Stochastic model of protein-protein interaction: why signaling proteins need to be colocalized

Competition between physical search and a weak-to-strong transition rate-limits kinesin binding times

The self-organization of cells relies on the profound complexity of protein-protein interactions. Challenges in directly observing these events have hindered progress toward understanding their diverse behaviors. One notable example is the interaction between molecular motors and cytoskeletal systems that combine to perform a variety of cellular functions. In this work, we leverage theory and experiments to identify and quantify the rate-limiting mechanism of the initial association between a cargo-bound kinesin motor and a microtubule track. Recent advances in optical tweezers provide binding times for several lengths of kinesin motors trapped at varying distances from a microtubule, empowering the investigation of competing models. We first explore a diffusion-limited model of binding. Through Brownian dynamics simulations and simulation-based inference, we find this simple diffusion model fails to explain the experimental binding times, but an extended model that accounts for the ADP state of the molecular motor agrees closely with the data, even under the scrutiny of penalizing for additional model complexity. We provide quantification of both kinetic rates and biophysical parameters underlying the proposed binding process. Our model suggests that a typical binding event is limited by ADP state rather than physical search. Lastly, we predict how these association rates can be modulated in distinct ways through variation of environmental concentrations and physical properties.

ADP release can explain spatially-dependent kinesin binding times

The self-organization of cells relies on the profound complexity of protein-protein interactions. Challenges in directly observing these events have hindered progress toward understanding their diverse behaviors. One notable example is the interaction between molecular motors and cytoskeletal systems that combine to perform a variety of cellular functions. In this work, we leverage theory and experiments to identify and quantify the rate-limiting mechanism of the initial association between a cargo-bound kinesin motor and a microtubule track. Recent advances in optical tweezers provide binding times for several lengths of kinesin motors trapped at varying distances from a microtubule, empowering the investigation of competing models. We first explore a diffusion-limited model of binding. Through Brownian dynamics simulations and simulation-based inference, we find this simple diffusion model fails to explain the experimental binding times, but an extended model that accounts for the ADP state of the molecular motor agrees closely with the data, even under the scrutiny of penalizing for additional model complexity. We provide quantification of both kinetic rates and biophysical parameters underlying the proposed binding process. Our model suggests that most but not every motor binding event is limited by their ADP state. Lastly, we predict how these association rates can be modulated in distinct ways through variation of environmental concentrations and spatial distances.

Puzzling out nuclear pore complex assembly

Nuclear pore complexes (NPCs) are sophisticated multiprotein assemblies embedded within the nuclear envelope and controlling the exchanges of molecules between the cytoplasm and the nucleus. In this review, we summarize the mechanisms by which these elaborate complexes are built from their subunits, the nucleoporins, based on our ever-growing knowledge of NPC structural organization and on the recent identification of additional features of this process. We present the constraints faced during the production of nucleoporins, their gathering into oligomeric complexes, and the formation of NPCs within nuclear envelopes, and review the cellular strategies at play, from co-translational assembly to the enrolment of a panel of cofactors. Remarkably, the study of NPCs can inform our perception of the biogenesis of multiprotein complexes in general - and vice versa.

A critical evaluation of protein kinase regulation by activation loop autophosphorylation

Phosphorylation of proteins is a ubiquitous mechanism of regulating their function, localization, or activity. Protein kinases, enzymes that use ATP to phosphorylate protein substrates are, therefore, powerful signal transducers in eukaryotic cells. The mechanism of phosphoryl-transfer is universally conserved among protein kinases, which necessitates the tight regulation of kinase activity for the orchestration of cellular processes with high spatial and temporal fidelity. In response to a stimulus, many kinases enhance their own activity by autophosphorylating a conserved amino acid in their activation loop, but precisely how this reaction is performed is controversial. Classically, kinases that autophosphorylate their activation loop are thought to perform the reaction in trans, mediated by transient dimerization of their kinase domains. However, motivated by the recently discovered regulation mechanism of activation loop cis-autophosphorylation by a kinase that is autoinhibited in trans, we here review the various mechanisms of autoregulation that have been proposed. We provide a framework for critically evaluating biochemical, kinetic, and structural evidence for protein kinase dimerization and autophosphorylation, and share some thoughts on the implications of these mechanisms within physiological signaling networks.

Cotranslational Mechanisms of Protein Biogenesis and Complex Assembly in Eukaryotes

The formation of protein complexes is crucial to most biological functions. The cellular mechanisms governing protein complex biogenesis are not yet well understood, but some principles of cotranslational and posttranslational assembly are beginning to emerge. In bacteria, this process is favored by operons encoding subunits of protein complexes. Eukaryotic cells do not have polycistronic mRNAs, raising the question of how they orchestrate the encounter of unassembled subunits. Here we review the constraints and mechanisms governing eukaryotic co- and posttranslational protein folding and assembly, including the influence of elongation rate on nascent chain targeting, folding, and chaperone interactions. Recent evidence shows that mRNAs encoding subunits of oligomeric assemblies can undergo localized translation and form cytoplasmic condensates that might facilitate the assembly of protein complexes. Understanding the interplay between localized mRNA translation and cotranslational proteostasis will be critical to defining protein complex assembly in vivo.

