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.

     

The metabolome 18 years on: a concept comes of age

Background

The term ‘metabolome’ was introduced to the scientific literature in September 1998.

Aim and key scientific concepts of the review

To mark its 18-year-old ‘coming of age’, two of the co-authors of that paper review the genesis of metabolomics, whence it has come and where it may be going.

     

Pseudomyxoma extraperitonei occurring 35 years after appendicectomy: a case report and review of literature

Background

Pseudomyxoma peritonei is a rare condition consisting of mucinous ascites, most commonly arising from mucinous tumors of the appendix and occasionally from the ovary. Very rarely mucinous implants arise in the retroperitoneum without any intra-peritoneal involvement. This has been termed as pseudomyxoma extraperitonei.

Case presentation

We report a case of a 57 year old man who developed pseudomyxoma extraperitonei, 35 years after undergoing an appendicectomy for a perforated appendix.

Conclusions

Pseudomyxoma extraperitonei has been previously reported, however we report the longest incubation period of 35 years for this condition.

     

NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics

Introduction

Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks.

Objectives

The objective of NMRProcFlow is to assist the expert in this task in the best way without requirement of programming skills.

Methods

NMRProcFlow was developed to be a graphical and interactive 1D NMR (¹H & ¹³C) spectra processing tool.

Results

NMRProcFlow (http://nmrprocflow.org), dedicated to metabolic fingerprinting and targeted metabolomics, covers all spectra processing steps including baseline correction, chemical shift calibration and alignment.

Conclusion

Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses.

     

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.

     

Targeted metabolomic profiling of low and high grade serous epithelial ovarian cancer tissues: a pilot study

Introduction

Epithelial ovarian cancer (EOC) remains the leading cause of death from gynecologic malignancies and has an alarming global fatality rate. Besides the differences in underlying pathogenesis, distinguishing between high grade (HG) and low grade (LG) EOC is imperative for the prediction of disease progression and responsiveness to chemotherapy.

Objectives

The aim of this study was to investigate, the tissue metabolome associated with HG and LG serous epithelial ovarian cancer.

Methods

A combination of one dimensional proton nuclear magnetic resonance (1D H NMR) spectroscopy and targeted mass spectrometry (MS) was employed to profile the tissue metabolome of HG, LG serous EOCs, and controls.

Results

Using partial least squares-discriminant analysis, we observed significant separation between all groups (p?<?0.05) following cross validation. We identified which metabolites were significantly perturbed in each EOC grade as compared with controls and report the biochemical pathways which were perturbed due to the disease. Among these metabolic pathways, ascorbate and aldarate metabolism was identified, for the first time, as being significantly altered in both LG and HG serous cancers. Further, we have identified potential biomarkers of EOC and generated predictive algorithms with AUC (CI)?=?0.940 and 0.929 for HG and LG, respectively.

Conclusion

These previously unreported biochemical changes provide a framework for future metabolomic studies for the development of EOC biomarkers. Finally, pharmacologic targeting of the key metabolic pathways identified herein could lead to novel and effective treatments of EOC.

     

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.

     

Ethnobotany in a coastal environmental protected area: shifts in plant use in two communities in southern Brazil

Background

We investigated local knowledge of plants in terms of plant use shifts and losses, in two coastal communities within a protected area in southern Brazil. Our hypothesis is that people’s livelihoods are associated with different ethnobotanical knowledge, and changes in these activities can reflect shifts in ethnobotanical knowledge such as stopping using some plants.

Methods

We interviewed 125 inhabitants after prior informed consent, asking her/him about their socioeconomic profile and to free list the plants they know. The free lists were analyzed by frequency of cited plants. To compare averages of cited plants and age in both communities, we used the Wilcoxon test with a significance of 5%. Spearman correlation was tested with number of plants cited in the past and the interviewees’ age. Permanence and change in economic activities in each community were represented using a graph and compared through a chi-squared test with a significance of 5%. Qualitative analyses of the interviews and a field diary were used to analyze driving forces for the abandonment of used plants.

