Boolean network model predicts cell cycle sequence of fission yeast

A Boolean network model of the cell-cycle regulatory network of fission yeast (Schizosaccharomyces Pombe) is constructed solely on the basis of the known biochemical interaction topology. Simulating the model in the computer, faithfully reproduces the known sequence of regulatory activity patterns along the cell cycle of the living cell. Contrary to existing differential equation models, no parameters enter the model except the structure of the regulatory circuitry. The dynamical properties of the model indicate that the biological dynamical sequence is robustly implemented in the regulatory network, with the biological stationary state G1 corresponding to the dominant attractor in state space, and with the biological regulatory sequence being a strongly attractive trajectory. Comparing the fission yeast cell-cycle model to a similar model of the corresponding network in S. cerevisiae, a remarkable difference in circuitry, as well as dynamics is observed. While the latter operates in a strongly damped mode, driven by external excitation, the S. pombe network represents an auto-excited system with external damping.Comment: 10 pages, 3 figure

Augmented visual feedback of movement performance to enhance walking recovery after stroke : study protocol for a pilot randomised controlled trial

Increasing evidence suggests that use of augmented visual feedback could be a useful approach to stroke rehabilitation. In current clinical practice, visual feedback of movement performance is often limited to the use of mirrors or video. However, neither approach is optimal since cognitive and self-image issues can distract or distress patients and their movement can be obscured by clothing or limited viewpoints. Three-dimensional motion capture has the potential to provide accurate kinematic data required for objective assessment and feedback in the clinical environment. However, such data are currently presented in numerical or graphical format, which is often impractical in a clinical setting. Our hypothesis is that presenting this kinematic data using bespoke visualisation software, which is tailored for gait rehabilitation after stroke, will provide a means whereby feedback of movement performance can be communicated in a more meaningful way to patients. This will result in increased patient understanding of their rehabilitation and will enable progress to be tracked in a more accessible way. The hypothesis will be assessed using an exploratory (phase II) randomised controlled trial. Stroke survivors eligible for this trial will be in the subacute stage of stroke and have impaired walking ability (Functional Ambulation Classification of 1 or more). Participants (n = 45) will be randomised into three groups to compare the use of the visualisation software during overground physical therapy gait training against an intensity-matched and attention-matched placebo group and a usual care control group. The primary outcome measure will be walking speed. Secondary measures will be Functional Ambulation Category, Timed Up and Go, Rivermead Visual Gait Assessment, Stroke Impact Scale-16 and spatiotemporal parameters associated with walking. Additional qualitative measures will be used to assess the participant's experience of the visual feedback provided in the study. Results from the trial will explore whether the early provision of visual feedback of biomechanical movement performance during gait rehabilitation demonstrates improved mobility outcomes after stroke and increased patient understanding of their rehabilitation

Dosage differences in 12-OXOPHYTODIENOATE REDUCTASE genes modulate wheat root growth

Wheat, an essential crop for global food security, is well adapted to a wide variety of soils. However, the gene networks shaping different root architectures remain poorly understood. We report here that dosage differences in a cluster of monocot-specific 12-OXOPHYTODIENOATE REDUCTASE genes from subfamily III (OPRIII) modulate key differences in wheat root architecture, which are associated with grain yield under water-limited conditions. Wheat plants with loss-of-function mutations in OPRIII show longer seminal roots, whereas increased OPRIII dosage or transgenic over-expression result in reduced seminal root growth, precocious development of lateral roots and increased jasmonic acid (JA and JA-Ile). Pharmacological inhibition of JA-biosynthesis abolishes root length differences, consistent with a JA-mediated mechanism. Transcriptome analyses of transgenic and wild-type lines show significant enriched JA-biosynthetic and reactive oxygen species (ROS) pathways, which parallel changes in ROS distribution. OPRIII genes provide a useful entry point to engineer root architecture in wheat and other cereals.Fil: Gabay, Gilad. University of California at Davis; Estados UnidosFil: Wang, Hanchao. University of California at Davis; Estados Unidos. University Of Haifa; IsraelFil: Zhang, Junli. University of California at Davis; Estados UnidosFil: Moriconi, Jorge Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Burguener, Germán Federico. University of California at Davis; Estados UnidosFil: Gualano, Leonardo David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Howell, Tyson. University of California at Davis; Estados UnidosFil: Lukaszewski, Adam. University of California; Estados UnidosFil: Staskawicz, Brian. University of California; Estados UnidosFil: Cho, Myeong-Je. University of California; Estados UnidosFil: Tanaka, Jaclyn. University of California; Estados UnidosFil: Fahima, Tzion. University Of Haifa; IsraelFil: Ke, Haiyan. University of California; Estados UnidosFil: Dehesh, Katayoon. University of California; Estados UnidosFil: Zhang, Guo-Liang. Fudan University; ChinaFil: Gou, Jin Ying. Beijing Key Laboratory Of Crop Genetic Improvement; China. Fudan University; ChinaFil: Hamberg, Mats. Karolinska Huddinge Hospital. Karolinska Institutet; SueciaFil: Santa Maria, Guillermo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Dubcovsky, Jorge. University of California at Davis; Estados Unidos. Howard Hughes Medical Institute; Estados Unido