Simulation of Biochemical Reactions with ANN-Dependent Kinetic Parameter Extraction Method

The measurement of thermodynamic properties of chemical or biological reactions were often confined to experimental means, which produced overall measurements of properties being investigated, but were usually susceptible to pitfalls of being too general. Among the thermodynamic properties that are of interest, reaction rates hold the greatest significance, as they play a critical role in reaction processes where speed is of essence, especially when fast association may enhance binding affinity of reaction molecules. Association reactions with high affinities often involve the formation of a intermediate state, which can be demonstrated by a hyperbolic reaction curve, but whose low abundance in reaction mixture often preclude the possibility of experimental measurement. Therefore, we resorted to computational methods using predefined reaction models that model the intermediate state as the reaction progresses. Here, we present a novel method called AKPE (ANN-Dependent Kinetic Parameter Extraction), our goal is to investigate the association/dissociation rate constants and the concentration dynamics of lowly-populated states (intermediate states) in the reaction landscape. To reach our goal, we simulated the chemical or biological reactions as system of differential equations, employed artificial neural networks (ANN) to model experimentally measured data, and utilized Particle Swarm Optimization (PSO) algorithm to obtain the globally optimum parameters in both the simulation and data fitting. In the Results section, we have successfully modeled a protein association reaction using AKPE, obtained the kinetic rate constants of the reaction, and constructed a full concentration versus reaction time curve of the intermediate state during the reaction. Furthermore, judging from the various validation methods that the method proposed in this paper has strong robustness and accuracy.

Prognostic Significance of Prostaglandin-Endoperoxide Synthase-2 Expressions in Human Breast Carcinoma: A Multiomic Approach

Prostaglandin-endoperoxide synthase-2 (PTGS2) plays a pivotal role in inflammation and carcinogenesis in human breast cancer. Our aim of the study is to find the prognostic value of PTGS2 in breast cancer. We conducted a multiomic analysis to determine whether PTGS2 functions as a prognostic biomarker in human breast cancer. We explored PTGS2 mRNA expressions using different public bioinformatics portals. Oncomine, Serial Analysis of Gene Expression (SAGE), GEPIA, ULCAN, PrognoScan database, Kaplan-Meier Plotter, bc-GenExMiner, USC XENA, and Cytoscape/STRING DB were used to identify the prognostic roles of PTGS2 in breast cancer. Based on the clinicopathological analysis, decreased PTGS2 expressions correlated positively with older age, lymph node status, the human epidermal growth factor receptor 2 (HER2) status (P < .0001), estrogen receptor (ER+) expression (P < .0001) Luminal A (P < .0001), and Luminal B (P < .0001). Interestingly, progesterone receptor (PR) (P < .0001) negative showed a high expression of PTGS2. Prostaglandin-endoperoxide synthase-2 was downregulated in breast cancer tissues than in normal tissues. In the PrognoScan database and, Kaplan-Meier Scanner, downregulated expressions of PTGS2 associated with poor overall survival (OS), relapse-free survival (RFS), and distant metastasis-free survival. The methylation levels were significantly higher in the Luminal B subtype. Through oncomine coexpressed gene analysis, we found a positive correlation between PTGS2 and interleukin-6 (IL-6) expression in breast cancer tissues. These results indicate that downregulated expressions of PTGS2 can be used as a promising prognostic biomarker and Luminal B hyper methylation may play an important role in the development of breast cancers. However, to clarify our results, extensive study is required.

Fractional diffusion on the human proteome as an alternative to the multi-organ damage of SARS-CoV-2

The coronavirus 2019 (COVID-19) respiratory disease is caused by the novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), which uses the enzyme ACE2 to enter human cells. This disease is characterized by important damage at a multi-organ level, partially due to the abundant expression of ACE2 in practically all human tissues. However, not every organ in which ACE2 is abundant is affected by SARS-CoV-2, which suggests the existence of other multi-organ routes for transmitting the perturbations produced by the virus. We consider here diffusive processes through the protein-protein interaction (PPI) network of proteins targeted by SARS-CoV-2 as an alternative route. We found a subdiffusive regime that allows the propagation of virus perturbations through the PPI network at a significant rate. By following the main subdiffusive routes across the PPI network, we identify proteins mainly expressed in the heart, cerebral cortex, thymus, testis, lymph node, kidney, among others of the organs reported to be affected by COVID-19.

Mechanisms and consequences of subcellular RNA localization across diverse cell types

Essentially all cells contain a variety of spatially restricted regions that are important for carrying out specialized functions. Often, these regions contain specialized transcriptomes that facilitate these functions by providing transcripts for localized translation. These transcripts play a functional role in maintaining cell physiology by enabling a quick response to changes in the cellular environment. Here, we review how RNA molecules are trafficked within cells, with a focus on the subcellular locations to which they are trafficked, mechanisms that regulate their transport and clinical disorders associated with misregulation of the process.

Growth, Cell Division, and Gene Expression of Escherichia coli at Elevated Concentrations of Magnesium Sulfate: Implications for Habitability of Europa and Mars

We perform quantitative studies of the growth, death, and gene expression of Escherichia coli in a wide range of magnesium sulfate (MgSO 4 ) concentrations (0-2.5 M). Elevated concentration of MgSO 4 causes the inhibition of cell growth, leading to an increase in the population doubling time. We find that cells exhibit three distinct morphological phenotypes-(i) normal, (ii) filamentous, and (iii) small cells at 1 . 25 M MgSO 4 . Filamentous cells arise due to the lack of cell division, while the small cells arise due to the partial plasmolysis of the cells. We further find that cell death starts for salt concentrations >1.25 M and increases with an increasing concentration of MgSO 4 . For salt concentrations ≥1.66 M, the growth of cells stops and all the cells become smaller than the control cells, suggesting the plasmolysis of the population. Cells grown at salt concentration up to 2 . 07 M are reversible in both the growth rate and morphology upon the removal of the salt stress. The time scale of reversibility increases with increasing salt concentration. Finally, we investigate the expression of an osmotically inducible gene (osmC), genes involved in magnesium transport (corA), sulfate transport (cysP), and osmotically driven transport of water (aqpZ). We find that a high concentration of magnesium sulfate leads to the upregulation of cysP and osmC.

