Density-Dependent Multiplication and Survival Rates in Heterodera glycines

Seasonal multiplication and overwinter survival are density-dependent in Heterodera glycines. At low to moderate population densities, the nematode is capable of large population increases on susceptible soybean cultivars and high rates of oversummer or overwinter survival in the absence of a host. To improve estimates of H. glycines multiplication and survival rates, egg densities were monitored for 12 cropping sequences across 10 years. Log-linear regression analysis was used to describe and compare density-dependent relationships. Growing-season change in H. glycines egg densities was density-dependent for all crops (susceptible soybean, resistant soybean, and nonhost), with slope estimates for the density-dependent relationship greater for susceptible soybean compared with a non-host crop. Overwinter population change also was density-dependent, with similar declines in survival rates observed for all crops as population densities increased. Survival was greater following susceptible soybean compared with resistant soybean, with an intermediate rate of survival associated with non-host crops. Survival estimates greater than 100% frequently were obtained at low population densities, despite attempts to account for sampling error. Rates of growing-season multiplication and survival, when standardized for population density, declined with year of the study. Standardized overwinter survival rates were inversely related to average daily minimum temperature and monthly snow cover.     

Quantitative Measurement of Melanoma Spread in Sentinel Lymph Nodes and Survival

Background

Sentinel lymph node spread is a crucial factor in melanoma outcome. We aimed to define the impact of minimal cancer spread and of increasing numbers of disseminated cancer cells on melanoma-specific survival.

Methods and Findings

We analyzed 1,834 sentinel nodes from 1,027 patients with ultrasound node-negative melanoma who underwent sentinel node biopsy between February 8, 2000, and June 19, 2008, by histopathology including immunohistochemistry and quantitative immunocytology. For immunocytology we recorded the number of disseminated cancer cells (DCCs) per million lymph node cells (DCC density [DCCD]) after disaggregation and immunostaining for the melanocytic marker gp100. None of the control lymph nodes from non-melanoma patients (n = 52) harbored gp100-positive cells. We analyzed gp100-positive cells from melanoma patients by comparative genomic hybridization and found, in 45 of 46 patients tested, gp100-positive cells displaying genomic alterations. At a median follow-up of 49 mo (range 3–123 mo), 138 patients (13.4%) had died from melanoma. Increased DCCD was associated with increased risk for death due to melanoma (univariable analysis; p<0.001; hazard ratio 1.81, 95% CI 1.61–2.01, for a 10-fold increase in DCCD + 1). Even patients with a positive DCCD ≤3 had an increased risk of dying from melanoma compared to patients with DCCD = 0 (p = 0.04; hazard ratio 1.63, 95% CI 1.02–2.58). Upon multivariable testing DCCD was a stronger predictor of death than histopathology. The final model included thickness, DCCD, and ulceration (all p<0.001) as the most relevant prognostic factors, was internally validated by bootstrapping, and provided superior survival prediction compared to the current American Joint Committee on Cancer staging categories.

Conclusions

Cancer cell dissemination to the sentinel node is a quantitative risk factor for melanoma death. A model based on the combined quantitative effects of DCCD, tumor thickness, and ulceration predicted outcome best, particularly at longer follow-up. If these results are validated in an independent study, establishing quantitative immunocytology in histopathological laboratories may be useful clinically. Please see later in the article for the Editors'' Summary     

Annual Survival and Turnover Rates of an Afrotropical Robin in a Fragmented Forest

Whether or not subdivided populations persist in fragmented landscapes primarily depends on how well individuals can survive within discrete habitat patches. Using data from six capture–recapture sessions, survival probabilities of the white-starred robin were estimated in seven indigenous forest patches in the highly fragmented Taita Hills forests, SE Kenya. We found no significant differences in survival probability either among fragment-size categories (large 135 ha, medium 95 ha, and small 2–8 ha) or between adult and first-year birds. However, males had a higher probability of survival from one year to the next than females. Turnover rates of adult birds were higher for females than males, but also higher in the medium and small patches than in the large one within each sex. That survival probability was similar among fragments, but turnover rates differed denoted that different processes caused extirpation from the patches. We suggest that mortality associated with dispersal was probably a more important cause of extirpation than within-patch mortality in the largest habitat, which had the lowest turnover rates. Conversely, high within-patch mortality, for instance due to predation during incubation, could have been more important in the smaller, more disturbed habitats. These results lend support to the proposition that avian conservation efforts should be focussed both at the landscape level to improve connectivity between fragments and reduce mortality during dispersal, and at the patch level to exclude other mortality sources such as nest predation.     

