Longitudinal data methods for evaluating genome-by-epigenome interactions in families

Background

Longitudinal measurement is commonly employed in health research and provides numerous benefits for understanding disease and trait progression over time. More broadly, it allows for proper treatment of correlated responses within clusters. We evaluated 3 methods for analyzing genome-by-epigenome interactions with longitudinal outcomes from family data.

Results

Linear mixed-effect models, generalized estimating equations, and quadratic inference functions were used to test a pharmacoepigenetic effect in 200 simulated posttreatment replicates. Adjustment for baseline outcome provided greater power and more accurate control of Type I error rates than computation of a pre-to-post change score.

Conclusions

Comparison of all modeling approaches indicated a need for bias correction in marginal models and similar power for each method, with quadratic inference functions providing a minor decrement in power compared to generalized estimating equations and linear mixed-effects models.

     

How the chemical features of molecules may have addressed the settlement of metabolic steps

Introduction

While the evolutionary adaptation of enzymes to their own substrates is a well assessed and rationalized field, how molecules have been originally selected in order to initiate and assemble convenient metabolic pathways is a fascinating, but still debated argument.

Objectives

Aim of the present study is to give a rationale for the preferential selection of specific molecules to generate metabolic pathways.

Methods

The comparison of structural features of molecules, through an inductive methodological approach, offer a reading key to cautiously propose a determining factor for their metabolic recruitment.

Results

Starting with some commonplaces occurring in the structural representation of relevant carbohydrates, such as glucose, fructose and ribose, arguments are presented in associating stable structural determinants of these molecules and their peculiar occurrence in metabolic pathways.

Conclusions

Among other possible factors, the reliability of the structural asset of a molecule may be relevant or its selection among structurally and, a priori, functionally similar molecules.

     

Assessing hydraulic redistribution with the compensated average gradient heat-pulse method on rain-fed olive trees

Aims

In this study on hydraulic redistribution (HR) in roots, we aimed to use the calibrated average-gradient (CAG) heat-pulse method, the novelty being the use of a much narrower averaging window for the signal analysis, in order to achieve a more linear calibration curve, allowing the HR quantification.

Methods

The study was conducted in 12 large roots of a rain-fed olive orchard, for 6 months without significant rain, when the predawn leaf water potential decreased to ?2.4 MPa, and immediately following the first autumn rains.

Results

Detailed numerical modelling of the CAG method allowed verification of the response of the measurement system to a range of drivers, improving the linear range of the calibration response function, which has remained stable over the observations period. On average, reverse flow was observed during 30% of the summer nights and, in a conservative estimate, it increased to about 5% of total daily root flow before first autumn rain.

Conclusions

Reverse flow accounted on average for 2.6% of the total daily root flow, enabling upper roots to stay active during the very dry late-summer period. In qualitative terms, our results confirm the CAG method as a reliable tool to identify reverse flow and quantify HR when it occurs.

     

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.

     

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.

     

Causal modeling in a multi-omic setting: insights from GAW20

Background

The GAW20 group formed on the theme of methods for association analyses of repeated measures comprised 4sets of investigators. The provided “real” data set included genotypes obtained from a human whole-genome association study based on longitudinal measurements of triglycerides (TGs) and high-density lipoprotein in addition to methylation levels before and after administration of fenofibrate. The simulated data set contained 200 replications of methylation levels and posttreatment TGs, mimicking the real data set.

Results

The different investigators in the group focused on the statistical challenges unique to family-based association analyses of phenotypes measured longitudinally and applied a wide spectrum of statistical methods such as linear mixed models, generalized estimating equations, and quasi-likelihood–based regression models. This article discusses the varying strategies explored by the group’s investigators with the common goal of improving the power to detect association with repeated measures of a phenotype.

Conclusions

Although it is difficult to identify a common message emanating from the different contributions because of the diversity in the issues addressed, the unifying theme of the contributions lie in the search for novel analytic strategies to circumvent the limitations of existing methodologies to detect genetic association.

     

From correlation to causation: analysis of metabolomics data using systems biology approaches

Introduction

Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

Objectives

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

Methods

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

Results

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

Conclusions

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

     

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.

     

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.

     

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.

     

Impaired mitochondrial calcium uptake caused by tacrolimus underlies beta-cell failure

Background

One of the most common side effects of the immunosuppressive drug tacrolimus (FK506) is the increased risk of new-onset diabetes mellitus. However, the molecular mechanisms underlying this association have not been fully clarified.

Methods

We studied the effects of the therapeutic dose of tacrolimus on mitochondrial fitness in beta-cells.

Results

We demonstrate that tacrolimus impairs glucose-stimulated insulin secretion (GSIS) in beta-cells through a previously unidentified mechanism. Indeed, tacrolimus causes a decrease in mitochondrial Ca²⁺ uptake, accompanied by altered mitochondrial respiration and reduced ATP production, eventually leading to impaired GSIS.

Conclusion

Our observations individuate a new fundamental mechanism responsible for the augmented incidence of diabetes following tacrolimus treatment. Indeed, this drug alters Ca²⁺ fluxes in mitochondria, thereby compromising metabolism-secretion coupling in beta-cells.

     

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.

     

Nonlinear analysis of biomagnetic signals recorded from uterine myomas

Objective

To determine if there is any non-linearity in the biomagnetic recordings of uterine myomas and to find any differences that may be present in the mechanisms underlying their signal dynamics.