PROM1 and PROM2 expression differentially modulates clinical prognosis of cancer: a multiomics analysis

Prominin 1 (PROM1) is considered a biomarker for cancer stem cells, although its biological role is unclear. Prominin 2 (PROM2) has also been associated with certain cancers. However, the prognostic value of PROM1 and PROM2 in cancer is controversial. Here, we performed a systematic data analysis to examine whether prominins can function as prognostic markers in human cancers. The expression of prominins was assessed and their prognostic value in human cancers was determined using univariate and multivariate survival analyses, via various online platforms. We selected a group of prominent functional protein partners of prominins by protein-protein interaction analysis. Subsequently, we investigated the relationship between mutations and copy number alterations in prominin genes and various types of cancers. Furthermore, we identified genes that correlated with PROM1 and PROM2 in certain cancers, based on their levels of expression. Gene ontology and pathway analyses were performed to assess the effect of these correlated genes on various cancers. We observed that PROM1 was frequently overexpressed in esophageal, liver, and ovarian cancers and its expression was negatively associated with prognosis, whereas PROM2 overexpression was associated with poor overall survival in lung and ovarian cancers. Based on the varying characteristics of prominins, we conclude that PROM1 and PROM2 expression differentially modulates the clinical outcomes of cancers.

The Balancing Act of Intrinsically Disordered Proteins: Enabling Functional Diversity while Minimizing Promiscuity

Intrinsically disordered proteins (IDPs) or regions (IDRs) perform diverse cellular functions, but are also prone to forming promiscuous and potentially deleterious interactions. We investigate the extent to which the properties of, and content in, IDRs have adapted to enable functional diversity while limiting interference from promiscuous interactions in the crowded cellular environment. Information on protein sequences, their predicted intrinsic disorder, and 3D structure contents is related to data on protein cellular concentrations, gene co-expression, and protein-protein interactions in the well-studied yeast Saccharomyces cerevisiae. Results reveal that both the protein IDR content and the frequency of "sticky" amino acids in IDRs (those more frequently involved in protein interfaces) decrease with increasing protein cellular concentration. This implies that the IDR content and the amino acid composition of IDRs experience negative selection as the protein concentration increases. In the S. cerevisiae protein-protein interaction network, the higher a protein's IDR content, the more frequently it interacts with IDR-containing partners, and the more functionally diverse the partners are. Employing a clustering analysis of Gene Ontology terms, we newly identify ~600 putative multifunctional proteins in S. cerevisiae. Strikingly, these proteins are enriched in IDRs and contribute significantly to all the observed trends. In particular, IDRs of multi-functional proteins feature more sticky amino acids than IDRs of their non-multifunctional counterparts, or the surfaces of structured yeast proteins. This property likely affords sufficient binding affinity for the functional interactions, commonly mediated by short IDR segments, thereby counterbalancing the loss in overall IDR conformational entropy upon binding.

Bioinformatics Approaches to Gain Insights into cis-Regulatory Motifs Involved in mRNA Localization

Messenger RNA (mRNA) is a fundamental intermediate in the expression of proteins. As an integral part of this important process, protein production can be localized by the targeting of mRNA to a specific subcellular compartment. The subcellular destination of mRNA is suggested to be governed by a region of its primary sequence or secondary structure, which consequently dictates the recruitment of trans-acting factors, such as RNA-binding proteins or regulatory RNAs, to form a messenger ribonucleoprotein particle. This molecular ensemble is requisite for precise and spatiotemporal control of gene expression. In the context of RNA localization, the description of the binding preferences of an RNA-binding protein defines a motif, and one, or more, instance of a given motif is defined as a localization element (zip code). In this chapter, we first discuss the cis-regulatory motifs previously identified as mRNA localization elements. We then describe motif representation in terms of entropy and information content and offer an overview of motif databases and search algorithms. Finally, we provide an outline of the motif topology of asymmetrically localized mRNA molecules.

Biological functions, regulatory mechanisms, and disease relevance of RNA localization pathways

The asymmetric subcellular distribution of RNA molecules from their sites of transcription to specific compartments of the cell is an important aspect of post-transcriptional gene regulation. This involves the interplay of intrinsic cis-regulatory elements within the RNA molecules with trans-acting RNA-binding proteins and associated factors. Together, these interactions dictate the intracellular localization route of RNAs, whose downstream impacts have wide-ranging implications in cellular physiology. In this review, we examine the mechanisms underlying RNA localization and discuss their biological significance. We also review the growing body of evidence pointing to aberrant RNA localization pathways in the development and progression of diseases.

RNA localization and transport

RNA localization serves numerous purposes from controlling development and differentiation to supporting the physiological activities of cells and organisms. After a brief introduction into the history of the study of mRNA localization I will focus on animal systems, describing in which cellular compartments and in which cell types mRNA localization was observed and studied. In recent years numerous novel localization patterns have been described, and countless mRNAs have been documented to accumulate in specific subcellular compartments. These fascinating revelations prompted speculations about the purpose of localizing all these mRNAs. In recent years experimental evidence for an unexpected variety of different functions has started to emerge. Aside from focusing on the functional aspects, I will discuss various ways of localizing mRNAs with a focus on the mechanism of active and directed transport on cytoskeletal tracks. Structural studies combined with imaging of transport and biochemical studies have contributed to the enormous recent progress, particularly in understanding how dynein/dynactin/BicD (DDB) dependent transport on microtubules works. This transport process actively localizes diverse cargo in similar ways to the minus end of microtubules and, at least in flies, also individual mRNA molecules. A sophisticated mechanism ensures that cargo loading licenses processive transport.

CeFra-seq reveals broad asymmetric mRNA and noncoding RNA distribution profiles in Drosophila and human cells

Cells are highly asymmetrical, a feature that relies on the sorting of molecular constituents, including proteins, lipids, and nucleic acids, to distinct subcellular locales. The localization of RNA molecules is an important layer of gene regulation required to modulate localized cellular activities, although its global prevalence remains unclear. We combine biochemical cell fractionation with RNA-sequencing (CeFra-seq) analysis to assess the prevalence and conservation of RNA asymmetric distribution on a transcriptome-wide scale in Drosophila and human cells. This approach reveals that the majority (∼80%) of cellular RNA species are asymmetrically distributed, whether considering coding or noncoding transcript populations, in patterns that are broadly conserved evolutionarily. Notably, a large number of Drosophila and human long noncoding RNAs and circular RNAs display enriched levels within specific cytoplasmic compartments, suggesting that these RNAs fulfill extra-nuclear functions. Moreover, fraction-specific mRNA populations exhibit distinctive sequence characteristics. Comparative analysis of mRNA fractionation profiles with that of their encoded proteins reveals a general lack of correlation in subcellular distribution, marked by strong cases of asymmetry. However, coincident distribution profiles are observed for mRNA/protein pairs related to a variety of functional protein modules, suggesting complex regulatory inputs of RNA localization to cellular organization.