Biomolecule Association Rates Do Not Provide a Complete Description of Bond Formation

The efficiency of many cell-surface receptors is dependent on the rate of binding soluble or surface-attached ligands. Much effort was exerted to measure association rates between soluble molecules (three-dimensional k_on) and, more recently, between surface-attached molecules (two-dimensional [2D] k_on). According to a generally accepted assumption, the probability of bond formation between receptors and ligands is proportional to the first power of encounter duration. Here we provide new experimental evidence and review published data demonstrating that this simple assumption is not always warranted. Using as a model system the (2D) interaction between ICAM-1-coated surfaces and flowing microspheres coated with specific anti-ICAM-1 antibodies, we show that the probability of bond formation may scale as a power of encounter duration that is significantly higher than 1. Further, we show that experimental data may be accounted for by modeling ligand-receptor interaction as a displacement along a single path of a rough energy landscape. Under a wide range of conditions, the probability that an encounter of duration t resulted in bond formation varied as erfc[(t₀/t)^1/2], where t₀ was on the order of 10 ms. We conclude that the minimum contact time for bond formation may be a useful parameter to describe a ligand-receptor interaction, in addition to conventional association rates.     

A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks

How to identify influential nodes is a key issue in complex networks. The degree centrality is simple, but is incapable to reflect the global characteristics of networks. Betweenness centrality and closeness centrality do not consider the location of nodes in the networks, and semi-local centrality, leaderRank and pageRank approaches can be only applied in unweighted networks. In this paper, a bio-inspired centrality measure model is proposed, which combines the Physarum centrality with the K-shell index obtained by K-shell decomposition analysis, to identify influential nodes in weighted networks. Then, we use the Susceptible-Infected (SI) model to evaluate the performance. Examples and applications are given to demonstrate the adaptivity and efficiency of the proposed method. In addition, the results are compared with existing methods.     

Temporal Percolation of the Susceptible Network in an Epidemic Spreading

In this work, we study the evolution of the susceptible individuals during the spread of an epidemic modeled by the susceptible-infected-recovered (SIR) process spreading on the top of complex networks. Using an edge-based compartmental approach and percolation tools, we find that a time-dependent quantity , namely, the probability that a given neighbor of a node is susceptible at time , is the control parameter of a node void percolation process involving those nodes on the network not-reached by the disease. We show that there exists a critical time above which the giant susceptible component is destroyed. As a consequence, in order to preserve a macroscopic connected fraction of the network composed by healthy individuals which guarantee its functionality, any mitigation strategy should be implemented before this critical time . Our theoretical results are confirmed by extensive simulations of the SIR process.     

Entrainment to Periodic Initiation and Transition Rates in a Computational Model for Gene Translation

Periodic oscillations play an important role in many biomedical systems. Proper functioning of biological systems that respond to periodic signals requires the ability to synchronize with the periodic excitation. For example, the sleep/wake cycle is a manifestation of an internal timing system that synchronizes to the solar day. In the terminology of systems theory, the biological system must entrain or phase-lock to the periodic excitation. Entrainment is also important in synthetic biology. For example, connecting several artificial biological systems that entrain to a common clock may lead to a well-functioning modular system. The cell-cycle is a periodic program that regulates DNA synthesis and cell division. Recent biological studies suggest that cell-cycle related genes entrain to this periodic program at the gene translation level, leading to periodically-varying protein levels of these genes. The ribosome flow model (RFM) is a deterministic model obtained via a mean-field approximation of a stochastic model from statistical physics that has been used to model numerous processes including ribosome flow along the mRNA. Here we analyze the RFM under the assumption that the initiation and/or transition rates vary periodically with a common period . We show that the ribosome distribution profile in the RFM entrains to this periodic excitation. In particular, the protein synthesis pattern converges to a unique periodic solution with period . To the best of our knowledge, this is the first proof of entrainment in a mathematical model for translation that encapsulates aspects such as initiation and termination rates, ribosomal movement and interactions, and non-homogeneous elongation speeds along the mRNA. Our results support the conjecture that periodic oscillations in tRNA levels and other factors related to the translation process can induce periodic oscillations in protein levels, and may suggest a new approach for re-engineering genetic systems to obtain a desired, periodic, protein synthesis rate.     

