scholarly journals The monoplastidic bottleneck in algae and plant evolution

2017 ◽  
Author(s):  
Jan de Vries ◽  
Sven B. Gould
Keyword(s):  
Cell Cycle ◽  
Vascular Plants ◽  
Plastid Genome ◽  
Eukaryotic Cell ◽  
Plant Evolution ◽  
Plastid Division ◽  
Complex Morphology ◽  
Bacterial Division ◽  
Associated Proteins ◽  
Plastid Transmission

AbstractPlant and algae plastids evolved from the endosymbiotic integration of a cyanobacterium by a heterotrophic eukaryote. A consequence of their ancestry is that new plastids can only emerge through fission and vital to organelle and host co-evolution was the early synchronization of bacterial division with the host’s eukaryotic cell cycle. Most of the sampled algae, including multicellular macroalgae, house a single plastid per cell — or nucleus in case of coenocytic cells — and basal branching relatives of polyplastidic lineages are all monoplastidic. The latter is also true regarding embryophytes, as some non-vascular plants are monoplastidic at least at some stage of their life cycle. Here we synthesize recent advances regarding plastid division and associated proteins, including those of the peptidoglycan wall biosynthesis, across the diversity of phototrophic eukaryotes. Through the comparison of the phenotype of 131 species harbouring plastids of primary or secondary origin, we uncover that one prerequisite for an algae or plant to house multiple plastids per nucleus appears the loss of the genes MinD and MinE from the plastid genome. Housing a single plastid whose division is coupled to host cytokinesis appears a prerequisite of plastid emergence; escaping that monoplastidic bottleneck succeeded rarely and appears tied to evolving a complex morphology. Considering how little we know about the mechanisms that guarantee proper organelle (and genome) inheritance raises the peculiar possibility that a quality control checkpoint of plastid transmission remains to be explored and which is tied to understanding the monoplastidic bottleneck.

Cyclin specificity: how many wheels do you need on a unicycle?

2001 ◽  
Vol 114 (10) ◽  
pp. 1811-1820 ◽  
Author(s):  
M.E. Miller ◽  
F.R. Cross
Keyword(s):  
Cell Cycle ◽  
Subcellular Localization ◽  
Cell Cycle Progression ◽  
Eukaryotic Cell ◽  
Cyclin Dependent Kinase ◽  
Temporal Expression ◽  
Cycle Progression

Cyclin-dependent kinase (CDK) activity is essential for eukaryotic cell cycle events. Multiple cyclins activate CDKs in all eukaryotes, but it is unclear whether multiple cyclins are really required for cell cycle progression. It has been argued that cyclins may predominantly act as simple enzymatic activators of CDKs; in opposition to this idea, it has been argued that cyclins might target the activated CDK to particular substrates or inhibitors. Such targeting might occur through a combination of factors, including temporal expression, protein associations, and subcellular localization.

Cyclomodulins: bacterial effectors that modulate the eukaryotic cell cycle

2005 ◽  
Vol 13 (3) ◽  
pp. 103-110 ◽  
Author(s):  
Jean-Philippe Nougayrède ◽  
Frédéric Taieb ◽  
Jean De Rycke ◽  
Eric Oswald
Keyword(s):  
Cell Cycle ◽  
Eukaryotic Cell ◽  
Bacterial Effectors

Eukaryotic Cell-Cycle Control

1992 ◽  
Vol 20 (2) ◽  
pp. 239-242 ◽  
Author(s):  
Paul Nurse
Keyword(s):  
Cell Cycle ◽  
Cell Cycle Control ◽  
Eukaryotic Cell

Microglial cell cycle-associated proteins control microglial proliferation in vivo and in vitro and are regulated by GM-CSF and density-dependent inhibition

2003 ◽  
Vol 74 (6) ◽  
pp. 898-905 ◽  
Author(s):  
Ken Koguchi ◽  
Yuji Nakatsuji ◽  
Tatsusada Okuno ◽  
Makoto Sawada ◽  
Saburo Sakoda
Keyword(s):  
Cell Cycle ◽  
Microglial Cell ◽  
Density Dependent ◽  
Gm Csf ◽  
Microglial Proliferation ◽  
Dependent Inhibition ◽  
Associated Proteins

scholarly journals Nuclear incorporation of iron during the eukaryotic cell cycle

2016 ◽  
Vol 23 (6) ◽  
pp. 1490-1497 ◽  
Author(s):  
Ian Robinson ◽  
Yang Yang ◽  
Fucai Zhang ◽  
Christophe Lynch ◽  
Mohammed Yusuf ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Fluorescence Microscopy ◽  
Phase Contrast ◽  
Eukaryotic Cell ◽  
Inhomogeneous Distribution ◽  
Cell Nuclei ◽  
X Ray ◽  
Microscopy Images ◽  
Different Levels