Three-Phase Coexistence in Lipid Membranes

Phospholipid ternary systems are useful model systems for understanding lipid-lipid interactions and their influence on biological properties such as cell signaling and protein translocation. Despite extensive studies, there are still open questions relating to membrane phase behavior, particularly relating to a proposed state of three-phase coexistence, due to the difficulty in clearly distinguishing the three phases. We look in and around the region of the phase diagram where three phases are expected and use a combination of different atomic force microscopy (AFM) modes to present the first images of three coexisting lipid phases in biomimetic cell lipid membranes. Domains form through either nucleation or spinodal decomposition dependent upon composition, with some exhibiting both mechanisms in different domains simultaneously. Slow cooling rates are necessary to sufficiently separate mixtures with high proportions of l_o and l_β phase. We probe domain heights and mechanical properties and demonstrate that the gel (l_β) domains have unusually low structural integrity in the three-phase region. This finding supports the hypothesis of a “disordered gel” state that has been proposed from NMR studies of similar systems, where the addition of small amounts of cholesterol was shown to disrupt the regular packing of the l_β state. We use NMR data from the literature on chain disorder in different mixtures and estimate an expected step height that is in excellent agreement with the AFM data. Alternatively, the disordered solid phase observed here and in the wider literature could be explained by the l_β phase being out of equilibrium, in a surface kinetically trapped state. This view is supported by the observation of unusual growth of nucleated domains, which we term “tree-ring growth,” reflecting compositional heterogeneity in large disordered l_β phase domains.

RNA localization: Making its way to the center stage

Cells are highly organized entities that rely on intricate addressing mechanisms to sort their constituent molecules to precise subcellular locations. These processes are crucial for cells to maintain their proper organization and carry out specialized functions in the body, consequently genetic perturbations that clog up these addressing systems can contribute to disease aetiology. The trafficking of RNA molecules represents an important layer in the control of cellular organization, a process that is both highly prevalent and for which features of the regulatory machineries have been deeply conserved evolutionarily. RNA localization is commonly driven by trans-regulatory factors, including RNA binding proteins at the core, which recognize specific cis-acting zipcode elements within the RNA transcripts. Here, we first review the functions and biological benefits of intracellular RNA trafficking, from the perspective of both coding and non-coding RNAs. Next, we discuss the molecular mechanisms that modulate this localization, emphasizing the diverse features of the cis- and trans-regulators involved, while also highlighting emerging technologies and resources that will prove instrumental in deciphering RNA targeting pathways. We then discuss recent findings that reveal how co-transcriptional regulatory mechanisms operating in the nucleus can dictate the downstream cytoplasmic localization of RNAs. Finally, we survey the growing number of human diseases in which RNA trafficking pathways are impacted, including spinal muscular atrophy, Alzheimer's disease, fragile X syndrome and myotonic dystrophy. Such examples highlight the need to further dissect RNA localization mechanisms, which could ultimately pave the way for the development of RNA-oriented diagnostic and therapeutic strategies. This article is part of a Special Issue entitled "Biochemistry of Synthetic Biology - Recent Developments" Guest Editor: Dr. Ilka Heinemann and Dr. Patrick O'Donoghue.

Effective molarity in a nucleic acid-controlled reaction

Positioning of reactive functional groups within a DNA duplex can enable chemical reactions that otherwise would not occur to an appreciable extent. However, few studies have quantitatively defined the extent to which the enforced proximity of reaction partners in duplex DNA can favor chemical processes. Here, we measured substantial effective molarities (as high as 25M) afforded by duplex DNA to a reaction involving interstrand cross-link formation between 2'-deoxyadenosine and a 2-deoxyribose abasic (Ap) site.

How spatial heterogeneity shapes multiscale biochemical reaction network dynamics

Spatial heterogeneity in cells can be modelled using distinct compartments connected by molecular movement between them. In addition to movement, changes in the amount of molecules are due to biochemical reactions within compartments, often such that some molecular types fluctuate on a slower timescale than others. It is natural to ask the following questions: how sensitive is the dynamics of molecular types to their own spatial distribution, and how sensitive are they to the distribution of others? What conditions lead to effective homogeneity in biochemical dynamics despite heterogeneity in molecular distribution? What kind of spatial distribution is optimal from the point of view of some downstream product? Within a spatially heterogeneous multiscale model, we consider two notions of dynamical homogeneity (full homogeneity and homogeneity for the fast subsystem), and consider their implications under different timescales for the motility of molecules between compartments. We derive rigorous results for their dynamics and long-term behaviour, and illustrate them with examples of a shared pathway, Michaelis-Menten enzymatic kinetics and autoregulating feedbacks. Using stochastic averaging of fast fluctuations to their quasi-steady-state distribution, we obtain simple analytic results that significantly reduce the complexity and expedite simulation of stochastic compartment models of chemical reactions.

