scholarly journals Coordination of Growth Rate, Cell Cycle, Stress Response, and Metabolic Activity in Yeast

2008 ◽  
Vol 19 (1) ◽  
pp. 352-367 ◽  
Author(s):  
Matthew J. Brauer ◽  
Curtis Huttenhower ◽  
Edoardo M. Airoldi ◽  
Rachel Rosenstein ◽  
John C. Matese ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Growth Rate ◽  
Stress Response ◽  
System Level ◽  
Cycle Phase ◽  
Instantaneous Growth Rate ◽  
Batch Cultures ◽  
Limiting Nutrient ◽  
Yeast Genes

We studied the relationship between growth rate and genome-wide gene expression, cell cycle progression, and glucose metabolism in 36 steady-state continuous cultures limited by one of six different nutrients (glucose, ammonium, sulfate, phosphate, uracil, or leucine). The expression of more than one quarter of all yeast genes is linearly correlated with growth rate, independent of the limiting nutrient. The subset of negatively growth-correlated genes is most enriched for peroxisomal functions, whereas positively correlated genes mainly encode ribosomal functions. Many (not all) genes associated with stress response are strongly correlated with growth rate, as are genes that are periodically expressed under conditions of metabolic cycling. We confirmed a linear relationship between growth rate and the fraction of the cell population in the G0/G1 cell cycle phase, independent of limiting nutrient. Cultures limited by auxotrophic requirements wasted excess glucose, whereas those limited on phosphate, sulfate, or ammonia did not; this phenomenon (reminiscent of the “Warburg effect” in cancer cells) was confirmed in batch cultures. Using an aggregate of gene expression values, we predict (in both continuous and batch cultures) an “instantaneous growth rate.” This concept is useful in interpreting the system-level connections among growth rate, metabolism, stress, and the cell cycle.

scholarly journals Nutritional Homeostasis in Batch and Steady-State Culture of Yeast

2004 ◽  
Vol 15 (9) ◽  
pp. 4089-4104 ◽  
Author(s):  
Alok J. Saldanha ◽  
Matthew J. Brauer ◽  
David Botstein
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Steady State ◽  
Stress Response ◽  
Stress Responses ◽  
Global Pattern ◽  
Batch Cultures ◽  
Cycle Arrest ◽  
Chemostat Cultures ◽  
Limiting Nutrient

We studied the physiological response to limitation by diverse nutrients in batch and steady-state (chemostat) cultures of S. cerevisiae. We found that the global pattern of transcription in steady-state cultures in limiting phosphate or sulfate is essentially identical to that of batch cultures growing in the same medium just before the limiting nutrient is completely exhausted. The massive stress response and complete arrest of the cell cycle that occurs when nutrients are fully exhausted in batch cultures is not observed in the chemostat, indicating that the cells in the chemostat are “poor, not starving.” Similar comparisons using leucine or uracil auxotrophs limited on leucine or uracil again showed patterns of gene expression in steady-state closely resembling those of corresponding batch cultures just before they exhaust the nutrient. Although there is also a strong stress response in the auxotrophic batch cultures, cell cycle arrest, if it occurs at all, is much less uniform. Many of the differences among the patterns of gene expression between the four nutrient limitations are interpretable in light of known involvement of the genes in stress responses or in the regulation or execution of particular metabolic pathways appropriate to the limiting nutrient. We conclude that cells adjust their growth rate to nutrient availability and maintain homeostasis in the same way in batch and steady state conditions; cells in steady-state cultures are in a physiological condition normally encountered in batch cultures.

scholarly journals Homeostatic Adjustment and Metabolic Remodeling in Glucose-limited Yeast Cultures

2005 ◽  
Vol 16 (5) ◽  
pp. 2503-2517 ◽  
Author(s):  
Matthew J. Brauer ◽  
Alok J. Saldanha ◽  
Kara Dolinski ◽  
David Botstein
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Steady State ◽  
Stress Response ◽  
The State ◽  
Second Phase ◽  
Diauxic Shift ◽  
Batch Cultures ◽  
Chemostat Cultures ◽  
Metabolic Remodeling

