contains RPKM values for all the genes in 1019 cell lines, covering all 935 CCLE RNA-seq set.The Cancer Cell Line Encyclopedia (CCLE) is a collaborative project focused on detailed genomic and pharmacologic characterization of a large panel of human cancer cell lines in order to link genomic patterns with distinct pharmacologic vulnerabilities and to translate cell line integrative genomics into clinic , 20. Genes (GATK ), and fies  whereas the second one has data for eight HCT116 cell lines (GSE101966) , 25. TheMEN1  and in Hd ARID1B  to alignd ARID1B , here toi, j 〉, the variants to be tested areTo assess the confirmation of two cell-lines to be either identical or highly similar in terms of their sequence variation profiles genome-wide or their expression levels, we choose to use Pearson Correlation to evaluate altered allele frequencies (FREQ) across two cell-lines or expression levels, facilitated by the number of non-zero FREQ shared between two cell-lines with at least 10 fold coverage in both cell lines. We choose FREQ, instead of direct counting of altered allele depth (AD), because that majority of altered allele fractions does not change with the expression level, and allele-specific expression may appear in cell lines with certain treatments but hopefully it will be a small proportion over a typically massive number of SNPs under consideration. To be specific, for any two cell lines 〈 di,k and fi,k are the depth of coverage (DP) and altered allele frequency at genomic location k of ith cell line, respectively. Note that we require variant has to exist in at least one cell line with 10 fold coverage. If a gene does not express, all mutations within this gene will not be considered unless its partner cell-line expresses this gene at a sufficient level. Therefore, the expression difference is already embedded in Pearson correlation, V. Similarly, correlations over gene expression levels between two cell lines are evaluated also by Pearson correlation coefficient, with requirement that genes with expression level > 0.1 (RPKM level) in at least one cell line. Hierarchical clustering was performed using MATLAB, using Pearson correlation of FREQ as the distance measure . Similar distribution is also observed in pair-wise correlation from WES samples. To estimate distribution parameters, we removed correlation coefficients less than 0 (unlikely) and greater than 0.8 (most likely due to replicate and derivative cell lines in CCLE collection), therefore it forms a truncated normal density function within an interval , as follows,To determine the significance of a detected correlation coefficient for a given cell line, we generated all pair-wise correlations for 934 RNA samples, and its distribution follows normal distribution a = 0, and b = 0.8. ϕ and Φ are standard normal density and distribution functions, respectively. We chose b = 0.8 as a cut-off threshold since pairs with correlation > 0.8 are derived from same parental lines or with some other biological relevance (see subsection Cell line authentication using variant comparisons in Results Section). Maximum-likelihood estimate (using MATLAB mle function) was employed in this study, and distribution parameters from distribution  for CCLE collection were estimated. For any given correlation coefficient ρi for the test sample against ith sample in CCLE, its p = P(ρ ≥ ρij) = 1 − F, where F is the cumulative distribution function of Eq. p < 0.001, and they are most likely derived from same cell origin if p < 10− 4. Multiple samples are identified as matching cells, we can revise Eq. where we fixed cut-off For gene expression level, the distribution of pair-wise correlation coefficient is more skewed towards 1.0; therefore, it is difficult to separate matching cells from mismatch cells (data not shown).x1 and x2, with unknown proportion q1 and q2, and we denoted the mixture cell as y, or,In addition to authenticate cells, one may also want to know whether or not the processed cells are contaminated by other cells, possibly from CCLE or additional cell lines collected in the lab, along with RNA-seq data. Assuming the test sample is a mixture of cell lines y, x1, x2 are vectors of FREQs from selected variant sites of test mixture sample and CCLE cell lines. Eq. Y = qX, where q = , if more than two cell mixture is hypothesized. To demonstrate the proof-of-concept, our current implementation takes top 200 sites, each direction that has most difference in FREQ comparing two samples . To further simplify the procedure, we also use our CeL-ID to identify the dominant cell, say x1 first. Following the similar studies for de-convoluting cell type proportions [x2, using robust linear model regression method (implemented in MATLAB fitlm function) to estimate q1 and q2, provided q1 + q2 ≤ 1. Slightly different to typical cell-type deconvolution methods, after determining the first contaminator, we can iteratively add other candidates from the entire CCLE collection and perform linear regression, and terminate the process until q value becomes negative or regression fails  is the Gaussian noise we added to q values  were downloaded and bam files were processed to call variants using an in-house pipeline described earlier in the methods section. Additionally, WES data for 326 cell lines available from GDC were also obtained and variants were identified. A total of 1,027,428 of variants were identified across all the cell lines with an average of 27,310 variants per cell line. As shown in Fig. RNA-seq data for 934 cell lines available from the NCI GDC legacy portal (ρ > 0.8) whereas most other pairs show poor correlation  were turned out to be known replicates, subclones, derived from same patients or have been known in the literature to share high SNP identity ; Fig. ρ = 0.97), and G27298.EKVX.1 & G41811.EKVX.1 (ρ = 0.96). Moreover, pair – G20492.HEL_92.1.7.2 & G28844.HEL.3 also identified to be very similar . Majority of cell line pairs rightly show poor correlation  but have different cells of origin and derived from different cancers. These cell lines may just happen to share high variant identity or somewhere during the cell culturing and maintenance cells got contaminated with each other. As expected, correlated cell lines tend to share more common mutations . Notice the difference of correlation coefficients of the best sample and the next-to-best samples are much smaller than those derived from variant profiles.Transcriptome profiles of any given cells are known to change during various treatments, and adapt to their environment as well. For base-line expression data provide through CCLE project, we can see their correlation holds for pair G20492.HEL_92.1.7.2 & G28844.HEL.3  = . Therefore, at L0.001 = 0.609, two samples will be considered similar with p < 0.001, or at L10-6 = 0.686 two samples will be unlikely similar (p < 10− 6). As a comparison, between RNA-seq and WES variant profiles  = , excluding all pair-wise comparison between same cell lines  .We tested the robustness of CeL-ID method by adding noise (Gaussian noise with zero mean) to the allele frequency of variant data for six pairs of cell lines as aforementioned. As evident from the Fig. p-value is much higher, which is due to the removal of common variants, only assess the agreement with variant sites perhaps differentiate MCF7 and KPL-1. Similar results were also obtained for HCT116 cells and are provided in Additional file Moreover, we tested the robustness of CeL-ID method on two independent test sets. First independent test set comprises of 12 RNA-seq datasets for MCF7 cells, which were downloaded from GEO (GSE86316) and represents mRNA expression profiles in MCF7 cells after silencing of MEN1 using small hairpin or chemical inhibition that affected expression profile of selected group of transcripts . The secFurthermore, to test the robustness of the system, effect of sequencing depth on the results was checked. We randomly selected nine cell lines and randomly subsampled it to 1 million (1 M), 2 million (2 M), 5 million (5 M), 10 million (10 M), 25 million (25 M), and 50 million (50 M) reads and ran the pipeline on subsampled subset of reads. As evident from the Fig. x1), and G20469.JHOS-2.2 as contaminant candidate (x2);For example, we select G20492.HEL_92.1.7.2 as test sample , and q2 ~ N. We also tested proportion of 0.70/0.30, as shown in Fig. Generate proportion z = q1 ∙ x1 + q2 ∙ x2 for both FREQ and DP;Following Eq. σnoise = , we perform,4.1.y = z + N only for FREQ, and then reset y to 0 if y < 0, and 100 if y > 100;Following Eq. 4.2.σ = 0.01, we obtained:Calling function CCLE_Identification to identify dominant cell-line, or provide cell line identification. For our particular example selection and for ρ = 0.97, p = 9.762 × 10− 271st match cell-line = G20492.HEL_92.1.7.2, with ρ = 0.90, p = 1.768 × 10− 202nd match cell-line = G28844.HEL.3, with ρ = 0.47, p = 0.5143rd match cell-line = G25242.K-562.3, with For each standard deviation Cell line contamination is a major issue facing biomedical sciences , 9. Huma4.3.q1 and q2. Results for our demonstrated case with ;Calling function CCLE_MixtureEstimate to identify contaminant and The possible mixture is G20469.JHOS-2.2, with proportion q2 = 82.3%, with t-stat = 210.0, p-value 0.000000e+00Note that 2nd matched cell line (HEL.3) is the cell line that has a high correlation with HEL_92.1.7.2 ; andσ from 0 to 6. With the increase of the s, the t stats for each proportion variable estimate decreases  for estimating cell line purity from RNA-seq data. A key advantage of using the CeL-ID method for cell line authentication is that it relies on a complete set of variants from the transcriptome instead of a fixed panel of small numbers of STRs or SNPs, and hence avoids the loss of statistical power caused by allelic dropout that affects STR-based authentication methods , 9–11. TCurrently, STR profiling is the ANSI standard for authenticating cell lines . STR proBenchmarking studies on independent test sets showed that CeL-ID method is precise and robust and can be used as a resource for cell line authentication. Genentech authenticated cell lines contain a consolidated list of 3587 cell lines , of whicIn summary, we have developed a new method called CeL-ID, for cell line authentication using variant profiles derived from RNA-seq data and has shown its robustness. CeL-ID successfully identifies identical, synonymous and derivative cell lines and also estimates about the possible contaminant. We have attempted to provide simple solution to problem associated with cell line authentication and hope this would help in adoption of regular cell line authentication.Additional file 1:List of sample names in CCLE dataset (BG-04-S1.xls). (XLS 92 kb)Additional file 2:Test results on an independent test set of eight HCT116 cells obtained from GSE101966 (BG-04-S2.xls). (XLS 38 kb)


