iTRAQ Analysis of a Cell Culture Model for Malignant Transformation, Including Comparison with 2D-PAGE and SILAC

To study human cancer development, cell culture models for malignant transformation can be used. In 1999 Hahn and Coworkers introduced such a model system and established herewith a basis for research on human tumorigenesis. Primary human fibroblasts are sequentially transduced with defined genetic elements (hTERT, SV40 ER, and H-Ras^V12), resulting in four defined cell lines, whereby the last has a fully transformed phenotype. In order to get a deeper insight into the molecular biology of human tumorigenesis, we compared the proteomes of these four cell lines following a multimethod concept. At the beginning we assumed SILAC and sample fractionation with COFRADIC is the method of choice to analyze the cell culture model for malignant transformation. Here, the compared samples are combined before sample preparation, thus avoiding differences in sample preparation, and using COFRADIC notably reduces sample complexity. Because 2D-PAGE is a standard method for the separation and visualization of closely related proteomes, we decided to analyze and compare the proteomes of these four cell lines in a first approach by differential 2D-PAGE. Surprisingly, we discovered much more unique results with iTRAQ and sample fractionation with SCX than with the combination of 2D-PAGE and SILAC-COFRADIC. Moreover, iTRAQ outperforms the other strategies not only in number of yielded results but also in analysis time. Here, we present the iTRAQ quantification results and compare them with the results of 2D-PAGE and SILAC-COFRADIC. We found changes in the protein level at each transition. Thereby, SV40 has the strongest impact on the proteome. In detail we identified 201 regulated proteins. Beside others, these proteins are involved in cytoskeleton, RNA processing, and cell cycle, such as CDC2, hnRNPs, snRNPs, collagens, and MCM proteins. For example, MCM proteins are up-regulated and collagens are down-regulated due to SV40 ER expression. Furthermore we made the observation that proteins containing the same domain have analogous regulation profiles during malignant transformation. For instance, several proteins containing a CH or LIM domain are down-regulated. Moreover, by this study and the defined cell culture model, changes could be clearly matched to specific steps during tumorigenesis

MGA, L3MBTL2 and E2F6 determine genomic binding of the non-canonical Polycomb repressive complex PRC1.6

<div><p>Diverse Polycomb repressive complexes 1 (PRC1) play essential roles in gene regulation, differentiation and development. Six major groups of PRC1 complexes that differ in their subunit composition have been identified in mammals. How the different PRC1 complexes are recruited to specific genomic sites is poorly understood. The Polycomb Ring finger protein PCGF6, the transcription factors MGA and E2F6, and the histone-binding protein L3MBTL2 are specific components of the non-canonical PRC1.6 complex. In this study, we have investigated their role in genomic targeting of PRC1.6. ChIP-seq analysis revealed colocalization of MGA, L3MBTL2, E2F6 and PCGF6 genome-wide. Ablation of MGA in a human cell line by CRISPR/Cas resulted in complete loss of PRC1.6 binding. Rescue experiments revealed that MGA recruits PRC1.6 to specific loci both by DNA binding-dependent and by DNA binding-independent mechanisms. Depletion of L3MBTL2 and E2F6 but not of PCGF6 resulted in differential, locus-specific loss of PRC1.6 binding illustrating that different subunits mediate PRC1.6 loading to distinct sets of promoters. Mga, L3mbtl2 and Pcgf6 colocalize also in mouse embryonic stem cells, where PRC1.6 has been linked to repression of germ cell-related genes. Our findings unveil strikingly different genomic recruitment mechanisms of the non-canonical PRC1.6 complex, which specify its cell type- and context-specific regulatory functions.</p></div

iTRAQ Analysis of a Cell Culture Model for Malignant Transformation, Including Comparison with 2D-PAGE and SILAC

