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2. Overshoot during phenotypic switching of cancer cell populations

Author

Sellerio, Alessandro L., Ciusani, Emilio, Ben-Moshe, Noa Bossel, Coco, Stefania, Piccinini, Andrea, Myers, Christopher R., Sethna, James P., Giampietro, Costanza, Zapperi, Stefano, and La Porta, Caterina A. M.

Subjects
Quantitative Biology - Cell Behavior, Physics - Biological Physics
Abstract

The dynamics of tumor cell populations is hotly debated: do populations derive hierarchically from a subpopulation of cancer stem cells (CSCs), or are stochastic transitions that mutate differentiated cancer cells to CSCs important? Here we argue that regulation must also be important. We sort human melanoma cells using three distinct cancer stem cell (CSC) markers - CXCR6, CD271 and ABCG2 - and observe that the fraction of non-CSC-marked cells first overshoots to a higher level and then returns to the level of unsorted cells. This clearly indicates that the CSC population is homeostatically regulated. Combining experimental measurements with theoretical modeling and numerical simulations, we show that the population dynamics of cancer cells is associated with a complex miRNA network regulating the Wnt and PI3K pathways. Hence phenotypic switching is not stochastic, but is tightly regulated by the balance between positive and negative cells in the population. Reducing the fraction of CSCs below a threshold triggers massive phenotypic switching, suggesting that a therapeutic strategy based on CSC eradication is unlikely to succeed.

Published
2015
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3. Data Supplement from Downregulation of microRNAs 145-3p and 145-5p Is a Long-term Predictor of Postmenopausal Breast Cancer Risk: The ORDET Prospective Study

Author

Muti, Paola, primary, Sacconi, Andrea, primary, Hossain, Ahmed, primary, Donzelli, Sara, primary, Ben Moshe, Noa Bossel, primary, Ganci, Federica, primary, Sieri, Sabina, primary, Krogh, Vittorio, primary, Berrino, Franco, primary, Biagioni, Francesca, primary, Strano, Sabrina, primary, Beyene, Joseph, primary, Yarden, Yosef, primary, and Blandino, Giovanni, primary

Published
2023
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4. Data from Downregulation of microRNAs 145-3p and 145-5p Is a Long-term Predictor of Postmenopausal Breast Cancer Risk: The ORDET Prospective Study

Author

Muti, Paola, primary, Sacconi, Andrea, primary, Hossain, Ahmed, primary, Donzelli, Sara, primary, Ben Moshe, Noa Bossel, primary, Ganci, Federica, primary, Sieri, Sabina, primary, Krogh, Vittorio, primary, Berrino, Franco, primary, Biagioni, Francesca, primary, Strano, Sabrina, primary, Beyene, Joseph, primary, Yarden, Yosef, primary, and Blandino, Giovanni, primary

Published
2023
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5. Paired single-cell host profiling with multiplex-tagged bacterial mutants reveals intracellular virulence-immune networks.

Author

Heyman, Ori, Yehezkel, Dror, Mattioli, Camilla Ciolli, Blumberger, Neta, Rosenberg, Gili, Solomon, Aryeh, Hoffman, Dotan, Ben-Moshe, Noa Bossel, and Avraham, Roi

Subjects
SALMONELLA typhimurium, RNA sequencing, INTRACELLULAR pathogens, BACTERIAL cells, PHENOTYPES
Abstract

Encounters between host cells and intracellular bacterial pathogens lead to complex phenotypes that determine the outcome of infection. Single-cell RNA sequencing (scRNA-seq) is increasingly used to study the host factors underlying diverse cellular phenotypes but has limited capacity to analyze the role of bacterial factors. Here, we developed scPAIR-seq, a single-cell approach to analyze infection with a pooled library of multiplex- tagged, barcoded bacterial mutants. Infected host cells and barcodes of intracellular bacterial mutants are both captured by scRNA-seq to functionally analyze mutant-dependent changes in host transcriptomes. We applied scPAIR-seq to mac- rophages infected with a library of Salmonella Typhimurium secretion system effector mutants. We analyzed redundancy between effectors and mutant- specific unique fin- gerprints and mapped the global virulence network of each individual effector by its impact on host immune pathways. ScPAIR-seq is a powerful tool to untangle bacterial virulence strategies and their complex interplay with host defense strategies that drive infection outcome. [ABSTRACT FROM AUTHOR]

Published
2023
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7. Host succinate is an activation signal for Salmonella virulence during intracellular infection