Compound 3, reported in the literature as a synthetic derivative of furoscalarol 2, was for the first time isolated from a natural source. During the separation performed using a silica column in the presence of methanol, 16-methoxy derivatives  were obtained from the unintended reaction of 2. The isolated natural products 3 and 4 and the artifact 5 showed moderate to high cytotoxicity against six human cancer cell lines, whereas compound 6, the C-16 epimer of 5, showed no cytotoxicity at a concentration of 60 μΜ.Three furan-containing scalarane sesterterpenoids ( Scalarispongia is a genus of sponges that was established in the year 2000; previously, it was assigned as Cacospongia scalaris + .Compound 4: a pale yellow amorphous solid; c 0.3, CHCl3); UV λmax (log ε) 255 (2.98) nm; IR (KBr) νmax 3330, 2926, 1733, 1643, 1462, 1370, 1262, 1027 cm−1; 1H and 13C-NMR , see m/z 426.3011 [M + H]+ .Compound 5: a pale yellow amorphous solid; c 0.3, CHCl3); UV λmax (log ε) 218 (4.05) nm; IR (KBr) νmax 2957, 1766, 1463, 1373, 1058 cm−1; 1H- and 13C-NMR , see m/z 443.3129 [M + H]+ , m/z 411.2901 [M-OCH3]+ .Compound 6: a pale yellow amorphous solid; c 0.3, CHCl3); UV λmax (log ε) 219 (4.32) nm; IR (KBr) νmax 2957, 1766, 1463, 1373, 1058 cm−1; 1H- and 13C-NMR , see m/z 443.3129 [M + H]+ , m/z 411.2901 [M-OCH3]+ .Compound 2. The growth inhibition assays were performed as reported previously [50 values were calculated using GraphPad Prism 4.0 software .Six human cancer cell lines, particularly HCT-15 , NCI-H23 , ACHN , MDA-MB-231 , NUGC-3 , and PC-3 , were used in this study. The cell lines were cultured in RPMI 1640 supplemented with 10% fetal bovine serum. Cell cultures were incubated at 37 °C under a humidified atmosphere of 5% CO


PCDH9, NSBP1, MCTP1 and SEMA3A genes in the PAC-resistant cell lines. Short-term exposure to PAC led to increased expression of the MDR1 and BCRP genes in the A2780 and W1 cell lines. In the A2780 cell line, we also observed increased expression of the C4orf18 gene and decreased expression of the PCDH9 and SEMA3A genes after PAC treatment. In the W1 cell line, short-term treatment with PAC upregulated the expression of the ALDH1A1 gene, a marker of Cancer stem cells (CSCs). Our results suggest that downregulation of the PCDH9, NSBP1, MCTP1 and SEMA3A genes and upregulation of the MDR1, BCRP, C4orf18 and ALDH1A1 genes may be related to PAC resistance.Development of drug resistance is the main reason for low chemotherapy effectiveness in treating ovarian cancer. Paclitaxel (PAC) is a chemotherapeutic drug used in the treatment of this cancer. We analysed the development of PAC resistance in two ovarian cancer cell lines. Exposure of drug-sensitive cell lines (A2780 and W1) to PAC was used to determine the primary response. An established response was determined in PAC-resistant sublines of the A2780 and W1 cell lines. qRT-PCR was performed to measure the expression levels of specific genes. We observed decreased expression of the  Epithelial ovarian cancer (EOC) is one of the most aggressive tumours of all gynaecological malignancies and is the fifth leading cause of death from gynaecological malignancies. At the time of diagnosis, the majority of patients have advanced intraperitoneal metastatic disease . AlthougTaxus brevifolia. It belongs to the family of antimitotic anticancer agents and blocks mitosis through stabilizing microtubules by binding to β-tubulin subunits. Consequently, PAC blocks cell division and leads to apoptotic cell death . Downregulation of the MCTP1 gene was observed in all PAC-resistant cell lines, although in the A2780PR1 cell line, downregulation of this gene was not statistically significant. In the A2780PR2 cell line, we observed approximately 120-fold downregulation of the MCTP1 gene (p < 0.001) (p < 0.001) and 20-fold (p < 0.01) downregulation of the MCTP1 gene  A. W1PR1 TP1 gene B.SEMA3A gene expression was observed in PAC-resistant cell lines. A 4-fold decrease in the SEMA3A transcript level was observed in the A2780PR1 cell line (p < 0.05) vs. 223-fold in the A2780PR2 cell line (p < 0.01) (SEMA3A transcript level—approximately 850-fold (p < 0.001) (SEMA3A transcript level (p < 0.05) (A different pattern of  < 0.01) A. Even l< 0.001) B. In con < 0.05) A.The second part of our study focused on the early response to PAC treatment. In these experiments, drug-sensitive cell lines A2780 and W1 were treated with low concentrations of PAC (20 ng/mL and 25 ng/mL for A2780) and (15 ng/mL and 20 ng/mL for W1) for 24, 48 and 72 h. Then, changes in gene expression were investigated.MDR1 and BCRP genes encoding the most important drug transporters can also be expressed during the first days of PAC treatment. In the A2780 cell line, we observed a dose- and time-dependent increase in the MDR1 transcript level (p < 0.05 or p < 0.01)  (We were interested in whether  < 0.01) A. In the< 0.001) B.BCRP transcript level was observed at all time points and concentrations (p < 0.05 or p < 0.01) (p < 0.05 or p < 0.01), with the exception of PAC 15 ng/mL after 72 h, for which the increase was close to significant (p = 0.07)  A. Simila = 0.07) B.PCDH9 and SEMA3A genes were investigated to determine if PAC could induce changes in the expression of these genes during the first days of treatment. We did not observe any changes in the expression of these genes in the W1 cell line (not shown). In the A2780 cell line, we observed statistically significant downregulation of the PCDH9 gene after 48 and 72 h of PAC treatment (p < 0.05)  only after 48 h of treatment and was close to significant (p = 0.09) after 72 h of treatment at a PAC concentration of 25 ng/mL  A. Downre25 ng/mL B.C4orf18 gene after PAC treatment was observed in only the A2780 cell line. At all time and concentration points, we observed statistically significant expression, with the highest C4orf18 transcript levels after 72 h of treatment (p < 0.05 and p < 0.01) (Increased expression of the  < 0.01) .ALDH1A1 transcript in the A2780 cell line (not shown). In contrast, we observed an increase in ALDH1A1 mRNA after PAC treatment in W1 cell line. The increase was statistically significant after 24 and 48 h at a PAC concentration of 20 ng/mL and after 48 h at a concentration of 15 ng/mL (p < 0.05 or p < 0.01). After 72 h, the increase was close to significant (p = 0.13 for 15 ng/mL and p = 0.07 for 20 ng/mL) (ALDH1A1 is the most popular marker of cancer stem cells (CSCs), and its expression was previously observed by us in PAC- and TOP-resistant cell lines . Therefo0 ng/mL) .MDR1 and MDR3 genes  at 37 °C in a 5% CO2 atmosphere.In our study, we used two ovarian cancer cell lines: the established ovarian cancer cell line A2780 and the primary ovarian cancer cell line W1. The human ovarian carcinoma A2780 cell line was purchased from ATCC . A2780 sublines that were resistant to PAC [A2780PR1 and A2780PR2 (A2780 paclitaxel resistant)] were generated by exposing A2780 cells to PAC at incrementally increasing concentrations. The human primary ovarian cancer cell line W1 was established using ovarian cancer tissue obtained from an untreated patient. The W1 sublines resistant to PAC [W1PR1 and W1PR2 (W1 paclitaxel resistant)] were obtained by exposing W1 cells to PAC at incrementally increasing concentrations. The final concentrations used for selecting the resistant cells was 1100 ng/mL of PAC and was two-fold higher than the plasma concentrations of PAC two hours after intravenous administration. The increase in resistance according to parental drug-sensitive cell lines were as follows: 146-fold for A2780PR1 vs. A2780; 1202-fold for A2780PR2 vs. A2780; 743-fold for W1PR1 vs. W1 and 967-fold for W1PR2 vs. W1 as described previously . All the6 (A2780 and W1) in 1 mL of medium per well of a 6-well plate. Cell count and viability were determined before the cells were used in the different assays. Viability was determined by the trypan blue exclusion criteria. Cells were harvested and used for RNA isolation 24, 48 and 72 h after treatment.In time course experiments, the A2780 and W1 cell lines were treated with PAC at concentrations of 20 ng/mL and 25 ng/mL for A2780 or 15 ng/mL and 20 ng/mL for W1. The starting cell concentration was 0.4 × 10PCDH9, MCTP1, NSBP1, SEMA3A, C4orf18, ALDH1A1, MDR1 and BCRP gene expression in A2780 and W1 cell lines as well as in the PAC-resistant cell lines were examined. RNA was isolated using a Gene Matrix Universal RNA Purification Kit  as described by the manufacturer’s protocol. Reverse transcription was performed using M-MLV reverse transcriptase  as described in the manufacturer’s protocol using a thermal cycler ; 2 μg of RNA was used for cDNA synthesis. Real-time PCR was performed using a 7900HT Fast Real-Time PCR System , Maxima SYBR Green/ROX qPCR Master Mix  and sequence-specific primers, as indicated in GADPH), β-actin, hypoxanthine-guanine phosphoribosyltransferase 1 (HRPT1) and beta-2-microglobulin (β2M) served as the normalizing genes (geometric mean) against which changes in the expression of examined genes were compared. Gene expression was analysed using the relative quantification (RQ) method. RQ estimates the differences in the level of gene expression against a calibrator . The drug-sensitive cell lines (A2780 and W1) were used as the calibrators. The analysis was conducted by employing the standard formula: RQ = [sample (drug-resistant line) calibrator (drug-sensitive line)]. The graphs were made using SigmaPlot .Changes in For amplification, 12.5 μL of Maxima SYBR Green/ROX qPCR Master Mix , 1 μL of each primer  followed by 45 cycles of denaturation at 95 °C for 15 s, annealing at 60 °C for 30 s, and extension at 72 °C for 30 s. After amplification, melting curve analysis was performed to analyse the melting temperature of the product. The amplification products were also resolved by 3% agarose gel electrophoresis and visualized by ethidium bromide staining.t-test. The statistical analysis was performed using Excel software . The statistical significance of the differences was determined by applying Student’s PCDH9, NSBP1, MCTP1 and SEMA3A genes suggests that downregulation of these genes is important in PAC resistance. Upregulation of the BCRP and C4orf18 genes was characteristic of only the primary response to PAC. The significance of these genes in the resistance to PAC has not been described previously and should be further investigated. We also confirmed the significance of the MDR1 gene in the PAC response. Upregulation of the ALDH1A1 gene suggests the role of CSCs in PAC resistance development in the W1 cell line.In summary, our results show the changes in the expression of new genes in response to PAC treatment in ovarian cancer cell lines. The decreased expression of


