Crop metabolomics: from diagnostics to assisted breeding

Background

Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.

Aim of Review

We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.

Key Scientific Concepts of Review

Translational metabolomics applied to crop breeding programs.

     

Key factors identified by proteomic analysis in maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.) seedlings’ response to long-term exposure to different phosphate levels

Background

Maize seedlings are constantly exposed to inorganic phosphate (Pi)-limited environments. To understand how maize cope with low Pi (LP) and high Pi (HP) conditions, physiological and global proteomic analysis of QXN233 genotype were performed under the long-term Pi starvation and supplementation.

Methods

We investigated the physiological response of QXN233 genotype to LP and HP conditions and detected the changes in ion fluxes by non-invasive micro-test technology and gene expression by quantitative real-time polymerase chain reaction. QXN233 was further assessed using vermiculite assay, and then proteins were isolated and identified by nano-liquid chromatography-mass spectrometry.

Results

A negative relationship was observed between Na⁺ and Pi, and Na⁺ efflux was enhanced under HP condition. Furthermore, a total of 681 and 1374 were identified in the leaves and roots, respectively, which were mostly involved in metabolism, ion transport, and stress response. Importantly, several key Pi transporters were identified for breeding potential. Several ion transporters demonstrated an elaborate interplay between Pi and other ions, together contributing to the growth of QXN233 seedlings.

Conclusion

The results from this study provide insights into the response of maize seedlings to long-term Pi exposure.

     

Sperm cryopreservation incidence in men with testicular cancer: towards a stabilization in testicular cancer incidence? Results from the CECOS network

Background

Testicular cancer (TC) represents 1% of all new male cancer cases but remains the most frequent cancer in adolescents and young adults in industrialized countries. In this study, we assessed time trends in use of sperm cryopreservation by men with TC from 1990 to 2013 in France.

Methods

We collected data from patients diagnosed with TC who underwent sperm cryopreservation in the French national network of sperm banks. Trends in the incidence of sperm cryopreservation were estimated through two statistical models: the commonly used Poisson regression model and the Verhulst model.

Results

Between 1990 and 2013, the overall incidence of sperm cryopreservation rose from 1.73 to 5.57 per 100,000 person-years. Poisson regression predicted an incidence of 9 per 100,000 [95% CI?=?8.66–9.34] in 2020. However, since 2005, the observed sperm cryopreservation rate seems to be attenuating. The Verhulst model predicted an incidence of 6 per 100,000 after 2020.

Conclusions

Limitations include the impossibility of analyzing age-standardized incidence. Based on the Verhulst model, results suggest that it is still relevant to follow up TC incidence and sperm cryopreservation in order to confirm or refute the potential decrease already observed in this disease.

     

Markov modeling in hepatitis B screening and linkage to care

Background

With up to 240 million people chronically infected with hepatitis B worldwide, including an estimated 2 million in the United States, widespread screening is needed to link the infected to care and decrease the possible consequences of untreated infection, including liver cancer, cirrhosis and death. Screening is currently fraught with challenges in both the developed and developing world. New point-of-care tests may have advantages over standard-of-care tests in terms of cost-effectiveness and linkage to care. Stochastic modeling is applied here for relative utility assessment of point-of-care tests and standard-of-care tests for screening.

Methods

We analyzed effects of point-of-care versus standard-of-care testing using Markov models for disease progression in individual patients. Simulations of large cohorts with distinctly quantified models permitted the assessment of particular screening schemes. The validity of the trends observed is supported by sensitivity analyses for the simulation parameters.

Results

Increased utilization of point-of-care screening was shown to decrease hepatitis B-related mortalities and increase life expectancy at low projected expense.

Conclusions

The results suggest that standard-of-care screening should be substituted by point-of-care tests resulting in improved linkage to care and decrease in long-term complications.

     

Modern and traditional approaches combined into an effective gray-box mathematical model of full-blood acid-base

Background

The acidity of human body fluids, expressed by the pH, is physiologically regulated in a narrow range, which is required for the proper function of cellular metabolism. Acid-base disorders are common especially in intensive care, and the acid-base status is one of the vital clinical signs for the patient management. Because acid-base balance is connected to many bodily processes and regulations, complex mathematical models are needed to get insight into the mixed disorders and to act accordingly. The goal of this study is to develop a full-blood acid-base model, designed to be further integrated into more complex human physiology models.

Results

We have developed computationally simple and robust full-blood model, yet thorough enough to cover most of the common pathologies. Thanks to its simplicity and usage of Modelica language, it is suitable to be embedded within more elaborate systems. We achieved the simplification by a combination of behavioral Siggaard-Andersen’s traditional approach for erythrocyte modeling and the mechanistic Stewart’s physicochemical approach for plasma modeling. The resulting model is capable of providing variations in arterial pCO2, base excess, strong ion difference, hematocrit, plasma protein, phosphates and hemodilution/hemoconcentration, but insensitive to DPG and CO concentrations.

