PLoS Computational Biology: New Articles

Circadian KaiC Phosphorylation: A Multi-Layer Network

2009-11-20T08:00:00Z

Author Summary

Circadian clocks are endogenous timing mechanisms that allow living organisms to coordinate their activities with daily environmental fluctuations. In cyanobacteria, almost all the genes are rhythmically expressed with the same ~24 h period yet exhibit a variety of phase relationships and waveforms. Remarkably, the core pacemaker ticks robustly via simple biochemical reactions carried out by three Kai proteins: KaiC undergoes circadian phosphorylation in the presence of KaiA, KaiB and ATP. In this work, we propose a reaction network modeling the Kai oscillator based on the differentiation of dual phosphorylation sites. We found a dynamic diversity in KaiC phosphorylation which may serve as a potential regulatory mechanism related to the diverse-phased genome-wide expressions in cyanobacteria. In addition, we deduce that each KaiC hexamer is a single oscillator in regulating its own phosphorylation and interactions with KaiA or/and KaiB. In complex organisms, a number of key clock components possess similar activities (e.g., phosphorylation) with multiple nonequivalent active sites, and they may also show some unusual dynamic features that are embedded in the proteins' own reaction networks. We hope our work could be helpful to study the correlations between gene expressions and circadian rhythm in prokaryotic cells, even in eukaryotic cells.

Spatial Analysis of Expression Patterns Predicts Genetic Interactions at the Mid-Hindbrain Boundary

2009-11-20T08:00:00Z

Author Summary

Understanding brain formation during development is a tantalizing challenge. It is also essential for the fight against neurodegenerative diseases. In vertebrates, the central nervous system arises from a structure called the neural plate. This tissue is divided into four regions, which continue to develop into forebrain, midbrain, hindbrain and spinal cord. Interactions between locally expressed genes and signaling molecules are responsible for this patterning. Two key signaling molecules in this process are Fgf8 and Wnt1 proteins. They are secreted from a signaling center located at the boundary between prospective mid- and hindbrain (mid-hindbrain boundary, MHB) and mediate development of these two brain regions. Here, we logically analyze the spatial gene expression patterns at the MHB and predict interactions involved in the differentiation of mid- and hindbrain. In particular, our analysis indicates that Wnt1 depends on Fgf8 for stable maintenance. A time-course analysis of Wnt1 expression after implantation of Fgf8-coated beads in mouse neural plate/tube explants experimentally validates our prediction about the interactions between these two key patterning molecules. Subsequently, we demonstrate that available data allows construction of a mathematical model able to explain the maintenance of the signaling center at the MHB. We begin to understand this small aspect of brain formation on a systems level.

A Novel Extended Granger Causal Model Approach Demonstrates Brain Hemispheric Differences during Face Recognition Learning

2009-11-20T08:00:00Z

Author Summary

The right temporal cortex has previously been shown to play a greater role in the discrimination of faces in both sheep and humans. In the frequency domain, analysis of the relative causal contributions of low (theta 4–8Hz) and high (gamma 30–70Hz) frequency oscillations reveals that prior to learning, theta activity is more predominant in right than in left hemisphere processing, and that learning reduces this so that high frequency oscillations gain more control. We have been able to demonstrate that the frequency of connections increases in the right hemisphere and decreases between the left and right hemispheres after learning. The results are obtained based upon a way to combine aspects of both the Granger and Dynamic Causal Models, which can be used to establish significant causal relations in both time and frequency domains and applied to local field potential recordings from multiple (64 channel) electrodes implanted in the inferotemporal cortex of both sides of the brain in sheep in order to establish changes in causal connections within and between the two hemispheres as a result of learning to discriminate visually between pairs of faces. It is anticipated that this new approach to the measurement of causality will not only help reveal how the two brain hemispheres interact, but will also be applicable to many different types of biological data where variations in both frequency and temporal domains can be measured.

Exon Array Analysis of Head and Neck Cancers Identifies a Hypoxia Related Splice Variant of LAMA3 Associated with a Poor Prognosis

2009-11-20T08:00:00Z

Author Summary

Alternative splicing is the process by which cells express a set of different, but related, transcripts from a single gene. When translated, each transcript results in a different protein, resulting in additional cellular complexity. Affymetrix Exon microarrays, which feature multiple probesets targeting different locations throughout each gene, allow the changes in transcription that result from alternative splicing to be investigated in a single genome-wide assay. In addition, the increased number of probesets targeting each gene offers the potential to combine signals in order to increase statistical power, allowing smaller changes to be detected reliably. We developed a novel algorithm to exploit both these aspects of exon arrays and applied it to tumour hypoxia in clinical samples. Our method identified 4 potential transcript variants upregulated in hypoxic cancers, including a splice variant of the Laminin alpha 3 gene, which we were then able to validate by other methods. On further investigation, we found that expression of this particular isoform in head and neck cancers was a strong adverse prognostic factor for survival following primary surgical treatment. This shows that exon arrays can be used to identify clinically relevant splicing events with potential utility as prognostic biomarkers.