Scanning X-ray fluorescence microscopy has been used to probe the distribution of S, P and Fe within cell nuclei. Nuclei, which may have originated at different phases of the cell cycle, are found to show very different levels of Fe present with a strongly inhomogeneous distribution. P and S signals, presumably from DNA and associated nucleosomes, are high and relatively uniform across all the nuclei; these agree with X-ray phase contrast projection microscopy images of the same samples. Possible reasons for the Fe incorporation are discussed.

scholarly journals Effects of cell cycle variability on lineage and population measurements of mRNA abundance

2020 ◽  
Author(s):  
Ruben Perez-Carrasco ◽  
Casper Beentjes ◽  
Ramon Grima
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Negative Binomial ◽  
Eukaryotic Cell ◽  
Cycle Duration ◽  
Cell Cycle Duration ◽  
Monotonically Decreasing Function ◽  
Binomial Mixture ◽  
A Cell ◽  
The Mean

AbstractMany models of gene expression do not explicitly incorporate a cell cycle description. Here we derive a theory describing how mRNA fluctuations for constitutive and bursty gene expression are influenced by stochasticity in the duration of the cell cycle and the timing of DNA replication. Analytical expressions for the moments show that omitting cell cycle duration introduces an error in the predicted mean number of mRNAs that is a monotonically decreasing function of η, which is proportional to the ratio of the mean cell cycle duration and the mRNA lifetime. By contrast, the error in the variance of the mRNA distribution is highest for intermediate values of η consistent with genome-wide measurements in many organisms. Using eukaryotic cell data, we estimate the errors in the mean and variance to be at most 3% and 25%, respectively. Furthermore, we derive an accurate negative binomial mixture approximation to the mRNA distribution. This indicates that stochasticity in the cell cycle can introduce fluctuations in mRNA numbers that are similar to the effect of bursty transcription. Finally, we show that for real experimental data, disregarding cell cycle stochasticity can introduce errors in the inference of transcription rates larger than 10%.

scholarly journals Successive Paradigm Shifts in the Bacterial Cell Cycle and Related Subjects

Life ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 27
Author(s):  
Vic Norris
Keyword(s):  
Cell Cycle ◽  
Steady State ◽  
Growth Rates ◽  
Bacterial Cell ◽  
Paradigm Shift ◽  
Eukaryotic Cell ◽  
Origins Of Life ◽  
Paradigm Shifts ◽  
Bacterial Physiology ◽  
Bacterial Cell Cycle

A paradigm shift in one field can trigger paradigm shifts in other fields. This is illustrated by the paradigm shifts that have occurred in bacterial physiology following the discoveries that bacteria are not unstructured, that the bacterial cell cycle is not controlled by the dynamics of peptidoglycan, and that the growth rates of bacteria in the same steady-state population are not at all the same. These paradigm shifts are having an effect on longstanding hypotheses about the regulation of the bacterial cell cycle, which appear increasingly to be inadequate. I argue that, just as one earthquake can trigger others, an imminent paradigm shift in the regulation of the bacterial cell cycle will have repercussions or “paradigm quakes” on hypotheses about the origins of life and about the regulation of the eukaryotic cell cycle.

Regulation of the Eukaryotic Cell Cycle

1992 ◽  
pp. 1-26
Author(s):  
R. F. Brooks
Keyword(s):  
Cell Cycle ◽  
Eukaryotic Cell

scholarly journals Microtubule Retrograde Motors and Their Role in Retroviral Transport

Viruses ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 483
Author(s):  
Gianfranco Pietrantoni ◽  
Rodrigo Ibarra-Karmy ◽  
Gloria Arriagada
Keyword(s):  
Host Cell ◽  
Reverse Transcription ◽  
Eukaryotic Cell ◽  
Chromosomal Dna ◽  
Viral Dna ◽  
Preintegration Complex ◽  
Retrograde Trafficking ◽  
Microtubule Associated Proteins ◽  
Associated Proteins ◽  
Crowded Environment

Following entry into the host cell, retroviruses generate a dsDNA copy of their genomes via reverse transcription, and this viral DNA is subsequently integrated into the chromosomal DNA of the host cell. Before integration can occur, however, retroviral DNA must be transported to the nucleus as part of a ‘preintegration complex’ (PIC). Transporting the PIC through the crowded environment of the cytoplasm is challenging, and retroviruses have evolved different mechanisms to accomplish this feat. Within a eukaryotic cell, microtubules act as the roads, while the microtubule-associated proteins dynein and kinesin are the vehicles that viruses exploit to achieve retrograde and anterograde trafficking. This review will examine the various mechanisms retroviruses have evolved in order to achieve retrograde trafficking, confirming that each retrovirus has its own strategy to functionally subvert microtubule associated proteins.

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