The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to study 3D genome organization. Owing to experimental costs, high resolution Hi-C datasets are limited to a few cell lines. Computational prediction of Hi-C counts can offer a scalable and inexpensive approach to examine 3D genome organization across multiple cellular contexts. Here we present HiC-Reg, an approach to predict contact counts from one-dimensional regulatory signals. HiC-Reg predictions identify topologically associating domains and significant interactions that are enriched for CCCTC-binding factor (CTCF) bidirectional motifs and interactions identified from complementary sources. CTCF and chromatin marks, especially repressive and elongation marks, are most important for HiC-Reg’s predictive performance. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions and higher-order organizational units of the genome. Existing computational approaches to predict long-range regulatory interactions do not fully exploit high-resolution Hi-C datasets. Here the authors present a Random Forests regression-based approach to predict high-resolution Hi-C counts using one-dimensional regulatory genomic signals. Chromosome conformation capture (3C) technologies such as, 4C, 5C, ChIA-PET, Hi-C8, and Capture-Hi-C9, used to measure 3D proximity of genomic loci have rapidly matured over the past decade. However, there are several challenges for identifying such interactions in diverse cell types and contexts. First, the majority of these experimental technologies have been applied to well-studied cell lines. Second, very few Hi-C datasets are available at resolutions high enough  to identify enhancer-gene interactions due to the tradeoff between measuring genome-wide chromosome organizations while achieving higher resolutions. Third, long-range gene regulation involves a complex interplay of transcription factors, histone marks and architectural proteins13, making it important to examine three-dimensional proximity in concert with these components of the transcription machinery.The three-dimensional (3D) organization of the genome has emerged as an important component of the gene regulation machinery that enables distal regulatory elements, such as enhancers, to control the expression of genes hundreds of kilobases away. These long-range interactions can have major roles in tissue-specific expression17, which leverage the fact that regulatory regions that participate in long-range regulatory interactions have characteristic one-dimensional genomic signatures18. However, these methods have all used a binary classification framework, which is not optimal because the interaction prediction problem is a one-class problem with no negative examples of interactions. In addition, these do not exploit the genome-wide nature of high-throughput chromosome capture datasets such as Hi-C as they focus on significantly interacting pairs, which can be sensitive to the method used to call significant interactions. Recent comparison of methods for calling significant interactions show that there are substantial differences in the interactions identified from different methods19.Recently, numerous computational approaches for predicting long-range interactions have been developedTo overcome the limitations of a classification approach and to maximally exploit the information in high-throughput assays such Hi-C, we develop a Random Forests regression-based approach, HiC-Reg. HiC-Reg integrates published Hi-C datasets with one-dimensional regulatory genomic datasets such as chromatin marks, architectural and transcription factor proteins, and chromatin accessibility, to predict interaction counts between two genomic loci in a cell line-specific manner. We apply our approach to high-resolution Hi-C data from five cell lines to predict interaction counts at 5 kb resolution and systematically evaluate its ability to predict interactions within the same cell line , across different chromosomes as well as different cell lines. Our work shows that a Random Forests-based regression framework can predict genome-wide Hi-C interaction matrices within cell lines and can generalize to new chromosomes and cell lines. Furthermore, modeling the signal between regions as well as integrating data from multiple cell lines is beneficial for improved predictive power. Feature analysis of the predicted interactions suggests that the CCCTC-binding factor (CTCF) and chromatin signals are both important for high-quality predictions. HiC-Reg predictions agree well with ChIA-PET datasets, recapitulate well-known examples of long-range interactions, exhibit bidirectional CTCF loops, and can recover topologically associating domains. Overall, HiC-Reg provides a computational approach for predicting the interaction counts discovered by Hi-C technology, which can be used for examining long-range interactions for individual loci as well as for global studies of chromosome conformation organization.20. We used features from 14 cell line-specific regulatory genomic datasets and genomic distance to represent a pair of regions. These datasets include the architectural protein CTCF, repressive marks , marks associated with active gene bodies and elongation , enhancer-specific marks , activating marks , cohesin component (RAD21), a general transcription factor (TBP) and DNase I (open chromatin)14. HiC-Reg uses a Random Forests regression model as its core predictive model. The Random Forests regression model significantly outperforms a linear regression model, suggesting that there are non-linear dependencies that are important to be captured to effectively solve the count prediction problem . The distance-stratified correlation measures the Pearson’s correlation between predicted and true counts for genomic pairs at a particular distance threshold. Distance stratification is important due to the high dependence of contact count on genomic distance21. As a baseline we compared the performance of these models to a Random Forests regression model trained on distance alone. The performance of HiC-Reg is much better than the performance of distance alone, demonstrating that addition of regulatory signals significantly improves performance . The AUC metric ranges from –1 to 1, with 1 representing the best performance. We find that across all cell lines and chromosomes, both the WINDOW and the MULTI-CELL features are significantly better than PAIR-CONCAT, suggesting that incorporating the signal between two regions is important for capturing these interaction counts at longer distances Fig. b. When et-test one-sided p-value To examine whether our approach can generalize to new chromosomes, we trained HiC-Reg on one chromosome and used it to predict counts in a different chromosome for the same cell line. We considered five chromosomes, chr9, chr11, chr14, chr17, and chr19, each as training and test chromosomes, in all five cell lines. HiC-Reg predictions across chromosomes were slightly worse than when training and testing on the same chromosome in all but the Gm12878 cell line, where the cross chromosome performance was much worse. Interestingly, while both feature types, WINDOW and MULTI-CELL were comparable when training and testing in the same chromosome setting  error . Briefly, we obtained region pairs with the lowest 5% test error and counted the number of times a feature or a pair of features was used on the tree path traversed for these region pairs. Restricting to the pairs with the 5% lowest errors did not change individual feature rankings and was computational more tractable  to select a small number of features that were important for all five cell lines. Datasets spanning these features  were used as input in the second step, which we iteratively refined using Random Forests (RF). We applied this approach on different subsamples of the data and generated a ranking of datasets based on the number of times it is selected as a contributor to an important feature. We averaged the rankings from all five cell lines and selected top six and eight datasets , we next asked if we can reduce the number of datasets needed for HiC-Reg without substantial loss in performance. We used a two-step approach (MTG-RF) to select a minimal feature set (Methods), similar to a previous approach applied in a classification settingWe next compared HiC-Reg trained on these reduced datasets against HiC-Reg trained on all 14 datasets in a cross-validation , (+ –), (– +), and (– –), where + corresponds to the motif on the forward strand and – corresponds to the motif being on the reverse strand. The pairs with the (+ –) configuration are most likely the true loops. This property of looping interactions was also used by Forcato et al.19 to compare different Hi-C peak-calling programs. To assess the occurrence of CTCF bidirectional motifs in HiC-Reg predictions, we identified significant interactions in both true and predicted counts (Methods). We used Fit-Hi-C to call significant interactions, however, there is substantial overlap among interactions when using another interaction caller  configuration . We obtained ten published datasets for different factors  and histone marks in multiple cell linesnts Fig. b. The CT24. Hence, as a third validation metric, we asked to what extent the HiC-Reg counts identify structural units of chromosomal organization, such as TADs25. We applied the Directionality Index (DI) Domain Caller method26 to HiC-Reg predicted counts and true counts and compared the similarity of the identified TADs using a metric derived from the Jaccard coefficient. The Jaccard coefficient assesses the overlap between two sets of objects  and is a number between 0 and 1, with 0 representing no overlap and 1 representing perfect overlap. We aggregated the Jaccard coefficient across all TADs identified on a chromosome into a single average Jaccard coefficient (Methods). Across all cell lines and chromosomes, the average Jaccard coefficient ranged between 0.79 and 0.83 indicating good agreement between TADs from true and predicted counts  finding algorithmsOverall, these validation results show that HiC-Reg predictions can be used to study three-dimensional genome organization at the level of individual loops, as well as the level of higher-order structural units such as TADs.Our analysis so far demonstrates the feasibility of using regression to predict contact counts in cell lines with available Hi-C data for at least some chromosomes. We next asked if we could apply this approach to predict interactions in test cell lines different from the training cell line. This would enable us to study the utility of HiC-Reg in cell lines where Hi-C data are not yet available. We applied HiC-Reg trained on one cell line to predict counts for pairs from a different test cell line. We evaluated the quality of the predictions in the test cell line using the distance-stratified Pearson’s correlation . We first applied Fit-Hi-C on the true counts from each of the cell lines and found that K562 had among the largest number of significant interactions , with 2 significantly interacting pairs from the K562 CV model  in rat mammary epithelial cells32. The TCE resides within the MCS5C genomic locus associated with breast cancer susceptibility and is conserved in human and mouse32. When analyzing true counts, we found the largest number of significant interactions in the Hmec cell line  cells33, which showed that CRISPR deletion of the implicated enhancer decreased the expression of the target gene. We examined the predicted counts in the 1 MB radius of ten such enhancers that could be mapped to the human genome (hg19) and identified significant interactions that overlapped genes. We found significant interactions around three genes MACF1, MCL1, and KIAA1217 in the Hmec cell line  compared to other genes , which suggests that these interactions may not be specific to embryonic stem cells (ESC) and could be detected in other contexts. We did not detect the interactions that reported 80–90% decrease in expression, likely because they are specific to ESCs.Beyond these two well-characterized