Stochastic Drift in Mitochondrial DNA Point Mutations: A Novel Perspective Ex Silico

2009-11-20T08:00:00Z

Author Summary

Aging is characterized by a systemic decline of an organism's capacity in responding to internal and external stresses, leading to increased mortality. The mitochondrial Free Radical Theory of Aging (mFRTA) attributes this decline to the accumulation of damages, in the form of mitochondrial DNA (mtDNA) mutations, caused by free radical byproducts of metabolism. However, there is still a great deal of uncertainty with this theory due to the difficulties in quantifying mtDNA mutation burden. In this modeling study, we have shown that a random drift in mtDNA point mutation during life, in combination with the experimental sampling can explain the variability seen in some of the reported experimental data. Particularly, we found that while the average mutation increases in a linear fashion, the variability in the mutation load data increases over time, and thus a low number of data replicates can often lead to a deceptive inference of the mutation burden dynamics. The model also predicted a significant contribution from the embryonic developmental phase to the accumulation of mtDNA mutation burden. Furthermore, the model revealed that the replication rate of mtDNA is a major determinant of new mutations during development and in fast-dividing tissues.

Evaluation of the Oscillatory Interference Model of Grid Cell Firing through Analysis and Measured Period Variance of Some Biological Oscillators

2009-11-20T08:00:00Z

Author Summary

For many animals, including rats, accurate spatial memory over relatively large areas is important in order to find food and shelter. Just as unique points in time can be efficiently represented by combinations of repeating elements like hours, days, and months, points in space can be represented as combinations of elements that repeat at different spatial scales. Just such a code has been identified in the brains of rats and it shows an intriguing triangular spacing of encoded locations. Two different explanations have been developed as to what general mechanism in the brain might be able to generate this unusual code. However, to date there is not conclusive experimental evidence indicating whether either of the two explanations is correct. Here we show in detail that one of the explanations, called oscillatory interference, has specific requirements regarding the amount of variability in the system that implements it. We then report data experimentally examining candidate systems to evaluate their levels of noise. The large amount of noise that we find presents a challenge to the currently suggested biological implementations of oscillatory interference, but it does not provide support for the alternative explanation.

Dynamic Allostery in the Methionine Repressor Revealed by Force Distribution Analysis

2009-11-20T08:00:00Z

Author Summary

Proteins carry out most of the cellular processes, from metabolic reactions to the regulation and expression of genes. Tight and effective regulation of the executing protein machinery is commonly achieved by allostery. The only general requirement for allosteric communication is the transmission of a signal, e.g., the binding of a cofactor, from the ligand binding site to the allosteric (active) protein site; in other words an internal propagation of strain. Based on molecular dynamics simulations, we recently presented a method that allows visualization of force distribution in proteins. We here applied this method to MetJ, a transcription factor whose activity is regulated by a co-repressor. Interestingly, co-repressor binding does not cause visible structural changes, yet increases DNA binding affinity manyfold. We were able to reveal a network linking fluctuations of distal parts of MetJ, including the DNA binding interface. Mechanical strain caused by SAM binding propagates to certain key residues, thereby altering fluctuations and finally resulting in increased DNA binding affinity. By directly monitoring ligand induced strain, instead of conformational changes, which might be absent or slow, our force distribution analysis suggests itself suitable to detect the mechanically crucial motifs in allosterically regulated protein machineries.