Colocalization Estimation Using Graphical Modeling and Variational Bayesian Expectation Maximization: Towards a Parameter-Free Approach

In microscopy imaging, colocalization between two biological entities (e.g., protein-protein or protein-cell) refers to the (stochastic) dependencies between the spatial locations of the two entities in the biological specimen. Measuring colocalization between two entities relies on fluorescence imaging of the specimen using two fluorescent chemicals, each of which indicates the presence/absence of one of the entities at any pixel location. State-of-the-art methods for estimating colocalization rely on post-processing image data using an adhoc sequence of algorithms with many free parameters that are tuned visually. This leads to loss of reproducibility of the results. This paper proposes a brand-new framework for estimating the nature and strength of colocalization directly from corrupted image data by solving a single unified optimization problem that automatically deals with noise, object labeling, and parameter tuning. The proposed framework relies on probabilistic graphical image modeling and a novel inference scheme using variational Bayesian expectation maximization for estimating all model parameters, including colocalization, from data. Results on simulated and real-world data demonstrate improved performance over the state of the art.

Membrane microdomains: from seeing to understanding

The plasma membrane is a composite material, which forms a semi-permeable barrier and an interface for communication between the intracellular and extracellular environments. While the existence of membrane microdomains with nanoscale organization has been proved by the application of numerous biochemical and physical methods, direct observation of these heterogeneities using optical microscopy has remained challenging for decades, partly due to the optical diffraction limit, which restricts the resolution to ~200 nm. During the past years, new optical methods which circumvent this fundamental limit have emerged. Not only do these techniques allow direct visualization, but also quantitative characterization of nanoscopic structures. We discuss how these emerging optical methods have refined our knowledge of membrane microdomains and how they may shed light on the basic principles of the mesoscopic membrane organization.

Use of somatic mutations to quantify random contributions to mouse development

Background

The C. elegans cell fate map, in which the lineage of its approximately 1000 cells is visibly charted beginning from the zygote, represents a developmental biology milestone. Nematode development is invariant from one specimen to the next, whereas in mammals, aspects of development are probabilistic, and development exhibits variation between even genetically identical individuals. Consequently, a single defined cell fate map applicable to all individuals cannot exist.

Results

To determine the extent to which patterns of cell lineage are conserved between different mice, we have employed the recently developed method of “phylogenetic fate mapping” to compare cell fate maps in siblings. In this approach, somatic mutations arising in individual cells are used to retrospectively deduce lineage relationships through phylogenetic and—as newly investigated here—related analytical approaches based on genetic distance. We have cataloged genomic mutations at an average of 110 mutation-prone polyguanine (polyG) tracts for about 100 cells clonally isolated from various corresponding tissues of each of two littermates of a hypermutable mouse strain.

Conclusions

We find that during mouse development, muscle and fat arise from a mixed progenitor cell pool in the germ layer, but, contrastingly, vascular endothelium in brain derives from a smaller source of progenitor cells. Additionally, formation of tissue primordia is marked by establishment of left and right lateral compartments, with restricted cell migration between divisions. We quantitatively demonstrate that development represents a combination of stochastic and deterministic events, offering insight into how chance influences normal development and may give rise to birth defects.

Arbitrary protein-protein docking targets biologically relevant interfaces

BACKGROUND

Protein-protein recognition is of fundamental importance in the vast majority of biological processes. However, it has already been demonstrated that it is very hard to distinguish true complexes from false complexes in so-called cross-docking experiments, where binary protein complexes are separated and the isolated proteins are all docked against each other and scored. Does this result, at least in part, reflect a physical reality? False complexes could reflect possible nonspecific or weak associations.

RESULTS

In this paper, we investigate the twilight zone of protein-protein interactions, building on an interesting outcome of cross-docking experiments: false complexes seem to favor residues from the true interaction site, suggesting that randomly chosen partners dock in a non-random fashion on protein surfaces. Here, we carry out arbitrary docking of a non-redundant data set of 198 proteins, with more than 300 randomly chosen "probe" proteins. We investigate the tendency of arbitrary partners to aggregate at localized regions of the protein surfaces, the shape and compositional bias of the generated interfaces, and the potential of this property to predict biologically relevant binding sites. We show that the non-random localization of arbitrary partners after protein-protein docking is a generic feature of protein structures. The interfaces generated in this way are not systematically planar or curved, but tend to be closer than average to the center of the proteins. These results can be used to predict biological interfaces with an AUC value up to 0.69 alone, and 0.72 when used in combination with evolutionary information. An appropriate choice of random partners and number of docking models make this method computationally practical. It is also noted that nonspecific interfaces can point to alternate interaction sites in the case of proteins with multiple interfaces. We illustrate the usefulness of arbitrary docking using PEBP (Phosphatidylethanolamine binding protein), a kinase inhibitor with multiple partners.

CONCLUSIONS

An approach using arbitrary docking, and based solely on physical properties, can successfully identify biologically pertinent protein interfaces.

Entrainment of peripheral clock genes by cortisol

Circadian rhythmicity in mammals is primarily driven by the suprachiasmatic nucleus (SCN), often called the central pacemaker, which converts the photic information of light and dark cycles into neuronal and hormonal signals in the periphery of the body. Cells of peripheral tissues respond to these centrally mediated cues by adjusting their molecular function to optimize organism performance. Numerous systemic cues orchestrate peripheral rhythmicity, such as feeding, body temperature, the autonomic nervous system, and hormones. We propose a semimechanistic model for the entrainment of peripheral clock genes by cortisol as a representative entrainer of peripheral cells. This model demonstrates the importance of entrainer's characteristics in terms of the synchronization and entrainment of peripheral clock genes, and predicts the loss of intercellular synchrony when cortisol moves out of its homeostatic amplitude and frequency range, as has been observed clinically in chronic stress and cancer. The model also predicts a dynamic regime of entrainment, when cortisol has a slightly decreased amplitude rhythm, where individual clock genes remain relatively synchronized among themselves but are phase shifted in relation to the entrainer. The model illustrates how the loss of communication between the SCN and peripheral tissues could result in desynchronization of peripheral clocks.

Translational regulation of the cell cycle: when, where, how and why?