Results

We identified 231 plant species that were currently used mainly for food and medicine. Despite being in a protected area, most of the cited plants were exotic and cultivated in home gardens. We do not confirm the hypothesis that changes in livelihoods are reflected in the plants used; however, qualitative analyses showed potential drivers for shifts and losses of plant use. “Environmental law” and “protected area” were the drivers most related to the abandonment of plant use.

Conclusions

While recognizing the importance of the protected area to maintain local people and their traditions, we documented a shift in plant use that is mainly correlated to construction activities that disappeared from daily practices.

     

Heterogeneity in arbuscular mycorrhizal fungal communities may contribute to inconsistent plant-soil feedback in a Neotropical forest

Background and aims

Plant-soil feedback may vary across host species and environmental gradients. The relative importance of these biotic versus abiotic drivers of feedback will determine the stability of plant and microbial communities across environments. If plant hosts are the main driver of soil microbial communities, plant-soil feedback may be stable across changing environments. However, if microbial communities vary with environmental gradients, feedback may also vary, limiting its capacity to predict plant distributions.

Methods

We characterized arbuscular mycorrhizal (AM) fungi across tree plantations and a primary Neotropical rainforest. We then performed a plant-soil feedback pot experiment of AM fungi from these plantations on three plant species and related feedback and AM fungal communities in the field.

Results

In the field, temporal and spatial variation in AM fungal composition was similar in magnitude to variation across plant host species. Composition of AM fungi in the pot experiment significantly differed from the field plots. Furthermore, differential feedback was explained by shifts in AM fungal composition only for one plant host species (Hyeronima alchorneoides) in the pot experiment.

Conclusions

Natural AM fungal communities were temporally and spatially heterogeneous and AM fungal communities in the greenhouse did not reflect natural soils. These factors led to heterogeneous and unpredictable feedback responses, which suggests that applying greenhouse derived plant-soil feedback trends to predict plant coexistence in natural systems may be misleading.

     

Dynamical crises,multistability and the influence of the duration of immunity in a seasonally-forced model of disease transmission

Background

Highly successful strategies to make populations more resilient to infectious diseases, such as childhood vaccinations programs, may nonetheless lead to unpredictable outcomes due to the interplay between seasonal variations in transmission and a population’s immune status.

Methods

Motivated by the study of diseases such as pertussis we introduce a seasonally-forced susceptible-infectious-recovered model of disease transmission with waning and boosting of immunity. We study the system’s dynamical properties using a combination of numerical simulations and bifurcation techniques, paying particular attention to the properties of the initial condition space.

Results

We find that highly unpredictable behaviour can be triggered by changes in biologically relevant model parameters such as the duration of immunity. In the particular system we analyse — previously used in the literature to study pertussis dynamics — we identify the presence of an initial-condition landscape containing three coexisting attractors. The system’s response to interventions which perturb population immunity (e.g. vaccination "catch-up" campaigns) is therefore difficult to predict.

Conclusion

Given the increasing use of models to inform policy decisions regarding vaccine introduction and scheduling and infectious diseases intervention policy more generally, our findings highlight the importance of thoroughly investigating the dynamical properties of those models to identify key areas of uncertainty. Our findings suggest that the often stated tension between capturing biological complexity and utilising mathematically simple models is perhaps more nuanced than generally suggested. Simple dynamical models, particularly those which include forcing terms, can give rise to incredibly complex behaviour.

     

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.

     

Systems biology analysis reveals new insights into invasive lung cancer

Background

Adenocarcinoma in situ (AIS) is a pre-invasive lesion in the lung and a subtype of lung adenocarcinoma. The patients with AIS can be cured by resecting the lesion completely. In contrast, the patients with invasive lung adenocarcinoma have very poor 5-year survival rate. AIS can develop into invasive lung adenocarcinoma. The investigation and comparison of AIS and invasive lung adenocarcinoma at the genomic level can deepen our understanding of the mechanisms underlying lung cancer development.

