In-vivo quantification of the revascularization of a human acellular dermis seeded with EPCs and MSCs in co-culture with fibroblasts and pericytes in the dorsal chamber model in pre-irradiated tissue

Introduction

Transplantation of a cell-seeded graft may improve wound healing after radiotherapy. However, the survival of the seeded cells depends on a rapid vascularization of the graft. Co-culturing of adult stem cells may be a promising strategy to accelerate the vessel formation inside the graft. Thus, we compared the in vivo angiogenic potency of mesenchymal stem cells (MSC) and endothelial progenitor cells (EPC) using dorsal skinfold chambers and intravital microscopy.

Materials and methods

Cells were isolated from rat bone marrow and adipose tissue and characterized by immunostaining and flow cytometry. Forty-eight rats received a dorsal skinfold chamber and were divided into 2 main groups, irradiated and non-irradiated. Each of these 2 groups were further subdivided into 4 groups: unseeded matrices, matrices + fibroblasts + pericytes, matrices + fibroblasts + pericytes + MSCs and matrices + fibroblasts + pericytes + EPCs. Vessel densities were quantified semi-automatically using FIJI.

Results

Fibroblasts + pericytes ? seeded matrices showed a significantly higher vascular density in all groups with an exception of non-irradiated rats at day 12 compared to unseeded matrices. Co-seeding of MSCs increased vessel densities in both, irradiated and non-irradiated groups. Co-seeding with EPCs did not result in an increase of vascularization in none of the groups.

Discussion

We demonstrated that the pre-radiation treatment led to a significant decreased vascularization of the implanted grafts. The augmentation of the matrices with fibroblasts and pericytes in co-culture increased the vascularization compared to the non-seeded matrices. A further significant enhancement of vessel ingrowth into the matrices could be achieved by the co-seeding with MSCs in both, irradiated and non-irradiated groups.

     

rDolphin: a GUI R package for proficient automatic profiling of 1D <Superscript>1</Superscript>H-NMR spectra of study datasets

Introduction

Adoption of automatic profiling tools for ¹H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices.

Objectives

To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process.

Methods

rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality.

Results

The information and quality achieved in two public datasets of complex matrices are maximized.

Conclusion

rDolphin is an open-source R package (http://github.com/danielcanueto/rDolphin) able to provide the best balance between accuracy, reproducibility and ease of use.

     

Lanthanum phosphate/chitosan scaffolds enhance cytocompatibility and osteogenic efficiency via the Wnt/β-catenin pathway

Background

Fabrication of porous scaffolds with great biocompatibility and osteoinductivity to promote bone defect healing has attracted extensive attention.

Methods

In a previous study, novel lanthanum phosphate (LaPO₄)/chitosan (CS) scaffolds were prepared by distributing 40- to 60-nm LaPO₄ nanoparticles throughout plate-like CS films.

Results

Interconnected three dimensional (3D) macropores within the scaffolds increased the scaffold osteoconductivity, thereby promoting cell adhesion and bone tissue in-growth. The LaPO₄/CS scaffolds showed no obvious toxicity and accelerated bone generation in a rat cranial defect model. Notably, the element La in the scaffolds was found to promote osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) through the Wnt/β-catenin signalling pathway and induced high expression of the osteogenesis-related genes alkaline phosphatase, osteocalcin and Collagen I (Col-I). Moreover, the LaPO₄/CS scaffolds enhanced bone regeneration and collagen fibre deposition in rat critical-sized calvarial defect sites.

Conclusion

The novel LaPO₄/CS scaffolds provide an admirable and promising platform for the repair of bone defects.

     

Automated metabolite identification from biological fluid <Superscript>1</Superscript>H NMR spectra

Introduction

Metabolite identification in biological samples using Nuclear Magnetic Resonance (NMR) spectra is a challenging task due to the complexity of the biological matrices.

Objectives

This paper introduces a new, automated computational scheme for the identification of metabolites in 1D ¹H NMR spectra based on the Human Metabolome Database.

Methods

The methodological scheme comprises of the sequential application of preprocessing, data reduction, metabolite screening and combination selection.

Results

The proposed scheme has been tested on the 1D ¹H NMR spectra of: (a) an amino acid mixture, (b) a serum sample spiked with the amino acid mixture, (c) 20 blood serum, (d) 20 human amniotic fluid samples, (e) 160 serum samples from publicly available database. The methodological scheme was compared against widely used software tools, exhibiting good performance in terms of correct assignment of the metabolites.