Translational regulation contributes to the control of archetypal and specialized cell cycles, such as the meiotic and early embryonic cycles. Late meiosis and early embryogenesis unfold in the absence of transcription, so they particularly rely on translational repression and activation of stored maternal mRNAs. Here, we present examples of cell cycle regulators that are translationally controlled during different cell cycle and developmental transitions in model organisms ranging from yeast to mouse. Our focus also is on the RNA-binding proteins that affect cell cycle progression by recognizing special features in untranslated regions of mRNAs. Recent research highlights the significance of the cytoplasmic polyadenylation element-binding protein (CPEB). CPEB determines polyadenylation status, and consequently translational efficiency, of its target mRNAs in both transcriptionally active somatic cells as well as in transcriptionally silent mature Xenopus oocytes and early embryos. We discuss the role of CPEB in mediating the translational timing and in some cases spindle-localized translation of critical regulators of Xenopus oogenesis and early embryogenesis. We conclude by outlining potential directions and approaches that may provide further insights into the translational control of the cell cycle.

Mathematical modeling of K-Ras nanocluster formation on the plasma membrane

K-Ras functions as a critical node in the mitogen-activated protein kinase (MAPK) pathway that regulates key cellular functions including proliferation, differentiation, and apoptosis. Following growth factor receptor activation K-Ras.GTP forms nanoclusters on the plasma membrane through interaction with the scaffold protein galectin-3. The generation of nanoclusters is essential for high fidelity signal transduction via the MAPK pathway. To explore the mechanisms underlying K-Ras.GTP nanocluster formation, we developed a mathematical model of K-Ras-galectin-3 interactions. We designed a computational method to calculate protein collision rates based on experimentally determined protein diffusion rates and diffusion mechanisms and used a genetic algorithm to search the values of key model parameters. The optimal estimated model parameters were validated using experimental data. The resulting model accurately replicates critical features of K-Ras nanoclustering, including a fixed ratio of clustered K-Ras.GTP to monomeric K-Ras.GTP that is independent of the concentration of K-Ras.GTP. The model reproduces experimental results showing that the cytosolic level of galectin-3 determines the magnitude of the K-Ras.GTP clustered fraction and illustrates that nanoclustering is regulated by key nonequilibrium processes. Our kinetic model identifies a potential biophysical mechanism for K-Ras nanoclustering and suggests general principles that may be relevant for other plasma-membrane-localized proteins.

Hydrophilicity matching - a potential prerequisite for the formation of protein-protein complexes in the cell

A binding event between two proteins typically consists of a diffusional search of binding partners for one another, followed by a specific recognition of the compatible binding sites resulting in the formation of the complex. However, it is unclear how binding partners find each other in the context of the crowded, constantly fluctuating, and interaction-rich cellular environment. Here we examine the non-specific component of protein-protein interactions, which refers to those physicochemical properties of the binding partners that are independent of the exact details of their binding sites, but which can affect their localization or diffusional search for one another. We show that, for a large set of high-resolution experimental 3D structures of binary, transient protein complexes taken from the DOCKGROUND database, the binding partners display a surprising, statistically significant similarity in terms of their total hydration free energies normalized by a size-dependent variable. We hypothesize that colocalization of binding partners, even within individual cellular compartments such as the cytoplasm, may be influenced by their relative hydrophilicity, potentially in response to local hydrophilic gradients.

Multi-scale spatio-temporal modeling: lifelines of microorganisms in bioreactors and tracking molecules in cells

Agent-based models are rigorous tools for simulating the interactions of individual entities, such as organisms or molecules within cells and assessing their effects on the dynamic behavior of the system as a whole. In context with bioprocess and biosystems engineering there are several interesting and important applications. This contribution aims at introducing this strategy with the aid of two examples characterized by striking distinctions in the scale of the individual entities and the mode of their interactions. In the first example a structured-segregated model is applied to travel along the lifelines of single cells in the environment of a three-dimensional turbulent field of a stirred bioreactor. The modeling approach is based on an Euler-Lagrange formulation of the system. The strategy permits one to account for the heterogeneity present in real reactors in both the fluid and cellular phases, respectively. The individual response of the cells to local variations in the extracellular concentrations is pictured by a dynamically structured model of the key reactions of the central metabolism. The approach permits analysis of the lifelines of individual cells in space and time.The second application of the individual modeling approach deals with dynamic modeling of signal transduction pathways in individual cells. Usually signal transduction networks are portrayed as being wired together in a spatially defined manner. Living circuitry, however, is placed in highly malleable internal architecture. Creating a homogenous bag of molecules, a well-mixed system, the dynamic behavior of which is modeled with a set of ordinary differential equations is normally not valid. The dynamics of the MAP kinase and a steroid hormone pathway serve as examples to illustrate how single molecule tracking can be linked with the stochasticity of biochemical reactions, where diffusion and reaction occur in a probabilistic manner. The problem of hindered diffusion caused by macromolecular crowding is also taken into account.

Global implications of mRNA localization pathways in cellular organization

Genome expression profiling has led to the important realization that RNA molecules are more numerous and diverse than previously expected. One aspect of RNA biology that is just beginning to be fully appreciated is the extent to which mRNAs are transported to specific subcellular destinations before being translated, an exquisite mechanism for targeting proteins where they are required in the cell. While several excellent reviews have discussed the subject of mRNA localization, it is only in recent years that high-throughput technologies have been applied to address issues such as the extent and diversity of RNA localization events and mechanisms. This review focuses on these recent functional genomic approaches, their implications, and the new tools and methods that will be needed to further elucidate mRNA localization pathways.

Modelling spatio-temporal interactions within the cell

Biological phenomena at the cellular level can be represented by various types of mathematical formulations. Such representations allow us to carry out numerical simulations that provide mechanistic insights into complex behaviours of biological systems and also generate hypotheses that can be experimentally tested. Currently, we are particularly interested in spatio-temporal representations of dynamic cellular phenomena and how such models can be used to understand biological specificity in functional responses. This review describes the capability and limitations of the approaches used to study spatio-temporal dynamics of cell signalling components.