Rates of Transition and Transversion in Coding Sequences since the Human-Rodent Divergence

Protein-coding sequences of 337 human genes were compared with those of homologous genes from rodent (mouse or rat). A composite alignment containing 477,189 nucleotide positions was constructed, and 21,570 amino acid replacements were inferred. The rates of transitional and transversional silent substitutions in fourfold degenerate sites are estimated as 1.71 × 10^-9 and 1.22 × 10^-9 site^-1 year^-1, respectively. Rates of substitutions in replacement sites, subject to selective constraints mediated by the genetic code, are lower, but also reflect a transitional bias. The amino acid exchange rejected least often during evolution is Asp/Glu, which is fixed at 30% the rate of transversions in silent sites. The most mutable amino acids in this survey are threonine and serine; serine coded by AGY is more mutable than serine coded by TCN. A scoring matrix for evaluating amino acid similarity was derived from this study.     

Radiation Survival of Food Pathogens in Complex Media

When 15 bacterial species representing genera associated with food-borne diseases were irradiated individually, all except Escherichia coli and Streptococcus faecalis showed typical linear dose-survival curves in Hartsell's broth. The minimal lethal dose (MLD) for the organisms tested ranged from 3.0 x 10(5) to 6.0 x 10(5) rad. Salmonella paratyphi B, S. wichita, S. typhi, E. coli, and S. faecalis were found to be the least sensitive to radiation. In commercially canned crabmeat the survival curves of S. typhi, S. paratyphi B, and S. wichita exhibited to varying degrees an initial linear death decline with increasing radiation doses, followed by a distinct tailing effect caused by survival of low numbers at the higher doses. The above species of Salmonella were further individually subjected to gamma-radiation in various dilutions of crabmeat. The "tailing effect" gradually disappeared, with the dose-survival curve tending to become linear as the concentration of the crabmeat decreased.     

Interplay Among Nitric Oxide and Reactive Oxygen Species: A Complex Network Determining Cell Survival or Death

Programmed cell death (PCD) is an integrated cellular process occurring in plant growth, development, and defense responses to facilitate normal growth and development and better survival against various stresses as a whole. As universal toxic chemicals in plant and animal cells, reactive oxygen or nitrogen species (ROS or RNS), mainly superoxide anion (O₂^−•), hydrogen peroxide (H₂O₂) or nitric oxide (^•NO), have been studied extensively for their roles in PCD induction. Physiological and genetic studies have convincingly shown their essential roles. However, the details and mechanisms by which ROS and ^•NO interplay and induce PCD are not well understood. Our recent study on Cupressus lusitanica culture cell death revealed the elicitor-induced co-accumulation of ROS and ^•NO and interactions between ^•NO and H₂O₂ or O₂-^• in different ways to regulate cell death. ^•NO and H₂O₂ reciprocally enhanced the production of each other whereas ^•NO and O₂^−• showed reciprocal suppression on each other''s production. It was the interaction between ^•NO and O₂-^• but not between ^•NO and H₂O₂ that induced PCD, probably through peroxynitrite (ONOO⁻). In this addendum, some unsolved issues in the study were discussed based on recent studies on the complex network of ROS and ^•NO leading to PCD in animals and plants.Key Words: cell death, nitric oxide, reactive oxygen species, interaction, posttranslational modification     

Survival of Plant Tissue at Super-Low Temperature VI. Effects of Cooling and Rewarming Rates on Survival

The survival rates of the cortical parenchymal cells of mulberry tree were determined as a function of cooling and rewarming rates. When cooling was carried out slowly at 1° to 15° per minute, all of the cells still remained viable even when rewarmed either rapidly or slowly. Survival rates gradually decreased to zero as the cooling rate increased from about 15° to 2000° per minute. In the intermediate cooling rates, when the cells were cooled at the rates lower than 14° per minute, from −2.2° to about −10°, these cells could survive subsequent rapid cooling and rewarming.