Results

In this study, we identified 61 lung adenocarcinoma (LUAD) invasive-specific differentially expressed genes, including nine long non-coding RNAs (lncRNAs) based on RNA sequencing techniques (RNA-seq) data from normal, AIS, and invasive tissue samples. These genes displayed concordant differential expression (DE) patterns in the independent stage III LUAD tissues obtained from The Cancer Genome Atlas (TCGA) RNA-seq dataset. For individual invasive-specific genes, we constructed subnetworks using the Genetic Algorithm (GA) based on protein-protein interactions, protein-DNA interactions and lncRNA regulations. A total of 19 core subnetworks that consisted of invasive-specific genes and at least one putative lung cancer driver gene were identified by our study. Functional analysis of the core subnetworks revealed their enrichment in known pathways and biological progresses responsible for tumor growth and invasion, including the VEGF signaling pathway and the negative regulation of cell growth.

Conclusions

Our comparison analysis of invasive cases, normal and AIS uncovered critical genes that involved in the LUAD invasion progression. Furthermore, the GA-based network method revealed gene clusters that may function in the pathways contributing to tumor invasion. The interactions between differentially expressed genes and putative driver genes identified through the network analysis can offer new targets for preventing the cancer invasion and potentially increase the survival rate for cancer patients.

     

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.

     

Iridoid and phenylethanoid/phenylpropanoid metabolite profiles of <Emphasis Type="Italic">Scrophularia</Emphasis> and <Emphasis Type="Italic">Verbascum</Emphasis> species used medicinally in North America

Introduction

Botanicals containing iridoid and phenylethanoid/phenylpropanoid glycosides are used worldwide for the treatment of inflammatory musculoskeletal conditions that are primary causes of human years lived with disability, such as arthritis and lower back pain.

Objectives

We report the analysis of candidate anti-inflammatory metabolites of several endemic Scrophularia species and Verbascum thapsus used medicinally by peoples of North America.

Methods

Leaves, stems, and roots were analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and partial least squares-discriminant analysis (PLS-DA) was performed in MetaboAnalyst 3.0 after processing the datasets in Progenesis QI.

Results

Comparison of the datasets revealed significant and differential accumulation of iridoid and phenylethanoid/phenylpropanoid glycosides in the tissues of the endemic Scrophularia species and Verbascum thapsus.

Conclusions

Our investigation identified several species of pharmacological interest as good sources for harpagoside and other important anti-inflammatory metabolites.

     

A framework to find the logic backbone of a biological network

Background

Cellular behaviors are governed by interaction networks among biomolecules, for example gene regulatory and signal transduction networks. An often used dynamic modeling framework for these networks, Boolean modeling, can obtain their attractors (which correspond to cell types and behaviors) and their trajectories from an initial state (e.g. a resting state) to the attractors, for example in response to an external signal. The existing methods however do not elucidate the causal relationships between distant nodes in the network.

Results

In this work, we propose a simple logic framework, based on categorizing causal relationships as sufficient or necessary, as a complement to Boolean networks. We identify and explore the properties of complex subnetworks that are distillable into a single logic relationship. We also identify cyclic subnetworks that ensure the stabilization of the state of participating nodes regardless of the rest of the network. We identify the logic backbone of biomolecular networks, consisting of external signals, self-sustaining cyclic subnetworks (stable motifs), and output nodes. Furthermore, we use the logic framework to identify crucial nodes whose override can drive the system from one steady state to another. We apply these techniques to two biological networks: the epithelial-to-mesenchymal transition network corresponding to a developmental process exploited in tumor invasion, and the network of abscisic acid induced stomatal closure in plants. We find interesting subnetworks with logical implications in these networks. Using these subgraphs and motifs, we efficiently reduce both networks to succinct backbone structures.

Conclusions

The logic representation identifies the causal relationships between distant nodes and subnetworks. This knowledge can form the basis of network control or used in the reverse engineering of networks.

     

PKM2 knockdown influences SREBP activation and lipid synthesis in bovine mammary-gland epithelial MAC-T cells

Objective

The purpose of the article is to evaluate the changes in lipid metabolism in bovine mammary-gland epithelial MAC-T cells after PKM2 knockdown.

Results

MAC-T cells stably expressing low levels of PKM2 were established with lentivirus-mediated small hairpin RNA. Although the knockdown of PKM2 had no effect on MAC-T cell growth, the reduced expression of PKM2 attenuated the mRNA and protein expression of key enzymes involved in sterol synthesis through the SREBP pathway.