examples, we examined other examples curated in the literature that were shown to have a distal enhancer regulating a geneTaken together, our fine-grained analysis of these curated loci known to be involved in long-range regulatory interactions provide further support of our predictions, highlight potentially important features that facilitate these interactions and serve as case studies of how HiC-Reg could be used to characterize a particular locus of interest. In many of these cases we found additional loci that are predicted to interact with these genes, which can be followed with future experiments.18, the output from HiC-Reg can be used to identify significant interactions using peak-calling algorithms , as well as examine more large-scale organizational properties using domain finding algorithms .The three-dimensional organization of the genome can affect the transcriptional status of a single gene locus, as well as larger chromosomal domains, both of which can have significant downstream consequences on complex phenotypes. Although high-throughput chromosome capture conformation assays are rapidly evolving, measuring cell line-specific interactions on a genome-wide scale and at high resolution is a significant challenge. In this work, we described a novel computational approach, HiC-Reg that can predict the contact count of two genomic regions from their one-dimensional regulatory signals, which are available for a large number of cell lines and experimentally more tractable to generate than Hi-C datasets. As HiC-Reg directly predicts counts, instead of classifying interactions from non-interactions as has been commonly doneA key challenge we addressed using HiC-Reg was to generate high-resolution interaction counts in a new chromosome or cell line of interest. The former is relevant to predict interactions among regions that might not have been experimentally assayed. The latter is useful for cell types and developmental stages that may not be amenable to large-scale high-throughput 3C experiments and computational predictions could prioritize regions for targeted experimental studies. Our cross-chromosome experiments showed that the performance decreases when training on one chromosome and testing on another. The features identified as important across different chromosomes are very similar  can further improve performance in a cross-chromosome or cell line setting, likely because it can capture additional variation in the Hi-C interaction profile in new cell lines. However, the MULTI-CELL features might be difficult to generate due to unavailability of data in other cell lines and might be more computationally intensive to train models. The WINDOW features provide a good balance between performance and computational resources needed to train a model.A third important issue in the count prediction problem is feature representation of a pair of genomic loci. We studied different ways of modeling pairwise information and found that incorporating the signal between the interacting regions (WINDOW) is important for generalization to new chromosomes and cell lines. Our results are consistent with the finding of Whalen et al.HBA1 locus, as well as a relatively less studied locus, PAPPA. Our analysis of the PAPPA locus provided support of long-range regulation of PAPPA, originally identified in rat, in a relevant human cell line. Compared to a classification-based approach, our HiC-Reg predictions have greater sensitivity and are able to rank interactions better . We experimented with different number of trees  same cell line, same chromosome cross-validation (CV), (ii) same cell line cross chromosome comparison, (iii) different cell line same chromosome comparison.For the same cell line same chromosome setting, HiC-Reg was trained and tested using fivefold cross-validation using training and test pairs generated as described above. In each fold, we trained Random Forests regression models on four folds, and predict contact counts for the left-out fold. We concatenated predictions from five folds and assessed performance using distance-stratified Pearson’s correlation of true and predicted counts. As each training/test example is a pair of regions, we need to consider two types of examples: those that share a region with the training data (easy examples) and those that do not share a region with the training data (hard examples). Our same cell line same chromosome cross-validation results are generated using hard pairs only. The cross-validation experiments were done in all autosomal chromosomes.For the same cell line cross chromosome setting, we used the five Random Forests regression models trained on each fold from the training chromosome to predict contact counts for all pairs in a test chromosome. Each pair in the test chromosome had five predictions and we took the average of these predictions as the final predicted count. We note that in this setting, all test pairs are hard. Cross-chromosome experiments were done on five chromosomes, 9, 11, 14, 17, and 19.PAPPA and HBA1 loci.For different cell line same chromosome setting, we again used the Random Forests regression models trained on the training data from each fold in one cell line and generated predictions for all pairs in the test cell line. Next, we took the average of these predictions as the final predicted count. When using MULTI-CELL features, we excluded the features derived from the regulatory signals measured in the test cell line. In this setting as well, all test pairs are hard pairs. Our ensemble approach took a further average of the predictions from all four training cell lines as the predictions in a test cell line. We made cross-cell line predictions in five chromosomes: 9, 16, 14, 17, and 19. Cross-cell predictions from chromosomes 14, 17, and 19 were examined using enrichment for ChIA-PET interactions and TAD recovery. Cross-cell line predictions in chromosomes 9 and 16 were used for analyzing the interaction profile for the 45, learning a separate model for each cell line. We selected 14 datasets that were measured in all five cell lines. These 14 datasets included ChIP-seq datasets for ten histone marks and CTCF, DNase I-seq and DNase I-seq-derived motifs of RAD21 and TBP, which we had previously found to be helpful for predicting enhancer-promoter interactions in a classification setting14. A ChIP-seq signal is represented as the average read count aggregated into a 5 kb non-overlapping bin. We obtained the raw fastq files from the ENCODE consortium45, aligned reads to the human hg19 assembly using bowtie246, retrieved reads aligned to a locus using SAMtools47 and applied BEDTools48 to obtain a base pair level read count. We next aggregated the read counts of each base pair in a 5 kb region. Next, we normalized aggregated signal by sequencing depth and collapsed replicates by taking the median. As TBP and RAD21 ChIP-seq data are not available in Huvec, Nhek  and Hmec cell lines, we predicted the binding sites using PIQ49 on the DNase I data and used the sum of purity scores for all motifs mapped to the same 5 kb bin as the signal value. We performed a simple depth normalization on the counts to enable comparison across cell lines. The depth normalization does not affect the overall performance based on the Area under Pearson’s correlation curve, but could affect evaluation metrics such as distance-stratified mean-square error if they were used to compare different predictions. This could be particularly an issue for cross-cell line predictions that simply transfers counts (one of our baseline models).To extract features for HiC-Reg’s regression framework, we used datasets from the ENCODE project for five cell lines: K562, Gm12878, Huvec, Nhek, and Hmec15. The feature with the intervening region is a mean signal value of the feature in the region. In the MULTI-CELL case, we concatenated the 42-dimensional feature vectors of the two regions from all five cell lines to obtain a merged feature vector of size 210. Finally, for all these feature representations, we included genomic distance between the two regions of a pair as an additional feature.We represented features of a region as a 14-dimensional feature vector, each dimension corresponding to one of the 14 genome-wide datasets  on all five cell lines simultaneously to select features predictive of contact counts in all five cell lines. Our second step used a greedy approach, based on Random Forests (RF) to iteratively refine the feature set selected by MTG-LASSO.https://github.com/jiayuzhou/SLEP).The MTG-LASSO approach is a regularized regression approach that is applicable to a problem with multiple predictive tasks. In our problem setting, the different tasks are different cell lines. The objective function for MTG-LASSO is defined as:To perform feature selection with MTG-LASSO, we used the WINDOW feature set for each of the five cell lines for chromosome 17. We considered a range of regularization parameters from The greedy feature refinement uses Random Forests as the regression model and works as follows. Starting with an initial set of features and one of the folds for training and testing, we train a Random Forests model on the training data and generate predictions for the test dataset. For each subsequent iteration, we randomly pick between adding or removing a feature, retrain the Random Forests model on the training data considering every candidate feature for addition  and select the best feature based on the change in prediction error on the test set. We add (or remove) the feature if there is gain in test error performance and do nothing if there is no feature with an improvement in test error. We repeat this procedure until a max number of iterations have been executed or the error does not change substantially. After convergence, we tabulate the features selected. We repeat this entire procedure for all five folds. As this process is compute intensive, we subsample 10% of the training data so that the pairs retain their distance distribution and select a feature set. We repeat the training data subsampling ten times for each fold resulting in a total of 50 feature sets. Finally, we rank a feature based on the fraction of times (out of 50) it is selected , Iterative Correction and Eigen vector decomposition (ICE), and Square Root Vanilla Coverage (SQRTVC). The normalized counts using the KR and SQRTVC methods are downloaded from the Rao et al. paperIn parallel, we examined HiC-Reg model training on data generated at different resolutions: 5 kb, 10 kb, 25 kb, and 50 kb in selected cell lines of varying depth , and different chromsomes (chromosome 14 and 17). As expected, the hardest task is to learn models at the highest resolution (5 kb), and the CV performance gets better with decreasing resolution  PAPPA promoter, (3) enhancers involved in enhancer-gene interactions validated with CRISPR33. The HBA1 promoter spans two 5 kb bins on chromosome 16, the PAPPA promoter spans two 5 kb bins on chromosome 9, while the enhancers span between 2 and 9 bins. We generated predictions for all pairs spanning the 1 MB radius around the bins overlapping our locus of interest. For the HBA1, which is situated at the beginning of chromosome 16, we had 225 kb on the