Conclusions

This new robust scheme accomplishes to automatically identify peak resonances in ¹H-NMR spectra with high accuracy and less human intervention with a wide range of applications in metabolic profiling.

     

Modeling the behavior of human induced pluripotent stem cells seeded on melt electrospun scaffolds

Background

Human induced pluripotent stem cells (hiPSCs) can form any tissue found in the body, making them attractive for regenerative medicine applications. Seeding hiPSC aggregates into biomaterial scaffolds can control their differentiation into specific tissue types. Here we develop and analyze a mathematical model of hiPSC aggregate behavior when seeded on melt electrospun scaffolds with defined topography.

Results

We used ordinary differential equations to model the different cellular populations (stem, progenitor, differentiated) present in our scaffolds based on experimental results and published literature. Our model successfully captures qualitative features of the cellular dynamics observed experimentally. We determined the optimal parameter sets to maximize specific cellular populations experimentally, showing that a physiologic oxygen level (～?5%) increases the number of neural progenitors and differentiated neurons compared to atmospheric oxygen levels (～?21%) and a scaffold porosity of ～?63% maximizes aggregate size.

Conclusions

Our mathematical model determined the key factors controlling hiPSC behavior on melt electrospun scaffolds, enabling optimization of experimental parameters.

     

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.

     

NMR-based metabolic characterization of chicken tissues and biofluids: a model for avian research

Introduction

Poultry is one of the most consumed meat in the world and its related industry is always looking for ways to improve animal welfare and productivity. It is therefore essential to understand the metabolic response of the chicken to new feed formulas, various supplements, infections and treatments.

Objectives

As a basis for future research investigating the impact of diet and infections on chicken’s metabolism, we established a high-resolution proton nuclear magnetic resonance (NMR)-based metabolic atlas of the healthy chicken (Gallus gallus).

Methods

Metabolic extractions were performed prior to ¹H-NMR and 2D NMR spectra acquisition on twelve biological matrices: liver, kidney, spleen, plasma, egg yolk and white, colon, caecum, faecal water, ileum, pectoral muscle and brain of 6 chickens. Metabolic profiles were then exhaustively characterized.

Results

Nearly 80 metabolites were identified. A cross-comparison of these matrices was performed to determine metabolic variations between and within each section and highlighted that only eight core metabolites were systematically found in every matrice.

Conclusion

This work constitutes a database for future NMR-based metabolomic investigations in relation to avian production and health.

     

A faster way to make GFP-based biosensors: Two new transposons for creating multicolored libraries of fluorescent fusion proteins

Background

There are now several ways to generate fluorescent fusion proteins by randomly inserting DNA encoding the Green Fluorescent Protein (GFP) into another protein's coding sequence. These approaches can be used to map regions in a protein that are permissive for GFP insertion or to create novel biosensors. While remarkably useful, the current insertional strategies have two major limitations: (1) they only produce one kind, or color, of fluorescent fusion protein and (2) one half of all GFP insertions within the target coding sequence are in the wrong orientation.

Results

We have overcome these limitations by incorporating two different fluorescent proteins coding sequences in a single transposon, either in tandem or antiparallel. Our initial tests targeted two mammalian integral membrane proteins: the voltage sensitive motor, Prestin, and an ER ligand gated Ca²⁺ channel (IP₃R).

Conclusions

These new designs increase the efficiency of random fusion protein generation in one of two ways: (1) by creating two different fusion proteins from each insertion or (2) by being independent of orientation.

     

Group-wise ANOVA simultaneous component analysis for designed <Emphasis Type="Italic">omics</Emphasis> experiments

Introduction

Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretation.

Objectives

We introduce here a sparse implementation of ASCA termed group-wise ANOVA-simultaneous component analysis (GASCA) with the aim of obtaining models that are easier to interpret.

Methods

GASCA is based on the concept of group-wise sparsity introduced in group-wise principal components analysis where structure to impose sparsity is defined in terms of groups of correlated variables found in the correlation matrices calculated from the effect matrices.

Results

The GASCA model, containing only selected subsets of the original variables, is easier to interpret and describes relevant biological processes.

Conclusions

GASCA is applicable to any kind of omics data obtained through designed experiments such as, but not limited to, metabolomic, proteomic and gene expression data.

     

Characterization of ankylosing spondylitis and rheumatoid arthritis using <Superscript>1</Superscript>H NMR-based metabolomics of human fecal extracts

Introduction

The differences in fecal metabolome between ankylosing spondylitis (AS)/rheumatoid arthritis (RA) patients and healthy individuals could be the reason for an autoimmune disorder.