Computational Model of Membrane Fission Catalyzed by ESCRT-III

2009-11-20T08:00:00Z

Author Summary

Membrane fission is a key step of fundamental intracellular processes such as endocytosis, membrane trafficking, cytokinesis and virus budding. The fission reaction requires substantial energy inputs provided by specialized proteins. Recently, the ESCRT-III proteins have been implicated in membrane budding and fission involved in multivesicular body formation, cytokinesis and virus budding. The ESCRT-III proteins self-assemble into circular filaments and flat spirals in the membrane plane and generate tubular structures with dome-like end caps. We suggest and elaborate computationally on a mechanism by which the ESCRT-III complexes can drive membrane fission. The essence of the mechanism is in generation in the course of membrane attachment to the dome-like surface of an ESCRT-III assembly of a thin membrane neck accumulating large elastic stresses. Relaxation of these stresses can drive the neck fission and formation of separate vesicles of biologically relevant sizes. Estimations of the membrane affinity to the protein surface required for the neck fission to occur and comparison of these values with the experimentally expected values justify quantitatively the proposed mechanism and demonstrate that ESCRT-III assemblies must be highly effective in promoting membrane fission.

Interactions between Connected Half-Sarcomeres Produce Emergent Mechanical Behavior in a Mathematical Model of Muscle

2009-11-13T08:00:00Z

Author Summary

Quantitative muscle biophysics has been dominated for the last 60 years by reductionist theories that try to explain the mechanical properties of an entire muscle fiber as the scaled behavior of a single half-sarcomere (typical muscle fibers contain ~10⁶ such structures). This work tests the hypothesis that a fiber's mechanical properties are irreducible, meaning that the fiber exhibits more complex behavior than the half-sarcomeres do. The key finding is that a system composed of many interacting half-sarcomeres has mechanical properties that are very different from that of a single half-sarcomere. This conclusion is based on the results of extensive computer modeling that reproduces the mechanical behavior of a fast mammalian muscle fiber during an imposed stretch without requiring that titin filaments become more than 3-fold stiffer in an activated muscle. This work is significant because it shows that it is probably not sufficient to attribute functional properties of whole muscle fibers solely to the behavior of a single half-sarcomere. Systems-level approaches are therefore likely to be required to explain how known structural and biochemical heterogeneities influence function in normal and diseased muscle tissue.

Dynamics of Trimming the Content of Face Representations for Categorization in the Brain

2009-11-13T08:00:00Z

Author Summary

How the brain uses visual information to construct representations of categories is a central question of cognitive neuroscience. With our methods we visualize how the brain transforms its representations of facial expressions. Using electroencephalographic data, we analyze how representations change over the first 450 ms of processing both in feature content (e.g., which aspects of the face, such as the eyes or the mouth are represented across time) and level of detail. We show that facial expressions are initially encoded with most of their features (i.e., mouth and eyes) across all levels of details in the occipito-temporal regions. In a later phase, we show that a gradual reorganization of representations occurs, whereby only task relevant face features are kept (e.g., the mouth in “happy”) at only the finest level of details. We describe this elimination of irrelevant and redundant information as ‘trimming’. We suggest that this may be an example of the brain optimizing categorical representations.

Discovery of Regulatory Elements is Improved by a Discriminatory Approach

2009-11-13T08:00:00Z

Author Summary

In the years following the sequencing of the human genome focus have shifted towards trying to understand how this blueprint results in the diversity of cells that we observe. Part of the answer lies in the regulation of transcription and how the proteins responsible for this recognize where they should attach to the DNA. This is a well studied problem, but most methods developed for this have a hard time dealing with the heterogeneity of the mammalian genomes. Here we present a method that greatly improves the efficiency of this search by contrasting the DNA with a large number of background DNA sequences. This enables us to handle repetitive segments of the genome that may be functional, but are usually considered intractable by most methods.

Identifying Changes in Selective Constraints: Host Shifts in Influenza

2009-11-13T08:00:00Z

Author Summary

Influenza A's natural reservoir is waterfowl. Sometimes avian virus genomic segments are able to shift to a human host, either in toto or by combining with those that underwent a previous host shift event. Such host shift events can cause worldwide pandemics in their immunologically naive hosts. In order for these host shifts to establish a stable lineage, the virus has to adapt to the new host. Identifying the changes that have occurred in the past can provide important clues about how this process happens, and how surveillance for new influenza threats should be targeted. Unfortunately, it is difficult to determine whether an amino acid has changed due to adaptation to the new host or whether the change occurred through random drift. Here we describe a novel phylogenetic approach to identifying locations where the nature of the selective pressure exerted on the location has changed corresponding to the host shift event. We identify a set of locations on a number of the genomic segments. The approach we describe is of wide applicability when the timing of the change of selective constraints is known in advance.