left of the gene. In parallel, we applied Fit-Hi-C to the true and predicted counts for each chromosome to find significant interactions, where predicted counts were generated from CV models, as well as models trained in the cross-cell line setting. To enable comparison between true and predicted counts, we restricted Fit-Hi-C only to pairs that had measured counts and therefore have q-values associated with only these pairs. Next, we extracted significant interactions associated with the 5 kb bins containing the locus of interest and the 1 MB radius around the promoter. For HBA1, we checked if these significant interactions overlap with interactions measured from 5C experiments28. For PAPPA, we checked if the significant interactions overlap with the TCE or MCS5C genomic locus on one end, and the PAPPA gene on the other end. For the enhancers from Moorthy et al.33, we extracted significant interactions associated with the 5 kb bins containing the enhancer. Finally, we checked to see if the other end of a significant interaction overlapped the 5 kb bin containing the promoter of the target gene of interest.To test if HiC-Reg can recover well-characterized long-range interactions, we focused on significant interactions associated with several well-studied loci: (a) the We used different metrics for assessing the quality of our predictions for contact counts. The distance-stratified Pearson’s correlation was used to directly measure the accuracy of the predicted counts. The other metrics were used to assess the quality of results after further downstream analysis of HiC-Reg predictions and compare them to similar results obtained from actual measured data. In particular, enrichment of CTCF bidirectional motifs and ChIA-PET datasets enabled us to study the quality of significant interactions identified from HiC-Reg predictions, while the TAD similarity enabled us to study the ability of HiC-Reg predictions to capture structural units of organization.Distance-stratified Pearson’s correlation: to assess the quality of predicted counts from HiC-Reg, we used Pearson’s correlation of predicted contact counts and true contact counts as a function of genomic distance. We grouped pairs of regions based on their genomic distance and calculated the Pearson’s correlation of predicted and true contact counts for pairs that fall into each distance bin. We considered all pairs upto a distance of 1 MB in distance bin intervals of 5 kb. To easily compare the performance between different methods, chromosomes and cell lines, we summarized the distance-stratified Pearson’s correlation curve into the area under the curve (AUC) using the MATLAB trapz function as trapzEnrichment of bidirectional CTCF motifs: to obtain the coordinates and orientation of the CTCF motifs, we applied the PIQ tool49 on cell line-specific DNase I-seq fastq files from ENCODE (http://hgdownload.soe.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeOpenChromDnase/). PIQ gives a score from 0.5 to 1, which is proportional to the true occurrence of the motif. We selected a threshold of 0.9 to identify high-confidence CTCF motifs. We use R1 to denote the region with the smaller starting coordinate, and R2 to denote the region with the larger starting coordinate. Following Rao et al.20, an interaction is labeled as having a convergent CTCF orientation if R1 contains CTCF motifs on the forward strand (+ orientation) and R2 contains CTCF motifs on the reverse strand (– orientation). We only focus on pairs with at least one CTCF motif mapped to R1 and at least one CTCF motif mapped to R2. A pair can have one of the four configurations: (i) (+ +) configuration where both R1 and R2 have the motifs in the + orientation, (ii) (+ –) configuration where R1 has CTCF motifs in + orientation and R2 has CTCF motifs in the – orientation, (iii) (– +) configuration, where R1 has motifs in the – orientation and R2 has motifs in the + orientation, (iv) (– –), where both R1 and R2 have motifs in the – orientation. We counted the total number of pairs with each of these configurations and compared this with the number of significant pairs called by Fit-Hi-C using the Hypergeometric test and fold enrichment. Briefly, assume we are testing the enrichment for the (+ –) configuration. Let the total number of possible pairs in the background be Comparing HiC-Reg interactions with published ChIA-PET data: we downloaded ten published ChIA-PET datasets: PolII in HeLa and K562, and CTCF in the K562 cell line22, and seven ChIA-PET datasets from Heidari et al.23, which included RNA PolII, CTCF, RAD21, and multiple chromatin marks in K562 and Gm12878 cell lines. Our metric for evaluating these genome-wide maps is fold enrichment, which assesses the fraction of significant interactions identified from HiC-Reg that overlapped with experimentally detected measurements, compared to the fraction of interactions expected by random chance. We mapped ChIA-PET interactions onto the pairs of regions used in HiC-Reg by requiring one region of an interaction from the ChIA-PET dataset to overlap with one region of a HiC-Reg pair , and the other ChIA-PET region to map to the second region . Fold enrichment is defined as Assessing TADs identified from true and predicted counts: to identify topologically associating domains (TADs), we applied the directionality index (DI) method described in Dixon et al.26. The method is based on Hidden Markov Model (HMM) segmentation of the DI. The DI is a score for a genomic region to measure the bias in the directionality of interactions for that region as measured in a Hi-C dataset. It is determined by the difference in the number of reads between the region and a genomic window  upstream of the region and the number of reads between the region and a window downstream of the region. The window is user-defined. The DI score is segmented into three states of upstream, downstream or no bias. A TAD is then defined by a contiguous stretch of downstream biased states.We transformed our upper triangle predicted matrix at 5 kb resolution into a symmetric interaction matrix and gave this to DI as input. We used the default parameters of the package with a window size of 2 Mb for defining the Directionality Index. For comparison, we applied the same procedure to the true count matrices to identify TADs. We compared the similarity of TADs identified from the HiC-Reg predicted counts and true counts for each chromosome using a Jaccard coefficient-based score. The Jaccard coefficient measures the overlap between two sets, 41, which computes importance of a feature based on the change in error on Out of Bag example pairs when the feature values are permuted. We used MATLAB’s implementation of this measure. In addition we devised another importance measure, feature usage count, which counted the number of times a particular feature was used to predict the count for an example pair when it is part of the test set. Briefly, for each test example To assess the importance of individual features we used the Out of Bag (OOB) Variable Importance measure52, which counts the number of times a pair of features co-occur on the path from the root to the leaf. The main difference between our approach and that of Foresight is that we estimate these counts on the test examples, while Foresight estimates these on the examples in each leaf node in the tree identified during training. By using the counts on the test examples, our approach is less prone to overfitting. As the feature rankings of individual features are similar when using all pairs and pairs with the top  5% errors, we computed these counts only for the top 5% error pairs.To assess the importance of pairs of features, we considered all pairs of features that occur on the tree path from root to a leaf for a test example (similar to above). This approach is similar to the Foresight method53, with node size proportional to the number of feature pairs they are associated with and edge weights corresponding to the strength of the association. The NMF-based feature analysis enabled us to extract groups of examples associated with sets of feature pairs.We developed a novel feature analysis method to identify feature sets associated with sets of region pairs based on NMF. The input to this approach is a Further information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1


Cancer is still the most common cause of morbidity in the world. Chitosan, a commonly used natural polymer, is consisted of different molecular weight with different biological activities.The purpose of this study was to determine cytotoxicity effect of high molecular weight (HMWC) and low molecular weight of chitosan (LMWC) on three cancerous cell lines MCF-7, HeLa and Saos-2 with different histological origin.The anticancer property of two types of chitosan on three cancerous cell lines and human fibroblast as normal cell line, was evaluated by cytotoxic activity including their apoptosis induction properties. Chitosan solutions 2% (w/v) were prepared. The cells were treated by different concentration of chitosan and viability was determined by MTT assay after 24, 48 and 72 h .Also the mode of cell death-apoptosis vs necrosis ,was determined by Annexin V staining assay and analyzed by flow cytometry.While both types of chitosan were effective in inhibiting cell proliferation of three cancerous cell lines, fibroblast cells showed somehow more compatibility with chitosan. Despite of a significant decrease in all 3 cell lines viability, up to 90%, but we didn't see a concentration dependent difference between both types of chitosan (HMWC and LMWC) in their cytotoxic effects. Flow cytometry analysis showed necrosis more observable with MCF7 while the apoptosis pattern of death was more in Saos-2 and HeLa. Also, higher viability with both types of chitosan was seen in fibroblast as normal cells.While chitosan is compatible with normal diploid fibroblast cells, it shows anticancerous effect against 3 cancerous cell lines. Furthermore, it seems that the molecular weight of chitosan does not affect its anticancerous property. Cancer is still a major public health problem and is one of the most common cause of morbidity globally. Shall be inhibited by new anticancer candidates, cell proliferation is one of the major steps in cancer cells metastasis of a multistep process that includes cell adhesion, invasion, cell proliferation, transport through circulatory system and growth in a secondary organ , 2. As tThere are many reports about antitumor activity of chitosan in which, its mechanism is related to membrane-disrupting and apoptosis-inducing activities . The antWe would like to investigate about anticancer activity of the (LMW 100-300 kDa) and HMW 600-800 kDa) chitosan using human breast cancer cell line (MCF-7), human epithelial cervical cancer (HeLa), osteosarcoma (Saos-2) and noncancer fibroblast. Their cytotoxic activity was studied by MTT assay, and anticancer activity of apoptosis and necrosis were analyzed by flow cytometry. We therefore performed the present research to explore the anticancer effects of LMWC on cancerous cell lines in comparison with HMWC in terms of cytoxicity and apoptosis.Materials: The following chemicals and reagents were obtained