A full body musculoskeletal model based on flexible multibody simulation approach utilised in bone strain analysis during human locomotion

Load-induced strains applied to bone can stimulate its development and adaptation. In order to quantify the incident strains within the skeleton, in vivo implementation of strain gauges on the surfaces of bone is typically used. However, in vivo strain measurements require invasive methodology that is challenging and limited to certain regions of superficial bones only such as the anterior surface of the tibia. Based on our previous study [Al Nazer et al. (2008) J Biomech. 41:1036–1043], an alternative numerical approach to analyse in vivo strains based on the flexible multibody simulation approach was proposed. The purpose of this study was to extend the idea of using the flexible multibody approach in the analysis of bone strains during physical activity through integrating the magnetic resonance imaging (MRI) technique within the framework. In order to investigate the reliability and validity of the proposed approach, a three-dimensional full body musculoskeletal model with a flexible tibia was used as a demonstration example. The model was used in a forward dynamics simulation in order to predict the tibial strains during walking on a level exercise. The flexible tibial model was developed using the actual geometry of human tibia, which was obtained from three-dimensional reconstruction of MRI. Motion capture data obtained from walking at constant velocity were used to drive the model during the inverse dynamics simulation in order to teach the muscles to reproduce the motion in the forward dynamics simulation. Based on the agreement between the literature-based in vivo strain measurements and the simulated strain results, it can be concluded that the flexible multibody approach enables reasonable predictions of bone strain in response to dynamic loading. The information obtained from the present approach can be useful in clinical applications including devising exercises to prevent bone fragility or to accelerate fracture healing.     

Localization of a potyvirus and the viral genome-linked protein in wild potato leaves at an early stage of systemic infection

The upper noninoculated 'sink' leaves of the wild potato species, Solanum commersonii, were studied for distribution of Potato virus A (PVA) at an early stage of systemic infection. Viral RNA was detected by in situ hybridization, and five viral proteins were localized using immunohistochemical staining in leaf sections. Initial systemic infection foci were found at the vicinity of major and minor veins. In these infection foci, the viral coat protein, cylindrical inclusion protein, and helper component-proteinase colocalized with viral RNA in parenchyma and mesophyll cells, but none of these were detected in companion cells (CC). In contrast, VPg, which is the N-proximal half of the NIa protein (separated from the C-terminal proteinase domain, NIapro, by an autocatalytic cleavage) and acts as a viral genome-linked protein, was detected in CC in the infection foci, but only at an early stage of virus unloading. Outside the infection foci, conspicuous signals for VPg were readily and exclusively detected in CC of many veins in all vein classes in the absence of signals for NIapro, other viral proteins, and viral RNA. Taken together, our data indicate that both major and minor veins may unload PVA in the sink leaves of potato. The data suggest that VPg is translocated from inoculated source leaves to the sink leaves, where it accumulates in CC at an early stage of systemic infection. These findings suggest that VPg may be a 'phloem protein' that specifically acts in CC in the sink leaves to facilitate virus unloading.     

Accounting for control mislabeling in case-control biomarker studies

In biomarker discovery studies, uncertainty associated with case and control labels is often overlooked. By omitting to take into account label uncertainty, model parameters and the predictive risk can become biased, sometimes severely. The most common situation is when the control set contains an unknown number of undiagnosed, or future, cases. This has a marked impact in situations where the model needs to be well-calibrated, e.g., when the prediction performance of a biomarker panel is evaluated. Failing to account for class label uncertainty may lead to underestimation of classification performance and bias in parameter estimates. This can further impact on meta-analysis for combining evidence from multiple studies. Using a simulation study, we outline how conventional statistical models can be modified to address class label uncertainty leading to well-calibrated prediction performance estimates and reduced bias in meta-analysis. We focus on the problem of mislabeled control subjects in case-control studies, i.e., when some of the control subjects are undiagnosed cases, although the procedures we report are generic. The uncertainty in control status is a particular situation common in biomarker discovery studies in the context of genomic and molecular epidemiology, where control subjects are commonly sampled from the general population with an established expected disease incidence rate.     

Statistically integrated metabonomic-proteomic studies on a human prostate cancer xenograft model in mice

A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies.     

K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space

Background  

Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.     

Potyviral VPg enhances viral RNA Translation and inhibits reporter mRNA translation in planta

Viral protein genome-linked (VPg) plays a central role in several stages of potyvirus infection. This study sought to answer questions about the role of Potato virus A (PVA; genus Potyvirus) VPg in viral and host RNA expression. When expressed in Nicotiana benthamiana leaves in trans, a dual role of VPg in translation is observed. It repressed the expression of monocistronic luciferase (luc) mRNA and simultaneously induced a significant upregulation in the expression of both replicating and nonreplicating PVA RNAs. This enhanced viral gene expression was due at least to the 5' untranslated region (UTR) of PVA RNA, eukaryotic initiation factors 4E and iso 4E [eIF4E/eIF(iso)4E], and the presence of a sufficient amount of VPg. Coexpression of VPg with viral RNA increased the viral RNA amount, which was not the case with the monocistronic mRNA. Both mutations at certain lysine residues in PVA VPg and eIF4E/eIF(iso)4E depletion reduced its ability to upregulate the viral RNA expression. These modifications were also involved in VPg-mediated downregulation of monocistronic luc expression. These results suggest that VPg can titrate eIF4Es from capped monocistronic RNAs. Because VPg-mediated enhancement of viral gene expression required eIF4Es, it is possible that VPg directs eIF4Es to promote viral RNA expression. From this study it is evident that VPg can serve as a specific regulator of PVA expression by boosting the viral RNA amounts as well as the accumulation of viral translation products. Such a mechanism could function to protect viral RNA from being degraded and to secure efficient production of coat protein (CP) for virion formation.     

Structural flexibility allows the functional diversity of potyvirus genome-linked protein VPg

Several viral genome-linked proteins (VPgs) of plant viruses are intrinsically disordered and undergo folding transitions in the presence of partners. This property has been postulated to be one of the factors that enable the functional diversity of the protein. We created a homology model of Potato virus A VPg and positioned the known functions and structural properties of potyviral VPgs on the novel structural model. The model suggests an elongated structure with a hydrophobic core composed of antiparallel β-sheets surrounded by helices and a positively charged contact surface where most of the known activities are localized. The model most probably represents the fold induced immediately after binding of VPg to a negatively charged lipid surface or to SDS. When the charge of the positive surface was lowered by lysine mutations, the efficiencies of in vitro NTP binding, uridylylation reaction, and unspecific RNA binding were reduced and in vivo the infectivity was debilitated. The most likely uridylylation site, Tyr63, locates to the positively charged surface. Surprisingly, a Tyr63Ala mutation did not prevent replication completely but blocked spreading of the virus. Based on the localization of Tyr119 in the model, it was hypothesized to serve as an alternative uridylylation site. Evidence to support the role of Tyr119 in replication was obtained which gives a positive example of the prediction power of the model. Taken together, our experimental data support the features presented in the model and the idea that the functional diversity is attributable to structural flexibility.