Applying Molecular Crowding Models to Simulations of Virus Capsid Assembly In?Vitro

Virus capsid assembly has been widely studied as a biophysical system, both for its biological and medical significance and as an important model for complex self-assembly processes. No current technology can monitor assembly in detail and what information we have on assembly kinetics comes exclusively from in vitro studies. There are many differences between the intracellular environment and that of an in vitro assembly assay, however, that might be expected to alter assembly pathways. Here, we explore one specific feature characteristic of the intracellular environment and known to have large effects on macromolecular assembly processes: molecular crowding. We combine prior particle simulation methods for estimating crowding effects with coarse-grained stochastic models of capsid assembly, using the crowding models to adjust kinetics of capsid simulations to examine possible effects of crowding on assembly pathways. Simulations suggest a striking difference depending on whether or not a system uses nucleation-limited assembly, with crowding tending to promote off-pathway growth in a nonnucleation-limited model but often enhancing assembly efficiency at high crowding levels even while impeding it at lower crowding levels in a nucleation-limited model. These models may help us understand how complicated assembly systems may have evolved to function with high efficiency and fidelity in the densely crowded environment of the cell.     

Using Hidden Markov Models to Improve Quantifying Physical Activity in Accelerometer Data – A Simulation Study

Introduction

The use of accelerometers to objectively measure physical activity (PA) has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM) are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA.

Methods

1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois]), the generalized Poisson distribution (HMM[GenPois]) and the Gaussian distribution (HMM[Gauss]) with regard to misclassification rate (MCR), bout detection, detection of the number of activities performed during the day and runtime.

Results

The cutpoint method had a misclassification rate (MCR) of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint), 2.0 minutes (HMM[Gauss]) and 14.2 minutes (HMM[GenPois]).

Conclusions

Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.     

Using Markov State Models to Develop a Mechanistic Understanding of Protein Kinase A Regulatory Subunit RIα Activation in Response to cAMP Binding

Protein kinase A (PKA) holoenzyme consists of two catalytic (C) subunits and a regulatory (R) subunit dimer (R₂C₂). The kinase is activated by the binding of cAMPs to the two cyclic nucleotide binding domains (CBDs), A and B, on each R-subunit. Despite extensive study, details of the allosteric mechanisms underlying the cooperativity of holoenzyme activation remain unclear. Several Markov state models of PKA-RIα were developed to test competing theories of activation for the R₂C₂ complex. We found that CBD-B plays an essential role in R-C interaction and promotes the release of the first C-subunit prior to the binding to CBD-A. This favors a conformational selection mechanism for release of the first C-subunit of PKA. However, the release of the second C-subunit requires all four cAMP sites to be occupied. These analyses elucidate R-C heterodimer interactions in the cooperative activation of PKA and cAMP binding and represent a new mechanistic model of R₂C₂ PKA-RIα activation.     

Cytogerontology at the Beginning of the Third Millennium: From “Correlative” to “Gist” Models