from the companies mentioned below. Chitosan, with two different MW: 600-800 kDa and 100-300 kDa with deacetylation degree of 75%-85% were purchased . RPMI 1640  Trypsin–EDTA , MTT -2, 5-Diphenyl tetrazoliumbromide , FBS , Penicillin/Streptomycin (PAA), DMSO , ApoFlowExFITC kit Exbio (ED7044).Cell-Lines: Three different human cell lines: MCF-7 human breast cancer), HeLa , Saos-2 (osteosarcoma) were purchased from the Pasteur Institute of Iran. Cancer cells were grown and maintained in Roswell Park Memorial Institute medium (RPMI) 1640 supplemented with 1% penicillin-streptomycin, 10% (v/v) heat-inactivated fetal bovine serum. The cells were maintained at 37º C under 5% CO2. The human fibroblast cell line was isolated from normal children’s foreskin by non- enzymatic method which had been cryopreserved since previous project with Ethical code: MUBABOL.REC.1390.8. (16). All cell lines were used to evaluate the effects of HMWC and LMWC in vitro. Preparation of chitosan solution: LMW and HMW chitosan were dispersed in a 1% (v/v) glacial acetic acid  for the preparation of chitosan solutions 2% (w/v) and kept stirring at 50  C overnight for complete dissolution of their particles. Then the solutions were autoclaved at 121 C for 15 min and stored at 4 C until used -2,5-diphenyltetrazolium bromide),which measures the activity of mitochondrial dehydrogenase in the cells. For cell treatment, 5*103 cells were seeded into each well of a 96-well flat bottom plate (Orange scientific) and the plate was incubated at 37°C in a humidified CO2 5%. After 24 h, the cell culture medium was replaced by a fresh medium that contained HMW and LMW chitosan at final concentrations of 0.25, 0.5,1,2,4 mg/mL separately. After 24, 48 and 72 h MTT solution (5 mg/mL) was freshly prepared and added to each well. Three hours after incubation, the formed formazan crystals were dissolved in DMSO and the resulting color was measured spectrophotometrically using a microplate reader  at a test wavelength of 570 nm and a reference wavelength of 630 nm. Each experiment was performed in triplicate. Same cell lines without exposure of both types of chitosan served as controls. Percentage cell viability was calculated by dividing the absorbance of individual wells by the mean absorbance of control wells , annexin V/PI test was carried out. Annexin V is intracellular protein that binds to phosphatidylserine (PS). PS is normally only found on the intracellular leaflet of the plasma membrane in healthy cells but during early apoptosis, membrane is removed and PS translocates from inner phospholipid layer to the cell surface. Fluorochrome-labeled annexin V can identify apoptotic cells and for distinguishing between the necrotic and apoptotic cells propidium iodide solution (PI) was used which is a fluorescent dye that binds to DNA. The flow cytometry test can differentiate early apoptotic cells (FITC+/PI-), late apoptotic cells (FITC-/PI+),necrotic cells (FITC+/PI+) and intact cells (FITC-/PI-)(ITC-/PI-),24. AnneStatistical analysis: Statistical analysis was performed using SPSS Version 19. The t-test and analysis of variance (ANOVA) via Tukey's multiple comparison were used to determine the different variable effects on cell lines. The difference between numerical results was considered in a significant level in p<0.05.Cell Cytotoxicity assessment: Three cancer cell lines MCF-7, HeLa, Saos-2 and fibroblast isolated from foreskin were treated with a different concentration,  of LMW and HMW chitosan and cytotoxity was measured after 24, 48 and 72 h incubation by MTT assay. The morphologic changes were seen in As shown in Comparision of cytotoxicity between cancer cells and fibroblast derived from foreskin as non-cancer cells showed that the viability of dermal fibroblast was higher than cancer cells after exposure to HMW and LMW chitosans and both types of chitosan have less cytotoxicity on dermal fibroblast than cancer cell lines, it means that their side effects are low.The effect of HMW and LMW chitosan on all types of cell lines was compared to each other and there was no significant difference between HMW and LMW chitosan (p>0.05) .Quantification of apoptosis by flow cytometry: Annexin V/PI test was carried out for the determination of mode of cell death after exposure to 1 and 2 mg/mL of HMW and LMW chitosan for 48 h. Apoptotic and necrotic incidence in MCF7, Saos-2, HeLa as cancer cell lines and dermal fibroblast as normal cell were determined by flow cytometry and overall obtained result confirmed the presence of apoptosis and necrosis in chitosan-treated cancer cells. The apoptotic incidence increased to 34% and 20.46% in Saos-2, 7.75% and 5.9% in MCF7, 63% and 44.68% in HeLa after being treated with 1mg/mL HMW and LMW chitosan, respectively. The necrotic incidence was 8.46% and 5.98% in Saos-2, 15.84% and 43.16% in MCF7, 2% and 1.27% in HeLa after being treated with 1mg/mL HMW and LMW chitosan, respectively. In contrast, fibroblast treated with 1 mg/mL HMW and LMW chitosan were like untreated cells and 94% to 96% of cells were intact .In this study, chitosan with high (600-800 kDa) and low (100-300 kDa) molecular weights were used to compare their effects on three cancer cell lines, including MCF-7, HeLa, Saos-2 and fibroblast derived foreskin as normal cell. The results of MTT assay in the present study, demonstrated that HMW and LMW chitosan could exert cytotoxicity against MCF-7, Saos-2, and HeLa cell lines and both types of chitosan have less cytotoxic properties on dermal fibroblast, as a normal cell, than cancerous cell lines. Exposure of three cancer cell lines to chitosan showed that there is a concentration-dependent decrease in cell biomassconcentration-dependent .According to the results, there is no significant difference between the cytotoxic effects of high and low molecular weight chitosan on three cancerous and normal fibroblast cell lines except for the concentration of 2 mg/mL on MCF-7 and 4 mg/mL on fibroblast (p<0.01) . In contThe results of flow cytometry analysis implied that the action mechanism of chitosan in the three cancer cell lines might be different. While apoptosis was the most obvious mode of death in HeLa and Saos-2 cell lines exposed to chitosan, necrosis was prevalent in the MCF-7 cell line. It seems that chitosan with HMW and LMW induces death in each cancer cell line. This finding was also observed with both different concentrations of chitosan .In a previous study, short-term LMWC exposure, was less cytotoxic to HaCaT cells, as normal keratinocyte cells, than to Ca9-22 cells as oral squamous cell carcinoma. The anticancer activity was exerted through induction of apoptosis suggesting that LMWC could be a promising natural anticancer agent with fewer side effects on normal cells .3 mol/L concentration, but not the normal human lung fibroblast cell line HLF, similar to our study.The effect of chitosan-copper complex on HeLa and 293 cell line showed that this complex inhibited tumor cell proliferation at 10As a consequence of difference in mechanism of cytotoxicity, there are contrary effects of chitosan on the cancer cell lines and fibroblast. Cancer cells have greater negative charge than normal cells which can be attracted more by positive charged amino groups of chitosan . ChitosaBased on Ming Jiang is study, it was found that 24-h exposure of MCF-7 cells to 50 μM chitosan derivatives such as sulfated chitosan (SCS) and the sulfated benzaldehyde chitosan (SBCS) significantly inhibited cell proliferation and induced apoptosis. SBCS had better inhibitory effects and a lower IC50 compared to SCS, but our study showed necrosis was prevalent in the MCF-7 cell line ,it may be the chitosan exposure, was 48 h and also only chitosan was used ,not chitosan compound .Antitumor effects of various low molecular weight chitosans  and HMW chitosan (average 650 kDa), in sarcoma 180–bearing mice showed that chitosan with molecular weights of 21 kDa and 46 Da had antitumor activity while this effect was not observed in the mice treated with 130-kDa and 650-kDa chitosan (Moreover human gastric carcinoma MGC803 cell line was sensitive to chitosan nanoparticles with an IC50 value of 3 µg/mL after 48 h treatment . The bioThe HMWC cytotoxicity test using MTT assay against HepG2, A549, and PC3, as the model tumor cell lines, has been carried out before with concentrations ranging from 0.75 to 50 µg/mL. While HMWC showed significantly higher cytotoxicity toward human HepG2 and A549 than PC3 cells, in concordance with our study ,those results demonstrated that HMWC has antitumor activities against various tumor cell lines in vitro and might be applicable as an antitumor agent based on the biodegradability and biocompatibility of HMWC. Galbiatin vivo experiments, drug formulation and clinical trials would be necessary in the future. In conclusion, two types of chitosan with different molecular weights and DDA are implicated to exhibit growth inhibitory effects against HeLa, Saos-2 and MCF-7 as tumor cell lines. It might be an option for treatment of cancer with fewer side effects and more studies involving


Unlike homogeneous cell lines, PDC comprised of debris and low-viability cells, which resulted in an inaccurate estimation of cell viability using total fluorescence intensity as determined by high IC50 values. However, the IC50 of these cells was lower and accurate when calculated based on the selected-colony-area method that eliminated the intensity offset associated with the heterogeneous nature of PDC. The selected-colony-area method was optimized to accurately predict drug response in micropillar environment using heterogeneous nature of PDCs.We aimed to establish a fluorescence intensity-based colony area sweeping method by selecting the area of highest viability among patient-derived cancer cells (PDC) which has high tumor heterogeneity. Five gastric cancer cell lines and PDCs were screened with 24 small molecule compounds using a 3D micropillar/microwell chip. 100 tumor cells per well were immobilized in alginate, treated with the compounds, and then stained and scanned for viable cells. Dose response curves and IC Despite advances in targeted therapy and immunotherapy for solid cancer, one of the most challenging problems in oncology is that development of active drugs is still a slow multi-layered, complicated process. Considering the time consumption, high cost, and low success rate of pre-clinical and clinical development of oncology drugs, more efficient and accurate platforms for oncology drug screening are urgently needed.in vivo microenvironment [The activity of oncology drugs has been studied in two-dimensionally (2D) cultured cancer cell lines. However, it has been long challenged that these preclinical model systems minimally reflect the ironment –6 and loironment , 8. In oironment –19.Patient-derived tumor cells are attractive as effective tools for preclinical evaluation of personalized medicine strategies –24, even2-humidifed atmosphere and passaged every four days.Human gastric cancer cells  were purchased from the Korean Cell Line Bank . All cell lines were cultured in RPMI 1640 medium (Gibco) supplemented with 10% foetal bovine serum. Cell lines were maintained at 37°C in a 5% CO2 culture