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Rethinking gene regulatory networks in light of alternative splicing, intrinsically disordered protein domains, and post-translational modifications.

Niklas KJ, Bondos SE, Dunker AK, Newman SA - Front Cell Dev Biol (2015)

Bottom Line: Evidence shows that AS, IDP, and PTM complicate gene expression and act synergistically to facilitate and promote time- and cell-specific protein modifications involved in cell signaling and cell fate specification and thereby disrupt a strict deterministic GRN-phenotype mapping.The combined effects of AS, IDP, and PTM give proteomes physiological plasticity, adaptive responsiveness, and developmental versatility without inefficiently expanding genome size.They also help us understand how protein functionalities can undergo major evolutionary changes by buffering mutational consequences.

View Article: PubMed Central - PubMed

Affiliation: Plant Biology Section, School of Integrative Plant Science, Cornell University Ithaca, NY, USA.

ABSTRACT
Models for genetic regulation and cell fate specification characteristically assume that gene regulatory networks (GRNs) are essentially deterministic and exhibit multiple stable states specifying alternative, but pre-figured cell fates. Mounting evidence shows, however, that most eukaryotic precursor RNAs undergo alternative splicing (AS) and that the majority of transcription factors contain intrinsically disordered protein (IDP) domains whose functionalities are context dependent as well as subject to post-translational modification (PTM). Consequently, many transcription factors do not have fixed cis-acting regulatory targets, and developmental determination by GRNs alone is untenable. Modeling these phenomena requires a multi-scale approach to explain how GRNs operationally interact with the intra- and intercellular environments. Evidence shows that AS, IDP, and PTM complicate gene expression and act synergistically to facilitate and promote time- and cell-specific protein modifications involved in cell signaling and cell fate specification and thereby disrupt a strict deterministic GRN-phenotype mapping. The combined effects of AS, IDP, and PTM give proteomes physiological plasticity, adaptive responsiveness, and developmental versatility without inefficiently expanding genome size. They also help us understand how protein functionalities can undergo major evolutionary changes by buffering mutational consequences.

No MeSH data available.


Schematics of cell fate specification viewed from the standard deterministic GRN perspective (A) and the non-deterministic GRN perspective described in the text (B). (A) In the standard view, pre-mRNAs undergo alternative splicing (AS), and transcription factors specified by the variant mRNAs undergo post-translational modifications (PTMs) to form a cadre of proteins involved in cell-fate specification networks (GRNs, represented as irregular shapes) via their cis-acting targets. Discrete cell types result from the deterministic properties of these GRNs. (B) In the proposed non-deterministic view, transcription factors are generated by AS and PTM operating in the context of intrinsically disordered protein (IDP) domains. Cell-fate determination in this case (represented by interactions among components of variable, context-dependent identity and specificity), is a consequence of the time- and spatial-context dependency of each of the levels shown in this schematic, which depend on internal and external cellular conditions in a fashion that eludes deterministic description at the level of GRNs.
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Figure 4: Schematics of cell fate specification viewed from the standard deterministic GRN perspective (A) and the non-deterministic GRN perspective described in the text (B). (A) In the standard view, pre-mRNAs undergo alternative splicing (AS), and transcription factors specified by the variant mRNAs undergo post-translational modifications (PTMs) to form a cadre of proteins involved in cell-fate specification networks (GRNs, represented as irregular shapes) via their cis-acting targets. Discrete cell types result from the deterministic properties of these GRNs. (B) In the proposed non-deterministic view, transcription factors are generated by AS and PTM operating in the context of intrinsically disordered protein (IDP) domains. Cell-fate determination in this case (represented by interactions among components of variable, context-dependent identity and specificity), is a consequence of the time- and spatial-context dependency of each of the levels shown in this schematic, which depend on internal and external cellular conditions in a fashion that eludes deterministic description at the level of GRNs.

Mentions: According to the most mathematically sophisticated deterministic GRN dynamics models (e.g., Foster et al., 2009; Jaeger and Monk, 2014), each cell type is an attractor. That is, if a cell's state at a given time is represented by a point in a multidimensional “state space” whose axes are the concentration ranges of key transcription factors, the point's position will change until it settles stably at one of a finite number of discrete sub-regions within the space (Figure 4). These sub-regions (i.e., system attractors) can be stationary points, periodic orbits, or a mixture of these behaviors, depending on the subset of the components involved in the system. Deterministic systems of sufficient complexity can also exhibit the so-called “butterfly effect,” in which an infinitesimal displacement of the system point can take it along widely divergent trajectories, as well as chaotic behaviors, characterized by “strange attractors,” i.e., regions within the state space in which a point remains bounded but wanders in an unpredictable fashion (Strogatz, 2001; Kaneko, 2006). Each attractor in a deterministic dynamical system is surrounded by a “basin of attraction” toward which a system point gravitates. Importantly, the number of attractors within a deterministic dynamical system is, in principle, a predictable function of its network topology and rate constants, and is always much smaller than the number of basic interacting components. The rationale for applying this mathematical formalism to GRNs and cell differentiation thus arises from observations like the fact that the human genome specifies more than 1300 transcription factors (Vaquerizas et al., 2009) but the human body contains only about 244 cell types (Niklas et al., 2014).