Objectives

The study explored the fecal metabolome difference between AS/RA patients and healthy controls to clarify human immune disturbance.

Methods

Fecal samples from 109 individuals (healthy controls 34, AS 40, and RA 35) were analyzed by ¹H NMR spectroscopy. Data were analyzed with principal component analysis (PCA) and orthogonal projection to latent structure discriminant (OPLS-DA) analysis.

Results

Significant differences in the fecal metabolic profiles could distinguish AS/RA patients from healthy controls but could not distinguish between AS and RA patients. The significantly decreased metabolites in AS/RA patients were butyrate, propionate, methionine, and hypoxanthine. Significantly increased metabolites in AS/RA patients were taurine, methanol, fumarate, and tryptophan.

Conclusion

The metabolome variations in feces indicated AS and RA were two homologous diseases that could not be distinguished by ¹H NMR metabolomics.

     

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.

     

Maximizing power generation from dark fermentation effluents in microbial fuel cell by selective enrichment of exoelectrogens and optimization of anodic operational parameters

Objective

To selectively enrich an electrogenic mixed consortium capable of utilizing dark fermentative effluents as substrates in microbial fuel cells and to further enhance the power outputs by optimization of influential anodic operational parameters.

Results

A maximum power density of 1.4 W/m³ was obtained by an enriched mixed electrogenic consortium in microbial fuel cells using acetate as substrate. This was further increased to 5.43 W/m³ by optimization of influential anodic parameters. By utilizing dark fermentative effluents as substrates, the maximum power densities ranged from 5.2 to 6.2 W/m³ with an average COD removal efficiency of 75% and a columbic efficiency of 10.6%.

Conclusion

A simple strategy is provided for selective enrichment of electrogenic bacteria that can be used in microbial fuel cells for generating power from various dark fermentative effluents.

     

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.

     

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.

     

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.

     

MicroRNA-190b confers radio-sensitivity through negative regulation of Bcl-2 in gastric cancer cells

Objectives

To determine the role of miR-190b in radio-sensitivity of gastric cancer (GC).

Results

In radio-resistant GC cells, down-regulation of miR-190b and up-regulation of Bcl-2 were observed. The protein expression of Bcl-2 was negatively regulated by miR-190b. Overexpression of miR-190b significantly decreased cell viability and enhanced radio-sensitivity of GC cells. Of note, these effects of miR-190b on GC cells radio-sensitivity were abolished by Bcl-2.

Conclusion

miR-190b confers radio-sensitivity of GC cells, possibly via negative regulation of Bcl-2.

     

Prediction of heme binding residues from protein sequences with integrative sequence profiles

Background

The heme-protein interactions are essential for various biological processes such as electron transfer, catalysis, signal transduction and the control of gene expression. The knowledge of heme binding residues can provide crucial clues to understand these activities and aid in functional annotation, however, insufficient work has been done on the research of heme binding residues from protein sequence information.

Methods

We propose a sequence-based approach for accurate prediction of heme binding residues by a novel integrative sequence profile coupling position specific scoring matrices with heme specific physicochemical properties. In order to select the informative physicochemical properties, we design an intuitive feature selection scheme by combining a greedy strategy with correlation analysis.

Results

Our integrative sequence profile approach for prediction of heme binding residues outperforms the conventional methods using amino acid and evolutionary information on the 5-fold cross validation and the independent tests.

Conclusions

The novel feature of an integrative sequence profile achieves good performance using a reduced set of feature vector elements.

     

Selective oxidation of UDP-glucose to UDP-glucuronic acid using permeabilized <Emphasis Type="Italic">Schizosaccharomyces pombe</Emphasis> expressing human UDP-glucose 6-dehydrogenase

Objectives

To use permeabilized cells of the fission yeast, Schizosaccharomyces pombe, that expresses human UDP-glucose 6-dehydrogenase (UGDH, EC 1.1.1.22), for the production of UDP-glucuronic acid from UDP-glucose.

Results

In cell extracts no activity was detected. Therefore, cells were permeabilized with 0.3 % (v/v) Triton X-100. After washing away all low molecular weight metabolites, the permeabilized cells were directly used as whole cell biocatalyst. Substrates were 5 mM UDP-glucose and 10 mM NAD⁺. Divalent cations were not added to the reaction medium as they promoted UDP-glucose hydrolysis. With this reaction system 5 mM UDP-glucose were converted into 5 mM UDP-glucuronic acid within 3 h.

Conclusions

Recombinant permeabilized cells of S. pombe can be used to synthesize UDP-glucuronic acid with 100 % yield and selectivity.

     

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.