Conclusions

This study presents a straightforward unification of Siggaard-Andersen’s and Stewart’s acid-base models. The resulting full-blood acid-base model is designed to be a core part of a complex dynamic whole-body acid-base and gas transfer model.

     

Accurate flexible refinement of atomic models against medium-resolution cryo-EM maps using damped dynamics

Background

Dramatic progress has recently been made in cryo-electron microscopy technologies, which now make possible the reconstruction of a growing number of biomolecular structures to near-atomic resolution. However, the need persists for fitting and refinement approaches that address those cases that require modeling assistance.

Methods

In this paper, we describe algorithms to optimize the performance of such medium-resolution refinement methods. These algorithms aim to automatically optimize the parameters that define the density shape of the flexibly fitted model, as well as the time-dependent damper cutoff distance. Atomic distance constraints can be prescribed for cases where extra containment of parts of the structure is helpful, such as in regions where the density map is poorly defined. Also, we propose a simple stopping criterion that estimates the probable onset of overfitting during the simulation.

Results

The new set of algorithms produce more accurate fitting and refinement results, and yield a faster rate of convergence of the trajectory toward the fitted conformation. The latter is also more reliable due to the overfitting warning provided to the user.

Conclusions

The algorithms described here were implemented in the new Damped-Dynamics Flexible Fitting simulation tool “DDforge” in the Situs package.

     

Bridge processes: a solution for LCI datasets independent of background databases

Introduction

New platforms are emerging that enable more data providers to publish life cycle inventory data.

Background

Providing datasets that are not complete LCA models results in fragments that are difficult for practitioners to integrate and use for LCA modeling. Additionally, when proxies are used to provide a technosphere input to a process that was not originally intended by the process authors, in most LCA software, this requires modifying the original process.

Results

The use of a bridge process, which is a process created to link two existing processes, is proposed as a solution.

Discussion

Benefits to bridge processes include increasing model transparency, facilitating dataset sharing and integration without compromising original dataset integrity and independence, providing a structure with which to make the data quality associated with process linkages explicit, and increasing model flexibility in the case that multiple bridges are provided. A drawback is that they add additional processes to existing LCA models which will increase their size.

Conclusions

Bridge processes can be an enabler in allowing users to integrate new datasets without modifying them to link to background databases or other processes they have available. They may not be the ideal long-term solution but provide a solution that works within the existing LCA data model.

     

Nucleotide binding domain 1 pharmacophore modeling for visualization and analysis of P-glycoprotein–flavonoid molecular interactions

Background

P-glycoprotein (P-gp) is a 170-kDa membrane protein. It provides a barrier function and help to excrete toxins from the body as a transporter. Some bioflavonoids have been shown to block P-gp activity.

Objective

To evaluate the important amino acid residues within nucleotide binding domain 1 (NBD1) of P-gp that play a key role in molecular interactions with flavonoids using structure-based pharmacophore model.

Methods

In the molecular docking with NBD1 models, a putative binding site of flavonoids was proposed and compared with the site for ATP. The binding modes for ligands were achieved using LigandScout to generate the P-gp–flavonoid pharmacophore models.

Results

The binding pocket for flavonoids was investigated and found these inhibitors compete with the ATP for binding site in NBD1 including the NBD1 amino acid residues identified by the in silico techniques to be involved in the hydrogen bonding and van der Waals (hydrophobic) interactions with flavonoids.

Conclusion

These flavonoids occupy with the same binding site of ATP in NBD1 proffering that they may act as an ATP competitive inhibitor.

     

Workflow to improve patient recruitment for clinical trials within hospital information systems – a case-study

Background

The identification of suitable patients is a common problem in clinical trials that is especially evident in tertiary care hospitals.

Methods

We developed and analysed a workflow, which uses routine data captured during patient care in a hospital information system (HIS), to identify potential trial subjects. Study nurses or physicians are notified automatically by email and verify eligibility.

Results

As a case study we implemented the system for acute myeloid leukemia (AML) trials in Münster. During a test period of 50 days 41 patients were identified by the system. 13 could be included as new trial patients, 7 were already included during earlier visits. According to review of paper records no AML trial patient was missed by the system. In addition, the hospital information system further allowed to preselect patients for specific trials based on their disease status and individual characteristics.

Conclusion

Routine HIS data can be used to support patient recruitment for clinical trials by means of an automated notification workflow.

     

Identification of broadly neutralizing antibody epitopes in the HIV-1 envelope glycoprotein using evolutionary models

Background

Identification of the epitopes targeted by antibodies that can neutralize diverse HIV-1 strains can provide important clues for the design of a preventative vaccine.