Rethinking gene regulatory networks in light of alternative splicing, intrinsically disordered protein domains, and post-translational modifications.

Niklas KJ, Bondos SE, Dunker AK, Newman SA - Front Cell Dev Biol (2015)

Schematics of cell fate specification viewed from the standard deterministic GRN perspective (A) and the non-deterministic GRN perspective described in the text (B). (A) In the standard view, pre-mRNAs undergo alternative splicing (AS), and transcription factors specified by the variant mRNAs undergo post-translational modifications (PTMs) to form a cadre of proteins involved in cell-fate specification networks (GRNs, represented as irregular shapes) via their cis-acting targets. Discrete cell types result from the deterministic properties of these GRNs. (B) In the proposed non-deterministic view, transcription factors are generated by AS and PTM operating in the context of intrinsically disordered protein (IDP) domains. Cell-fate determination in this case (represented by interactions among components of variable, context-dependent identity and specificity), is a consequence of the time- and spatial-context dependency of each of the levels shown in this schematic, which depend on internal and external cellular conditions in a fashion that eludes deterministic description at the level of GRNs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4341551&req=5

Figure 4: Schematics of cell fate specification viewed from the standard deterministic GRN perspective (A) and the non-deterministic GRN perspective described in the text (B). (A) In the standard view, pre-mRNAs undergo alternative splicing (AS), and transcription factors specified by the variant mRNAs undergo post-translational modifications (PTMs) to form a cadre of proteins involved in cell-fate specification networks (GRNs, represented as irregular shapes) via their cis-acting targets. Discrete cell types result from the deterministic properties of these GRNs. (B) In the proposed non-deterministic view, transcription factors are generated by AS and PTM operating in the context of intrinsically disordered protein (IDP) domains. Cell-fate determination in this case (represented by interactions among components of variable, context-dependent identity and specificity), is a consequence of the time- and spatial-context dependency of each of the levels shown in this schematic, which depend on internal and external cellular conditions in a fashion that eludes deterministic description at the level of GRNs.
Mentions: According to the most mathematically sophisticated deterministic GRN dynamics models (e.g., Foster et al., 2009; Jaeger and Monk, 2014), each cell type is an attractor. That is, if a cell's state at a given time is represented by a point in a multidimensional “state space” whose axes are the concentration ranges of key transcription factors, the point's position will change until it settles stably at one of a finite number of discrete sub-regions within the space (Figure 4). These sub-regions (i.e., system attractors) can be stationary points, periodic orbits, or a mixture of these behaviors, depending on the subset of the components involved in the system. Deterministic systems of sufficient complexity can also exhibit the so-called “butterfly effect,” in which an infinitesimal displacement of the system point can take it along widely divergent trajectories, as well as chaotic behaviors, characterized by “strange attractors,” i.e., regions within the state space in which a point remains bounded but wanders in an unpredictable fashion (Strogatz, 2001; Kaneko, 2006). Each attractor in a deterministic dynamical system is surrounded by a “basin of attraction” toward which a system point gravitates. Importantly, the number of attractors within a deterministic dynamical system is, in principle, a predictable function of its network topology and rate constants, and is always much smaller than the number of basic interacting components. The rationale for applying this mathematical formalism to GRNs and cell differentiation thus arises from observations like the fact that the human genome specifies more than 1300 transcription factors (Vaquerizas et al., 2009) but the human body contains only about 244 cell types (Niklas et al., 2014).

Bottom Line: Evidence shows that AS, IDP, and PTM complicate gene expression and act synergistically to facilitate and promote time- and cell-specific protein modifications involved in cell signaling and cell fate specification and thereby disrupt a strict deterministic GRN-phenotype mapping.The combined effects of AS, IDP, and PTM give proteomes physiological plasticity, adaptive responsiveness, and developmental versatility without inefficiently expanding genome size.They also help us understand how protein functionalities can undergo major evolutionary changes by buffering mutational consequences.

View Article: PubMed Central - PubMed

Affiliation: Plant Biology Section, School of Integrative Plant Science, Cornell University Ithaca, NY, USA.

ABSTRACT
Models for genetic regulation and cell fate specification characteristically assume that gene regulatory networks (GRNs) are essentially deterministic and exhibit multiple stable states specifying alternative, but pre-figured cell fates. Mounting evidence shows, however, that most eukaryotic precursor RNAs undergo alternative splicing (AS) and that the majority of transcription factors contain intrinsically disordered protein (IDP) domains whose functionalities are context dependent as well as subject to post-translational modification (PTM). Consequently, many transcription factors do not have fixed cis-acting regulatory targets, and developmental determination by GRNs alone is untenable. Modeling these phenomena requires a multi-scale approach to explain how GRNs operationally interact with the intra- and intercellular environments. Evidence shows that AS, IDP, and PTM complicate gene expression and act synergistically to facilitate and promote time- and cell-specific protein modifications involved in cell signaling and cell fate specification and thereby disrupt a strict deterministic GRN-phenotype mapping. The combined effects of AS, IDP, and PTM give proteomes physiological plasticity, adaptive responsiveness, and developmental versatility without inefficiently expanding genome size. They also help us understand how protein functionalities can undergo major evolutionary changes by buffering mutational consequences.

No MeSH data available.