To study human cancer development, cell culture models for malignant transformation can be used. In 1999 Hahn and Coworkers introduced such a model system and established herewith a basis for research on human tumorigenesis. Primary human fibroblasts are sequentially transduced with defined genetic elements (hTERT, SV40 ER, and H-Ras^V12), resulting in four defined cell lines, whereby the last has a fully transformed phenotype. In order to get a deeper insight into the molecular biology of human tumorigenesis, we compared the proteomes of these four cell lines following a multimethod concept. At the beginning we assumed SILAC and sample fractionation with COFRADIC is the method of choice to analyze the cell culture model for malignant transformation. Here, the compared samples are combined before sample preparation, thus avoiding differences in sample preparation, and using COFRADIC notably reduces sample complexity. Because 2D-PAGE is a standard method for the separation and visualization of closely related proteomes, we decided to analyze and compare the proteomes of these four cell lines in a first approach by differential 2D-PAGE. Surprisingly, we discovered much more unique results with iTRAQ and sample fractionation with SCX than with the combination of 2D-PAGE and SILAC-COFRADIC. Moreover, iTRAQ outperforms the other strategies not only in number of yielded results but also in analysis time. Here, we present the iTRAQ quantification results and compare them with the results of 2D-PAGE and SILAC-COFRADIC. We found changes in the protein level at each transition. Thereby, SV40 has the strongest impact on the proteome. In detail we identified 201 regulated proteins. Beside others, these proteins are involved in cytoskeleton, RNA processing, and cell cycle, such as CDC2, hnRNPs, snRNPs, collagens, and MCM proteins. For example, MCM proteins are up-regulated and collagens are down-regulated due to SV40 ER expression. Furthermore we made the observation that proteins containing the same domain have analogous regulation profiles during malignant transformation. For instance, several proteins containing a CH or LIM domain are down-regulated. Moreover, by this study and the defined cell culture model, changes could be clearly matched to specific steps during tumorigenesis

iTRAQ Analysis of a Cell Culture Model for Malignant Transformation, Including Comparison with 2D-PAGE and SILAC

To study human cancer development, cell culture models for malignant transformation can be used. In 1999 Hahn and Coworkers introduced such a model system and established herewith a basis for research on human tumorigenesis. Primary human fibroblasts are sequentially transduced with defined genetic elements (hTERT, SV40 ER, and H-Ras^V12), resulting in four defined cell lines, whereby the last has a fully transformed phenotype. In order to get a deeper insight into the molecular biology of human tumorigenesis, we compared the proteomes of these four cell lines following a multimethod concept. At the beginning we assumed SILAC and sample fractionation with COFRADIC is the method of choice to analyze the cell culture model for malignant transformation. Here, the compared samples are combined before sample preparation, thus avoiding differences in sample preparation, and using COFRADIC notably reduces sample complexity. Because 2D-PAGE is a standard method for the separation and visualization of closely related proteomes, we decided to analyze and compare the proteomes of these four cell lines in a first approach by differential 2D-PAGE. Surprisingly, we discovered much more unique results with iTRAQ and sample fractionation with SCX than with the combination of 2D-PAGE and SILAC-COFRADIC. Moreover, iTRAQ outperforms the other strategies not only in number of yielded results but also in analysis time. Here, we present the iTRAQ quantification results and compare them with the results of 2D-PAGE and SILAC-COFRADIC. We found changes in the protein level at each transition. Thereby, SV40 has the strongest impact on the proteome. In detail we identified 201 regulated proteins. Beside others, these proteins are involved in cytoskeleton, RNA processing, and cell cycle, such as CDC2, hnRNPs, snRNPs, collagens, and MCM proteins. For example, MCM proteins are up-regulated and collagens are down-regulated due to SV40 ER expression. Furthermore we made the observation that proteins containing the same domain have analogous regulation profiles during malignant transformation. For instance, several proteins containing a CH or LIM domain are down-regulated. Moreover, by this study and the defined cell culture model, changes could be clearly matched to specific steps during tumorigenesis

iTRAQ Analysis of a Cell Culture Model for Malignant Transformation, Including Comparison with 2D-PAGE and SILAC

To study human cancer development, cell culture models for malignant transformation can be used. In 1999 Hahn and Coworkers introduced such a model system and established herewith a basis for research on human tumorigenesis. Primary human fibroblasts are sequentially transduced with defined genetic elements (hTERT, SV40 ER, and H-Ras^V12), resulting in four defined cell lines, whereby the last has a fully transformed phenotype. In order to get a deeper insight into the molecular biology of human tumorigenesis, we compared the proteomes of these four cell lines following a multimethod concept. At the beginning we assumed SILAC and sample fractionation with COFRADIC is the method of choice to analyze the cell culture model for malignant transformation. Here, the compared samples are combined before sample preparation, thus avoiding differences in sample preparation, and using COFRADIC notably reduces sample complexity. Because 2D-PAGE is a standard method for the separation and visualization of closely related proteomes, we decided to analyze and compare the proteomes of these four cell lines in a first approach by differential 2D-PAGE. Surprisingly, we discovered much more unique results with iTRAQ and sample fractionation with SCX than with the combination of 2D-PAGE and SILAC-COFRADIC. Moreover, iTRAQ outperforms the other strategies not only in number of yielded results but also in analysis time. Here, we present the iTRAQ quantification results and compare them with the results of 2D-PAGE and SILAC-COFRADIC. We found changes in the protein level at each transition. Thereby, SV40 has the strongest impact on the proteome. In detail we identified 201 regulated proteins. Beside others, these proteins are involved in cytoskeleton, RNA processing, and cell cycle, such as CDC2, hnRNPs, snRNPs, collagens, and MCM proteins. For example, MCM proteins are up-regulated and collagens are down-regulated due to SV40 ER expression. Furthermore we made the observation that proteins containing the same domain have analogous regulation profiles during malignant transformation. For instance, several proteins containing a CH or LIM domain are down-regulated. Moreover, by this study and the defined cell culture model, changes could be clearly matched to specific steps during tumorigenesis

iTRAQ Analysis of a Cell Culture Model for Malignant Transformation, Including Comparison with 2D-PAGE and SILAC