The Evolutionary Dynamics of a Rapidly Mutating Virus within and between Hosts: The Case of Hepatitis C Virus

2009-11-13T08:00:00Z

Author Summary

Rapidly mutating viruses, such as hepatitis C virus, can escape host immunity by generating new strains that avoid the immune system. Existing data support the idea that such within-host evolution affects the outcome of the infection. Few theoretical models address this question and most follow viral diversity or qualitative traits, such as drug resistance. Here, we study the evolution of two virus quantitative traits—the replication rate and the ability to be recognised by the immune response—during an infection. We develop an epidemiological framework where transmission events are driven by within-host dynamics. We find that the replication rate of the virus that initially infects the host has a strong influence on the epidemiological success of the disease. Furthermore, we show that the cross-reactive immune response is key to determining the outcome of the infection (acute or chronic). Finally, we show that the timing of the start of an anti-viral treatment has a strong effect on viral evolution, which impacts the efficiency of the treatment. Our analysis suggests a new mechanism to explain infection outcomes and proposes testable predictions that can drive future experimental approaches.

Discovery and Annotation of Functional Chromatin Signatures in the Human Genome

2009-11-13T08:00:00Z

Author Summary

Recent studies have observed that histone tails can be modified in a variety of ways. Analyzing a collection of 21 histone modifications, we attempted to determine what common signatures are associated with different classes of regulatory elements and whether they mark places of distinct function. Indeed, at promoters, we identified a number of distinct signatures, each associated with a different class of expressed and functional genes. We also observed several unexpected signatures marking exons that directly correlate with the expression of exons. Finally, we recovered many places marked by two distinct repressive modifications, and showed that they mark distinct populations of repetitive elements associated with distinct modes of gene repression. Together, these results highlight the rich information embedded in the human epigenome and underscore its value in studying gene regulation.

A Threading-Based Method for the Prediction of DNA-Binding Proteins with Application to the Human Genome

2009-11-13T08:00:00Z

Author Summary

DNA-binding proteins represent only a small fraction of proteins encoded in genomes, yet they play a critical role in a variety of biological activities. Identifying these proteins and understanding how they function are important issues. The structures of solved DNA protein complexes of different protein families provide an invaluable knowledge base not only for understanding DNA-protein interactions, but also for developing methods that predict whether or not a protein binds DNA. While such methods are useful, they require an experimental structure as input. To overcome this obstacle, we have developed a threading-based method for the prediction of DNA-binding domains and associated DNA-binding protein residues from protein sequence. The method has higher accuracy in large scale benchmarking than methods based on sequence similarity alone. Application to the human proteome identified potential targets of not only previously unknown DNA-binding proteins, but also of biologically interesting ones that are related to, yet evolved from, DNA-binding proteins.

Alternative Splicing in the Differentiation of Human Embryonic Stem Cells into Cardiac Precursors

2009-11-06T08:00:00Z

Author Summary

The reprogramming of pluripotent stem cells from adult cells is a crucial step toward producing patient-specific cells for transplant therapy. Critical to this goal is the ability to reproducibly drive the differentiation of these cells to specific fates, such as cardiac and neural cells. While gene expression is important in tissue specific differentiation, the impact of alternative splicing on the biology of differentiating cells has not been fully realized. To identify specific splicing events that may determine cell-type-specific differentiation, we compared splicing profiles of human embryonic stem cells (ESCs) and derived cardiac and neural precursors using Affymetrix exon tiling arrays. Segregation of splicing profiles into cardiac-restricted and common cardiac/neural differentiation pattern groups revealed unique groups of genes with clear implications for the biology of cardiomyocyte function and the maintenance of pluripotent ESCs. Alternative splicing of many of these genes, notably regulators of cell death and proliferation, were often predicted to impact protein domain or microRNA binding site inclusion, suggesting that the function or expression of these proteins is altered during differentiation. These results provide further evidence that alternative splicing is important in shaping the functional repertoire of ESCs and differentiated cells.

Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology

2009-11-06T08:00:00Z

Author Summary

Building artificial vision systems that work robustly in a variety of environments has been difficult, with systems often only performing well under restricted conditions. In contrast, animal vision operates effectively under extremely variable situations. Many attempts to emulate biological vision have met with limited success, often because multiple seemingly appropriate approximations to neural coding resulted in a compromised system. We have constructed a full model for motion processing in the insect visual pathway incorporating known or suspected elements in as much detail as possible. We have found that it is only once all elements are present that the system performs robustly, with reduction or removal of elements dramatically limiting performance. The implementation of this new algorithm could provide a very useful and robust velocity estimator for artificial navigation systems.