Author

Rosenberg, Gili, primary, Yehezkel, Dror, additional, Hoffman, Dotan, additional, Mattioli, Camilla Ciolli, additional, Fremder, Moran, additional, Ben-Arosh, Hadar, additional, Vainman, Leia, additional, Nissani, Noa, additional, Hen-Avivi, Shelly, additional, Brenner, Shirley, additional, Itkin, Maxim, additional, Malitsky, Sergey, additional, Ohana, Ehud, additional, Ben-Moshe, Noa Bossel, additional, and Avraham, Roi, additional

Published
2021
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13. Additional file 2: of mRNA-seq whole transcriptome profiling of fresh frozen versus archived fixed tissues

Author

Ben-Moshe, Noa Bossel, Gilad, Shlomit, Perry, Gili, Benjamin, Sima, Balint-Lahat, Nora, Pavlovsky, Anya, Halperin, Sharon, Markus, Barak, Yosepovich, Ady, Barshack, Iris, Gal-Yam, Einav, Domany, Eytan, Kaufman, Bella, and Dadiani, Maya

Subjects
body regions, nervous system, fungi
Abstract

Table S2. Number of exonic and intronic reads. (PDF 167Â kb)

Published
2018
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14. Additional file 5: of mRNA-seq whole transcriptome profiling of fresh frozen versus archived fixed tissues

Author

Ben-Moshe, Noa Bossel, Gilad, Shlomit, Perry, Gili, Benjamin, Sima, Balint-Lahat, Nora, Pavlovsky, Anya, Halperin, Sharon, Markus, Barak, Yosepovich, Ady, Barshack, Iris, Gal-Yam, Einav, Domany, Eytan, Kaufman, Bella, and Dadiani, Maya

Abstract

Figure S2. Comparison of fold-changes measured for FFPE samples vs. matched FF samples using mRNA-seq. (A) Scatter plot for the expression fold changes (log2 scale) of genes measured in T1 vs.T3, obtained from FF samples (x-axis) compared to matched FFPE samples (y-axis) by mRNA-seq protocol (purple). r-square and correlation coefficient are presented at the plot. B) Scatter plot for the expression fold changes (log2 scale) of genes measured in T2 vs.T3, obtained from FF samples (x-axis) compared to matched FFPE samples (y-axis) by mRNA-seq protocol (purple). r-square and correlation coefficient are presented at the plot. (PDF 936Â kb)

Published
2018
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16. Additional file 6: of mRNA-seq whole transcriptome profiling of fresh frozen versus archived fixed tissues

Author

Ben-Moshe, Noa Bossel, Gilad, Shlomit, Perry, Gili, Benjamin, Sima, Balint-Lahat, Nora, Pavlovsky, Anya, Halperin, Sharon, Markus, Barak, Yosepovich, Ady, Barshack, Iris, Gal-Yam, Einav, Domany, Eytan, Kaufman, Bella, and Dadiani, Maya

Abstract

Figure S3. Expression of non-coding RNAs in FFPE samples by mRNAseq and RiboZero protocols. (A) Scatter plot of the expression levels of annotated lincRNAs as measured on T1 FFPE sample by mRNAseq (x-axis) versus RiboZero protocol (y-axis). Correlation coefficients between the two protocols for the expression of these lincRNAs are indicated at the fig. (B) Same as (A) for miRNAs expression. (PDF 185Â kb)

Published
2018
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17. Additional file 4: of mRNA-seq whole transcriptome profiling of fresh frozen versus archived fixed tissues

Author

Ben-Moshe, Noa Bossel, Gilad, Shlomit, Perry, Gili, Benjamin, Sima, Balint-Lahat, Nora, Pavlovsky, Anya, Halperin, Sharon, Markus, Barak, Yosepovich, Ady, Barshack, Iris, Gal-Yam, Einav, Domany, Eytan, Kaufman, Bella, and Dadiani, Maya

Abstract

Figure S1. Number of detected genes as a function of exonic reads. The number of detected genes as a function of exonic reads is shown for each library (total of 29 libraries from the different sample types and protocols, see legend). The black line is a smooth monotonic curve extrapolated from all 29 data points. (PDF 722Â kb)

Published
2018
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18. Overshoot during phenotypic switching of cancer cell populations

Author

Sellerio, Alessandro L., primary, Ciusani, Emilio, additional, Ben-Moshe, Noa Bossel, additional, Coco, Stefania, additional, Piccinini, Andrea, additional, Myers, Christopher R., additional, Sethna, James P., additional, Giampietro, Costanza, additional, Zapperi, Stefano, additional, and La Porta, Caterina A. M., additional

Published
2015
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19. EGF induces microRNAs that target suppressors of cell migration: miR-15b targets MTSS1 in breast cancer