However, at cooling rates above 1000° per minute and with rapid rewarming, the effect of cooling rate reversed and survival increased, reaching a maximum at about 200,000° per minute. As the cooling rate increased above 15° per minute, survival rates became increasingly dependent on the rewarming rate, with rapid rewarming becoming less deleterious than slow rewarming.

The temperature range at which damage occurred during rewarming following removal from liquid nitrogen and in which growth rate of ice crystallization was greatest, was −30° to −40°. The survival rates even in the prefrozen cells at −30° decreased considerably by keeping them at −30° for 10 minutes after removal from liquid nitrogen. This fact indicates that intracellular freezable water remains to some degree even in the prefrozen cells at −30°. After removal from liquid nitrogen, all cells retained their viability, when they were passed rapidly through a temperature range between −50° and −2.5° within about 2 seconds, namely at the rates greater than 1000° per minute.

These observations are explained in terms of the size of the crystals formed within the cortical cells.

     

Network Signatures of Survival in Glioblastoma Multiforme

To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included “protein kinase cascade,” “IκB kinase/NFκB cascade,” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.     

Laminin Network Formation Studied by Reconstitution of Ternary Nodes in Solution

The polymerization of laminins into a cell-associated network is a key process in basement membrane assembly. Network formation is mediated by the homologous short arm tips of the laminin heterotrimer, each consisting of a globular laminin N-terminal (LN) domain followed by a tandem of laminin-type epidermal growth factor-like (LEa) domains. How the short arms interact in the laminin network is unclear. Here, we have addressed this question by reconstituting laminin network nodes in solution and analyzing them by size exclusion chromatography and light scattering. Recombinant LN-LEa1–4 fragments of the laminin α1, α2, α5, β1, and γ1 chains were monomeric in solution. The β1 and γ1 fragments formed the only detectable binary complex and ternary complexes of 1:1:1 stoichiometry with all α chain fragments. Ternary complex formation required calcium and did not occur at 4 °C, like the polymerization of full-length laminins. Experiments with chimeric short arm fragments demonstrated that the LEa2–4 regions of the β1 and γ1 fragments are dispensable for ternary complex formation, and an engineered glycan in the β1 LEa1 domain was also tolerated. In contrast, mutation of Ser-68 in the β1 LN domain (corresponding to a Pierson syndrome mutation in the closely related β2 chain) abolished ternary complex formation. We conclude that authentic ternary nodes of the laminin network can be reconstituted for structure-function studies.     

Photoperiodic Control of the Floral Transition through a Distinct Polycomb Repressive Complex

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An Extended N-Player Network Game and Simulation of Four Investment Strategies on a Complex Innovation Network

As computer science and complex network theory develop, non-cooperative games and their formation and application on complex networks have been important research topics. In the inter-firm innovation network, it is a typical game behavior for firms to invest in their alliance partners. Accounting for the possibility that firms can be resource constrained, this paper analyzes a coordination game using the Nash bargaining solution as allocation rules between firms in an inter-firm innovation network. We build an extended inter-firm n-player game based on nonidealized conditions, describe four investment strategies and simulate the strategies on an inter-firm innovation network in order to compare their performance. By analyzing the results of our experiments, we find that our proposed greedy strategy is the best-performing in most situations. We hope this study provides a theoretical insight into how firms make investment decisions.     

Probability Fluxes and Transition Paths in a Markovian Model Describing Complex Subunit Cooperativity in HCN2 Channels

Hyperpolarization-activated cyclic nucleotide-modulated (HCN) channels are voltage-gated tetrameric cation channels that generate electrical rhythmicity in neurons and cardiomyocytes. Activation can be enhanced by the binding of adenosine-3′,5′-cyclic monophosphate (cAMP) to an intracellular cyclic nucleotide binding domain. Based on previously determined rate constants for a complex Markovian model describing the gating of homotetrameric HCN2 channels, we analyzed probability fluxes within this model, including unidirectional probability fluxes and the probability flux along transition paths. The time-dependent probability fluxes quantify the contributions of all 13 transitions of the model to channel activation. The binding of the first, third and fourth ligand evoked robust channel opening whereas the binding of the second ligand obstructed channel opening similar to the empty channel. Analysis of the net probability fluxes in terms of the transition path theory revealed pronounced hysteresis for channel activation and deactivation. These results provide quantitative insight into the complex interaction of the four structurally equal subunits, leading to non-equality in their function.     