We studied the physiological response to glucose limitation in batch and steady-state (chemostat) cultures of Saccharomyces cerevisiae by following global patterns of gene expression. Glucose-limited batch cultures of yeast go through two sequential exponential growth phases, beginning with a largely fermentative phase, followed by an essentially completely aerobic use of residual glucose and evolved ethanol. Judging from the patterns of gene expression, the state of the cells growing at steady state in glucose-limited chemostats corresponds most closely with the state of cells in batch cultures just before they undergo this “diauxic shift.” Essentially the same pattern was found between chemostats having a fivefold difference in steady-state growth rate (the lower rate approximating that of the second phase respiratory growth rate in batch cultures). Although in both cases the cells in the chemostat consumed most of the glucose, in neither case did they seem to be metabolizing it primarily through respiration. Although there was some indication of a modest oxidative stress response, the chemostat cultures did not exhibit the massive environmental stress response associated with starvation that also is observed, at least in part, during the diauxic shift in batch cultures. We conclude that despite the theoretical possibility of a switch to fully aerobic metabolism of glucose in the chemostat under conditions of glucose scarcity, homeostatic mechanisms are able to carry out metabolic adjustment as if fermentation of the glucose is the preferred option until the glucose is entirely depleted. These results suggest that some aspect of actual starvation, possibly a component of the stress response, may be required for triggering the metabolic remodeling associated with the diauxic shift.

Effects of temperature and nutritional conditions on the mitotic cell cycle of Saccharomyces cerevisiae

1978 ◽  
Vol 31 (1) ◽  
pp. 71-78
Author(s):  
M.N. Jagadish ◽  
B.L. Carter
Keyword(s):  
Cell Cycle ◽  
Growth Rate ◽  
Growth Rates ◽  
Mitotic Cell ◽  
S Phase ◽  
Yeast Cells ◽  
Batch Cultures ◽  
Nutritional Conditions ◽  
Limiting Nutrient ◽  
Effects Of Temperature

Yeast cells were cultivated at different growth rates in a chemostat by alterations in the flow of the limiting nutrient glucose and in batch cultures where variations in growth rate were achieved by alterations in the composition of nutrients. It was observed that the stage in the cycle at which S-phase was completed varied with growth rate. The faster the growth rate, the earlier the stage in the cycle in which completion of S-phase occurred. When stage in the cycle is converted into time before division it was observed that the time from completion of S-phase to cell division varied only slightly with growth rate except at extremely slow growth rates. Expansion of cell cycle transit time as the growth rate was slowed was achieved primarily by an expansion in time of the period from division to the completion of S-phase. In contrast, when cells were grown at different rates by alterations in the temperature of cultivation, completion of S-phase occurred at approximately the same stage in the cell cycle at all growth rates.

Neighbourhoods in the yeast regulatory network in different physiological states

2020 ◽  
Author(s):  
Arthur M Lesk ◽  
Arun S Konagurthu
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Dna Damage ◽  
Transcription Factor ◽  
Stress Response ◽  
Local Structure ◽  
Regulatory Network ◽  
Diauxic Shift ◽  
Central Node ◽  
Similarities And Differences

Abstract Motivation The gene expression regulatory network in yeast controls the selective implementation of the information contained in the genome sequence. We seek to understand how, in different physiological states, the network reconfigures itself to produce a different proteome. Results This article analyses this reconfiguration, focussing on changes in the local structure of the network. In particular, we define, extract and compare the 1-neighbourhoods of each transcription factor, where a 1-neighbourhood of a node in a network is the minimal subgraph of the network containing all nodes connected to the central node by an edge. We report the similarities and differences in the topologies and connectivities of these neighbourhoods in five physiological states for which data are available: cell cycle, DNA damage, stress response, diauxic shift and sporulation. Based on our analysis, it seems apt to regard the components of the regulatory network as ‘software’, and the responses to changes in state, ‘reprogramming’.