For the most part, research in the area of cytogerontology, i.e., investigation of the mechanisms of aging in the experiments on cultured cells, is carried out using the Hayflick's model. More than forty years have passed since the appearance of that model, and during this period of time, very much data were obtained on its basis. These data contributed significantly to our knowledge of the behavior of both animal and human cultured cells. Specifically, we already know of the mechanisms underlying the aging in vitro. On the other hand, in my opinion, little has changed in our knowledge of the aging of the whole organism. In all likelihood, this can be explained by that the Hayflick's model is, like many others used in the experimental gerontology, correlative, i.e. based on a number of detected correlations. In the case of Hayflick's model, these are correlations between the mitotic potential of cells (cell population doubling potential) and some gerontological parameters and indices: species life-span, donor age, evidence of progeroid syndromes, etc., as well as various changes of normal (diploid) cells during long-term cultivation and during aging of the organism. It is, however, well known that very frequently a good correlation has nothing to do with the essence (gist) of the phenomenon. For example, we do know that the amount of gray hair correlates quite well with the age of an individual but is in no way related to the mechanisms of his/her aging and probability of death. In this case, the absence of cause-effect relationships is evident, which are, at the same time, indispensable for the development of gist models. These models, as distinct from the correlative ones, are based on a certain concept of aging. In the case of Hayflick's model, such a concept is absent: we cannot explain, using the Hayflick's limit, why our organism ages. This conclusion was convincingly confirmed by the discovery of telomere mechanism which determines the aging of cellsin vitro. That discovery initiated the appearance of theories attempting to explain the process of aging in vivo also on its basis. However, it has become clear that the mechanisms of aging of the entire organism, located, apparently, in its postmitotic cells, such as neurons or cardiomyocytes, cannot be explained in the framework of this approach. Hence, we believe that it is essential to develop gist models of aging using cultured cells. The mechanisms of cell aging in such models should be similar to the mechanisms of cell aging in the entire organism. Our stationary phase aging model could be one of such models, which is based on the assumption of the leading role of cell proliferation restriction in the processes of aging. We assume that the accumulation of senile damage is caused by the restriction of cell proliferation either due to the formation of differentiated cell populations during development (in vivo) or to the existence of saturation density phenomenon (in vitro). Cell proliferation changes themselves do not induce aging, they only lead to the accumulation of macromolecular defects, which, in turn, lead to the deterioration of tissues, organs, and, eventually, of the entire organism, increasing the probability of its death. Within the framework of our model, we define cell aging as the accumulation in a cell population of various types of damage identical to the damage arising in senescing multicellular organism. And, finally, it is essential to determine how the cell is dying and what the death of the cell is. These definitions will help to draw real parallels between the genuine aging of cells (i.e., increasing probability of their death with age) and the aging of multicellular organisms.     

Models of β-amyloid induced Tau-pathology: the long and “folded” road to understand the mechanism

The amyloid cascade hypothesis has been the prevailing hypothesis in Alzheimer’s Disease research, although the final and most wanted proof i.e. fully successful anti-amyloid clinical trials in patients, is still lacking. This may require a better in depth understanding of the cascade. Particularly, the exact toxic forms of Aβ and Tau, the molecular link between them and their respective contributions to the disease process need to be identified in detail. Although the lack of final proof has raised substantial criticism on the hypothesis per se, accumulating experimental evidence in in vitro models, in vivo models and from biomarkers analysis in patients supports the amyloid cascade and particularly Aβ-induced Tau-pathology, which is the focus of this review. We here discuss available models that recapitulate Aβ-induced Tau-pathology and review some potential underlying mechanisms. The availability and diversity of these models that mimic the amyloid cascade partially or more complete, provide tools to study remaining questions, which are crucial for development of therapeutic strategies for Alzheimer’s Disease.     

Murine Models for Trypanosoma brucei gambiense Disease Progression��From Silent to Chronic Infections and Early Brain Tropism

BackgroundHuman African trypanosomiasis (HAT) caused by Trypanosoma brucei gambiense remains highly prevalent in west and central Africa and is lethal if left untreated. The major problem is that the disease often evolves toward chronic or asymptomatic forms with low and fluctuating parasitaemia producing apparently aparasitaemic serological suspects who remain untreated because of the toxicity of the chemotherapy. Whether the different types of infections are due to host or parasite factors has been difficult to address, since T. b. gambiense isolated from patients is often not infectious in rodents thus limiting the variety of isolates.Conclusions/SignificanceWhereas trypanosome characterisation assigned all these isolates to the homogeneous Group I of T. b. gambiense, they clearly induce very different infections in mice thus mimicking the broad clinical diversity observed in HAT due to T. b. gambiense. Therefore, these murine models will be very useful for the understanding of different aspects of the physiopathology of HAT and for the development of new diagnostic tools and drugs.     

Using Non-Homogeneous Models of Nucleotide Substitution to Identify Host Shift Events: Application to the Origin of the 1918 ‘Spanish’ Influenza Pandemic Virus

Nonhomogeneous Markov models of nucleotide substitution have received scant attention. Here we explore the possibility of using nonhomogeneous models to identify host shift nodes along phylogenetic trees of pathogens evolving in different hosts. It has been noticed that influenza viruses show marked differences in nucleotide composition in human and avian hosts. We take advantage of this fact to identify the host shift event that led to the 1918 ‘Spanish’ influenza. This disease killed over 50 million people worldwide, ranking it as the deadliest pandemic in recorded history. Our model suggests that the eight RNA segments which eventually became the 1918 viral genome were introduced into a mammalian host around 1882–1913. The viruses later diverged into the classical swine and human H1N1 influenza lineages around 1913–1915. The last common ancestor of human strains dates from February 1917 to April 1918. Because pigs are more readily infected with avian influenza viruses than humans, it would seem that they were the original recipient of the virus. This would suggest that the virus was introduced into humans sometime between 1913 and 1918.     

