A 3D numerical study of the collateral capacity of the circle of Willis with anatomical variation in the posterior circulation

Background

The Circle of Willis (CoW) is the most important collateral pathway of the cerebral artery. The present study aims to investigate the collateral capacity of CoW with anatomical variation when unilateral internalcarotid artery (ICA) is occluded.

Methods

Basing on MRI data, we have reconstructed eight 3D models with variations in the posterior circulation of the CoW and set four different degrees of stenosis in the right ICA, namely 24%, 43%, 64% and 79%, respectively. Finally, a total of 40 models are performed with computational fluid dynamics simulations. All of the simulations share the same boundary condition with static pressure and the volume flow rate (VFR) are obtained to evaluate their collateral capacity.

Results

As for the middle cerebral artery (MCA) and the anterior cerebral artery (ACA), the transitional-type model possesses the best collateral capacity. But for the posterior cerebral artery (PCA), unilateral stenosis of ICA has the weakest influence on the unilateral posterior communicating artery (PCoA) absent model. We also find that the full fetal-type posterior circle of Willis is an utmost dangerous variation which must be paid more attention.

Conclusion

The results demonstrate that different models have different collateral capacities in coping stenosis of unilateral ICA and these differences can be reflected by different outlets. The study could be used as a reference for neurosurgeon in choosing the best treatment strategy.

     

Definition of common carotid wall thickness affects risk classification in relation to degree of internal carotid artery stenosis: the Plaque At RISK (PARISK) study

Background

Mean or maximal intima-media thickness (IMT) is commonly used as surrogate endpoint in intervention studies. However, the effect of normalization by surrounding or median IMT or by diameter is unknown. In addition, it is unclear whether IMT inhomogeneity is a useful predictor beyond common wall parameters like maximal wall thickness, either absolute or normalized to IMT or lumen size. We investigated the interrelationship of common carotid artery (CCA) thickness parameters and their association with the ipsilateral internal carotid artery (ICA) stenosis degree.

Methods

CCA thickness parameters were extracted by edge detection applied to ultrasound B-mode recordings of 240 patients. Degree of ICA stenosis was determined from CT angiography.

Results

Normalization of maximal CCA wall thickness to median IMT leads to large variations. Higher CCA thickness parameter values are associated with a higher degree of ipsilateral ICA stenosis (p?<?0.001), though IMT inhomogeneity does not provide extra information. When the ratio of wall thickness and diameter instead of absolute maximal wall thickness is used as risk marker for having moderate ipsilateral ICA stenosis (>50%), 55 arteries (15%) are reclassified to another risk category.

Conclusions

It is more reasonable to normalize maximal wall thickness to end-diastolic diameter rather than to IMT, affecting risk classification and suggesting modification of the Mannheim criteria.

Trial registration

Clinical trials.gov NCT01208025.

     

Prediction of heme binding residues from protein sequences with integrative sequence profiles

Background

The heme-protein interactions are essential for various biological processes such as electron transfer, catalysis, signal transduction and the control of gene expression. The knowledge of heme binding residues can provide crucial clues to understand these activities and aid in functional annotation, however, insufficient work has been done on the research of heme binding residues from protein sequence information.

Methods

We propose a sequence-based approach for accurate prediction of heme binding residues by a novel integrative sequence profile coupling position specific scoring matrices with heme specific physicochemical properties. In order to select the informative physicochemical properties, we design an intuitive feature selection scheme by combining a greedy strategy with correlation analysis.

Results

Our integrative sequence profile approach for prediction of heme binding residues outperforms the conventional methods using amino acid and evolutionary information on the 5-fold cross validation and the independent tests.

Conclusions

The novel feature of an integrative sequence profile achieves good performance using a reduced set of feature vector elements.

     

Genomic integration occurs in the packaging cell via unexported lentiviral precursors

Objective

To use HIV-1 based lentivirus components to produce gene integration and the formation of a stable cell line in the packaging cell line without viral infection.

Results

A co-transfection of a Human Embryonic Kidney (HEK) 293 packaging cell line with Gag–pol (GP) and a transfer vector, without the envelope vector, produces a stable cell line after 2 weeks of selection. Furthermore, a matrix protein deficient GP in the packaging vector enhances this integration. This supports that, in theory, unexported lentiviral cores produced within the packaging cell can infect itself without requiring the release of any lentiviral particles.