Gene Expression Analysis of Leukemic Blasts Persisting at Day 8 of Induction Therapy in Childhood ALL.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 522-522
Author(s):  
Peter Rhein ◽  
Richard Ratei ◽  
Stefanie Scheid ◽  
Christian Hagemeier ◽  
Karl Seeger ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Experimental Approach ◽  
Response To Therapy ◽  
Cycle Phase ◽  
Flow Sorting ◽  
Blast Cells ◽  
Survival Signaling

Abstract In childhood acute lymphoblastic leukemia (ALL), early response to therapy is of crucial prognostic significance. In the frontline ALL-BFM (Berlin-Frankfurt-Münster) trial, treatment stratification is based on blast count estimation in peripheral blood at day 8 of induction prephase with prednisone and one dose of intrathecal methotrexate at day 1. To approach yet unknown mechanisms of therapy resistance and to characterize cells persisting under therapy on molecular level, we investigated gene expression profiles of leukemic blasts at day 8 of therapy (“day 8” cells) and their changes as compared with blast cells at initial diagnosis (“day 0” cells). To this end, an experimental procedure has been established including flow sorting of leukemic cells by their leukemia-associated immunophenotype and preparation of cRNA, starting from a small number of cells and using an additional amplification step. Experiments have shown that flow sorting procedure does not affect RNA quality, and this experimental approach facilitates investigation of patient samples with blast cell counts as low as 50 blast cells/μl. Blast cells from ten patients with B-cell precursor ALL were investigated using Affymetrix HG U133A microarrays, and gene expression data were processed by normalization procedure on probe level by variance stabilization. Genes commonly up- or downregulated in blast cells under therapy were identified in matched pairs of day 8 and day 0 samples using Significance Analysis of Microarrays (SAM) and a filtering criterion of at least two-fold mean change. By this procedure a group of 84 genes with a false discovery rate of less than 10 % was identified. In this group, 24 genes, reportedly involved at different levels of the cell cycle regulation, including cell cycle progression (CDC2, cyclin B2), DNA replication (e.g. thymidylate synthetase TYMS, ribonucleotide reductase RRM2, proteins MCM 4 and 6) and execution of mitosis (e.g. cell cycle checkpoint kinases CHEK1 and BUB1B, kinesin-like proteins 1 and 7, and MAD2L1), were found to be downregulated in the day 8 cells. In contrast, genes (n=7) encoding for proteins involved in survival signaling (e.g. IL-4/IL-13 common receptor chain, membrane-spanning 4-domains MS4A1 and CD20) showed increased expression in day 8 blasts. Taken together, the described experimental approach enabled gene expression analysis of ALL cells persisting under therapy and pointed to increased survival signaling in day 8 blasts and their preferential positioning in the G1 cell cycle phase.

scholarly journals Role for a YY1-Binding Element in Replication-Dependent Mouse Histone Gene Expression

1998 ◽  
Vol 18 (12) ◽  
pp. 7106-7118 ◽  
Author(s):  
Katherine A. Eliassen ◽  
Amy Baldwin ◽  
Eric M. Sikorski ◽  
Myra M. Hurt
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Transcription Factor ◽  
Protein Interaction ◽  
Histone Gene ◽  
Cycle Phase ◽  
Histone Gene Expression ◽  
Dna Protein Interaction

ABSTRACT Expression of the highly conserved replication-dependent histone gene family increases dramatically as a cell enters the S phase of the eukaryotic cell cycle. Requirements for normal histone gene expression in vivo include an element, designated α, located within the protein-encoding sequence of nucleosomal histone genes. Mutation of 5 of 7 nucleotides of the mouse H3.2 α element to yield the sequence found in an H3.3 replication-independent variant abolishes the DNA-protein interaction in vitro and reduces expression fourfold in vivo. A yeast one-hybrid screen of a HeLa cell cDNA library identified the protein responsible for recognition of the histone H3.2 α sequence as the transcription factor Yin Yang 1 (YY1). YY1 is a ubiquitous and highly conserved transcription factor reported to be involved in both activation and repression of gene expression. Here we report that the in vitro histone α DNA-protein interaction depends on YY1 and that mutation of the nucleotides required for the in vitro histone α DNA-YY1 interaction alters the cell cycle phase-specific up-regulation of the mouse H3.2 gene in vivo. Because all mutations or deletions of the histone α sequence both abolish interactions in vitro and cause an in vivo decrease in histone gene expression, the recognition of the histone α element by YY1 is implicated in the correct temporal regulation of replication-dependent histone gene expression in vivo.

scholarly journals Fibroblast Growth Factor 2 Reactivates G1 Checkpoint in SK-N-MC Cells via Regulation of p21, Inhibitor of Differentiation Genes (Id1-3), and Epithelium-Mesenchyme Transition-Like Events