Explanatory Models and Mental Health Treatment: Is Vodou an Obstacle to Psychiatric Treatment in Rural Haiti?

Vodou as an explanatory framework for illness has been considered an impediment to biomedical psychiatric treatment in rural Haiti by some scholars and Haitian professionals. According to this perspective, attribution of mental illness to supernatural possession drives individuals to seek care from houngan-s (Vodou priests) and other folk practitioners, rather than physicians, psychologists, or psychiatrists. This study investigates whether explanatory models of mental illness invoking supernatural causation result in care-seeking from folk practitioners and resistance to biomedical treatment. The study comprised 31 semi-structured interviews with community leaders, traditional healers, religious leaders, and biomedical providers, 10 focus group discussions with community members, community health workers, health promoters, community leaders, and church members; and four in-depth case studies of individuals exhibiting mental illness symptoms conducted in Haiti's Central Plateau. Respondents invoked multiple explanatory models for mental illness and expressed willingness to receive treatment from both traditional and biomedical practitioners. Folk practitioners expressed a desire to collaborate with biomedical providers and often referred patients to hospitals. At the same time, respondents perceived the biomedical system as largely ineffective for treating mental health problems. Explanatory models rooted in Vodou ethnopsychology were not primary barriers to pursuing psychiatric treatment. Rather, structural factors including scarcity of treatment resources and lack of psychiatric training among health practitioners created the greatest impediments to biomedical care for mental health concerns in rural Haiti.     

Does Raising Type 1 Error Rate Improve Power to Detect Interactions in Linear Regression Models? A Simulation Study

Introduction

Statistical interactions are a common component of data analysis across a broad range of scientific disciplines. However, the statistical power to detect interactions is often undesirably low. One solution is to elevate the Type 1 error rate so that important interactions are not missed in a low power situation. To date, no study has quantified the effects of this practice on power in a linear regression model.

Methods

A Monte Carlo simulation study was performed. A continuous dependent variable was specified, along with three types of interactions: continuous variable by continuous variable; continuous by dichotomous; and dichotomous by dichotomous. For each of the three scenarios, the interaction effect sizes, sample sizes, and Type 1 error rate were varied, resulting in a total of 240 unique simulations.

Results

In general, power to detect the interaction effect was either so low or so high at α = 0.05 that raising the Type 1 error rate only served to increase the probability of including a spurious interaction in the model. A small number of scenarios were identified in which an elevated Type 1 error rate may be justified.

Conclusions

Routinely elevating Type 1 error rate when testing interaction effects is not an advisable practice. Researchers are best served by positing interaction effects a priori and accounting for them when conducting sample size calculations.     

Are Mosses Required to Accurately Predict Upland Black Spruce Forest Soil Carbon in National-Scale Forest C Accounting Models?

The boreal forest plays a key role in the global carbon (C) cycle, and black spruce (Picea mariana (Mill.) BSP) forests are the dominant coniferous forest type in the Canadian boreal forest. National-scale forest C models currently do not account for the contribution of moss-derived organic matter that we hypothesize to be significant in the C budget of black spruce ecosystems. One such model, the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3), is designed to meet Canada’s forest-related greenhouse gas reporting requirements. In this study our goal was to determine if black spruce forest soil C stocks are significantly underestimated by the CBM-CFS3, and if so, to determine if estimates could be improved by adding moss-derived C. We conclude that in black spruce sites, organic layer C is significantly underestimated by CBM-CFS3 compared to sites with all other leading tree species analyzed. We compiled and used published moss net primary productivity rates for upland forest systems, with decomposition rates, in mass-balance calculations to estimate mean moss-derived C in black spruce forests for feather mosses at 64 Mg C ha^?1, and for sphagnum mosses at 103 Mg C ha^?1. These C pools are similar to the CBM-CFS3 mean underestimation of black spruce soil organic layers (63 Mg C ha^?1). We conclude that the contribution of mosses is sufficiently large that a moss C pool should be added to national-scale models including the CBM-CFS3, to reduce uncertainties in boreal forest C budget estimation. Feather and sphagnum mosses should be parameterized separately.     