Conclusion

If the packaging cell is also the target cell, then gene integration leading to a stable cell line can be accomplished without viral particle infection.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Expression and purification of classical swine fever virus E2 protein from Sf9 cells using a modified vector

Objective

To develop a simple method for efficient expression of classical swine fever virus (CSFV) E2 protein.

Results

The pFastBac HT B vector (pFastHTB-M1) was modified by adding a melittin signal peptide sequence. The E2 gene fragment without the transmembrane region was cloned into pFastHTB-M1. The modified vector has clear advantage over the original one, as evidenced by the purified recombinant E2 protein that was detected significantly by SDS-PAGE.

Conclusions

The modified vector has the potential for large-scale production and easy purification of the CSFV E2 protein or other proteins of interests.

     

<Emphasis Type="Italic">massPix</Emphasis>: an R package for annotation and interpretation of mass spectrometry imaging data for lipidomics

Introduction

Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools.

Objectives

We have developed massPix—an R package for analysing and interpreting data from MSI of lipids in tissue.

Methods

massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries.

Results

Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering.

Conclusion

massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.

     

Pathway-based analyses

Background

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation andpenalties for multiple testing.

Methods

The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge.

Results

Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data.

Conclusions

The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.

     

Predicting adverse drug reactions through interpretable deep learning framework

Background

Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs.

Methods

In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori.

Results

We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis.

Conclusions

The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.

     

Antibiotic treatment modulates protein components of cytotoxic outer membrane vesicles of multidrug-resistant clinical strain, <Emphasis Type="Italic">Acinetobacter baumannii</Emphasis> DU202

Background

Outer membrane vesicles (OMVs) of Acinetobacter baumannii are cytotoxic and elicit a potent innate immune response. OMVs were first identified in A. baumannii DU202, an extensively drug-resistant clinical strain. Herein, we investigated protein components of A. baumannii DU202 OMVs following antibiotic treatment by proteogenomic analysis.

Methods

Purified OMVs from A. baumannii DU202 grown in different antibiotic culture conditions were screened for pathogenic and immunogenic effects, and subjected to quantitative proteomic analysis by one-dimensional electrophoresis and liquid chromatography combined with tandem mass spectrometry (1DE-LC-MS/MS). Protein components modulated by imipenem were identified and discussed.

Results

OMV secretion was increased >?twofold following imipenem treatment, and cytotoxicity toward A549 human lung carcinoma cells was elevated. A total of 277 proteins were identified as components of OMVs by imipenem treatment, among which β-lactamase OXA-23, various proteases, outer membrane proteins, β-barrel assembly machine proteins, peptidyl-prolyl cis–trans isomerases and inherent prophage head subunit proteins were significantly upregulated.

Conclusion

In vitro stress such as antibiotic treatment can modulate proteome components in A. baumannii OMVs and thereby influence pathogenicity.

     

Pseudo current density maps of electrophysiological heart,nerve or brain function and their physical basis

Background

In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.

Methods

The physical basis of these intuitive maps is clarified by means of analytically solvable problems.

Results

Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.

Conclusion

Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Deep learning for DNase I hypersensitive sites identification

Background

The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood.

Methods

Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens.

Results

Our method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens.

Conclusions

Our method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning.

     

Neuropsychopathological comorbidities in learning disorders

Background

Learning Disorders (LD) are complex diseases that affect about 2-10% of the school-age population. We performed neuropsychological and psychopathological evaluation, in order to investigate comorbidity in children with LD.

Methods

Our sample consisted of 448 patients from 7 to 16 years of age with a diagnosis of LD, divided in two subgroups: Specific Learning Disorders (SLD), including reading, writing, mathematics disorders, and Learning Disorders Not Otherwise Specified (LD NOS).

Results

Comorbidity with neuropsychopathologies was found in 62.2% of the total sample. In the LSD subgroup, ADHD was present in 33%, Anxiety Disorder in 28.8%, Developmental Coordination Disorder in 17.8%, Language Disorder in 11% and Mood Disorder in 9.4% of patients. In LD NOS subgroup, Language Disorder was present in 28.6%, Developmental Coordination Disorder in 27.5%, ADHD in 25.4%, Anxiety Disorder in 16.4%, Mood Disorder in 2.1% of patients. A statistically significant presence was respectively found for Language and Developmental Coordination Disorder comorbidity in LD NOS and for ADHD, mood and anxiety disorder comorbidity in SLD subgroup.

