Antimicrobial nodule-specific cysteine-rich peptides disturb the integrity of bacterial outer and inner membranes and cause loss of membrane potential

Background

Certain legume plants produce a plethora of AMP-like peptides in their symbiotic cells. The cationic subgroup of the nodule-specific cysteine-rich (NCR) peptides has potent antimicrobial activity against gram-negative and gram-positive bacteria as well as unicellular and filamentous fungi.

Findings

It was shown by scanning and atomic force microscopies that the cationic peptides NCR335, NCR247 and Polymyxin B (PMB) affect differentially on the surfaces of Sinorhizobium meliloti bacteria. Similarly to PMB, both NCR peptides caused damages of the outer and inner membranes but at different extent and resulted in the loss of membrane potential that could be the primary reason of their antimicrobial activity.

Conclusions

The primary reason for bacterial cell death upon treatment with cationic NCR peptides is the loss of membrane potential.

     

TarMet: a reactive GUI tool for efficient and confident quantification of MS based targeted metabolic and stable isotope tracer analysis

Introduction

Untargeted and targeted analyses are two classes of metabolic study. Both strategies have been advanced by high resolution mass spectrometers coupled with chromatography, which have the advantages of high mass sensitivity and accuracy. State-of-art methods for mass spectrometric data sets do not always quantify metabolites of interest in a targeted assay efficiently and accurately.

Objectives

TarMet can quantify targeted metabolites as well as their isotopologues through a reactive and user-friendly graphical user interface.

Methods

TarMet accepts vendor-neutral data files (NetCDF, mzXML and mzML) as inputs. Then it extracts ion chromatograms, detects peak position and bounds and confirms the metabolites via the isotope patterns. It can integrate peak areas for all isotopologues automatically.

Results

TarMet detects more isotopologues and quantify them better than state-of-art methods, and it can process isotope tracer assay well.

Conclusion

TarMet is a better tool for targeted metabolic and stable isotope tracer analyses.

     

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.

     

New molecular evidence of wine yeast-bacteria interaction unraveled by non-targeted exometabolomic profiling

Introduction

Bacterial malolactic fermentation (MLF) has a considerable impact on wine quality. The yeast strain used for primary fermentation can systematically stimulate (MLF+ phenotype) or inhibit (MLF?) bacteria and the MLF process as a function of numerous winemaking practices, but the underlying molecular evidence still remains a mystery.

Objectives

The goal of the study was to elucidate such evidence by the direct comparison of extracellular metabolic profiles of MLF+ and MLF? phenotypes.

Methods

We have applied a non-targeted metabolomic approach combining ultrahigh-resolution FT-ICR-MS analysis, powerful statistical tools and a comprehensive wine metabolite database.

Results

We discovered around 2500 unknown masses and 800 putative biomarkers involved in phenotypic distinction. For the putative biomarkers, we also developed a biomarker identification workflow and elucidated the exact structure (by UPLC-Q-ToF–MS²) and/or exact physiological impact (by in vitro tests) of several novel biomarkers, such as D-gluconic acid, citric acid, trehalose and tripeptide Pro-Phe-Val. In addition to valid biomarkers, molecular evidence was reflected by unprecedented chemical diversity (around 3000 discriminant masses) that characterized both the yeast phenotypes. While distinct chemical families such as phenolic compounds, carbohydrates, amino acids and peptides characterize the extracellular metabolic profiles of the MLF+ phenotype, the MLF? phenotype is associated with sulphur-containing peptides.

Conclusion

The non-targeted approach used in this study played an important role in finding new and unexpected molecular evidence.

     

A metabolomics guided exploration of marine natural product chemical space

Introduction

Natural products from culture collections have enormous impact in advancing discovery programs for metabolites of biotechnological importance. These discovery efforts rely on the metabolomic characterization of strain collections.

Objective

Many emerging approaches compare metabolomic profiles of such collections, but few enable the analysis and prioritization of thousands of samples from diverse organisms while delivering chemistry specific read outs.

Method

In this work we utilize untargeted LC–MS/MS based metabolomics together with molecular networking to inventory the chemistries associated with 1000 marine microorganisms.