MiR-335 Regulates Hif-1α to Reduce Cell Death in Both Mouse Cell Line and Rat Ischemic Models

Hypoxia inducible factor-1α facilitates cellular adaptation to hypoxic conditions. Hence its tight regulation is crucial in hypoxia related diseases such as cerebral ischemia. Changes in hypoxia inducible factor-1α expression upon cerebral ischemia influence the expression of its downstream genes which eventually determines the extent of cellular damage. MicroRNAs are endogenous regulators of gene expression that have rapidly emerged as promising therapeutic targets in several diseases. In this study, we have identified miR-335 as a direct regulator of hypoxia inducible factor-1α and as a potential therapeutic target in cerebral ischemia. MiR-335 and hypoxia inducible factor-1α mRNA showed an inverse expression profile, both in vivo and in vitro ischemic conditions. Given the biphasic nature of hypoxia inducible factor-1α expression during cerebral ischemia, miR-335 mimic was found to reduce infarct volume in the early time (immediately after middle cerebral artery occlusion) of embolic stroke animal models while the miR-335 inhibitor appears to be beneficial at the late time of stroke (24 hrs after middle cerebral artery occlusion). Modulation of hypoxia inducible factor-1α expression by miR-335 also influenced the expression of crucial genes implicated in neurovascular permeability, cell death and maintenance of the blood brain barrier. These concerted effects, resulting in a reduction in infarct volume bring about a beneficial outcome in ischemic stroke.     

High Resolution Niche Models of Malaria Vectors in Northern Tanzania: A New Capacity to Predict Malaria Risk?

Background

Malaria transmission rates in Africa can vary dramatically over the space of a few kilometres. This spatial heterogeneity reflects variation in vector mosquito habitat and presents an important obstacle to the efficient allocation of malaria control resources. Malaria control is further complicated by combinations of vector species that respond differently to control interventions. Recent modelling innovations make it possible to predict vector distributions and extrapolate malaria risk continentally, but these risk mapping efforts have not yet bridged the spatial gap to guide on-the-ground control efforts.

Methodology/Principal Findings

We used Maximum Entropy with purpose-built, high resolution land cover data and other environmental factors to model the spatial distributions of the three dominant malaria vector species in a 94,000 km² region of east Africa. Remotely sensed land cover was necessary in each vector''s niche model. Seasonality of precipitation and maximum annual temperature also contributed to niche models for Anopheles arabiensis and An. funestus s.l. (AUC 0.989 and 0.991, respectively), but cold season precipitation and elevation were important for An. gambiae s.s. (AUC 0.997). Although these niche models appear highly accurate, the critical test is whether they improve predictions of malaria prevalence in human populations. Vector habitat within 1.5 km of community-based malaria prevalence measurements interacts with elevation to substantially improve predictions of Plasmodium falciparum prevalence in children. The inclusion of the mechanistic link between malaria prevalence and vector habitat greatly improves the precision and accuracy of prevalence predictions (r² = 0.83 including vector habitat, or r² = 0.50 without vector habitat). Predictions including vector habitat are unbiased (observations vs. model predictions of prevalence: slope = 1.02). Using this model, we generate a high resolution map of predicted malaria prevalence throughout the study region.

Conclusions/Significance

The interaction between mosquito niche space and microclimate along elevational gradients indicates worrisome potential for climate and land use changes to exacerbate malaria resurgence in the east African highlands. Nevertheless, it is possible to direct interventions precisely to ameliorate potential impacts.     

