Neutrophil extracellular traps are downregulated by glucocorticosteroids in lungs in an equine model of asthma

Background

Severe neutrophilic asthma is poorly responsive to glucocorticosteroids (GC). Neutrophil extracellular traps (NETs) within the lungs have been associated with the severity of airway obstruction and inflammation in asthma, and were found to be unaffected by GC in vitro. As IL-17 is overexpressed in neutrophilic asthma and contributes to steroid insensitivity in different cell types, we hypothesized that NETs formation in asthmatic airways would be resistant to GC through an IL-17 mediated pathway.

Methods

Six neutrophilic severe asthmatic horses and six healthy controls were studied while being treated with dexamethasone. Lung function, bronchoalveolar lavage fluid (BALF) cytology and NETs formation, as well as the expression of CD11b and CD13 by blood and airway neutrophils were evaluated. The expression of IL-17 and its role in NETs formation were also studied.

Results

Airway neutrophils from asthmatic horses, as opposed to blood neutrophils, enhanced NETs formation, which was then decreased by GC. GC also tended to decrease the expression of CD11b in blood neutrophils, but not in airway neutrophils. IL-17 mRNA was increased in BALF cells of asthmatic horses and was unaffected by GC. However, both GC and IL-17 inhibited NETs formation in vitro.

Conclusion

GC decreased NETs formation in vitro and also in vivo in the lungs of asthmatic horses. However, airway neutrophil activation during asthmatic inflammation was otherwise relatively insensitive to GC. The contribution of IL-17 to these responses requires further study.

     

The clinicopathological significance of angiogenesis in hindgut neuroendocrine tumors obtained via an endoscopic procedure

Background

As the World Health Organization grading system for gastroenteropancreatic-neuroendocrine tumors (GEP-NETs) may not always correlate with tumor progression, it is imperative that other independent predictors of tumor progression be established. To identify such predictors, we conducted a retrospective histopathological study of hindgut NETs, obtained from endoscopic procedures, and used statistical analyses to evaluate predictive factors.

Methods

We first obtained clinicopathological data of cases of hindgut NETs. Tissue sections from tumor samples were prepared and subjected to pathological examination. In particular, we calculated the microvessel density (MVD) and lymphatic microvessel density (LMVD) values, and performed appropriate statistical analyses.

Results

A total of 42 cases of hindgut NETs were selected for the study, 41 from the rectum and 1 from the sigmoid colon. Based on the Ki-67 labeling index, 34 cases were classified as NET G1 tumors and 8 as NET G2 tumors. MVD values ranged from 1.4/mm² to 73.9/mm² and LMVD values from 0/mm² to 22.9/mm². MVD and LMVD were identified as risk factors for venous and lymphatic invasion of hindgut NETs. Moreover, MVD positively correlated with the maximum diameter of the tumor.

Conclusions

Tumor progression of NETs may cause angiogenesis and lymphangiogenesis, via an unknown mechanism, as well as lymphovascular invasion. Angiogenesis likely plays an important role in occurrence and progression in the initial phase of hindgut NETs.

     

Extraction of natural products from bark of <Emphasis Type="Italic">Betula pendula</Emphasis> using ionic liquids

Introduction

Aqueous–methanol mixtures have successfully been applied to extract a broad range of metabolites from plant tissue. However, a certain amount of material remains insoluble.

Objectives

To enlarge the metabolic compendium, two ionic liquids were selected to extract the methanol insoluble part of trunk from Betula pendula.

Methods

The extracted compounds were analyzed by LC/MS and GC/MS.

Results

The results show that 1-butyl-3-methylimidazolium acetate (IL-Ac) predominantly resulted in fatty acids, whereas 1-ethyl-3-methylimidazolium tosylate (IL-Tos) mostly yielded phenolic structures. Interestingly, bark yielded more ionic liquid soluble metabolites compared to interior wood.

Conclusion

From this one can conclude that the application of ionic liquids may expand the metabolic snapshot.