flasks. Cell lines and PDCs were grown to 80–90% confluency and passaged using TrypLE Express (Gibco BRL) and seeded with 3D culture medium consisting of DMEM F/12 supplemented with 10 mM HEPES, 1% antibiotic-antimycotic solution, 2% 50× B27, 1% 20× N2, 1% 100× Glutamax (Gibco BRL), 10 mM human gastrin I, 1 mM N-acetyl-L-cysteine (Sigma Aldrich), 10 μg/mL insulin, 20 ng/mL basic fibroblast growth factor (bFGF), and 50 ng/mL EGF (PeproTech)., approximately 100 cells in 50 nL with a 0.5% alginate concentration by volume (0.5w/w) were automatically dispensed onto a micropillar chip by using ASFASpotter ST . The ASFA Spotter ST uses a solenoid valve  for dispensing the 50 nL droplets of the cell–alginate mixture and 950 nL of media or compound. After dispensing the cells, as shown as , the micropillar chip containing cell lines and PDCs in alginate was sandwiched (or “stamped”) with the microwell chip for 3D cell culture and compound efficacy tests. After 1 day of incubation at 37°C to stabilize the cells, as shown in , the micropillar chip containing the cells was moved to a new microwell chip filled with various test compounds. A single chip can screen 12 compounds for 6 replicates. Next, the combined chips were incubated for 3 days (cell lines) or 5 days (PDCs). After 3 or 5 days, cell viability against the compounds was measured with Calcein AM live cell staining dye (4 mM stock from Invitrogen), which stains viable cells with green fluorescence. The staining dye solution was prepared by adding 1.0 μL of Calcein AM (4 mM stock from Invitrogen) in 8 mL staining buffer . For staining, as shown , micropillar chips were moved into a staining buffer in Calcein AM for 45 minutes. The stained chip was washed for 30 minutes in the staining buffer solution and then dried completely in a dark environment. To measure cell viability quantitatively after staining the alginate spots, cells on the micropillar chip were scanned using optical scanner  shown in . Scanned images were evaluated using an image analysis software  shown in . Fig 1 shows a schematic representation of the experimental procedure described above.The basic layout of the micropillar/microwell chip for a 12-compound screening was shown in our previous study [50 values  were obtained with the following equation:50 is the midpoint of the curve; nH is the hill slope; X is the logarithm of the compound concentration, and Y is the response (cell viability). The ASFAScanner ST  software sets the Bottom as zero and the Top as 100% when the data are fit to a curve.Cell viability values were normalized to their corresponding control wells (no drug treatment), because not all control conditions exhibited 100% cell viability. The sigmoidal dose-response curves (variable slope) and ICThis investigation was conducted in accordance with the ethical standards of the Declaration of Helsinki and national and international guidelines and was approved by the Institutional Review Board at Samsung Medical Center in Seoul, Korea (IRB No. 2015-10-062 & 2011-07-089).To validate fluorescence intensity-based colony area sweeping method, five patient-derived cancer cells  were scr50 values calculated using the total intensity with the selected colony area method. Colony formation was seen with most MKN1 cells, but only with some of the cells from the patient-derived cancer cell line #1 , calculated using both methods, were similar between the cancer cell lines , suggesting no significant differences between the two methods. However, for the five patient-derived cancer cell samples, IC50 values calculated by the selected colony area method were generally lower than those calculated using total fluorescence intensity. In IC50 calibration by total intensity, the intensity offset increased cell viability in a high dosage of the drug. Specifically, we observed 10 outliers with large differences of more than 1 dose (3 times) ( line #1 . In the ll lines . The dif3 times) .50 value that was very high even though staurosporine exerted considerable inhibitory effect on patient-derived cancer cell line #5. To solve this problem, we removed the intensity offset by selecting a colony area of high viability using an intensity threshold. The black and red lines represent the dose response curves with cell viability calculated using total fluorescence intensity and the selected colony area, respectively. Even though cell viability of patient-derived cancer cell line #5 was affected by 10 μM staurosporine, the debris and cells with low viability produced a large intensity offset and artificially increased the cell viability calculation in the dose response curve Click here for additional data file.


For several leukemia patients, allogeneic stem cell transplantation  is the unique therapeutic modality that could potentially cure their disease. Despite significant progress made in clinical management of allogeneic-SCT, acute graft-versus-host disease (aGVHD) and infectious complications remain the second and third cause of death after disease recurrence. Clinical options to restore immunocompetence after allogeneic-SCT are very limited as studies have raised awareness about the safety with regards to graft-versus-host disease (GVHD). Preclinical works are now focusing on strategies to improve thymic functions and to restore the peripheral niche that have been damaged by alloreactive T cells. In this mini review, we will provide a brief overview about the adverse effects of GVHD on the thymus and the peripheral niche and the resulting negative outcome on peripheral T cell homeostasis. Finally, we will discuss the potential relevance of coordinating our studies on thymic rejuvenation and improvement of the peripheral lymphoid niche to achieve optimal T cell regeneration in GVHD patients. GVHD effect on the thymus.GVHD effect on the peripheral niche.Addressing the dysfunction of the thymus and the peripheral niche to improve T cell regeneration in GVHD patients.Allogeneic-SCT was developed to treat leukemia and lymphoma as well as congenital or acquired hematologic conditions. Despite important side effects, allogeneic-SCT remains the only curative treatment for several patients with high risk refractory hematologic cancers. Acute graft-versus-host disease (aGVHD) is a serious complication of allogeneic-SCT that occurs when donor lymphocytes react to normal host-tissues. Paradoxically, alloreactive T cells can improve survival by eliminating residual leukemia cells that survive SCT; a reaction known as graft-versus-leukemia effect . Followivia homeostatic proliferation (HP) of mature lymphocytes  on T cell regeneration is not as well understood. cGVHD occurs normally after aGVHD and during this period, T cell regeneration is already compromised. While aGVHD is mediated by mature lymphocytes contained in the graft, the origin of cGVHD appears related to leakage and release of donor-derived autoreactive lymphocytes by the thymus Figure . As a reImmunosuppressive therapies use to control GVHD can affect lymphocyte reconstitution. Cyclosporine A and methotrexate alter B cell differentiation and thymocytes and peripheral T cell survival by interfering with TCR signaling , 25, 26.The rich microenvironment of the thymus in MHC-I and II expression by thymic epithelial cells (TECs) and antigen-presenting cells render the thymus a significant target for alloreactive lymphocytes . While sEvaluation of thymopoiesis can be measured by T-cell receptor excision circles (TREC) that are generated from the rearrangement of the V and J segments at the TCRα locus and thus correlate with thymic output of naïve T cells , 36. Dep+ regulatory CD4+ T cells  and medullary thymic epithelial cells (mTECs) have distinct functions and play a critical role during positive and negative selection of thymocytes to produce conventional T cells and non-conventional FOXP3 T cells –48. Foll T cells , 50. All T cells –53. Loss T cells . While G T cells , 56. Fur T cells –60. Thus+ T cell HP depends on MHC-II presentation by DCs, such that this effect alone could explain the lack of CD4 regeneration. However, it does not necessarily explain lack of CD8+ T cell HP, as MHC-I is ubiquitously expressed, and CD8 HP is probably not exclusively dependent on MHC-I expressed by DCs. Additional studies are needed to identify elements of the peripheral niche that limit CD8 recovery in GVHD hosts.Several studies have demonstrated that GVHD induces significant damage to the peripheral niche that controls T cell homeostasis –23. DuriSeveral studies have demonstrated that DC numbers are reduced during GVHD and depletion/inactivation of recipient DCs before allogeneic-SCT can reduce GVHD , 68–71. Damage to the BM microenvironment can induce myelosuppression after allogeneic-SCT. BM stromal cells provide growth factors such as GM-CSF, M-CSF, and Flt3-L, which are essential for HSC differentiation into mature cells. However, little is known about how these factors are affected by GVHD in the BM. While serum analysis can provide some insight about variation of these factors, the evaluation of BM biopsy is probably more accurate since several of these factors are also produced by immune cells . For exa+ T cell recovery in HIV patients but the benefits were quite modest  to improve thymopoiesis. However, these studies were performed in a non-GVHD setting and although CLPs are decreased during GVHD , it is ue modest . Thymic e modest .−Sca1+c-kit+ lymphoid progenitors after allogeneic-SCT  can be administered in patients to diminish GVHD , 99. MSC−/− BM cells (In most clinical settings of human lymphopenia, the dysfunction of the thymus represents the primary limiting factor for T cell regeneration. However, the dysfunction of the peripheral niche is now well documented and added to the dysfunction of the thymus and together they contribute to the very poor immune reconstitution that typically occurs in GVHD patients. In a setting where the peripheral niche remains dysfunctional, it is not clear whether restoring thymopoiesis would be sufficient to increase peripheral T cell counts. DCs and IL-7 provide critical survival signals to RTEs for their expansion in the periphery. Depending of the robustness of the thymic output, T cell immune reconstitution may vary considerably when the peripheral niche is dysfunctional. The absence of peripheral DCs could result into greater competition between T cells for access to MHC class I–II, which could affect the number and the diversity of the T cell repertoire. Under these conditions, the repertoire may be skewed toward lymphocytes with the highest levels of self-reactivity. In addition, the absence of peripheral DCs could impede self-tolerance imposed by thymopoiesis since the peripheral DCs pick up peripheral self-antigens, migrate to the thymus and induce negative selection of developing self-reactive thymocytes , 117. ThBM cells . As a reDespite significant progress in our understanding of the biology of T cell depletion, therapies aimed at accelerating T cell regeneration remain limited and are still in clinical development , 120. ThAll authors contributed intellectually to the writing of this work.The authors declare no commercial or financial relationships that could constitute a conflict of interest.