Is the Climate Right for Pleistocene Rewilding? Using Species Distribution Models to Extrapolate Climatic Suitability for Mammals across Continents

Species distribution models (SDMs) are increasingly used for extrapolation, or predicting suitable regions for species under new geographic or temporal scenarios. However, SDM predictions may be prone to errors if species are not at equilibrium with climatic conditions in the current range and if training samples are not representative. Here the controversial “Pleistocene rewilding” proposal was used as a novel example to address some of the challenges of extrapolating modeled species-climate relationships outside of current ranges. Climatic suitability for three proposed proxy species (Asian elephant, African cheetah and African lion) was extrapolated to the American southwest and Great Plains using Maxent, a machine-learning species distribution model. Similar models were fit for Oryx gazella, a species native to Africa that has naturalized in North America, to test model predictions. To overcome biases introduced by contracted modern ranges and limited occurrence data, random pseudo-presence points generated from modern and historical ranges were used for model training. For all species except the oryx, models of climatic suitability fit to training data from historical ranges produced larger areas of predicted suitability in North America than models fit to training data from modern ranges. Four naturalized oryx populations in the American southwest were correctly predicted with a generous model threshold, but none of these locations were predicted with a more stringent threshold. In general, the northern Great Plains had low climatic suitability for all focal species and scenarios considered, while portions of the southern Great Plains and American southwest had low to intermediate suitability for some species in some scenarios. The results suggest that the use of historical, in addition to modern, range information and randomly sampled pseudo-presence points may improve model accuracy. This has implications for modeling range shifts of organisms in response to climate change.     

Improving Transferability of Introduced Species’ Distribution Models: New Tools to Forecast the Spread of a Highly Invasive Seaweed

The utility of species distribution models for applications in invasion and global change biology is critically dependent on their transferability between regions or points in time, respectively. We introduce two methods that aim to improve the transferability of presence-only models: density-based occurrence thinning and performance-based predictor selection. We evaluate the effect of these methods along with the impact of the choice of model complexity and geographic background on the transferability of a species distribution model between geographic regions. Our multifactorial experiment focuses on the notorious invasive seaweed Caulerpacylindracea (previously Caulerpa racemosa var. cylindracea ) and uses Maxent, a commonly used presence-only modeling technique. We show that model transferability is markedly improved by appropriate predictor selection, with occurrence thinning, model complexity and background choice having relatively minor effects. The data shows that, if available, occurrence records from the native and invaded regions should be combined as this leads to models with high predictive power while reducing the sensitivity to choices made in the modeling process. The inferred distribution model of Caulerpacylindracea shows the potential for this species to further spread along the coasts of Western Europe, western Africa and the south coast of Australia.     

Herpes Simplex Virus-1 Fine-Tunes Host’s Autophagic Response to Infection: A Comprehensive Analysis in Productive Infection Models

Herpes simplex virus-1 (HSV-1) infection causes severe conditions, with serious complications, including corneal blindness from uncontrolled ocular infections. An important cellular defense mechanism against HSV-1 infection is autophagy. The autophagic response of the host cell was suggested to be regulated by HSV-1. In this study, we performed a detailed analysis of autophagy in multiple HSV-1-targeted cell types, and under various infection conditions that recapitulate a productive infection model. We found that autophagy was slightly inhibited in one cell type, while in other cell types autophagy maintained its basal levels mostly unchanged during productive infection. This study refines the concept of HSV-1-mediated autophagy regulation to imply either inhibition, or prevention of activation, of the innate immune pathway.     

Homogenization of Large-Scale Movement Models in Ecology

A difficulty in using diffusion models to predict large scale animal population dispersal is that individuals move differently based on local information (as opposed to gradients) in differing habitat types. This can be accommodated by using ecological diffusion. However, real environments are often spatially complex, limiting application of a direct approach. Homogenization for partial differential equations has long been applied to Fickian diffusion (in which average individual movement is organized along gradients of habitat and population density). We derive a homogenization procedure for ecological diffusion and apply it to a simple model for chronic wasting disease in mule deer. Homogenization allows us to determine the impact of small scale (10–100 m) habitat variability on large scale (10–100 km) movement. The procedure generates asymptotic equations for solutions on the large scale with parameters defined by small-scale variation. The simplicity of this homogenization procedure is striking when compared to the multi-dimensional homogenization procedure for Fickian diffusion,and the method will be equally straightforward for more complex models.