Rapid HILIC-Z ion mobility mass spectrometry (RHIMMS) method for untargeted metabolomics of complex biological samples

Gallium and silver-doped titanium surfaces provide enhanced osteogenesis, reduce bone resorption and prevent bacterial infection in co-culture

Bacterial infection remains a significant problem associated with orthopaedic surgeries leading to surgical site infection (SSI). This unmet medical need can become an even greater complication when surgery is due to malignant bone tumor. In the present study, we evaluated in vitro titanium (Ti) implants subjected to gallium (Ga) and silver (Ag)-doped thermochemical treatment as strategy to prevent SSI and improve osteointegration in bone defects caused by diseases such as osteoporosis, bone tumor, or bone metastasis. Firstly, as Ga has been reported to be an osteoinductive and anti-resorptive agent, its performance in the mixture was proved by studying human mesenchymal stem cells (hMSC) and pre-osteoclasts (RAW264.7) behaviour. Then, the antibacterial potential provided by Ag was assessed by resembling "The Race for the Surface" between hMSC and Pseudomonas aeruginosa in two co-culture methods. Moreover, the presence of quorum sensing molecules in the co-culture was evaluated. The results highlighted the suitability of the mixture to induce osteodifferentiation and reduce osteoclastogenesis in vitro. Furthermore, the GaAg surface promoted strong survival rate and retained osteoinduction potential of hMSCs even after bacterial inoculation. Therefore, GaAg-modified titanium may be an ideal candidate to repair bone defects caused by excessive bone resorption, in addition to preventing SSI. STATEMENT OF SIGNIFICANCE: This article provides important insights into titanium for fractures caused by osteoporosis or bone metastases with high incidence in surgical site infection (SSI) because in this situation bacterial infection can become a major disaster. In order to solve this unmet medical need, we propose a titanium implant modified with gallium and silver to improve osteointegration, reduce bone resorption and avoid bacterial infection. For that aim, we study osteoblast and osteoclast behavior with the main novelty focused on the antibacterial evaluation. In this work, we recreate "the race for the surface" in long-term experiments and study bacterial virulence factors (quorum sensing). Therefore, we believe that our article could be of great interest, providing a great impact on future orthopedic applications.

Proline Metabolism in WHO G4 Gliomas Is Altered as Compared to Unaffected Brain Tissue

Proline metabolism has been identified as a significant player in several neoplasms, but knowledge of its role in gliomas is limited despite it providing a promising line of pursuit. Data on proline metabolism in the brain are somewhat historical. This study aims to investigate alterations of proline metabolism in gliomas of WHO grade 4 (GG4) in the context of the brain. A total of 20 pairs of samples were studied, consisting of excised tumor and unaffected brain tissue, obtained when partial brain resection was required to reach deep-seated lesions. Levels of proline oxidase/proline dehydrogenase (POX/PRODH), Δ1-pyrroline-5-carboxylate reductases (PYCR1/2/3), prolidase (PEPD), and metalloproteinases (MMP-2, MMP-9) were assessed, along with the concentration of proline and proline-related metabolites. In comparison to normal brain tissue, POX/PRODH expression in GG4 was found to be suppressed, while PYCR1 expression and activity of PEPD, MMP-2, and -9 were upregulated. The GG4 proline concentration was 358% higher. Hence, rewiring of the proline metabolism in GG4 was confirmed for the first time, with a low-POX/PRODH/high-PYCR profile. High PEPD and MMPs activity is in keeping with GG4-increased collagen turnover and local aggressiveness. Further studies on the mechanisms of the interplay between altered proline metabolism and the GG4 microenvironment are warranted.