     

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.

     

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.

     

Crystal structure of <Emphasis Type="Italic">Escherichia coli</Emphasis> protein ybgI,a toroidal structure with a dinuclear metal site

Background

The protein encoded by the gene ybgI was chosen as a target for a structural genomics project emphasizing the relation of protein structure to function.

Results

The structure of the ybgI protein is a toroid composed of six polypeptide chains forming a trimer of dimers. Each polypeptide chain binds two metal ions on the inside of the toroid.

Conclusion

The toroidal structure is comparable to that of some proteins that are involved in DNA metabolism. The di-nuclear metal site could imply that the specific function of this protein is as a hydrolase-oxidase enzyme.

     

Trapping of lead (Pb) by corn and pea root border cells

Aims

Most plants produce a root tip extracellular matrix that includes viable border cell populations programmed to disperse into soil. Like neutrophils, border cells export structures that trap pathogens and prevent root tip infection. Border cells also trap metals. The goal of this study was to determine if border cells trap Pb.

Methods

Border cell responses to Pb were observed microscopically. Border cell impact on Pb-induced injury to roots was assessed using root growth assays. Pb removal from solution was measured using inductively coupled plasma mass spectrometry (ICP-MS). Speciation of Pb associated with border cells was evaluated by synchrotron X-ray absorption spectroscopy (XAS).

Results

Increased border cell trap size and number occurred within minutes in response to Pb but not silicon (Si). Transient immersion of root tips into Pb after border cells were removed resulted in growth inhibition. Immersion of root tips and border cells into Pb solution resulted in significant removal of Pb. Si levels in the presence of root tips remained unchanged. The Pb speciation, measured with Pb L_III XAS, altered when reacted with border cells, indicating that direct binding by extracellular traps occurred.

Conclusions

Border cells can trap Pb and prevent damage to the root tip.

     

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.

     

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.

     

MIDAS-G: a computational platform for investigating fragmentation rules of tandem mass spectrometry in metabolomics

Introduction

Tandem mass spectrometry (MS/MS) has been widely used for identifying metabolites in many areas. However, computationally identifying metabolites from MS/MS data is challenging due to the unknown of fragmentation rules, which determine the precedence of chemical bond dissociation. Although this problem has been tackled by different ways, the lack of computational tools to flexibly represent adjacent structures of chemical bonds is still a long-term bottleneck for studying fragmentation rules.

Objectives

This study aimed to develop computational methods for investigating fragmentation rules by analyzing annotated MS/MS data.

Methods

We implemented a computational platform, MIDAS-G, for investigating fragmentation rules. MIDAS-G processes a metabolite as a simple graph and uses graph grammars to recognize specific chemical bonds and their adjacent structures. We can apply MIDAS-G to investigate fragmentation rules by adjusting bond weights in the scoring model of the metabolite identification tool and comparing metabolite identification performances.

Results

We used MIDAS-G to investigate four bond types on real annotated MS/MS data in experiments. The experimental results matched data collected from wet labs and literature. The effectiveness of MIDAS-G was confirmed.

Conclusion

We developed a computational platform for investigating fragmentation rules of tandem mass spectrometry. This platform is freely available for download.

     

Leaf extracts from <Emphasis Type="Italic">Nitraria retusa</Emphasis> promote cell population growth of human cancer cells by inducing apoptosis

Background

In this report the phytochemical profile of Nitraria. Retusa (N. Retusa) leaf extracts were identified and their ability to induce apoptosis in human chronic myelogenous erythroleukaemia (K562) was evaluated.

Methods

Apoptosis of the human chronic myelogenous erythroleukaemia (K562) was evidenced by investigating DNA fragmentation, PARP cleavage and caspases 3 and 8 inducing activities, in the presence of N. retusa extracts.