Three-dimensional Monte Carlo simulations of intracellular diffusion and reaction of signaling proteins

We show that the Monte Carlo technique makes it possible to perform three-dimensional simulations of intracellular protein-mediated signal transduction with realistic ratio of the rates of protein diffusion and association with genes. Specifically, we illustrate that in the simplest case when the protein degradation and phosphorylation/dephosphorylation are negligible the distribution of the first passage time for this process is close to exponential provided that the number of target genes is between 1 and 100.

Stratus not altocumulus: a new view of the yeast protein interaction network

Systems biology approaches can reveal intermediary levels of organization between genotype and phenotype that often underlie biological phenomena such as polygenic effects and protein dispensability. An important conceptualization is the module, which is loosely defined as a cohort of proteins that perform a dedicated cellular task. Based on a computational analysis of limited interaction datasets in the budding yeast Saccharomyces cerevisiae, it has been suggested that the global protein interaction network is segregated such that highly connected proteins, called hubs, tend not to link to each other. Moreover, it has been suggested that hubs fall into two distinct classes: “party” hubs are co-expressed and co-localized with their partners, whereas “date” hubs interact with incoherently expressed and diversely localized partners, and thereby cohere disparate parts of the global network. This structure may be compared with altocumulus clouds, i.e., cotton ball–like structures sparsely connected by thin wisps. However, this organization might reflect a small and/or biased sample set of interactions. In a multi-validated high-confidence (HC) interaction network, assembled from all extant S. cerevisiae interaction data, including recently available proteome-wide interaction data and a large set of reliable literature-derived interactions, we find that hub–hub interactions are not suppressed. In fact, the number of interactions a hub has with other hubs is a good predictor of whether a hub protein is essential or not. We find that date hubs are neither required for network tolerance to node deletion, nor do date hubs have distinct biological attributes compared to other hubs. Date and party hubs do not, for example, evolve at different rates. Our analysis suggests that the organization of global protein interaction network is highly interconnected and hence interdependent, more like the continuous dense aggregations of stratus clouds than the segregated configuration of altocumulus clouds. If the network is configured in a stratus format, cross-talk between proteins is potentially a major source of noise. In turn, control of the activity of the most highly connected proteins may be vital. Indeed, we find that a fluctuation in steady-state levels of the most connected proteins is minimized.

Analysis of multi-validated protein interaction data reveals networks with greater interconnectivity than the more segregated structures seen in previously available data. To help visualize this, the authors draw comparisons between continuous stratus clouds and altocumulus clouds.

Spontaneous separation of bi-stable biochemical systems into spatial domains of opposite phases

Bi-stable chemical systems are the basic building blocks for intracellular memory and cell fate decision circuits. These circuits are built from molecules, which are present at low copy numbers and are slowly diffusing in complex intracellular geometries. The stochastic reaction-diffusion kinetics of a double-negative feedback system and a MAPK phosphorylation-dephosphorylation system is analysed with Monte-Carlo simulations of the reaction-diffusion master equation. The results show the geometry of intracellular reaction compartments to be important both for the duration and the locality of biochemical memory. Rules for when the systems lose global hysteresis by spontaneous separation into spatial domains in opposite phases are formulated in terms of geometrical constraints, diffusion rates and attractor escape times. The analysis is facilitated by a new efficient algorithm for exact sampling of the Markov process corresponding to the reaction-diffusion master equation.

An optimal number of molecules for signal amplification and discrimination in a chemical cascade

Understanding the information processing ability of signal transduction pathways is of great importance because of their crucial roles in triggering various cellular responses. Despite continuing theoretical investigation, some important aspects of signal transduction such as a transient response and its connection to stochasticity originating from a small number of molecules have not yet been well understood. It is, however, through these aspects that unexpected and nontrivial properties of the information processing emerge. In this article, we analyze the transient behavior of a simple signaling cascade by taking into account the stochasticity originating from the small number of molecules. We identify several properties of the signaling cascade that emerge as a result of the interplay between the stochasticity and transient dynamics of the cascade. We specifically demonstrate that each step of the cascade has an optimal number of signaling molecules at which the average signal amplitude becomes maximal. We further investigate the connection between a finite number of molecules and the ability of the cascade to discriminate between true and error signals, which cannot be inferred from deterministic descriptions. The implications of our results are discussed from both biological and mathematical viewpoints.

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae

BACKGROUND

The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference.

RESULTS

We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID (http://www.thebiogrid.org) and SGD (http://www.yeastgenome.org/) databases.

CONCLUSION

Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks.

Spatial regulation and the rate of signal transduction activation

Of the many important signaling events that take place on the surface of a mammalian cell, activation of signal transduction pathways via interactions of cell surface receptors is one of the most important. Evidence suggests that cell surface proteins are not as freely diffusible as implied by the classic fluid mosaic model and that their confinement to membrane domains is regulated. It is unknown whether these dynamic localization mechanisms function to enhance signal transduction activation rate or to minimize cross talk among pathways that share common intermediates. To determine which of these two possibilities is more likely, we derive an explicit equation for the rate at which cell surface membrane proteins interact based on a Brownian motion model in the presence of endocytosis and exocytosis. We find that in the absence of any diffusion constraints, cell surface protein interaction rate is extremely high relative to cytoplasmic protein interaction rate even in a large mammalian cell with a receptor abundance of a mere two hundred molecules. Since a larger number of downstream signaling events needs to take place, each occurring at a much slower rate than the initial activation via association of cell surface proteins, we conclude that the role of co-localization is most likely that of cross-talk reduction rather than coupling efficiency enhancement.

