Identification of pathogens in culture-negative infective endocarditis cases by metagenomic analysis

Background

Pathogens identification is critical for the proper diagnosis and precise treatment of infective endocarditis (IE). Although blood and valve cultures are the gold standard for IE pathogens detection, many cases are culture-negative, especially in patients who had received long-term antibiotic treatment, and precise diagnosis has therefore become a major challenge in the clinic. Metagenomic sequencing can provide both information on the pathogenic strain and the antibiotic susceptibility profile of patient samples without culturing, offering a powerful method to deal with culture-negative cases.

Methods

To assess the feasibility of a metagenomic approach to detect the causative pathogens in resected valves from IE patients, we employed both next-generation sequencing and Oxford Nanopore Technologies MinION nanopore sequencing for pathogens and antimicrobial resistance detection in seven culture-negative IE patients. Using our in-house developed bioinformatics pipeline, we analyzed the sequencing results generated from both platforms for the direct identification of pathogens from the resected valves of seven clinically culture-negative IE patients according to the modified Duke criteria.

Results

Our results showed both metagenomics methods can be applied for the causative pathogen detection in all IE samples. Moreover, we were able to simultaneously characterize respective antimicrobial resistance features.

Conclusion

Metagenomic methods for IE detection can provide clinicians with valuable information to diagnose and treat IE patients after valve replacement surgery. However, more efforts should be made to optimize protocols for sample processing, sequencing and bioinformatics analysis.

     

Next-generation sequencing facilitates detection of the classic E13-A20 EML4-ALK fusion in an ALK-FISH/IHC inconclusive biopsy of a stage IV lung cancer patient: a case report

Background

Inhibition of the oncogenic fusion-gene EML4-ALK is a current first-line approach for patients with stage IV non-small cell lung cancer. While FISH was established as the gold standard for identifying these patients, there is accumulating evidence that other methods of detection, i.e., immunohistochemistry and next-generation sequencing (NGS), exist that may be equally successful. However, the concordance of these methods is under investigation.

Case presentation

Adding to the current literature, we here report a 56 year old female never-smoker with stage IV lung adenocarcinoma whose biopsy was IHC and FISH inconclusive but positive in NGS. Retroactive profiling of the resection specimen corroborated fusion reads obtained by NGS, FISH-positivity and showed weak ALK-positivity by IHC. Consequently, we diagnosed the case as ALK-positive rendering the patient eligible to crizotinib treatment.

Conclusions

With IHC on biopsy material only, this case would have been overlooked withholding effective therapy.

     

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.

     

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.

     

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.

     

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.

     

Using multitask classification methods to investigate the kinase-specific phosphorylation sites

Background

Identification of phosphorylation sites by computational methods is becoming increasingly important because it reduces labor-intensive and costly experiments and can improve our understanding of the common properties and underlying mechanisms of protein phosphorylation.

Methods

A multitask learning framework for learning four kinase families simultaneously, instead of studying each kinase family of phosphorylation sites separately, is presented in the study. The framework includes two multitask classification methods: the Multi-Task Least Squares Support Vector Machines (MTLS-SVMs) and the Multi-Task Feature Selection (MT-Feat3).

Results

Using the multitask learning framework, we successfully identify 18 common features shared by four kinase families of phosphorylation sites. The reliability of selected features is demonstrated by the consistent performance in two multi-task learning methods.

Conclusions

The selected features can be used to build efficient multitask classifiers with good performance, suggesting they are important to protein phosphorylation across 4 kinase families.

     

Hypocapnia is a biological marker for orthostatic intolerance in some patients with chronic fatigue syndrome

Context

Patients with chronic fatigue syndrome and those with orthostatic intolerance share many symptoms, yet questions exist as to whether CFS patients have physiological evidence of orthostatic intolerance.

Objective

To determine if some CFS patients have increased rates of orthostatic hypotension, hypertension, tachycardia, or hypocapnia relative to age-matched controls.

Design

Assess blood pressure, heart rate, respiratory rate, end tidal CO2 and visual analog scales for orthostatic symptoms when supine and when standing for 8 minutes without moving legs.

Setting

Referral practice and research center.

Participants

60 women and 15 men with CFS and 36 women and 4 men serving as age matched controls with analyses confined to 62 patients and 35 controls showing either normal orthostatic testing or a physiological abnormal test.

Main outcome measures

Orthostatic tachycardia; orthostatic hypotension; orthostatic hypertension; orthostatic hypocapnia or combinations thereof.

Results

CFS patients had higher rates of abnormal tests than controls (53% vs 20%, p < .002), but rates of orthostatic tachycardia, orthostatic hypotension, and orthostatic hypertension did not differ significantly between patients and controls (11.3% vs 5.7%, 6.5% vs 2.9%, 19.4% vs 11.4%, respectively). In contrast, rates of orthostatic hypocapnia were significantly higher in CFS than in controls (20.6% vs 2.9%, p < .02). This CFS group reported significantly more feelings of illness and shortness of breath than either controls or CFS patients with normal physiological tests.