Result

This approach annotated 76 molecular families (a spectral match rate of 28 %), including clinically and biotechnologically important molecules such as valinomycin, actinomycin D, and desferrioxamine E. Targeting a molecular family produced primarily by one microorganism led to the isolation and structure elucidation of two new molecules designated maridric acids A and B.

Conclusion

Molecular networking guided exploration of large culture collections allows for rapid dereplication of know molecules and can highlight producers of uniques metabolites. These methods, together with large culture collections and growing databases, allow for data driven strain prioritization with a focus on novel chemistries.

     

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.

     

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.

     

Phosphotyrosine profiling of human cerebrospinal fluid

Background

Cerebrospinal fluid (CSF) is an important source of potential biomarkers that affect the brain. Biomarkers for neurodegenerative disorders are needed to assist in diagnosis, monitoring disease progression and evaluating efficacy of therapies. Recent studies have demonstrated the involvement of tyrosine kinases in neuronal cell death. Thus, neurodegeneration in the brain is related to altered tyrosine phosphorylation of proteins in the brain and identification of abnormally phosphorylated tyrosine peptides in CSF has the potential to ascertain candidate biomarkers for neurodegenerative disorders.

Methods

In this study, we used an antibody-based tyrosine phosphopeptide enrichment method coupled with high resolution Orbitrap Fusion Tribrid Lumos Fourier transform mass spectrometer to catalog tyrosine phosphorylated peptides from cerebrospinal fluid. The subset of identified tyrosine phosphorylated peptides was also validated using parallel reaction monitoring (PRM)-based targeted approach.

Results

To date, there are no published studies on global profiling of phosphotyrosine modifications of CSF proteins. We carried out phosphotyrosine profiling of CSF using an anti-phosphotyrosine antibody-based enrichment and analysis using high resolution Orbitrap Fusion Lumos mass spectrometer. We identified 111 phosphotyrosine peptides mapping to 66 proteins, which included 24 proteins which have not been identified in CSF previously. We then validated a set of 5 tyrosine phosphorylated peptides in an independent set of CSF samples from cognitively normal subjects, using a PRM-based targeted approach.

Conclusions

The findings from this deep phosphotyrosine profiling of CSF samples have the potential to identify novel disease-related phosphotyrosine-containing peptides in CSF.

     

Improved de novo peptide sequencing using LC retention time information

Background

Liquid chromatography combined with tandem mass spectrometry is an important tool in proteomics for peptide identification. Liquid chromatography temporally separates the peptides in a sample. The peptides that elute one after another are analyzed via tandem mass spectrometry by measuring the mass-to-charge ratio of a peptide and its fragments. De novo peptide sequencing is the problem of reconstructing the amino acid sequences of a peptide from this measurement data. Past de novo sequencing algorithms solely consider the mass spectrum of the fragments for reconstructing a sequence.

Results

We propose to additionally exploit the information obtained from liquid chromatography. We study the problem of computing a sequence that is not only in accordance with the experimental mass spectrum, but also with the chromatographic retention time. We consider three models for predicting the retention time and develop algorithms for de novo sequencing for each model.

Conclusions

Based on an evaluation for two prediction models on experimental data from synthesized peptides we conclude that the identification rates are improved by exploiting the chromatographic information. In our evaluation, we compare our algorithms using the retention time information with algorithms using the same scoring model, but not the retention time.

     

Cationic antimicrobial peptide: LL-37 and its role in periodontitis

Background

Periodontitis i.e. inflammation of the periodontium is a multifactorial disease. Antimicrobial peptides (AMPs) which demonstrate a broad-spectrum of activity against varied number of bacteria, fungi, viruses, and parasites, and cancerous cells have been linked to periodontitis. The AMPs even possess the caliber of immunomodulation, and are significantly responsive to innate immuno-stimulation and infections. LL-37 plays a salubrious role by preventing and in treatment of chronic forms of periodontitis.

Objective

In the present work we will review the role of antimicrobial peptide LL-37 in periodontitis.

Methods

A systematic search was carried out from the beginning till August, 2016 using the Pubmed search engine. The keywords included “LL-37,” “periodontitis,” “Papillon–Lefevre syndrome,” “Morbus Kostmann,” “Haim-Munk syndrome” along with use of Boolean operator “and.”