Compensation effect of the MAPK cascade on formation of phospho-protein gradients

The signal-transfer process in the mitogen-activated protein kinase (MAPK) cascade is formulated as a reaction-diffusion system describing the complete three-step phospho-protein reactions and the diffusion process in the direction from the cell membrane to the nucleus. The simulation analysis of the model demonstrates that MAPK cascade can work as a signal amplifier so as to compensate the signal attenuation due to formation of phospho-protein gradients. It also is found to be attainable for eukaryotic cells that a steep gradient of phosphorylated MAPK is not formed in a certain range of the system parameter values. One of the distinctive features in the formation of phospho-protein gradients is revealed to be its high sensitivity to a change in parameter values such as diffusion distance, diffusion coefficients and enzymatic activities of the phosphatases, suggesting that these parameters may act as the key factors for regulation of the signal transduction systems.

Subcellular mobility of the calpain/calpastatin network: an organelle transient

Calpain (Cp) is a calcium (Ca(2+))-dependent cysteine protease. Activation of the major isoforms of Cp, CpI and CpII, are required for a number of important cellular processes including adherence, shape change and migration. The current concept that cytoplasmic Cp locates and associates with its regulatory subunit (Rs) and substrates as well as translocates throughout the cell via random diffusion is not compatible with the spatial and temporal constraints of cellular metabolism. The novel finding that Cp and Rs function relies upon tenacious hydrophobic interactions with organelle membranes offers a unifying explanation for the paradoxical and puzzling features of Cp activation and regulation such as how nM concentrations of intracellular Ca(2+) can activate Cp molecules requiring muM to mM concentrations of Ca(2+) for in vitro activation, and how this protease can spatially and temporally locate specific substrates and translocate throughout the cell. We hypothesize that Cp and its regulatory moieties associate with organelles to facilitate the activation of this protease resulting in the cleavage of substrates and aid in its translocation throughout the cell.

Bridging proteomics and systems biology: What are the roads to be traveled?

The comprehensive study of proteomes has become an important part of attempts to uncover the systemic properties of biological systems. Proteomics provides data of a quality which increasingly fulfills strict requirements of systems biology for quantitative and qualitative information. Notably, proteomics can generate rich datasets that describe dynamic changes of proteomes. On the other hand, large-scale modeling requires the development of mathematic tools that are adequate for the processing of largely uncertain biological data. In this review, recent developments that pave the way for the integration of proteomics into systems biology are discussed. These developments include the standardization of data acquisition and presentation, the increased comprehensiveness of proteomics studies in description of functional status, localization and dynamics of proteins, and advanced modeling approaches.

The evolution of Ca2+ signalling in photosynthetic eukaryotes

It is likely that cytosolic Ca2+ elevations have played a part in eukaryotic signal transduction for about the last 2 Gyr, being mediated by a group of molecules which are collectively known as the [Ca2+]cyt signalling toolkit. Different eukaryotes often display strikingly similar [Ca2+]cyt signalling elevations, which may reflect conservation of toolkit components (homology) or similar constraints acting on different toolkits (homoplasy). Certain toolkit components, which are presumably ancestral, are shared by plants and animals, but some components are unique to photosynthetic organisms. We propose that the structure of modern plant [Ca2+]cyt signalling toolkits may be explained by their modular adaptation from earlier pathways.

Space as the final frontier in stochastic simulations of biological systems

Recent technological and theoretical advances are only now allowing the simulation of detailed kinetic models of biological systems that reflect the stochastic movement and reactivity of individual molecules within cellular compartments. The behavior of many systems could not be properly understood without this level of resolution, opening up new perspectives of using computer simulations to accelerate biological research. We review the modeling methodology applied to stochastic spatial models, also to the attention of non-expert potential users. Modeling choices, current limitations and perspectives of improvement of current general-purpose modeling/simulation platforms for biological systems are discussed.

Calstabin deficiency, ryanodine receptors, and sudden cardiac death

Altered cardiac ryanodine receptor (RyR2) function has an important role in heart failure and genetic forms of arrhythmias. RyR2 constitutes the major intracellular Ca2+ release channel in the cardiac sarcoplasmic reticulum (SR). The peptidyl-prolyl isomerase calstabin2 (FKBP12.6) is a component of the RyR2 macromolecular signaling complex. Calstabin2 binding to RyR2 is regulated by PKA phosphorylation of Ser2809 in RyR2. PKA phosphorylation of RyR2 decreases the binding affinity for calstabin2 and increases RyR2 open probability and sensitivity to Ca2+-dependent activation. In heart failure, a majority of studies have found that RyR2 becomes chronically PKA hyper-phosphorylated which depletes calstabin2 from the channel complex. Calstabin2 dissociation causes a diastolic SR Ca2+ leak contributing to depressed intracellular Ca2+ cycling and decreased cardiac contractility. Missense mutations linked to genetic forms of exercise-induced arrhythmias and sudden cardiac death also cause decreased calstabin2-binding affinity and leaky RyR2 channels. We review the importance of calstabin2 for RyR2 function and excitation-contraction coupling, and discuss new observations that implicate dysregulation of calstabin2 binding as a central mechanism for abnormal calcium cycling in heart failure and triggered arrhythmias.

Polyanions and the proteome

The behavior of the proteome reflects spatial and temporal organization both within and without cells. We propose that various macromolecular entities possessing polyanionic character such as proteoglycans, lipid bilayer surfaces, microtubules, microfilaments, and polynucleotides may provide a functional network that mediates a variety of cellular phenomena. The interaction of proteins with this array of polyanions is characterized by a lower degree of specificity than seen with most commonly recognized macromolecular interactions. In this commentary, potential roles for this polyanion network in diverse functions such as protein/protein interactions, protein folding and stabilization, macromolecular transport, and various disease processes are all considered, as well as the use of polyanions as therapeutic agents. The role of small polyanions in the regulation of protein/polyanion interactions is also postulated. We provide preliminary experimental analysis of the extent to which proteins interact with polyanions inside cells using a combination of two-dimensional chromatographic and electrophoretic methods and antibody arrays. We conclude that many hundreds to thousands of such interactions are present in cells and argue that future understanding of the proteome will require that the "polyanion world" be taken into account.