A Hidden Markov Model for Single Particle Tracks Quantifies Dynamic Interactions between LFA-1 and the Actin Cytoskeleton

2009-11-06T08:00:00Z

Author Summary

Many important biological processes begin when a target molecule binds to a cell surface receptor protein. This event leads to a series of biochemical reactions involving the receptor and signalling molecules, and ultimately a cellular response. Surface receptors are mobile on the cell surface and their mobility is influenced by their interaction with intracellular proteins. We wish to understand the details of these interactions and how they are affected by cellular activation. An experimental technique called single particle tracking (SPT) uses optical microscopy to study the motion of cell-surface receptors, revealing important details about the organization of the cell membrane. In this paper, we propose a new method of analyzing SPT data to identify reduced receptor mobility as a result of transient binding to intracellular proteins. Using our analysis we are able to reliably differentiate receptor motion when a receptor is freely diffusing on the membrane versus when it is interacting with an intracellular protein. By observing the frequency of transitions between free and bound states, we are able to estimate reaction rates for the interaction. We apply our method to the receptor LFA-1 in T cells and draw conclusions about its interactions with the T cell cytoskeleton.

Evolution of Resistance to Targeted Anti-Cancer Therapies during Continuous and Pulsed Administration Strategies

2009-11-06T08:00:00Z

Author Summary

Recently, the field of anti-cancer therapy has witnessed a revolution by the discovery of targeted therapy, which refers to compounds targeting specific pathways causing abnormal growth of cancer cells. The clinical success of such drugs has been limited by the evolution of acquired resistance to these compounds, which leads to a relapse after initial response to therapy. Current dosing procedures are not designed to optimally delay the emergence of resistance; the identification of such optimal dosing schedules represents an important challenge in clinical cancer research. Here, we design a novel methodology to identify the optimum drug administration strategies that reach this clinical goal. Our model describes the evolutionary dynamics of a tumor cell population during therapy. We consider drug resistance emerging due to a single (epi)genetic alteration and calculate the probability of resistance arising during specific dosing strategies. We then optimize treatment protocols such that the risk of resistance is minimal while considering drug toxicity and side effects as constraints. Since this methodology can be extended to describe situations arising during administration of cytotoxic chemotherapy as well, it can be used to identify optimum drug administration schedules to avoid resistance for any cancer and treatment type.

Optimal Experimental Design for Parameter Estimation of a Cell Signaling Model

2009-11-06T08:00:00Z

Author Summary

Differential equation models of signaling processes are useful to gain a molecular and quantitative understanding of cellular information flow. Although these models are typically based on simple kinetic rules, they can often qualitatively describe the behavior of biological systems. However, in the quest to transform biomedical research into an engineering discipline, biologists face the challenge of estimating important parameters of such models from laboratory data. Measurement noise as well as the robust architecture of biological circuits are causes for large uncertainty of parameter estimates. This makes it difficult to plan informative experiments. Here, we used a computational method to predict and minimize the uncertainty of parameter estimates we would obtain from prospective experiments given a cancer-relevant signaling model. This was achieved by optimizing the concentrations and time points for adding drugs in a live-cell microscopy experiment. Our experimental results demonstrated that the advice given by this algorithm resulted in many-fold more informative data than we would obtain by repeating an intuitively planned experiment. Our study shows that significant experimental effort and time can be saved by adopting an optimal experimental design strategy for inferring relevant parameters from biomedical experiments.

Looking at Cerebellar Malformations through Text-Mined Interactomes of Mice and Humans

2009-11-06T08:00:00Z

Author Summary

We described and made publicly available the largest existing set of text-mined statements; we also presented its application to an important biological problem. We have extracted and purified two large molecular networks, one for humans and one for mouse. We characterized the data sets, described the methods we used to generate them, and presented a novel biological application of the networks to study the etiology of five cerebellum phenotypes. We demonstrated quantitatively that the development-related malformations differ in their system-level properties from degeneration-related genes. We showed that there is a high degree of overlap among the genes implicated in the developmental malformations, that these genes have a strong tendency to be highly connected within the molecular network, and that they also tend to be clustered together, forming a compact molecular network neighborhood. In contrast, the genes involved in malformations due to degeneration do not have a high degree of connectivity, are not strongly clustered in the network, and do not overlap significantly with the development related genes. In addition, taking into account the above-mentioned system-level properties and the gene-specific network interactions, we made highly confident predictions about novel genes that are likely also involved in the etiology of the analyzed phenotypes.