Results

The search resulted in identifying 67 articles which included articles linking LL-37 with periodontitis, articles on Papillon–Lefevre syndrome, Morbus Kostmann, Haim-Munk syndrome, LL-37 and periodontitis and articles on pathogenicity of periodontitis.

Conclusion

The literature search concluded that LL-37 plays a pivotal role in preventing and treatment of severe form of periodontitis.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.

     

Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome

Background

Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40–50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.

Results

Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.

Conclusion

We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.

Reviewers

This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers’ comments section.

     

Molecular dynamics in germinating,endophyte-colonized quinoa seeds

Aims

The pseudo-cereal quinoa has an outstanding nutritional value. Seed germination is unusually fast, and plant tolerance to salt stress exceptionally high. Seemingly all seeds harbor bacterial endophytes. This work examines mitogen-activated protein kinase (MAPK) activities during early development. It evaluates possible contribution of endophytes to rapid germination and plant robustness.

Methods

MAPK activities were monitored in water- and NaCl-imbibed seeds over a 4-h-period using an immunoblot-based approach. Cellulolytic and pectinolytic abilities of bacteria were assessed biochemically, and cellular movement, biofilm, elicitor and antimicrobial compound synthesis genes sequenced. GyrA-based, cultivation-independent studies provided first insight into endophyte diversity.

Results

Quinoa seeds and seedlings exhibit remarkably complex and dynamic MAPK activity profiles. Depending on seed origin, variances exist in MAPK patterns and probably also in endophyte assemblages. Mucilage-degrading activities enable endophytes to colonize seed surfaces of a non-host species, chia, without apparent adverse effects.

Conclusions

Owing to their motility, cell wall-loosening and elicitor-generating abilities, quinoa endophytes have the potential to drive cell expansion, move across cell walls, generate damage-associated molecular patterns and activate MAPKs in their host. Bacteria may thus facilitate rapid germination and confer a primed state directly upon seed rehydration. Transfer into non-native crops appears both desirable and feasible.

     

Metabolic fingerprints of human primary endothelial and fibroblast cells

Introduction

Human primary cells originating from different locations within the body could differ greatly in their metabolic phenotypes, influencing both how they act during physiological/pathological processes and how susceptible/resistant they are to a variety of disease risk factors. A novel way to monitor cellular metabolism is through cell energetics assays, so we explored this approach with human primary cell types, as models of sclerotic disorders.

Objectives

In order to better understand pathophysiological processes at the cellular level, our goals were to measure metabolic pathway activities of endothelial cells and fibroblasts, and determine their metabolic phenotype profiles.

Methods

Biolog Phenotype MicroArray? technology was used for the first time to characterize metabolic phenotypes of diverse primary cells. These colorimetric assays enable detection of utilization of 367 specific biochemical substrates by human endothelial cells from the coronary artery (HCAEC), umbilical vein (HUVEC) and normal, healthy lung fibroblasts (NHLF).

Results

Adenosine, inosine, d-mannose and dextrin were strongly utilized by all three cell types, comparable to glucose. Substrates metabolized solely by HCAEC were mannan, pectin, gelatin and prevalently tricarballylic acid. HUVEC did not show any uniquely metabolized substrates whereas NHLF exhibited strong utilization of sugars and carboxylic acids along with amino acids and peptides.

Conclusion

Taken together, we show for the first time that this simple energetics assay platform enables metabolic characterization of primary cells and that each of the three human cell types examined gives a unique and distinguishable profile.

     

A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles

Introduction

It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.

Objectives

This work simplifies this process.

Methods

A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.

Results

The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.

Conclusion

This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.

     

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.

     

Measuring phenotype-phenotype similarity through the interactome

Background

Recently, measuring phenotype similarity began to play an important role in disease diagnosis. Researchers have begun to pay attention to develop phenotype similarity measurement. However, existing methods ignore the interactions between phenotype-associated proteins, which may lead to inaccurate phenotype similarity.

Results

We proposed a network-based method PhenoNet to calculate the similarity between phenotypes. We localized phenotypes in the network and calculated the similarity between phenotype-associated modules by modeling both the inter- and intra-similarity.

Conclusions

PhenoNet was evaluated on two independent evaluation datasets: gene ontology and gene expression data. The result shows that PhenoNet performs better than the state-of-art methods on all evaluation tests.