To study human cancer development, cell culture models for malignant transformation can be used. In 1999 Hahn and Coworkers introduced such a model system and established herewith a basis for research on human tumorigenesis. Primary human fibroblasts are sequentially transduced with defined genetic elements (hTERT, SV40 ER, and H-Ras^V12), resulting in four defined cell lines, whereby the last has a fully transformed phenotype. In order to get a deeper insight into the molecular biology of human tumorigenesis, we compared the proteomes of these four cell lines following a multimethod concept. At the beginning we assumed SILAC and sample fractionation with COFRADIC is the method of choice to analyze the cell culture model for malignant transformation. Here, the compared samples are combined before sample preparation, thus avoiding differences in sample preparation, and using COFRADIC notably reduces sample complexity. Because 2D-PAGE is a standard method for the separation and visualization of closely related proteomes, we decided to analyze and compare the proteomes of these four cell lines in a first approach by differential 2D-PAGE. Surprisingly, we discovered much more unique results with iTRAQ and sample fractionation with SCX than with the combination of 2D-PAGE and SILAC-COFRADIC. Moreover, iTRAQ outperforms the other strategies not only in number of yielded results but also in analysis time. Here, we present the iTRAQ quantification results and compare them with the results of 2D-PAGE and SILAC-COFRADIC. We found changes in the protein level at each transition. Thereby, SV40 has the strongest impact on the proteome. In detail we identified 201 regulated proteins. Beside others, these proteins are involved in cytoskeleton, RNA processing, and cell cycle, such as CDC2, hnRNPs, snRNPs, collagens, and MCM proteins. For example, MCM proteins are up-regulated and collagens are down-regulated due to SV40 ER expression. Furthermore we made the observation that proteins containing the same domain have analogous regulation profiles during malignant transformation. For instance, several proteins containing a CH or LIM domain are down-regulated. Moreover, by this study and the defined cell culture model, changes could be clearly matched to specific steps during tumorigenesis

iTRAQ Analysis of a Cell Culture Model for Malignant Transformation, Including Comparison with 2D-PAGE and SILAC

To study human cancer development, cell culture models for malignant transformation can be used. In 1999 Hahn and Coworkers introduced such a model system and established herewith a basis for research on human tumorigenesis. Primary human fibroblasts are sequentially transduced with defined genetic elements (hTERT, SV40 ER, and H-Ras^V12), resulting in four defined cell lines, whereby the last has a fully transformed phenotype. In order to get a deeper insight into the molecular biology of human tumorigenesis, we compared the proteomes of these four cell lines following a multimethod concept. At the beginning we assumed SILAC and sample fractionation with COFRADIC is the method of choice to analyze the cell culture model for malignant transformation. Here, the compared samples are combined before sample preparation, thus avoiding differences in sample preparation, and using COFRADIC notably reduces sample complexity. Because 2D-PAGE is a standard method for the separation and visualization of closely related proteomes, we decided to analyze and compare the proteomes of these four cell lines in a first approach by differential 2D-PAGE. Surprisingly, we discovered much more unique results with iTRAQ and sample fractionation with SCX than with the combination of 2D-PAGE and SILAC-COFRADIC. Moreover, iTRAQ outperforms the other strategies not only in number of yielded results but also in analysis time. Here, we present the iTRAQ quantification results and compare them with the results of 2D-PAGE and SILAC-COFRADIC. We found changes in the protein level at each transition. Thereby, SV40 has the strongest impact on the proteome. In detail we identified 201 regulated proteins. Beside others, these proteins are involved in cytoskeleton, RNA processing, and cell cycle, such as CDC2, hnRNPs, snRNPs, collagens, and MCM proteins. For example, MCM proteins are up-regulated and collagens are down-regulated due to SV40 ER expression. Furthermore we made the observation that proteins containing the same domain have analogous regulation profiles during malignant transformation. For instance, several proteins containing a CH or LIM domain are down-regulated. Moreover, by this study and the defined cell culture model, changes could be clearly matched to specific steps during tumorigenesis

Primers for RT-qPCR analyses.

