Controlling cell cycle and cell fate: common strategies in prokaryotes and eukaryotes

Effects of host genetics and environment on egg-associated microbiotas in brown trout (Salmo trutta)

Recent studies found fish egg-specific bacterial communities that changed over the course of embryogenesis, suggesting an interaction between the developing host and its microbiota. Indeed, single-strain infections demonstrated that the virulence of opportunistic bacteria is influenced by environmental factors and host immune genes. However, the interplay between a fish embryo host and its microbiota has not been studied yet at the community level. To test whether host genetics affects the assemblage of egg-associated bacteria, adult brown trout (Salmo trutta) were sampled from a natural population. Their gametes were used for full-factorial in vitro fertilizations to separate sire from dam effects. In total, 2520 embryos were singly raised under experimental conditions that differently support microbial growth. High-throughput 16S rRNA amplicon sequencing was applied to characterize bacterial communities on milt and fertilized eggs across treatments. Dam and sire identity influenced embryo mortality, time until hatching and composition of egg-associated microbiotas, but no link between bacterial communities on milt and on fertilized eggs could be found. Elevated resources increased embryo mortality and modified bacterial communities with a shift in their putative functional potential. Resource availability did not significantly affect any parental effects on embryo performance. Sire identity affected bacterial diversity that turned out to be a significant predictor of hatching time: embryos associated with high bacterial diversity hatched later. We conclude that both host genetics and the availability of resources define diversity and composition of egg-associated bacterial communities that then affect the life history of their hosts.

Temporal controls of the asymmetric cell division cycle in Caulobacter crescentus

The asymmetric cell division cycle of Caulobacter crescentus is orchestrated by an elaborate gene-protein regulatory network, centered on three major control proteins, DnaA, GcrA and CtrA. The regulatory network is cast into a quantitative computational model to investigate in a systematic fashion how these three proteins control the relevant genetic, biochemical and physiological properties of proliferating bacteria. Different controls for both swarmer and stalked cell cycles are represented in the mathematical scheme. The model is validated against observed phenotypes of wild-type cells and relevant mutants, and it predicts the phenotypes of novel mutants and of known mutants under novel experimental conditions. Because the cell cycle control proteins of Caulobacter are conserved across many species of alpha-proteobacteria, the model we are proposing here may be applicable to other genera of importance to agriculture and medicine (e.g., Rhizobium, Brucella).

An ensemble learning approach to reverse-engineering transcriptional regulatory networks from time-series gene expression data

BACKGROUND

One of the most challenging tasks in the post-genomic era is to reconstruct the transcriptional regulatory networks. The goal is to reveal, for each gene that responds to a certain biological event, which transcription factors affect its expression, and how a set of transcription factors coordinate to accomplish temporal and spatial specific regulations.

RESULTS

Here we propose a supervised machine learning approach to address these questions. We focus our study on the gene transcriptional regulation of the cell cycle in the budding yeast, thanks to the large amount of data available and relatively well-understood biology, although the main ideas of our method can be applied to other data as well. Our method starts with building an ensemble of decision trees for each microarray data to capture the association between the expression levels of yeast genes and the binding of transcription factors to gene promoter regions, as determined by chromatin immunoprecipitation microarray (ChIP-chip) experiment. Cross-validation experiments show that the method is more accurate and reliable than the naive decision tree algorithm and several other ensemble learning methods. From the decision tree ensembles, we extract logical rules that explain how a set of transcription factors act in concert to regulate the expression of their targets. We further compute a profile for each rule to show its regulation strengths at different time points. We also propose a spline interpolation method to integrate the rule profiles learned from several time series expression data sets that measure the same biological process. We then combine these rule profiles to build a transcriptional regulatory network for the yeast cell cycle. Compared to the results in the literature, our method correctly identifies all major known yeast cell cycle transcription factors, and assigns them into appropriate cell cycle phases. Our method also identifies many interesting synergetic relationships among these transcription factors, most of which are well known, while many of the rest can also be supported by other evidences.

CONCLUSION

The high accuracy of our method indicates that our method is valid and robust. As more gene expression and transcription factor binding data become available, we believe that our method is useful for reconstructing large-scale transcriptional regulatory networks in other species as well.