Methods

We have developed a computational approach that can identify key amino acids within the HIV-1 envelope glycoprotein that influence sensitivity to broadly cross-neutralizing antibodies. Given a sequence alignment and neutralization titers for a panel of viruses, the method works by fitting a phylogenetic model that allows the amino acid frequencies at each site to depend on neutralization sensitivities. Sites at which viral evolution influences neutralization sensitivity were identified using Bayes factors (BFs) to compare the fit of this model to that of a null model in which sequences evolved independently of antibody sensitivity. Conformational epitopes were identified with a Metropolis algorithm that searched for a cluster of sites with large Bayes factors on the tertiary structure of the viral envelope.

Results

We applied our method to ID₅₀ neutralization data generated from seven HIV-1 subtype C serum samples with neutralization breadth that had been tested against a multi-clade panel of 225 pseudoviruses for which envelope sequences were also available. For each sample, between two and four sites were identified that were strongly associated with neutralization sensitivity (2ln(BF)?>?6), a subset of which were experimentally confirmed using site-directed mutagenesis.

Conclusions

Our results provide strong support for the use of evolutionary models applied to cross-sectional viral neutralization data to identify the epitopes of serum antibodies that confer neutralization breadth.

     

The contraceptive efficacy of intravas injection of Vasalgel™ for adult male rhesus monkeys

Background

Options for male contraception are limited. The purpose of this study was to use a nonhuman primate model to evaluate Vasalgel?, a high molecular weight polymer being developed as a contraceptive device for men.

Methods

Sixteen adult male rhesus monkeys received intravas injections of Vasalgel, consisting of 25% styrene maleic acid in dimethyl sulfoxide. After a one-week recovery, males were returned to outdoor group housing, which included at least 3 and up to 9 intact, breeding females with a successful reproductive history.

Results

Treated males have had no conceptions since Vasalgel injections. All males were housed with intact females for at least one breeding season and seven have been almost continually housed with females for 2 years. Complications were minor and included one incident of incorrect placement of Vasalgel into the vas deferens and the development of a sperm granuloma in one animal. Three unilateral vasectomies were performed, one was necessary for incorrect placement of Vasalgel, the other two were elective.

Conclusions

Intravas injection of Vasalgel in sexually mature adult male rhesus monkeys was effective in preventing conception in a free-living, group environment. Complications were few and similar to those associated with traditional vasectomy.

     

Preclinical and clinical studies on cancer-associated cachexia

Background

Cancer cachexia is the wasting condition that is often seen in advanced stage cancer patients. This wasting is largely attributable to a systemic and progressive loss of skeletal muscle mass that greatly hinders performance of normal daily activities, resulting in reduced quality of life. Moreover, it negatively influences the prognosis of cancer patients. A general consensus in the field is that the loss of muscle mass is due both to an increase in protein degradation and a decrease in protein synthesis. Recent studies using preclinical models for studying cachexia have been useful in identifying the contribution of inflammatory cytokines (e.g. tumor necrosis factor-α and Interleukin-6), and myostatin receptors (e.g. the type IIB activin receptor) to cachexia development, and have led to several clinical trials. However, many questions remain about the molecular mechanisms thought to play a role in the development of cachexia.

Methods

We conducted a literature search using search engines, such as PubMed and Google Scholar to identify publications within the cancer cachexia field.

Results

We summarized our current knowledge of: 1) the driving mechanisms of cancer cachexia, 2) the preclinical models available for studying the condition, and 3) the findings of recent clinical trials.

Conclusion

Cancer cachexia is a complex and variable condition that currently has no standard effective therapeutic treatment. Further studies are desperately needed to better understand this condition and develop effective combination treatments for patients.

     

Forecasting infectious disease emergence subject to seasonal forcing

Background

Despite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also anticipate disease emergence in seasonal models.

Methods

We simulated the dynamics of an immunizing infectious pathogen approaching the tipping point to disease endemicity. To explore the effect of seasonality on the reliability of early warning statistics, we varied the amplitude of fluctuations around the average transmission. We proposed and analyzed two new early warning signals based on the wavelet spectrum. We measured the reliability of the early warning signals depending on the strength of their trend preceding the tipping point and then calculated the Area Under the Curve (AUC) statistic.

Results

Early warning signals were reliable when disease transmission was subject to seasonal forcing. Wavelet-based early warning signals were as reliable as other conventional early warning signals. We found that removing seasonal trends, prior to analysis, did not improve early warning statistics uniformly.

Conclusions

Early warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing. Wavelet-based early warning statistics can also be used to forecast infectious disease.

     

Differentially private distributed logistic regression using private and public data

Background

Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced.

Methodology

In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data.

Experiments and results

We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios.

Conclusion

Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee.