<p>Primers for RT-qPCR analyses.</p

iTRAQ Analysis of a Cell Culture Model for Malignant Transformation, Including Comparison with 2D-PAGE and SILAC

To study human cancer development, cell culture models for malignant transformation can be used. In 1999 Hahn and Coworkers introduced such a model system and established herewith a basis for research on human tumorigenesis. Primary human fibroblasts are sequentially transduced with defined genetic elements (hTERT, SV40 ER, and H-Ras^V12), resulting in four defined cell lines, whereby the last has a fully transformed phenotype. In order to get a deeper insight into the molecular biology of human tumorigenesis, we compared the proteomes of these four cell lines following a multimethod concept. At the beginning we assumed SILAC and sample fractionation with COFRADIC is the method of choice to analyze the cell culture model for malignant transformation. Here, the compared samples are combined before sample preparation, thus avoiding differences in sample preparation, and using COFRADIC notably reduces sample complexity. Because 2D-PAGE is a standard method for the separation and visualization of closely related proteomes, we decided to analyze and compare the proteomes of these four cell lines in a first approach by differential 2D-PAGE. Surprisingly, we discovered much more unique results with iTRAQ and sample fractionation with SCX than with the combination of 2D-PAGE and SILAC-COFRADIC. Moreover, iTRAQ outperforms the other strategies not only in number of yielded results but also in analysis time. Here, we present the iTRAQ quantification results and compare them with the results of 2D-PAGE and SILAC-COFRADIC. We found changes in the protein level at each transition. Thereby, SV40 has the strongest impact on the proteome. In detail we identified 201 regulated proteins. Beside others, these proteins are involved in cytoskeleton, RNA processing, and cell cycle, such as CDC2, hnRNPs, snRNPs, collagens, and MCM proteins. For example, MCM proteins are up-regulated and collagens are down-regulated due to SV40 ER expression. Furthermore we made the observation that proteins containing the same domain have analogous regulation profiles during malignant transformation. For instance, several proteins containing a CH or LIM domain are down-regulated. Moreover, by this study and the defined cell culture model, changes could be clearly matched to specific steps during tumorigenesis

Mga, L3mbtl2 and Pcgf6 colocalize in mouse ESCs and repress genes involved in differentiation.

<p>(A) Venn diagrams representing the overlap of Mga, L3mbtl2 and Pcgf6 peaks in mouse ESCs. The total number of filtered (≥30 tags and ≥3-fold enrichment over IgG control) ChIP-seq peaks and their overlap is shown. (B) A heat map view of the distribution of the top 8000 union Mga-L3mbtl2-Pcgf6 peaks in mouse ES cells at +/- 2 kb regions centred over the MGA peaks. (C) Representative genome browser screenshot of a 100 kb region of chromosome 1 showing co-binding of Mga, L3mbtl2 and Pcgf6 to four promoter regions. (D) Sequence motifs enriched in Mga-L3mbtl2-Pcgf6 binding regions in mouse ESCs. Top, logos were obtained by running MEME-ChIP with 300 bp summits of the top 600 union Mga-L3mbtl2-Pcgf6 ChIP-seq peaks. The numbers next to the logos indicate the occurrence of the motifs, the statistical significance (E-value) and the transcription factors that bind to the motif. Bottom, local motif enrichment analysis (CentriMo) showing central enrichment of the Mga/Max bHLH domain E-box binding motif and the motif that identified MEME Tomtom as a T-box as well as a E2f6 recognition sequence. The Nrf1 motif was not centrally enriched within the 300 bp peak regions. (E) Distribution of Mga, L3mbtl2 and Pcgf6 peaks relative to positions -2000 bp upstream to +2000 bp downstream of gene bodies. TSS, transcription start site; TES, transcription end site. (F) Middle panel, Venn diagram illustrating the overlap of PRC1.6-bound genes and genes up-regulated in Pcgf6<i>ko</i> cells [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007193#pgen.1007193.ref026" target="_blank">26</a>] and in L3mbtl2<i>ko</i> cells [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007193#pgen.1007193.ref013" target="_blank">13</a>]. Left panel, GO analyses of biological functions of PRC1.6-bound genes that were de-repressed ≥2-fold in Pcgf6<i>ko</i> cells. Right panel, GO analyses of biological functions of PRC1.6-bound genes that were de-repressed ≥2-fold in L3mbtl2<i>ko</i> cells. Enriched GO terms were retrieved using DAVID 6.8. (GOTERM_BP_DIRECT, Functional Annotation Chart). Benjamini values are plotted in log10 scale.</p