Effects of Photo and Genotype-Based Misidentification Error on Estimates of Survival,Detection and State Transition using Multistate Survival Models

We simulated multistate capture histories (CHs) by varying state survival (ϕ), detection (p) and transition (ψ), number of total capture occasions and releases per capture occasion and then modified these scenarios to mimic false rejection error (FRE), a common misidentification error, resulting from the failure to match samples of the same individual. We then fit a multistate model and estimated accuracy, bias and precision of state-specific ϕ, p and ψ to better understand the effects of FRE on different simulation scenarios. As expected, ϕ, and p, decreased in accuracy with FRE, with lower accuracy when CHs were simulated under a shorter-term study and a lower number of releases per capture occasion (lower sample size). Accuracy of ψ estimates were robust to FRE except in those CH scenarios simulated using low sample size. The effect of FRE on bias was not consistent among parameters and differed by CH scenario. As expected, ϕ was negatively biased with increased FRE (except for the low ϕ low p CH scenario simulated with a low sample size), but we found that the magnitude of bias differed by scenario (high p CH scenarios were more negatively biased). State transition was relatively unbiased, except for the low p CH scenarios simulated with a low sample size, which were positively biased with FRE, and high p CH scenarios simulated with a low sample size. The effect of FRE on precision was not consistent among parameters and differed by scenario and sample size. Precision of ϕ decreased with FRE and was lowest with the low ϕ low p CH scenarios. Precision of p estimates also decreased with FRE under all scenarios, except the low ϕ high p CH scenarios. However, precision of ψ increased with FRE, except for those CH scenarios simulated with a low sample size. Our results demonstrate how FRE leads to loss of accuracy in parameter estimates in a multistate model with the exception of ψ when estimated using an adequate sample size.     

System-Wide Analysis Reveals a Complex Network of Tumor-Fibroblast Interactions Involved in Tumorigenicity

Many fibroblast-secreted proteins promote tumorigenicity, and several factors secreted by cancer cells have in turn been proposed to induce these proteins. It is not clear whether there are single dominant pathways underlying these interactions or whether they involve multiple pathways acting in parallel. Here, we identified 42 fibroblast-secreted factors induced by breast cancer cells using comparative genomic analysis. To determine what fraction was active in promoting tumorigenicity, we chose five representative fibroblast-secreted factors for in vivo analysis. We found that the majority (three out of five) played equally major roles in promoting tumorigenicity, and intriguingly, each one had distinct effects on the tumor microenvironment. Specifically, fibroblast-secreted amphiregulin promoted breast cancer cell survival, whereas the chemokine CCL7 stimulated tumor cell proliferation while CCL2 promoted innate immune cell infiltration and angiogenesis. The other two factors tested had minor (CCL8) or minimally (STC1) significant effects on the ability of fibroblasts to promote tumor growth. The importance of parallel interactions between fibroblasts and cancer cells was tested by simultaneously targeting fibroblast-secreted amphiregulin and the CCL7 receptor on cancer cells, and this was significantly more efficacious than blocking either pathway alone. We further explored the concept of parallel interactions by testing the extent to which induction of critical fibroblast-secreted proteins could be achieved by single, previously identified, factors produced by breast cancer cells. We found that although single factors could induce a subset of genes, even combinations of factors failed to induce the full repertoire of functionally important fibroblast-secreted proteins. Together, these results delineate a complex network of tumor-fibroblast interactions that act in parallel to promote tumorigenicity and suggest that effective anti-stromal therapeutic strategies will need to be multi-targeted.     

Identification of Important Nodes in Directed Biological Networks: A Network Motif Approach

Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA), this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC) curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.