Dosage differences in 12-OXOPHYTODIENOATE REDUCTASE genes modulate wheat root growth

Wheat, an essential crop for global food security, is well adapted to a wide variety of soils. However, the gene networks shaping different root architectures remain poorly understood. We report here that dosage differences in a cluster of monocot-specific 12-OXOPHYTODIENOATE REDUCTASE genes from subfamily III (OPRIII) modulate key differences in wheat root architecture, which are associated with grain yield under water-limited conditions. Wheat plants with loss-of-function mutations in OPRIII show longer seminal roots, whereas increased OPRIII dosage or transgenic over-expression result in reduced seminal root growth, precocious development of lateral roots and increased jasmonic acid (JA and JA-Ile). Pharmacological inhibition of JA-biosynthesis abolishes root length differences, consistent with a JA-mediated mechanism. Transcriptome analyses of transgenic and wild-type lines show significant enriched JA-biosynthetic and reactive oxygen species (ROS) pathways, which parallel changes in ROS distribution. OPRIII genes provide a useful entry point to engineer root architecture in wheat and other cereals.Fil: Gabay, Gilad. University of California at Davis; Estados UnidosFil: Wang, Hanchao. University of California at Davis; Estados Unidos. University Of Haifa; IsraelFil: Zhang, Junli. University of California at Davis; Estados UnidosFil: Moriconi, Jorge Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Burguener, Germán Federico. University of California at Davis; Estados UnidosFil: Gualano, Leonardo David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Howell, Tyson. University of California at Davis; Estados UnidosFil: Lukaszewski, Adam. University of California; Estados UnidosFil: Staskawicz, Brian. University of California; Estados UnidosFil: Cho, Myeong-Je. University of California; Estados UnidosFil: Tanaka, Jaclyn. University of California; Estados UnidosFil: Fahima, Tzion. University Of Haifa; IsraelFil: Ke, Haiyan. University of California; Estados UnidosFil: Dehesh, Katayoon. University of California; Estados UnidosFil: Zhang, Guo-Liang. Fudan University; ChinaFil: Gou, Jin Ying. Beijing Key Laboratory Of Crop Genetic Improvement; China. Fudan University; ChinaFil: Hamberg, Mats. Karolinska Huddinge Hospital. Karolinska Institutet; SueciaFil: Santa Maria, Guillermo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Dubcovsky, Jorge. University of California at Davis; Estados Unidos. Howard Hughes Medical Institute; Estados Unido

Mathematical Model of a Cell Size Checkpoint

How cells regulate their size from one generation to the next has remained an enigma for decades. Recently, a molecular mechanism that links cell size and cell cycle was proposed in fission yeast. This mechanism involves changes in the spatial cellular distribution of two proteins, Pom1 and Cdr2, as the cell grows. Pom1 inhibits Cdr2 while Cdr2 promotes the G2 → M transition. Cdr2 is localized in the middle cell region (midcell) whereas the concentration of Pom1 is highest at the cell tips and declines towards the midcell. In short cells, Pom1 efficiently inhibits Cdr2. However, as cells grow, the Pom1 concentration at midcell decreases such that Cdr2 becomes activated at some critical size. In this study, the chemistry of Pom1 and Cdr2 was modeled using a deterministic reaction-diffusion-convection system interacting with a deterministic model describing microtubule dynamics. Simulations mimicked experimental data from wild-type (WT) fission yeast growing at normal and reduced rates; they also mimicked the behavior of a Pom1 overexpression mutant and WT yeast exposed to a microtubule depolymerizing drug. A mechanism linking cell size and cell cycle, involving the downstream action of Cdr2 on Wee1 phosphorylation, is proposed