PLoS Computational Biology Issue Image | Vol. 5(10) October 2009

2009-10-30T07:00:00Z

Cloud topology in the yeast protein interaction network.

This figure represents the protein-protein interaction network topology of budding yeast. The outermost circle shows proteins with smaller numbers of interactions (gray dots) and proteins that interact with these are located on inner circles. Most large hubs are placed here (blue dots). Inner circles are dominated by medium size hubs (red dots) that are extensively connected to each other. This topology is mathematically similar to the router-level topology of the internet and represents networks according to highly optimized tolerance (see Hase et al., doi:10.1371/journal.pcbi.1000550).

Image Credit: Hiroaki Kitano and Takeshi Hase

Computational Biology in Colombia

2009-10-30T07:00:00Z

Getting Started in Gene Expression Microarray Analysis

2009-10-30T07:00:00Z

Investigation of the Interaction between the Large and Small Subunits of Potato ADP-Glucose Pyrophosphorylase

2009-10-30T07:00:00Z

Author Summary

ADP-glucose pyrophosphorylase (AGPase) is a key heterotetrameric allosteric enzyme involved in plant starch biosynthesis. In this study, we have applied computational and experimental methods to identify critical amino acids of the AGPase large and small subunits that interact with each other during the heterotetrameric structure formation. During the comparison of the computational with the experimental results we also noted that the backbone energy contribution of the interface residues is more important in identifying critical residues. This study will enable us to use a rational approach to obtain better assembled mutant AGPase variants and use them for the improvement of the plant yield.

Mechanical Strength of 17 134 Model Proteins and Cysteine Slipknots

2009-10-30T07:00:00Z

Author Summary

The advances in nanotechnology have allowed for manipulation of single biomolecules and determination of their elastic properties. Titin was among the first proteins studied in this way. Its unravelling by stretching requires a 204 pN force. The resistance to stretching comes mostly from a localized region known as a force clamp. In titin, the force clamp is simple as it is formed by two parallel β-strands that are sheared on pulling. Studies of a set of under a hundred proteins accomplished in the last decade have revealed a variety of the force clamps that lead to forces ranging from under 20 pN to about 500 pN. This set comprises only a tiny fraction of proteins known. Thus one needs guidance as to what proteins should be considered for specific mechanical properties. Such a guidance is provided here through simulations within simplified coarse-grained models on 17 134 proteins that are stretched at constant speed. We correlate their unravelling forces with two structure classification schemes. We identify proteins with large resistance to unravelling and characterize their force clamps. Quite a few top strength proteins owe their sturdiness to a new type of the force clamp: the cystein slipknot in which the force peak is due to dragging of a piece of the backbone through a closed ring formed by two other pieces of the backbone and two connecting disulphide bonds.

Gene Circuit Analysis of the Terminal Gap Gene huckebein

2009-10-30T07:00:00Z

Author Summary

Currently, there are two very different approaches to the study of pattern formation: Traditional developmental genetics investigates the role of particular factors in great mechanistic detail, while newly developed systems-biology methods study many factors in parallel but usually remain rather general in their conclusions. Here, we attempt to bridge the gap between the two by studying the expression pattern and function of a particular developmental gene—the terminal gap gene huckebein (hkb) in the fruit fly Drosophila melanogaster—in great quantitative detail using a systems-level approach called the gene circuit method. Gene circuits are mathematical models which allow us to reconstitute a developmental process in the computer. This allows us to study the function of the hkb gene in its wild-type regulatory context with unprecedented accuracy and resolution. Our results confirm earlier, qualitative evidence, and show that hkb plays a small, but crucial role in gap gene regulation. Understanding hkb's regulatory contributions is essential for our wider understanding of dynamic shifts in the position of gap gene expression domains which play important roles during both development and evolution.

Tipping the Balance: Robustness of Tip Cell Selection, Migration and Fusion in Angiogenesis

2009-10-30T07:00:00Z

Author Summary

Abnormal vasculature exacerbates many diseases such as cancer and diabetic retinopathy. In angiogenesis new blood vessels, headed by a migrating tip cell, sprout from pre-existing vessels in response to chemical signals. The signals are released from newly oxygen deficient tissue. The signals are known to be different in disease and are thought to cause the process of angiogenesis to progress abnormally, though the reasons for this remain unclear. Normalisation of angiogenesis has great potential as a therapeutic strategy; it has been shown to reduce metastasis and improve drug delivery in tumours. Here we focus on the behaviours of three inter-related initial angiogenic pathways associated with changes in tissue signal conditions, utilising both in silico and in vivo approaches. By the construction and implementation of a novel computational model for cell motility and signal processing we present a new theory on why angiogenesis exhibits such sensitivity to signal changes and show that the behaviour in disease is surprisingly more robust than normal functioning. This we attribute to the positive feedback of cell migration reinforcing abnormal oscillations in cell fate selection. We make the unique prediction that normalisation could be achieved by reducing cell migration alone.