Conclusions

The different findings emerging in this study suggested to promote further investigations to better define the difference between SLD and LD NOS, in order to improve specific interventions to reduce the long range consequences.

     

Normalization and integration of large-scale metabolomics data using support vector regression

Introduction

Untargeted metabolomics studies for biomarker discovery often have hundreds to thousands of human samples. Data acquisition of large-scale samples has to be divided into several batches and may span from months to as long as several years. The signal drift of metabolites during data acquisition (intra- and inter-batch) is unavoidable and is a major confounding factor for large-scale metabolomics studies.

Objectives

We aim to develop a data normalization method to reduce unwanted variations and integrate multiple batches in large-scale metabolomics studies prior to statistical analyses.

Methods

We developed a machine learning algorithm-based method, support vector regression (SVR), for large-scale metabolomics data normalization and integration. An R package named MetNormalizer was developed and provided for data processing using SVR normalization.

Results

After SVR normalization, the portion of metabolite ion peaks with relative standard deviations (RSDs) less than 30 % increased to more than 90 % of the total peaks, which is much better than other common normalization methods. The reduction of unwanted analytical variations helps to improve the performance of multivariate statistical analyses, both unsupervised and supervised, in terms of classification and prediction accuracy so that subtle metabolic changes in epidemiological studies can be detected.

Conclusion

SVR normalization can effectively remove the unwanted intra- and inter-batch variations, and is much better than other common normalization methods.

     

Metabolomic profiling of maternal hair suggests rapid development of intrahepatic cholestasis of pregnancy

Introduction

Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.

Objective

Investigate the maternal hair metabolome for predictive biomarkers of ICP.

Methods

The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.

Results

Of 105 metabolites detected in hair, none were significantly associated with ICP.

Conclusion

Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

Reliable RT-qPCR-based titration of retroviral and lentiviral vectors via quantification of residual vector plasmid DNA in samples

Objectives

To develop a method for reliable quantification of viral vectors, which is necessary for determining the optimal dose of vector particles in clinical trials to obtain the desired effects without severe unwanted immune responses.

Results

A significant level of vector plasmid remained in retroviral and lentiviral vector samples, which led to overestimation of viral titers when using the conventional RT-qPCR-based genomic titration method. To address this problem, we developed a new method in which the residual plasmid was quantified by an additional RT-qPCR step, and standard molecules and primer sets were optimized. The obtained counts were then used to correct the conventionally measured genomic titers of viral samples. While the conventional method produced significantly higher genomic titers for mutant retroviral vectors than for wild-type vectors, our method produced slightly higher or equivalent titers, corresponding with the general idea that mutation of viral components mostly results in reduced or, at best, retained titers.

Conclusion

Subtraction of the number of residual vector plasmid molecules from the conventionally measured genomic titer can yield reliable quantification of retroviral and lentiviral vector samples, a prerequisite to advancing the safety of gene therapy applications.

     

Quantitative analysis of tetrahydrofolate metabolites from <Emphasis Type="Italic">clostridium autoethanogenum</Emphasis>

Introduction

Quantification of tetrahydrofolates (THFs), important metabolites in the Wood–Ljungdahl pathway (WLP) of acetogens, is challenging given their sensitivity to oxygen.

Objective

To develop a simple anaerobic protocol to enable reliable THFs quantification from bioreactors.

Methods

Anaerobic cultures were mixed with anaerobic acetonitrile for extraction. Targeted LC–MS/MS was used for quantification.

Results

Tetrahydrofolates can only be quantified if sampled anaerobically. THF levels showed a strong correlation to acetyl-CoA, the end product of the WLP.

Conclusion

Our method is useful for relative quantification of THFs across different growth conditions. Absolute quantification of THFs requires the use of labelled standards.

     

Statistical analysis of fractionation resistance by functional category and expression

Background

The current literature establishes the importance of gene functional category and expression in promoting or suppressing duplicate gene loss after whole genome doubling in plants, a process known as fractionation. Inspired by studies that have reported gene expression to be the dominating factor in preventing duplicate gene loss, we analyzed the relative effect of functional category and expression.

Methods

We use multivariate methods to study data sets on gene retention, function and expression in rosids and asterids to estimate effects and assess their interaction.

Results

Our results suggest that the effect on duplicate gene retention fractionation by functional category and expression are independent and have no statistical interaction.

Conclusion

In plants, functional category is the more dominant factor in explaining duplicate gene loss.