Active Brownian Particles. From Individual to Collective Stochastic Dynamics

We review theoretical models of individual motility as well as collective dynamics and pattern formation of active particles. We focus on simple models of active dynamics with a particular emphasis on nonlinear and stochastic dynamics of such self-propelled entities in the framework of statistical mechanics. Examples of such active units in complex physico-chemical and biological systems are chemically powered nano-rods, localized patterns in reaction-diffusion system, motile cells or macroscopic animals. Based on the description of individual motion of point-like active particles by stochastic differential equations, we discuss different velocity-dependent friction functions, the impact of various types of fluctuations and calculate characteristic observables such as stationary velocity distributions or diffusion coefficients. Finally, we consider not only the free and confined individual active dynamics but also different types of interaction between active particles. The resulting collective dynamical behavior of large assemblies and aggregates of active units is discussed and an overview over some recent results on spatiotemporal pattern formation in such systems is given.Comment: 161 pages, Review, Eur Phys J Special-Topics, accepte

Germ Warfare in a Microbial Mat Community: CRISPRs Provide Insights into the Co-Evolution of Host and Viral Genomes

CRISPR arrays and associated cas genes are widespread in bacteria and archaea and confer acquired resistance to viruses. To examine viral immunity in the context of naturally evolving microbial populations we analyzed genomic data from two thermophilic Synechococcus isolates (Syn OS-A and Syn OS-B′) as well as a prokaryotic metagenome and viral metagenome derived from microbial mats in hotsprings at Yellowstone National Park. Two distinct CRISPR types, distinguished by the repeat sequence, are found in both the Syn OS-A and Syn OS-B′ genomes. The genome of Syn OS-A contains a third CRISPR type with a distinct repeat sequence, which is not found in Syn OS-B′, but appears to be shared with other microorganisms that inhabit the mat. The CRISPR repeats identified in the microbial metagenome are highly conserved, while the spacer sequences (hereafter referred to as “viritopes” to emphasize their critical role in viral immunity) were mostly unique and had no high identity matches when searched against GenBank. Searching the viritopes against the viral metagenome, however, yielded several matches with high similarity some of which were within a gene identified as a likely viral lysozyme/lysin protein. Analysis of viral metagenome sequences corresponding to this lysozyme/lysin protein revealed several mutations all of which translate into silent or conservative mutations which are unlikely to affect protein function, but may help the virus evade the host CRISPR resistance mechanism. These results demonstrate the varied challenges presented by a natural virus population, and support the notion that the CRISPR/viritope system must be able to adapt quickly to provide host immunity. The ability of metagenomics to track population-level variation in viritope sequences allows for a culture-independent method for evaluating the fast co-evolution of host and viral genomes and its consequence on the structuring of complex microbial communities

Accurate Genome Relative Abundance Estimation Based on Shotgun Metagenomic Reads

Accurate estimation of microbial community composition based on metagenomic sequencing data is fundamental for subsequent metagenomics analysis. Prevalent estimation methods are mainly based on directly summarizing alignment results or its variants; often result in biased and/or unstable estimates. We have developed a unified probabilistic framework (named GRAMMy) by explicitly modeling read assignment ambiguities, genome size biases and read distributions along the genomes. Maximum likelihood method is employed to compute Genome Relative Abundance of microbial communities using the Mixture Model theory (GRAMMy). GRAMMy has been demonstrated to give estimates that are accurate and robust across both simulated and real read benchmark datasets. We applied GRAMMy to a collection of 34 metagenomic read sets from four metagenomics projects and identified 99 frequent species (minimally 0.5% abundant in at least 50% of the data- sets) in the human gut samples. Our results show substantial improvements over previous studies, such as adjusting the over-estimated abundance for Bacteroides species for human gut samples, by providing a new reference-based strategy for metagenomic sample comparisons. GRAMMy can be used flexibly with many read assignment tools (mapping, alignment or composition-based) even with low-sensitivity mapping results from huge short-read datasets. It will be increasingly useful as an accurate and robust tool for abundance estimation with the growing size of read sets and the expanding database of reference genomes

Gene prediction in metagenomic fragments: A large scale machine learning approach

<p>Abstract</p> <p>Background</p> <p>Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions.</p> <p>Results</p> <p>We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability.</p> <p>Conclusion</p> <p>Large scale machine learning methods are well-suited for gene prediction in metagenomic DNA fragments. In particular, the combination of linear discriminants and neural networks is promising and should be considered for integration into metagenomic analysis pipelines. The data sets can be downloaded from the URL provided (see Availability and requirements section).</p