Arabidopsis CDKA;1, a cdc2 homologue, controls proliferation of generative cells in male gametogenesis

The protein kinase cdc2 is conserved throughout eukaryotes and acts as a key regulator of the cell cycle. In plants, A-type cyclin-dependent kinase (CDKA), a homologue of cdc2, has a role throughout the cell cycle. Here we show that a loss-of-function mutation in CDKA;1, encoding the only Arabidopsis CDKA, results in lethality of the male gametophyte. Heterozygous plants produced mature siliques containing about 50% aborted seeds, and segregation distortion was observed in paternal inheritance. Microspores normally undergo an asymmetric cell division, pollen mitosis I (PMI), to produce bicellular pollen grains. The larger vegetative cell does not divide, but the smaller generative cell undergoes mitosis, PMII, to form the two sperm cells, thereby generating tricellular pollen grains. The cdka-1 mutant, however, produces mature bicellular pollen grains, consisting of a single sperm-like cell and a vegetative cell, due to failure of PMII. The mutant sperm-like cell is fertile, and preferentially fuses with the egg cell to initiate embryogenesis. As the central cell nucleus remains unfertilized, however, double fertilization does not occur. In heterozygous plants, the embryo is arrested at the globular stage, most likely because of loss of endosperm development, whereas it is arrested at the one- or two-cell stage in presumptive homozygous plants. Thus, CDKA;1 is essential for cell division of the generative cell in male gametogenesis.

A DNA integrity network in the yeast Saccharomyces cerevisiae

A network governing DNA integrity was identified in yeast by a global genetic analysis of synthetic fitness or lethality defect (SFL) interactions. Within this network, 16 functional modules or minipathways were defined based on patterns of global SFL interactions. Modules or genes involved in DNA replication, DNA-replication checkpoint (DRC) signaling, and oxidative stress response were identified as the major guardians against lethal spontaneous DNA damage, efficient repair of which requires the functions of the DNA-damage checkpoint signaling and multiple DNA-repair pathways. This genome-wide genetic interaction network also identified novel components (DIA2, NPT1, HST3, HST4, and the CSM1 module) that potentially contribute to mitotic DNA replication and genomic stability and revealed novel functions of well-studied genes (the CTF18 module) in DRC signaling. This network will guide more detailed characterization of mechanisms governing DNA integrity in yeast and other organisms.

Asymmetric cell divisions in flowering plants - one mother, "two-many" daughters

Plant development shows a fascinating range of asymmetric cell divisions. Over the years, however, cellular differentiation has been interpreted mostly in terms of a mother cell dividing mitotically to produce two daughter cells of different fates. This popular view has masked the significance of an entirely different cell fate specification pathway, where the mother cell first becomes a coenocyte and then cellularizes to simultaneously produce more than two specialized daughter cells. The "one mother - two different daughters" pathways rely on spindle-assisted mechanisms, such as translocation of the nucleus/spindle to a specific cellular site and orientation of the spindle, which are coordinated with cell-specific allocation of cell fate determinants and cytokinesis. By contrast, during "coenocyte-cellularization" pathways, the spindle-assisted mechanisms are irrelevant since cell fate specification emerges only after the nuclear divisions are complete, and the number of specialized daughter cells produced depends on the developmental context. The key events, such as the formation of a coenocyte and migration of the nuclei to specific cellular locations, are coordinated with cellularization by unique types of cell wall formation. Both one mother - two different daughters and the coenocyte-cellularization pathways are used by higher plants in precise spatial and time windows during development. In both the pathways, epigenetic regulation of gene expression is crucial not only for cell fate specification but also for its maintenance through cell lineage. In this review, the focus is on the coenocyte-cellularization pathways in the context of our current understanding of the asymmetric cell divisions. Instances where cell differentiation does not involve an asymmetric division are also discussed to provide a comprehensive account of cell differentiation.

