Comparative cell cycle transcriptomics reveals synchronization of developmental transcription factor networks in cancer cells

Boström, Johan; Šrámková, Zuzana; Salašová, Alena; Johard, Helena A.D.; Mahdessian, Diana; Fedr, Radek; Marks, Carolyn; Medalová, Jiřina; Souček, Karel; Lundberg, Emma; Linnarsson, Sten; Bryja, Vı́tězslav; Sekyrová, Petra; Altun, Mikael; Andäng, Michael

doi:10.1371/journal.pone.0188772

Cited by 22 publications

(16 citation statements)

References 70 publications

(86 reference statements)

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“…This was also evident from the differential expression analysis, where 52 of the downregulated and 22 of the upregulated genes had an interaction between BR and BRE over time ( P < .05). The propensity of the genes that were reversed by the exercise countermeasure were further analyzed based on co‐variance between BR and BRE 39 . This approach allowed us to visualize the effects of the two interventions on the gene‐level and to stratify genes based on their propensity to become reversed (or amplified) in the BRE condition.…”

Section: Resultsmentioning

confidence: 99%

Three months of bed rest induce a residual transcriptomic signature resilient to resistance exercise countermeasures

et al. 2020

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This study explored the muscle genome‐wide response to long‐term unloading (84‐day bed rest) in 21 men. We hypothesized that a part of the bed rest‐induced gene expression signature would be resilient to a concurrent flywheel resistance exercise (RE) countermeasure. Using DNA microarray technology analyzing 35 345 gene‐level probe‐sets, we identified 335 annotated probe‐sets that were downregulated, and 315 that were upregulated after bed rest (P < .01). Besides a predictable differential expression of genes and pathways related to mitochondria (downregulation; false‐discovery rates (FDR) <1E‐04), ubiquitin system (upregulation; FDR = 3E‐02), and skeletal muscle energy metabolism and structure (downregulation; FDR ≤ 3E‐03), 84‐day bed rest also altered circadian rhythm regulation (upregulation; FDR = 3E‐02). While most of the bed rest‐induced changes were counteracted by RE, 209 transcripts were resilient to the exercise countermeasure. Genes upregulated after bed rest were particularly resistant to training (P < .001 vs downregulated, non‐reversed genes). Specifically, “Translation Factors,” “Proteasome Degradation,” “Cell Cycle,” and “Nucleotide Metabolism” pathways were not normalized by RE. This study provides an unbiased high‐throughput transcriptomic signature of one of the longest unloading periods in humans to date. Classical disuse‐related changes in structural and metabolic genes/pathways were identified, together with a novel upregulation of circadian rhythm transcripts. In the context of previous bed rest campaigns, the latter seemed to be related to the duration of unloading, suggesting the transcriptomic machinery continues to adapt throughout extended disuse periods. Despite that the RE training offset most of the bed rest‐induced muscle‐phenotypic and transcriptomic alterations, we contend that the human skeletal muscle also displays a residual transcriptomic signature of unloading that is resistant to an established exercise countermeasure.

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Section: Resultsmentioning

confidence: 99%

Three months of bed rest induce a residual transcriptomic signature resilient to resistance exercise countermeasures

et al. 2020

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“…We thus classify these as exhibiting oscillatory behavior. For visualization of oscillatory patterns and to be able to compare patterns of oscillation between experiments, the algorithm TriComp was used according to (9). A generalized version of the TriComp code in R script can be found in Suppl File 1.…”

Section: Discussionmentioning

confidence: 99%

“…Cells were regularly checked for Mycoplasma contamination (Lonza, MycoAlert). This cell line was the same as used in Boström et al to generate the transcriptomic data that we compared our data with, and was thawed up from freezing vials from a similar passage number (9).…”

Section: Cell Culturementioning

confidence: 99%

“…We combined this system with fluorescence-activated cell sorting (FACS), cellular fractionation, proteomics and phospho-proteomics analyses. Furthermore, we integrated our dataset with a cell cycle transcriptomics expression profile (9) and in vitro essentiality data summarized from ten different CRISPR/Cas9 survivability cell line experiments (10).…”