BRCA1 and BRCA2 genes. In this study, we investigated the antiproliferative effects and mechanism of PARP inhibitors ABT-888 (Veliparib), BSI-201 (Iniparib) and AZD228 (Olaparib) in breast cancer cell lines with BRCA1 or BRCA2 mutations and 9 different BRCA wild-type cell lines with BRCA1 allelic loss. We found that AZD2281 was the most potent in the PARP inhibitors and induces significant growth inhibition (~95%) in BRCA1 mutant  and BRCA2 mutant (HCC-1428) cell lines. AZD2281 treatment also resulted in growth inhibition ranging from 20 to 50% in cells with BRCA1 allelic loss, including ER(+), HER2/Neu(+) and triple-negative breast cancer (TNBC) cells, but showed no effect in cells without with type BRCA without allelic loss. Knocking down of BRCA1 or BRCA2 in TNBC cells with BRCA1 allelic loss by RNA interference significantly enhanced AZD2281-induced growth inhibition and induced significant autophagy that was associated with mitophagy in cells with BRCA mutations. Inhibition of autophagy by gene knockdown significantly diminished AZD2281-induced mitophagy and apoptosis, indicating that autophagic process mediates some of the downstream effects of PARP inhibitors. In conclusion, our data provide the first evidence of PARP inhibitor AZD2281 autophagy and mitophagy in breast cancer cell lines with BRCA mutations or BRCA-allelic loss. In addition, our results indicate that the patients with BRCA1 allelic loss may also benefit from PARP inhibitor therapy if BRCA is further inhibited.PARP inhibitors are considered promising anti-cancer agents and currently being tested in clinical trials in hereditary breast cancer patients harboring mutations in  BRCA1 and BRCA2, several other genes associated with breast cancer predisposition have been identified, including ATM, CHEK2, PALB2, RAD51C and BRIP1. Many of these genes are associated with BRCA1 and BRCA2 in the DNA damage response (DDR) pathway  for DNA repair , are involved in a number of critical cellular processes, including DNA damage repair and programmed cell death . When acor BRCA2 . PARP inor BRCA2 ,20. HoweBRCA1 or BRCA2 mutant breast cancer cell lines and in wild-type BRCA cell lines with and without BRCA1 allelic loss. We provide evidence that the PARP inhibitor AZD2281 inhibits the growth of breast cancer cells with BRCA1 allelic loss lacking mutation in BRCA1. These results might lead the way to new approaches for treating a broad spectrum of breast cancer subtypes. We also demonstrated that the PARP inhibitor AZD2281 induces autophagy in BRCA mutated breast cancer cells as well as breast cancer cells with BRCA1 allelic loss lacking mutation in BRCA1. Our results also indicate importance of selection of patients who would benefit from PARP inhibitor therapy and molecular subclassifications of BRCA-related breast cancers.In this study, we investigated the effects of PARP inhibitors in BRCA1 mutant lines with BRCA1 allelic loss , 1 BRCA2 mutant line with BRCA2 allelic loss (HCC-1428), 9 BRCA wild-type lines with BRCA1 allelic loss , and 1 BRCA wild-type line without BRCA1 allelic loss (T47D). T47D, MCF-7, ZR75, MDA-MB-361, BT-474, SKBR3, MDA-MB-231, BT-549, MDA-MB-468, and BT-20 cells were cultured at 37°C in DMEM supplemented with 10% FBS in a humid incubator with 5% CO2. SUM-149PT cells were cultured in Ham’s F-12 supplemented with 5% FBS, insulin, and hydrocortisone. The PARP inhibitors veliparib (ABT-888), olaparib (AZD2281), and iniparib (BSI-201) were purchased from Selleck Chemicals .We studied 14 human breast cancer cell lines: 3 Cell viability was assayed by applying the cell proliferation reagent WST-1 (Roche Applied Science). First, a suspension of 4,000 cells per 90 μl was seeded into each well of a 96-well plate and cultured overnight. Then, the necessary amount of PARP inhibitor was added to the individual wells. After 3 days of PARP inhibitor treatment, 10 μl of the ready-to-use WST-1 reagent was added directly into the medium, the plates were incubated at 37°C for 30 min, and absorbance was measured on a plate reader at 450 nm. All experiments were done in triplicate. Cell viability was calculated as the percentage of cells killed by the treatment as measured by the difference in absorbance between treated and untreated wells.Lentiviral particles expressing BRCA1, BRCA2, ATG5, or control shRNA were purchased from Sigma. MDA-MB-231, BT-20, and HCC-1428 cells were transfected at a multiplicity of infection of 5. Five days after transfection, cells were treated with 5 μg/ml of puromycin concentration to select cells stably expressing shRNA. Lentiviral vector expressing mitochondrial yellow fluorescent protein (mYFP) was purchased from Biogenova. HCC-1428 cells were transfected at a multiplicity of infection of 5.After treatment, the cells were trypsinized and collected by centrifugation, and whole-cell lysates were obtained by using a cell lysis buffer. Total protein concentration was determined by using a detergent-compatible protein assay kit (Bio-Rad Laboratories). Aliquots containing 30 μg of total protein from each sample were subjected to SDS-PAGE with a 12% gradient and electrotransferred to nitrocellulose membranes. The membranes were blocked with 5% dry milk in TBS-Tween-20 and probed with primary antibodies against BRCA1 and BRCA2  and LC3 (Sigma). The antibodies were diluted in TBS-Tween-20 containing 2.5% dry milk and incubated at 4°C overnight. After the membranes were washed with TBS-Tween-20, they were incubated with horseradish peroxidase-conjugated anti-rabbit or anti-mouse secondary antibody (Amersham Life Sciences). Mouse anti-β-actin and donkey anti-mouse secondary antibodies (Sigma) were used to monitor β-actin expression to ensure equal loading of proteins. Chemiluminescence was detected with ChemiGlow detection reagents (Alpha Innotech). The blots were visualized with a FluorChem 8900 imager and quantified with densitometer software (Alpha Innotech).To detect and quantify acidic vesicular organelles, cells were stained with acridine orange as described previously . The num4 in the same buffer and then subjected to the electron microscopic analysis as described previously. Representative areas were chosen for ultrathin sectioning and viewed with a Hitachi 7600 electron microscope (Japan).Cells were grown on 6-well plates, treated with AZD2281, ATG5 shRNA, or control shRNA, fixed for 2 h with 2.5% glutaraldehyde in 0.1 mol/l cacodylate buffer (pH 7.4), and postfixed in 1% OsOCells were collected and double-stained with Annexin V-fluorescein isothiocyanate (FITC) and propidium iodide using an Annexin V-FITC apoptosis detection kit (BD Pharmingen) and evaluated with a flow cytometer.BRCA1 allelic loss. To investigate the effects of PARP inhibitors in BRCA wild-type breast cancer cell lines with BRCA allelic loss we treated BRCA wild-type ER/PR+, ER+, HER2+, and triple-negative cell lines with 3 different PARP inhibitors, ABT-888, BSI-201, and AZD2281, for 4 days. Growth rates were measured with the WST-1 assay. Whereas AZD2281 induced an average growth inhibition of 33% in BRCA wild-type cell lines at 2 μM, ABT-888 and BSI-201 did not induce growth inhibition in the same cell lines at 2 μM concentration. The growth inhibition effect of AZD2281 was significantly higher in the BRCA wild-type cell lines with BRCA1 allelic loss than in the BRCA wild-type cell line without BRCA1 allelic loss  and BRCA2 mutant (HCC-1428) cell lines. AZD2281 at 2 μM significantly inhibits cell survival in all 4 cell lines, whereas ABT-888 and BSI-201 did not induce cell death at 2 μM  of 41 breast cancer cell lines have BRCA mutations and 28 (68%) of the 41 cell lines have lic loss . We also at 2 μM . We also at 2 μM .To determine the effect of BRCA1 or BRCA2 in response to AZD2281 treatment, the BRCA wild-type MDA-MB-231 and BT-20 cells were stably transfected with BRCA1, BRCA2, or control lentiviral shRNA. BRCA1 or BRCA2 downregulation was demonstrated by western blot analysis . The 3 dBRCA1 mutant (SUM-149PT) and BRCA2 mutant (HCC-1428) breast cancer cells with 2 μM AZD2281 for 1 day and stained them with acridine orange. Acridine orange positive cells were counted using flow cytometry. AZD2281 induced significant autophagy (37 and 44%) in BRCA1 mutant SUM-149PT and BRCA2 mutant HCC-1428 breast cancer breast cancers, respectively, in 24 of treatment  before and after AZD2281 treatment. TEM images clearly demonstrated that AZD2281 induces autophagy, which results in mitochondrial degradation. AZD2281-treated cells had fewer mitochondria and more autophagosomes compared with untreated cells .To investigate the roles of autophagy and mitochondrial degradation under AZD2281 treatment, we stably transfected HCC-1428 cells with mYFP using lentiviral vector. Fluorescence microscope images clearly demonstrated the presence of mYFP in the mitochondrial compartment of HCC-1428-mYFP cells . HCC-142BRCA mutant cell lines. In addition, we demonstrated that AZD2281 induces growth inhibition in BRCA wild-type breast cancer cell lines with BRCA1 allelic loss, indicating that breast cancer patients with BRCA1 allelic loss may benefit from PARP inhibitors.In this study, we show for the first time that a PARP inhibitor as a single agent induces significant autophagy/mitophagy in et al, reported a detailed BRCA1 mutation analysis of 41 breast cancer cell lines  of the 41 cell lines had BRCA1 allelic loss (BRCA1 allelic loss, 9 BRCA wild-type lines with BRCA1 allelic loss, and 1 BRCA wild-type line without BRCA1 allelic loss. Our data clearly demonstrated that BRCA mutant breast cancer cell lines with BRCA allelic loss were highly sensitive to AZD2281 as monotherapy (Previously, AZD2281 was evaluated in a genetically engineered mouse model of BRCA1 breast cancer . Treatmell lines . Before lic loss . On the otherapy . UnfortuAutophagy is lysosomal degradation pathway that is induced as a protective and prosurvival pathway against nuclear DNA damage and metabolic and therapeutic stress, if excessive this process can also lead to cell death in breast and other cancers –25,29,30