Structure of Protein Interaction Networks and Their Implications on Drug Design

2009-10-30T07:00:00Z

Author Summary

Genome-wide data on interactions between proteins are now available, and networks of protein interactions are the keys to understanding diseases and finding accurate drug targets. This study revealed that the architectural properties of the backbones of protein interaction networks (PINs) were similar to those of the Internet router-level topology by using statistical analyses of genome-wide budding yeast and human PINs. This type of network is known as a highly optimized tolerance (HOT) network that is robust against failures in its components and that ensures high levels of communication. Moreover, we also found that a large number of the most successful drug-target proteins are on the backbone of the human PIN. We made a list of proteins on the backbone of the human PIN, which may help drug companies to search more efficiently for new drug targets.

Specific Entrainment of Mitral Cells during Gamma Oscillation in the Rat Olfactory Bulb

2009-10-30T07:00:00Z

Author Summary

Olfactory function relies on a chain of neural relays that extends from the periphery to the central nervous system and implies neural activity with various timescales. A central question in neuroscience is how information is encoded by the neural activity. In the mammalian olfactory bulb, local neural activity oscillations in the 40–80 Hz range (gamma) may influence the timing of individual neuron activities such that olfactory information may be encoded in this way. In this study, we first characterize in vivo the detailed activity of individual neurons relative to the oscillation and find that, depending on their state, neurons can exhibit periodic activity patterns. We also find, at least qualitatively, a relation between this activity and a particular odor. This is reminiscent of general physical phenomena—the entrainment by an oscillation—and to verify this hypothesis, in a second phase, we build a biologically realistic model mimicking these in vivo conditions. Our model confirms quantitatively this hypothesis and reveals that entrainment is maximal in the gamma range. Taken together, our results suggest that the neuronal activity may be specifically formatted in time during the gamma oscillation in such a way that it could, at this stage, encode the odor.

Intrinsic Structural Disorder Confers Cellular Viability on Oncogenic Fusion Proteins

2009-10-30T07:00:00Z

Author Summary

Chromosomal translocations generate chimeric proteins by fusing segments of two distinct genes and are frequently associated with cancer. The proteins involved are large and fairly heterogeneous in sequence and typically have only a few dispersed structural domains connected by long uncharacterized regions. It has never been studied from a structural perspective how these chimeras survive losing significant portions of the original proteins and acquire new oncogenic functions. By analyzing a collection of 406 human translocation proteins we show here that the answer to both questions lies to a large extent in the high level of structural disorder in the fusion partner proteins (on average, they are twice as disordered as all human proteins). The translocation breakpoints usually avoid globular domains. In rare cases when a globular domain is truncated by the fusion, it happens at a location in the domain where the hydrophobicity exposed by the split is favorable (i.e., not too high). Disorder on average is significantly higher in the vicinity of the breakpoint than in the rest of the fusion proteins. Disorder also plays a pivotal role in the acquired oncogenic function by bringing distant/disparate fusion segments together that enables novel intra- and/or intermolecular interactions.

Computational Resources in Infectious Disease: Limitations and Challenges

2009-10-26T07:00:00Z

Discovering the Phylodynamics of RNA Viruses

2009-10-26T07:00:00Z

The Role of Medical Structural Genomics in Discovering New Drugs for Infectious Diseases

2009-10-26T07:00:00Z

A Model of Cardiovascular Disease Giving a Plausible Mechanism for the Effect of Fractionated Low-Dose Ionizing Radiation Exposure

2009-10-23T07:00:00Z

Author Summary

Atherosclerosis is the main cause of coronary heart disease and stroke, the two major causes of death in developed society. There is emerging evidence of excess risk of cardiovascular disease in various occupationally exposed groups, exposed to fractionated radiation doses with small doses/fraction. The mechanisms for such effects of fractionated low-dose radiation exposures on cardiovascular disease are unclear. We outline a spatial reaction-diffusion model for early stage atherosclerotic lesion formation and perform a stability analysis, based on experimentally derived parameters. We show that following multiple small radiation doses the chemo-attractant (MCP-1) concentration increases proportionally to cumulative dose; this is driven by radiation-induced monocyte death. This will result in risk of atherosclerosis increasing approximately linearly with cumulative dose. This proposed mechanism would be testable. If true, it also has substantive implications for radiological protection, which at present does not take cardiovascular disease into account. The major uncertainty in assessing low-dose risk of cardiovascular disease is the shape of the dose response relationship, which is unclear in high dose data. Our analysis suggests that linear extrapolation would be appropriate.