Metabolic networks: a signal-oriented approach to cellular models

Complete genomes, far advanced proteomes, and even 'metabolomes' are available for at least a few organisms, e.g., Escherichia coli. Systematic functional analyses of such complete data sets will produce a wealth of information and promise an understanding of the dynamics of complex biological networks and perhaps even of entire living organisms. Such complete and holistic descriptions of biological systems, however, will increasingly require a quantitative analysis and the help of mathematical models for simulating whole systems. In particular, new procedures are required that allow a meaningful reduction of the information derived from complex systems that will consequently be used in the modeling process. In this review the biological elements of such a modeling procedure will be described. In a first step, complex living systems must be structured into well-defined and clearly delimited functional units, the elements of which have a common physiological goal, belong to a single genetic unit, and respond to the signals of a signal transduction system that senses changes in physiological states of the organism. These functional units occur at each level of complexity and more complex units originate by grouping several lower level elements into a single, more complex unit. To each complexity level corresponds a global regulator that is epistatic over lower level regulators. After its structuring into modules (functional units), a biological system is converted in a second step into mathematical submodels that by progressive combination can also be assembled into more aggregated model structures. Such a simplification of a cell (an organism) reduces its complexity to a level amenable to present modeling capacities. The universal biochemistry, however, promises a set of rules valid for modeling biological systems, from unicellular microorganisms and cells, to multicellular organisms and to populations.

Cell polarity in yeast

Subcellular asymmetry, cell polarity, is fundamental to the diverse specialized functions of eukaryotic cells. In yeast, cell polarization is essential to division and mating. As a result, this highly accessible experimental system serves as a paradigm for deciphering the molecular mechanisms underlying the generation of polarity. Beyond yeast, cell polarity is essential to the partitioning of cell fate in embryonic development, the generation of axons and their guidance during neuronal development, and the intimate communication between lymphocytes within the immune system. The polarization of yeast cells shares many features with that of these more complex examples, including regulation by both intrinsic and extrinsic cues, conserved regulatory molecules such as Cdc42 GTPase, and asymmetry of the cytoskeleton as its centerpiece. This review summarizes the molecular pathways governing the generation of cell polarity in yeast.

Differential localization of two histidine kinases controlling bacterial cell differentiation

The bacterium C. crescentus coordinates cellular differentiation and cell cycle progression via a network of signal transduction proteins. Here, we demonstrate that the antagonistic DivJ and PleC histidine kinases that regulate polar differentiation are differentially localized as a function of the cell cycle. The DivJ kinase localizes to the stalked pole in response to a signal at the G1-to-S transition, while the PleC kinase is localized to the flagellar pole in swarmer and predivisional cells but is dispersed throughout the cell in the stalked cell. PleC, which is required for DivJ localization, may provide the cue at the G1-to-S transition that directs the polar positioning of DivJ. The dynamic positioning of signal transduction proteins may contribute to the regulation of polar differentiation at specific times during the bacterial cell cycle.

Association of the class V myosin Myo4p with a localised messenger RNA in budding yeast depends on She proteins

Asymmetric distribution of messenger RNAs is a widespread mechanism to localize synthesis of specific protein to distinct sites in the cell. Although not proven yet there is considerable evidence that mRNA localisation is an active process that depends on the activity of cytoskeletal motor proteins. To date, the only motor protein with a specific role in mRNA localisation is the budding yeast type V myosin Myo4p. Myo4p is required for the localisation of ASH1 mRNA, encoding a transcriptional repressor that is essential for differential expression of the HO gene and mating type switching in budding yeast. Mutations in Myo4p, in proteins of the actin cytoskeleton, and in four other specific genes, SHE2-SHE5 disrupt the daughter-specific localisation of ASH1 mRNA. In order to understand if Myo4p is directly participating in mRNA transport, we used in situ colocalisation and coprecipitation of Myo4p and ASH1 mRNA to test for their interaction. Our results indicate an association of Myo4p and ASH1 mRNA that depends on the activity of two other genes involved in ASH1 mRNA localisation, SHE2 and SHE3. This strongly suggests a direct role of Myo4p myosin as a transporter of localised mRNAs, convincingly supporting the concept of motor-protein based mRNA localisation.

Protein localization during the Caulobacter crescentus cell cycle

New research on bacterial cells has demonstrated that they have a dynamic and complex subcellular organization. Work in Caulobacter crescentus shows that essential and nonessential proteins localize to discrete positions in the cell as a function of cell-cycle progression. The flagellum and chemotaxis receptor are asymmetrically localized to a single pole in the predivisional cell by coordinated proteolysis and transcriptional regulation. Cell type- and compartment-specific localization of the CtrA global transcriptional regulator is essential for proper cell-cycle progression, and subcellular localization of key chromosome partitioning proteins is correlated with proper nucleoid segregation. Given this structural complexity, we are driven to ask how localization is achieved, and to what end.