Right in two: capabilities of ion mobility spectrometry for untargeted metabolomics

This mini review focuses on the opportunities provided by current and emerging separation techniques for mass spectrometry metabolomics. The purpose of separation technologies in metabolomics is primarily to reduce complexity of the heterogeneous systems studied, and to provide concentration enrichment by increasing sensitivity towards the quantification of low abundance metabolites. For this reason, a wide variety of separation systems, from column chemistries to solvent compositions and multidimensional separations, have been applied in the field. Multidimensional separations are a common method in both proteomics applications and gas chromatography mass spectrometry, allowing orthogonal separations to further reduce analytical complexity and expand peak capacity. These applications contribute to exponential increases in run times concomitant with first dimension fractionation followed by second dimension separations. Multidimensional liquid chromatography to increase peak capacity in metabolomics, when compared to the potential of running additional samples or replicates and increasing statistical confidence, mean that uptake of these methods has been minimal. In contrast, in the last 15 years there have been significant advances in the resolution and sensitivity of ion mobility spectrometry, to the point where high-resolution separation of analytes based on their collision cross section approaches chromatographic separation, with minimal loss in sensitivity. Additionally, ion mobility separations can be performed on a chromatographic timescale with little reduction in instrument duty cycle. In this review, we compare ion mobility separation to liquid chromatographic separation, highlight the history of the use of ion mobility separations in metabolomics, outline the current state-of-the-art in the field, and discuss the future outlook of the technology. "Where there is one, you're bound to divide it. Right in two", James Maynard Keenan.

Liquid chromatography and differential mobility spectrometry-data-independent mass spectrometry for comprehensive multidimensional separations in metabolomics

The benefits of combining drift time ion mobility (DTIMS) with liquid chromatography-high-resolution mass spectrometry (HRMS) have been reported for metabolomics but the use of differential time mobility spectrometry (DMS) is less obvious due to the need for rapid scanning of the DMS cell. Drift DTIMS provides additional precursor ion selectivity and collisional cross-section information but the separation resolution between analytes remains cell- and component-dependent. With DMS, the addition of 2-propanol modifier can improve the selectivity but on cost of analyte MS response. In the present work, we investigate the liquid chromatography-mass spectrometry (LC-MS) analysis of a mix of 50 analytes, representative for urine and plasma metabolites, using scanning DMS with the single modifiers cyclohexane (Ch), toluene (Tol), acetonitrile (ACN), ethanol (EtOH), and 2-propanol (IPA), and a binary modifier mixture (cyclohexane/2-propanol) with emphasis on selectivity and signal sensitivity. 1.5% IPA in the N₂ stream was found to suppress the signal of 50% of the analytes which could be partially recovered with the use of IPA to 0.05% as a Ch/IPA mixture. The potential to use the separation voltage/compensation voltage/modifier (SV/CoV/Mod) feature as an additional analyte identifier for qualitative analysis is also presented and applied to a data-independent LCxDMS-SWATH-MS workflow for the analysis of endogenous metabolites and drugs of abuse in human urine samples from traffic control.

Guidelines and considerations for building multidimensional libraries for untargeted MS-based metabolomics

INTRODUCTION

Feature annotation is crucial in untargeted metabolomics but remains a major challenge. The large pool of metabolites collected under various instrumental conditions is underrepresented in publicly available databases. Retention time (RT) and collision cross section (CCS) measurements from liquid chromatography ion mobility high-resolution mass spectrometers can be employed in addition to MS/MS spectra to improve the confidence of metabolite annotation. Recent advancements in machine learning focus on improving the accuracy of predictions for CCS and RT values. Therefore, high-quality experimental data are crucial to be used either as training datasets or as a reference for high-confidence matching.

METHODS

This manuscript provides an easy-to-use workflow for the creation of an in-house metabolite library, offers an overview of alternative solutions, and discusses the challenges and advantages of using open-source software. A total of 100 metabolite standards from various classes were analyzed and subjected to the described workflow for library generation.

RESULTS AND DISCUSSION

The outcome was an open-access available NIST format metabolite library (.msp) with multidimensional information. The library was used to evaluate CCS prediction tools, MS/MS spectra heterogeneities (e.g., multiple adducts, in-source fragmentation, radical fragment ions using collision-induced dissociation), and the reporting of RT.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.