Steps in the Bacterial Flagellar Motor

2009-10-23T07:00:00Z

Author Summary

Many species of bacteria swim to find food or to avoid toxins. Swimming motility depends on helical flagella that act as propellers. Each flagellum is driven by a rotary molecular engine–the bacterial flagellar motor–which draws its energy from an ion flux entering the cell. Despite much progress, the detailed mechanisms underlying the motor's extraordinary power output, as well as its near 100% efficiency, have yet to be understood. Surprisingly, recent experiments have shown that, at low speeds, the motor proceeds by small steps (~26 per rotation), providing new insight into motor operation. Here we show that a simple physical model can quantitatively account for this stepping behavior as well as the motor's near-perfect efficiency and many other known properties of the motor. In our model, torque is generated via protein-springs that pull on the rotor; the steps arise from contact forces between static components of the motor and a 26-fold periodic ring that forms part of the rotor. Our model allows us to explain some curious properties of the motor, including the observation that backward steps are shorter on average than forward steps, and to make novel, experimentally testable predictions on the motor's speed and diffusion properties.

An Atlas of the Thioredoxin Fold Class Reveals the Complexity of Function-Enabling Adaptations

2009-10-23T07:00:00Z

Author Summary

For any large class of proteins, far more protein sequences are known than can be examined experimentally. This is the case with the thioredoxin fold class, a large and diverse collection of proteins, some of which are known to catalyze important steps in metabolism. Some others participate in key processes like protein folding and detoxification of foreign compounds. Many of the unstudied proteins likely participate in other important biological processes and have useful applications in medicine and industry. We used a new network-based computational approach to create similarity-based maps of the thioredoxin fold class. These maps juxtapose unstudied proteins with similar well-characterized proteins, helping to show where existing knowledge can help predict properties of uncharacterized sequences. This information can be used to identify which of these sequences are interesting and deserve experimental characterization. We also used the maps to gain insight about how shared structural features are used and modified to affect catalysis in the different subclasses, leading to a better understanding of the interplay between structure and function in the thioredoxin fold class.

Perturbation-Response Scanning Reveals Ligand Entry-Exit Mechanisms of Ferric Binding Protein

2009-10-23T07:00:00Z

Author Summary

Upon binding ligands, many proteins undergo structural changes compared to the unbound form. We introduce a methodology to monitor these changes and to study which mechanisms arrange conformational shifts between the liganded and free forms. Our method is simple, yet it efficiently characterizes the response of proteins to a given perturbation on systematically selected residues. The coherent responses predicted are validated by molecular dynamics simulations. The results indicate that the iron uptake by the ferric binding protein is favorable in a thermally fluctuating environment, while release of iron is allosterically moderated. Since ferric binding protein exhibits a high sequence identity with human transferrin whose allosteric anion binding sites generate large conformational changes around the binding region, we suggest mutational studies on remotely controlling sites identified in this work.

Invariant Distribution of Promoter Activities in Escherichia coli

2009-10-23T07:00:00Z

Author Summary

Cells respond to a changing environment by regulating the activity of genes. Here, we sought to understand how E. coli cells distribute their limited transcriptional resources among their target genes, and how this allocation varies with growth rate and growth conditions. To achieve this, we assayed the expression of a comprehensive library of transcriptional reporter strains under different conditions. High-temporal resolution measurements of promoter activities were obtained for different growth rates spanning recovery from stationary phase into exponential phase and eventually deep stationary phase again. We find that the genome-wide promoter activity follows a power-law distribution, which depends solely on growth rate and is independent of the specific growth conditions. Moreover, we find that the power-law distribution can be decomposed into two log-normal distributions: metabolic promoters that make up the low end of the distribution, and ribosomal promoters that make up the high end of the distribution. While distributions remained constant for a given growth rate, the ranked expression of metabolic promoters differed according to the specific condition. Thus, the invariant distribution may suggest optimal resource allocation under constrained resources. A mathematical theory is presented to explain these results.