Conclusions

The downregulation of PKM2 significantly influenced lipid synthesis in bovine mammary-gland epithelial MAC-T cells. These findings extend our understanding of the crosstalk between glycolysis and lipid metabolism in bovine mammary-gland epithelial cells.

     

Potential for synergy in soil inoculation for nature restoration by mixing inocula from different successional stages

Background and aims

Due to the well-known importance of biocrusts for several ecosystem properties linked to soil functionality, we aim to go deeper into the physiological performance of biocrusts components. Possible functional convergences in the physiology of biocrust constituents would facilitate the understanding of both species and genus distributional patterns and improve the possibility of modelling their response to climate change.

Methods

We measured gas exchange in the laboratory under controlled conditions of lichen- and moss-dominated biocrusts from four environmentally different locations in Europe. Field data were used to determine the natural hydration sources that drive metabolic activity of biocrusts.

Results

Our results show different activity drivers at the four sites. Within site analyses showed similar C fixation for the different crust types in the three sites without hydric stress whilst light use related parameters and respiration at 15 °C were similar in the between sites analyses. There were significant differences in water relations between the biocrusts types, with moss-dominated crusts showing higher maximum and optimum water contents.

Conclusions

The functional type approach for biocrusts can be justified from a physiological perspective when similar values are found in the within and between site analyses, the latter indicating habitat independent adaptation patterns. Our multi-site analyses for biocrusts functional performance provide comparisons of C fluxes and water relations in the plant-soil interface that will help to understand the adaptation ability of these communities to possible environmental changes.

     

Non-invasive urinary metabolomic profiling discriminates prostate cancer from benign prostatic hyperplasia

Introduction

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. Serum prostate specific antigen (PSA) level has been extensively used as a biomarker to detect PCa. However, PSA is not cancer-specific and various non-malignant conditions, including benign prostatic hyperplasia (BPH), can cause a rise in PSA blood levels, thus leading to many false positive results.

Objectives

In this study, we evaluated the potential of urinary metabolomic profiling for discriminating PCa from BPH.

Methods

Urine samples from 64 PCa patients and 51 individuals diagnosed with BPH were analysed using ¹H nuclear magnetic resonance (¹H-NMR). Comparative analysis of urinary metabolomic profiles was carried out using multivariate and univariate statistical approaches.

Results

The urine metabolomic profile of PCa patients is characterised by increased concentrations of branched-chain amino acids (BCAA), glutamate and pseudouridine, and decreased concentrations of glycine, dimethylglycine, fumarate and 4-imidazole-acetate compared with individuals diagnosed with BPH.

Conclusion

PCa patients have a specific urinary metabolomic profile. The results of our study underscore the clinical potential of metabolomic profiling to uncover metabolic changes that could be useful to discriminate PCa from BPH in a clinical context.

     

An efficient algorithm for identifying primary phenotype attractors of a large-scale Boolean network

Background

Boolean network modeling has been widely used to model large-scale biomolecular regulatory networks as it can describe the essential dynamical characteristics of complicated networks in a relatively simple way. When we analyze such Boolean network models, we often need to find out attractor states to investigate the converging state features that represent particular cell phenotypes. This is, however, very difficult (often impossible) for a large network due to computational complexity.

Results

There have been some attempts to resolve this problem by partitioning the original network into smaller subnetworks and reconstructing the attractor states by integrating the local attractors obtained from each subnetwork. But, in many cases, the partitioned subnetworks are still too large and such an approach is no longer useful. So, we have investigated the fundamental reason underlying this problem and proposed a novel efficient way of hierarchically partitioning a given large network into smaller subnetworks by focusing on some attractors corresponding to a particular phenotype of interest instead of considering all attractors at the same time. Using the definition of attractors, we can have a simplified update rule with fixed state values for some nodes. The resulting subnetworks were small enough to find out the corresponding local attractors which can be integrated for reconstruction of the global attractor states of the original large network.

Conclusions

The proposed approach can substantially extend the current limit of Boolean network modeling for converging state analysis of biological networks.