Author

Kedmi, Merav, primary, Ben-Chetrit, Nir, additional, Körner, Cindy, additional, Mancini, Maicol, additional, Ben-Moshe, Noa Bossel, additional, Lauriola, Mattia, additional, Lavi, Sara, additional, Biagioni, Francesca, additional, Carvalho, Silvia, additional, Cohen-Dvashi, Hadas, additional, Schmitt, Fernando, additional, Wiemann, Stefan, additional, Blandino, Giovanni, additional, and Yarden, Yosef, additional

Published
2015
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21. Downregulation of microRNAs 145-3p and 145-5p Is a Long-term Predictor of Postmenopausal Breast Cancer Risk: The ORDET Prospective Study

Author

Muti, Paola, primary, Sacconi, Andrea, additional, Hossain, Ahmed, additional, Donzelli, Sara, additional, Ben Moshe, Noa Bossel, additional, Ganci, Federica, additional, Sieri, Sabina, additional, Krogh, Vittorio, additional, Berrino, Franco, additional, Biagioni, Francesca, additional, Strano, Sabrina, additional, Beyene, Joseph, additional, Yarden, Yosef, additional, and Blandino, Giovanni, additional

Published
2014
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24. EGF induces microRNAs that target suppressors of cell migration: miR-15b targets MTSS1in breast cancer

Author

Kedmi, Merav, Ben-Chetrit, Nir, Körner, Cindy, Mancini, Maicol, Ben-Moshe, Noa Bossel, Lauriola, Mattia, Lavi, Sara, Biagioni, Francesca, Carvalho, Silvia, Cohen-Dvashi, Hadas, Schmitt, Fernando, Wiemann, Stefan, Blandino, Giovanni, and Yarden, Yosef

Abstract

Growth factor–induced metastasis involves microRNA-mediated repression of a tumor suppressor.

Published
2015
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25. EGF induces microRNAs that target suppressors of cell migration: miR-15b targets MTSS1 in breast cancer

Author

Cindy Körner, Noa Bossel Ben-Moshe, Maicol Mancini, Fernando Schmitt, Sara Lavi, Silvia Carvalho, Giovanni Blandino, Yosef Yarden, Nir Ben-Chetrit, Francesca Biagioni, Mattia Lauriola, Merav Kedmi, Hadas Cohen-Dvashi, Stefan Wiemann, Kedmi, Merav, Ben-Chetrit, Nir, Körner, Cindy, Mancini, Maicol, Ben-Moshe, Noa Bossel, Lauriola, Mattia, Lavi, Sara, Biagioni, Francesca, Carvalho, Silvia, Cohen-Dvashi, Hada, Schmitt, Fernando, Wiemann, Stefan, Blandino, Giovanni, and Yarden, Yosef

Subjects
Breast Neoplasms, Mice, SCID, Biology, Biochemistry, Metastasis, Mice, Breast cancer, Cell Movement, Epidermal growth factor, Cell Line, Tumor, microRNA, medicine, Animals, Humans, EGFR pathway, MICRORNA (MIRNA), Molecular Biology, GENE EXPRESSION PROFILE, Epidermal Growth Factor, Microfilament Proteins, Cancer, Cell migration, Cell Biology, medicine.disease, Molecular biology, Neoplasm Proteins, MicroRNAs, Invadopodia, Cancer research, Heterografts, Female, Neoplasm Transplantation, Metastasis Suppressor Protein
Abstract

Growth factors promote tumor growth and metastasis. We found that epidermal growth factor (EGF) induced a set of 22 microRNAs (miRNAs) before promoting the migration of mammary cells. These miRNAs were more abundant in human breast tumors relative to the surrounding tissue, and their abundance varied among breast cancer subtypes. One of these miRNAs, miR-15b, targeted the 3' untranslated region of MTSS1 (metastasis suppressor protein 1). Although xenografts in which MTSS1 was knocked down grew more slowly in mice initially, longer-term growth was unaffected. Knocking down MTSS1 increased migration and Matrigel invasion of nontransformed mammary epithelial cells. Overexpressing MTSS1 in an invasive cell line decreased cell migration and invasiveness, decreased the formation of invadopodia and actin stress fibers, and increased the formation of cellular junctions. In tissues from breast cancer patients with the aggressive basal subtype, an inverse correlation occurred with the high expression of miRNA-15b and the low expression of MTSS1. Furthermore, low abundance of MTSS1 correlated with poor patient prognosis. Thus, growth factor-inducible miRNAs mediate mechanisms underlying the progression of cancer.

Published
2015
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