Conclusion

A substantial number of CFS patients have orthostatic intolerance in the form of orthostatic hypocapnia. This allows subgrouping of patients with CFS and thus reduces patient pool heterogeneity engendered by use of a clinical case definition.

     

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.

     

Insights into gemcitabine resistance and the potential for therapeutic monitoring

Introduction

Gemcitabine is an important component of pancreatic cancer clinical management. Unfortunately, acquired gemcitabine resistance is widespread and there are limitations to predicting and monitoring therapeutic outcomes.

Objective

To investigate the potential of metabolomics to differentiate pancreatic cancer cells that develops resistance or respond to gemcitabine treatment.

Results

We applied 1D ¹H and 2D ¹H–¹³C HSQC NMR methods to profile the metabolic signature of pancreatic cancer cells. ¹³C₆-glucose labeling identified 30 key metabolites uniquely altered between wild-type and gemcitabine-resistant cells upon gemcitabine treatment. Gemcitabine resistance was observed to reprogram glucose metabolism and to enhance the pyrimidine synthesis pathway. Myo-inositol, taurine, glycerophosphocholine and creatinine phosphate exhibited a “binary switch” in response to gemcitabine treatment and acquired resistance.

Conclusion

Metabolic differences between naïve and resistant pancreatic cancer cells and, accordingly, their unique responses to gemcitabine treatment were revealed, which may be useful in the clinical setting for monitoring a patient’s therapeutic response.

     

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.

     

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.

     

Association of mitochondrial dysfunction and lipid metabolism with type 2 diabetes mellitus: A review of literature

Background

Diabetes mellitus (DM) is one of the most prevalent chronic diseases, and its prevalence continues to increase globally. The impact of mitochondrial dysfunction and lipid metabolism on diabetes mellitus and insulin resistance (IR) has been implicated in several previous reports; however, the results of studies are confusing despite four decades of study.

Methods/Results

This review has evaluated updated understanding of the role of mitochondrial dysfunction and lipid metabolism on type 2 diabetes, and found that mitochondrial dysfunction and lipid metabolism disorder induce the dysregulation of liver and pancreatic beta cells, insulin resistance, and type 2 diabetes.

Conclusion

Mitochondrial dysfunction and lipid metabolism induce metabolic dysregulation and finally increasing the possibility of diabetes.

     

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 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.

     

Genomic amplification and high expression of EGFR are key targetable oncogenic events in malignant peripheral nerve sheath tumor

Background

The dismal outcome of malignant peripheral nerve sheath tumor (MPNST) highlights the necessity of finding new therapeutic methods to benefit patients with this aggressive sarcoma. Our purpose was to investigate epidermal growth factor receptor (EGFR) as a potential therapeutic target in MPNSTs.

Patients and methods

We performed a microarray based-comparative genomic hybridization (aCGH) profiling of two cohorts of primary MPNST tissue samples including 25 patients treated at The University of Texas MD Anderson Cancer Center (MD Anderson) and 26 patients from Tianjin Medical University Cancer Institute & Hospital (TMUCIH). Fluorescence in situ hybridization (FISH) method was used to validate the gene amplification detected by aCGH analysis. Another independent cohort of 56 formalin fixed paraffin embedded (FFPE) MPNST samples was obtained to explore EGFR protein expression by immunohistochemical analysis. Cell biology detection and validation were performed on human MPNST cell lines ST88-14 and STS26T.

Results

aCGH and pathway analysis of the 51 MPNSTs identified significant gene amplification events in EGFR pathway, including frequent amplifications of EGFR gene itself, which was subsequently validated by FISH assay. High expression of EGFR protein was associated with poor disease-free and overall survival of human MPNST patients. In human MPNST cell lines ST88-14 and STS26T, inhibition of EGFR by siRNA or Gefitinib led to decreased cell proliferation, migration, and invasion accompanied by attenuation of PI3K/AKT and MAPK pathways.

Conclusion

These results suggest that EGFR is a potential therapeutic target for MPNST.

     

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.

     

Metabolites predict cardiovascular disease events in persons living with HIV: a pilot case–control study

Introduction

Persons living with HIV (PLWH) are at higher risk for cardiovascular disease (CVD) events than uninfected persons. Current risk-stratification methods to define PLWH at highest risk for CVD events are lacking.

Methods

Using tandem flow injection mass spectrometry, we quantified plasma levels of 60 metabolites in 24 matched pairs of PLWH [1:1 with and without known coronary artery disease (CAD)]. Metabolite levels were reduced to interpretable factors using principal components analysis.

Results

Factors derived from short-chain dicarboxylacylcarnitines (SCDA) (p?=?0.08) and glutamine/valine (p?=?0.003) were elevated in CAD cases compared to controls.

Conclusion

SCDAs and glutamine/valine may be valuable markers of cardiovascular risk among persons living with HIV in the future, pending validation in larger cohorts.

     

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.