Endocrinology ◽  
2009 ◽  
Vol 150 (9) ◽  
pp. 4044-4055 ◽  
Author(s):  
S. Higgins ◽  
S. H. X. Wong ◽  
M. Richner ◽  
C. L. Rowe ◽  
D. F. Newgreen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Rna Interference ◽  
Growth Factor ◽  
Fibroblast Growth Factor ◽  
Nuclear Accumulation ◽  
Cycle Phase ◽  
Fibroblast Growth ◽  
Igf I ◽  
E Cadherin

Abstract We have recently demonstrated that fibroblast growth factor (FGF)-2 promotes neuroblastoma cell differentiation and overrides their mitogenic response to IGF-I. However, the mechanisms involved are unknown. SK-N-MC cells were cultured with FGF-2 (50 ng/ml) and/or IGF-I (100 ng/ml) up to 48 h. Fluorescence-activated cell sorting analysis indicated that FGF-2 promotes G1/G0 cell cycle phase arrest. Gene expression by RT2-PCR and cellular localization showed up-regulation of p21. We then investigated whether FGF-2-induced differentiation of SK-N-MC cells (by GAP43 and NeuroD-6 expression) involves epithelium-mesenchyme transition interconversion. Real-time PCR (RT2-PCR) showed modulation of genes involved in maintenance of the epithelial phenotype and cell-matrix interactions (E-cadherin, Snail-1, MMPs). Zymography confirmed FGF-2 up-regulated MMP2 and induced MMP9, known to contribute to neuronal differentiation and neurite extension. Id1-3 expression was determined by RT2-PCR. FGF-2 induced Id2, while down-regulating Id1 and Id3. FGF-2 induced nuclear accumulation of ID2 protein, while ID1 and ID3 remained cytoplasmic. RNA interference demonstrated that Id3 regulates differentiation and cell cycle (increased Neuro-D6 and p21 mRNA), while d Id2 modulates epithelium-mesenchyme transition-like events (increased E-cadherin mRNA). In conclusion, we have shown for the first time that FGF-2 induces differentiation of neuroblastoma cells via activation of a complex gene expression program enabling modulation of cell cycle, transcription factors, and suppression of the cancer phenotype. The use of RNA interference indicated that Id-3 is a key regulator of these events, thus pointing to a novel therapeutic target for this devastating childhood cancer.

scholarly journals Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

2019 ◽  
Author(s):  
Chiaowen Joyce Hsiao ◽  
PoYuan Tung ◽  
John D. Blischak ◽  
Jonathan E. Burnett ◽  
Kenneth A. Barr ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Cycle Progression ◽  
Cell Types ◽  
Cell Cycle Phase ◽  
Cellular Heterogeneity ◽  
Cycle Phase ◽  
Cycle Progression ◽  
Cellular Environment

AbstractCellular heterogeneity in gene expression is driven by cellular processes such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity, and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). Using these data, we developed a novel approach to characterize cell cycle progression. While standard methods assign cells to discrete cell cycle stages, our method goes beyond this, and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell’s position on the cell cycle continuum to within 14% of the entire cycle, and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell-cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

scholarly journals Growth-Rate Dependent And Nutrient-Specific Gene Expression Resource Allocation In Fission Yeast

2021 ◽  
Author(s):  
Istvan T. Kleijn ◽  
Amalia Martínez-Segura ◽  
François Bertaux ◽  
Malika Saint ◽  
Holger Kramer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Resource Allocation ◽  
Fission Yeast ◽  
Metabolic Enzymes ◽  
Specific Gene ◽  
Specific Gene Expression ◽  
Rate Dependent ◽  
Specific Regulation ◽  
Yeast Genes

Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Quantitative studies using various growth conditions have singled out growth rate as a major physiological variable explaining relative protein abundances. Here, we used the simple eukaryote Schizosaccharomyces pombe to determine the importance of growth rate in explaining relative changes in protein and mRNA levels during growth on a series of non-limiting nitrogen sources. Although half of fission yeast genes were significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production with the notable exception of RNA polymerase II, whereas those negatively correlated mainly belonged to the environmental stress response programme. Critically, metabolic enzymes, which represent ~55-70% of the proteome by mass, showed mainly condition-specific regulation. Specifically, many enzymes involved in glycolysis and NAD-dependent metabolism as well as the fermentative and respiratory pathways were condition-dependent and not consistently correlated with growth. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate dependent and nutrient-specific gene expression.

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