Methods

Twenty-four women were included in the study. Sixteen of them were characterised with large myomas and 8 with small ones. Uterine artery waveform measurements were evaluated by use of Pulsatility Index (PI) (normal value PI<1.45).

Results

Applying nonlinear analysis to the biomagnetic signals of the uterine myomas, we observed a clear saturation value for the group of large ones (mean = 11.35 ± 1.49) and no saturation for the small ones.

Conclusion

The comparison of the saturation values in the biomagnetic recordings of large and small myomas may be a valuable tool in the evaluation of functional changes in their dynamic behavior.

     

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.

     

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.

     

Metabolomic analysis of tomato seed germination

Introduction

Seed germination is inherently related to seed metabolism, which changes throughout its maturation, desiccation and germination processes. The metabolite content of a seed and its ability to germinate are determined by underlying genetic architecture and environmental effects during development.

Objective

This study aimed to assess an integrative approach to explore genetics modulating seed metabolism in different developmental stages and the link between seed metabolic- and germination traits.

Methods

We have utilized gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) metabolite profiling to characterize tomato seeds during dry and imbibed stages. We describe, for the first time in tomato, the use of a so-called generalized genetical genomics (GGG) model to study the interaction between genetics, environment and seed metabolism using 100 tomato recombinant inbred lines (RILs) derived from a cross between Solanum lycopersicum and Solanum pimpinellifolium.

Results

QTLs were found for over two-thirds of the metabolites within several QTL hotspots. The transition from dry to 6 h imbibed seeds was associated with programmed metabolic switches. Significant correlations varied among individual metabolites and the obtained clusters were significantly enriched for metabolites involved in specific biochemical pathways.

Conclusions

Extensive genetic variation in metabolite abundance was uncovered. Numerous identified genetic regions that coordinate groups of metabolites were detected and these will contain plausible candidate genes. The combined analysis of germination phenotypes and metabolite profiles provides a strong indication for the hypothesis that metabolic composition is related to germination phenotypes and thus to seed performance.

     

Untargeted Antifungal Treatment Strategies for Invasive Candidiasis in Non-neutropenic Critically Ill Patients: Current Evidence and Insights

Purpose of Review

The purpose of this study was to provide an overview and insights on important new concepts on untargeted antifungal treatment strategies, namely prophylaxis pre-emptive and empiric treatments for the management of invasive candidiasis (IC) in non-neutropenic critically ill patients.

Recent Findings

Recently, clinical practice guidelines provided recommendation for the management of IC. However, results from recent trials and systematic reviews questioned the effect of untargeted antifungal treatment strategies, especially in terms of survival benefits in non-neutropenic patients, even with septic shock.

Summary

Widespread use of untargeted antifungal treatment strategies seems not to be justified anymore. Future research should evaluate comprehensive diagnostic-therapeutic approaches, including the implementation of de-escalation. In the meanwhile, clinicians should take into account all available sources of information including clinical evaluation, risk factor assessment, scores, and surrogate biomarkers to tailor antifungal treatment before definitive microbiological diagnosis.

     

Occlusion of left atrial appendage affects metabolomic profile: focus on glycolysis,tricarboxylic acid and urea metabolism

Background

Left atrial appendage (LAA) closure (LAAC) by implantation of an occlusion device is an established cardiac intervention to reduce risk of stroke while avoiding intake of oral anticoagulation medication during atrial fibrillation. Cardiac interventions can alter local or systemic gene and protein expression. Effects of LAAC on systemic metabolism have not been studied yet.

Objectives

We aimed to study the effects of interventional LAAC on systemic metabolism.

Methods

Products of glycolysis, tricarboxylic acid and urea metabolism were analyzed by ESI-LC-MS/MS and MS/MS using the AbsoluteIDQ? p180 Kit in plasma of 44 patients undergoing successful interventional LAAC at baseline (T0) and after 6 months (T1).

Results

During follow up, plasma concentrations of several parameters of glycolysis and tricarboxylic acid cycle (TCA) and urea metabolism increased (alanine, hexose, proline, sarcosine), while others decreased (aspartate, glycine, SDMA, serine). Multivariate linear regression analysis showed that time after interventional LAAC was an independent predictor for metabolite changes, including the decrease of SDMA (beta ?0.19, p?<?0.01) and the increase of sarcosine (beta 0.16, p?<?0.01).

Conclusions

Successful interventional LAAC affects different pathways of the metabolome, which are probably related to cardiac remodeling. The underlying mechanisms as well as the long term effects have to be studied in the future.

     

Cerebral infarcts associated with adenomyosis: a rare risk factor for stroke in middle-aged women: a case series

Background

Adenomyosis is a benign disease with elevated CA125 level.

Case presentation

We report 3 cases with adenomyosis who developed ischemic stroke during menstruation. The levels of CA125, CA19–9, and D-dimer were elevated, which dropped markedly after the menstrual phase. The development of nonbacterial thrombotic endocarditis (NBTE) and stenosis of the cerebral arteries associated with hypercoagulable state and the hyperviscosity nature of the mucinous protein may be the underlying mechanisms.

Conclusion

Our report suggests that adenomyosis might be a risk factor for ischemic stroke in middle-aged patients.

     

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.