Results

Our study revealed that the tested extracts from N. Retusa contain many useful bioactive compounds. They induced in a time-dependent manner the apoptosis the tested cancerous our cell line. This result was confirmed by ladder DNA fragmentation profile and PARP cleavage, as well as a release in caspase-3 and caspase-8 level.

Conclusion

Our results indicate that the tested compounds have a significant antiproliferative effect which may be due to their involvement in the induction of the extrinsic apoptosic pathway.

     

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.

     

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.

     

A novel chimeric influenza virosome containing <Emphasis Type="Italic">Vesicular stomatitis</Emphasis> G protein as a more efficient gene delivery system

Objectives

To enhance the efficiency of influenza virosome-mediated gene delivery by engineering this virosome.

Results

A novel chimeric influenza virosome was constructed containing the glycoprotein of Vesicular stomatitis virus (VSV-G), along with its own hemagglutinin protein. To optimize the transfection efficiency of both chimeric and influenza cationic virosomes, HEK cells were transfected with plasmid DNA and virosomes and the transfection efficiency was assessed by FACS analysis. The chimeric virosome was significantly more efficient in mediating transfection for all amounts of DNA and virosomes compared to the influenza virosome.

Conclusions

Chimeric influenza virosome, including VSV-G, is superior to the conventional influenza virosome for gene delivery.

     

COGcollator: a web server for analysis of distant relationships between homologous protein families

Background

The Clusters of Orthologous Groups (COGs) of proteins systematize evolutionary related proteins into specific groups with similar functions. However, the available databases do not provide means to assess the extent of similarity between the COGs.

Aim

We intended to provide a method for identification and visualization of evolutionary relationships between the COGs, as well as a respective web server.

Results

Here we introduce the COGcollator, a web tool for identification of evolutionarily related COGs and their further analysis. We demonstrate the utility of this tool by identifying the COGs that contain distant homologs of (i) the catalytic subunit of bacterial rotary membrane ATP synthases and (ii) the DNA/RNA helicases of the superfamily 1.

Reviewers

This article was reviewed by Drs. Igor N. Berezovsky, Igor Zhulin and Yuri Wolf.

     

Application of recombinant hemagglutinin proteins as alternative antigen standards for pandemic influenza vaccines

Objectives

The single radial immunodiffusion (SRID) assay, used to quantify hemagglutinin (HA) in influenza vaccines, requires reference reagents; however, because centralized production of reference reagents may slow the emergency deployment of vaccines, alternatives are needed.

Results

We investigated the production of HA proteins using recombinant DNA technology, rather than a traditional egg-based production process. The HA proteins were then used in an SRID assay as a reference antigen. We found that HA can be quantified in both egg-based and cell-based influenza vaccines when recombinant HAs (rHAs) are used as the reference antigen. Furthermore, we confirmed that rHAs obtained from strains with pandemic potential, such as H5N1, H7N3, H7N9, and H9N2 strains, can be utilized in the SRID assay. The rHA production process takes just one month, in contrast to the traditional process that takes three to four months.

Conclusions

The use of rHAs may reduce the time required to produce reference reagents and facilitate timely introduction of vaccines during emergencies.

     

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.

     

Metabolic response of porcine colon explants to in vitro infection by <Emphasis Type="Italic">Brachyspira hyodysenteriae</Emphasis>: a leap into disease pathophysiology

Introduction

Swine dysentery caused by Brachyspira hyodysenteriae is a production limiting disease in pig farming. Currently antimicrobial therapy is the only treatment and control method available.

Objective

The aim of this study was to characterize the metabolic response of porcine colon explants to infection by B. hyodysenteriae.

Methods

Porcine colon explants exposed to B. hyodysenteriae were analyzed for histopathological, metabolic and pro-inflammatory gene expression changes.

Results

Significant epithelial necrosis, increased levels of l-citrulline and IL-1α were observed on explants infected with B. hyodysenteriae.

Conclusions

The spirochete induces necrosis in vitro likely through an inflammatory process mediated by IL-1α and NO.