Section: Introductionmentioning

confidence: 99%

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Cell Cycle Profiling Reveals Protein Oscillation, Phosphorylation, and Localization Dynamics

Herr

Boström

Rullman

et al. 2020

Molecular & Cellular Proteomics

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The cell cycle is a highly conserved process involving the coordinated separation of a single cell into two daughter cells. To relate transcriptional regulation across the cell cycle with oscillatory changes in protein abundance and activity, we carried out a proteome- and phospho-proteome-wide mass spectrometry profiling. We compared protein dynamics with gene transcription, revealing many transcriptionally regulated G2 mRNAs that only produce a protein shift after mitosis. Integration of CRISPR/Cas9 survivability studies further highlighted proteins essential for cell viability. Analyzing the dynamics of phosphorylation events and protein solubility dynamics over the cell cycle, we characterize predicted phospho-peptide motif distributions and predict cell cycle-dependent translocating proteins, as exemplified by the S-adenosylmethionine synthase MAT2A. Our study implicates this enzyme in translocating to the nucleus after the G1/S-checkpoint, which enables epigenetic histone methylation maintenance during DNA replication. Taken together, this data set provides a unique integrated resource with novel insights on cell cycle dynamics.

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“…A reduction in the expression of cell cycle genes at the population level indicates a change in the proportion of cells at different stages of the cell cycle. Due to the massive transcriptional changes that occur during the progression through cell cycle [61][62][63][64], this may obscure other gene regulatory programs that are associated with alterations to cell function. Despite this limitation, we found evidence of a subset of TNF-α-regulated genes that were upregulated in TNF-treated granulocytes and following macrophage differentiation, but downregulated after granulocytic differentiation.…”

Section: Discussionmentioning

confidence: 99%

TNF-α differentially regulates cell cycle genes in promyelocytic and granulocytic HL-60/S4 cells

Jacobson

Perry

Vickers

et al. 2018

Preprint

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Tumor necrosis factor alpha (TNF-α) is a potent cytokine involved in systemic inflammation and immune modulation. Signaling responses that involve TNF-α are context dependent and capable of stimulating pathways promoting both cell death and survival. TNF-α treatment has been investigated as part of a combined therapy for acute myeloid leukemia due to its modifying effects on all-trans retinoic acid (ATRA) mediated differentiation into granulocytes.To investigate the interaction between cellular differentiation and TNF-α, we performed RNA-sequencing on two forms of the human HL-60/S4 promyelocytic leukemia cell line treated with TNF-α. The ATRA-differentiated granulocytic form of HL-60/S4 cells had an enhanced transcriptional response to TNF-α treatment compared to the undifferentiated promyelocytes. The observed TNF-α responses included differential expression of cell cycle gene sets, which were generally upregulated in TNF-α treated promyelocytes, and downregulated in TNF-α treated granulocytes. This is consistent with TNF-α induced cell cycle repression in granulocytes and cell cycle progression in promyelocytes. Moreover, comparisons with gene expression changes associated with differentiation indicated that TNF-α treatment of granulocytes shifts the transcriptome towards that of a macrophage.We conclude that TNF-α treatment promotes a divergent transcriptional program in promyelocytes and granulocytes. TNF-α promotes cell cycle associated gene expression in promyelocytes. In contrast, TNF-α stimulated granulocytes have reduced cell cycle gene expression, and a macrophage-like transcriptional program.

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Comparative cell cycle transcriptomics reveals synchronization of developmental transcription factor networks in cancer cells

Cited by 22 publications

References 70 publications

Three months of bed rest induce a residual transcriptomic signature resilient to resistance exercise countermeasures

Three months of bed rest induce a residual transcriptomic signature resilient to resistance exercise countermeasures

Cell Cycle Profiling Reveals Protein Oscillation, Phosphorylation, and Localization Dynamics

TNF-α differentially regulates cell cycle genes in promyelocytic and granulocytic HL-60/S4 cells

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