     

Probability-based collaborative filtering model for predicting gene–disease associations

Background

Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses.

Methods

We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used.

Results

We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches.

Conclusions

PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

     

Finite Element Analysis of Hepatic Radiofrequency Ablation Probes using Temperature-Dependent Electrical Conductivity

Background

Few finite element models (FEM) have been developed to describe the electric field, specific absorption rate (SAR), and the temperature distribution surrounding hepatic radiofrequency ablation probes. To date, a coupled finite element model that accounts for the temperature-dependent electrical conductivity changes has not been developed for ablation type devices. While it is widely acknowledged that accounting for temperature dependent phenomena may affect the outcome of these models, the effect has not been assessed.

Methods

The results of four finite element models are compared: constant electrical conductivity without tissue perfusion, temperature-dependent conductivity without tissue perfusion, constant electrical conductivity with tissue perfusion, and temperature-dependent conductivity with tissue perfusion.

Results

The data demonstrate that significant errors are generated when constant electrical conductivity is assumed in coupled electrical-heat transfer problems that operate at high temperatures. These errors appear to be closely related to the temperature at which the ablation device operates and not to the amount of power applied by the device or the state of tissue perfusion.

Conclusion

Accounting for temperature-dependent phenomena may be critically important in the safe operation of radiofrequency ablation device that operate near 100°C.

     

An outbreak vector-host epidemic model with spatial structure: the 2015–2016 Zika outbreak in Rio De Janeiro

Background

A deterministic model is developed for the spatial spread of an epidemic disease in a geographical setting. The disease is borne by vectors tosusceptible hosts through criss-cross dynamics. The model is focused on an outbreak that arises from a small number of infected hosts imported into a subregion of the geographical setting. The goal is to understand how spatial heterogeneity of the vector and host populations influences the dynamics of the outbreak, in both the geographical spread and the final size of the epidemic.

Methods

Partial differential equations are formulated to describe the spatial interaction of the hosts and vectors. The partial differential equations have reaction-diffusion terms to describe the criss-cross interactions of hosts and vectors. The partial differential equations of the model are analyzed and proven to be well-posed. A local basic reproduction number for the epidemic is analyzed.

Results

The epidemic outcomes of the model are correlated to the spatially dependent parameters and initial conditions of the model. The partial differential equations of the model are adapted to seasonality of the vector population, and applied to the 2015–2016 Zika seasonal outbreak in Rio de Janeiro Municipality in Brazil.

Conclusions

The results for the model simulations of the 2015–2016 Zika seasonal outbreak in Rio de Janeiro Municipality indicate that the spatial distribution and final size of the epidemic at the end of the season are strongly dependent on the location and magnitude of local outbreaks at the beginning of the season. The application of the model to the Rio de Janeiro Municipality Zika 2015–2016 outbreak is limited by incompleteness of the epidemic data and by uncertainties in the parametric assumptions of the model.

     

Modeling evolution of spatially distributed bacterial communities: a simulation with the haploid evolutionary constructor

Background

Multiscale approaches for integrating submodels of various levels of biological organization into a single model became the major tool of systems biology. In this paper, we have constructed and simulated a set of multiscale models of spatially distributed microbial communities and study an influence of unevenly distributed environmental factors on the genetic diversity and evolution of the community members.

Results

Haploid Evolutionary Constructor software http://evol-constructor.bionet.nsc.ru/ was expanded by adding the tool for the spatial modeling of a microbial community (1D, 2D and 3D versions). A set of the models of spatially distributed communities was built to demonstrate that the spatial distribution of cells affects both intensity of selection and evolution rate.

Conclusion

In spatially heterogeneous communities, the change in the direction of the environmental flow might be reflected in local irregular population dynamics, while the genetic structure of populations (frequencies of the alleles) remains stable. Furthermore, in spatially heterogeneous communities, the chemotaxis might dramatically affect the evolution of community members.

     

An adaptive gene-level association test for pedigree data

Background

We propose a gene-level association test that accounts for individual relatedness and population structures in pedigree data in the framework of linear mixed models (LMMs). Our method data-adaptively combines the results across a class of score-based tests, only requiring fitting a single null model (under the null hypothesis) for the whole genome, thereby being computationally efficient.

Results

We applied our approach to test for association with the high-density lipoprotein (HDL) ratio of post- and pretreatments in GAW20 data. Using the LMM similar to that used by Aslibekyan et al. (PLos One, 7:48663, 2012), our method identified 2 nearly significant genes (APOA5 and ZNF259) near rs964184, whereas neither the other gene-level tests nor the standard test on each individual single-nucleotide polymorphism (SNP) detected any significant gene in a genome-wide scan.

Conclusions

Gene-level association testing can be a complementary approach to the SNP-level association testing and our method is adaptive and efficient compared to several other existing gene-level association tests.