Nanotopography Influences Host-Pathogen Quorum Sensing and Facilitates Selection of Bioactive Metabolites in Mesenchymal Stromal Cells and Pseudomonas aeruginosa Co-Cultures.

Orthopedic implant-related bacterial infections and resultant antibiotic-resistant biofilms hinder implant-tissue integration and failure. Biofilm quorum sensing (QS) communication determines the pathogen colonization success. However, it remains unclear how implant modifications and host cells are influenced by, or influence, QS. High aspect ratio nanotopographies have shown to reduce biofilm formation of Pseudomonas aeruginosa, a sepsis causing pathogen with well-defined QS molecules. Producing such nanotopographies in relevant orthopedic materials (i.e., titanium) allows for probing QS using mass spectrometry-based metabolomics. However, nanotopographies can reduce host cell adhesion and regeneration. Therefore, we developed a polymer (poly(ethyl acrylate), PEA) coating that organizes extracellular matrix proteins, promoting bioactivity to host cells such as human mesenchymal stromal cells (hMSCs), maintaining biofilm reduction. This allowed us to investigate how hMSCs, after winning the race for the surface against pathogenic cells, interact with the biofilm. Our approach revealed that nanotopographies reduced major virulence pathways, such as LasR. The enhanced hMSCs support provided by the coated nanotopographies was shown to suppress virulence pathways and biofilm formation. Finally, we selected bioactive metabolites and demonstrated that these could be used as adjuncts to the nanostructured surfaces to reduce biofilm formation and enhance hMSC activity. These surfaces make excellent models to study hMSC-pathogen interactions and could be envisaged for use in novel orthopedic implants.

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.

Optimisation of surfactin yield in Bacillus using data-efficient active learning and high-throughput mass spectrometry.

Integration of machine learning and high throughput measurements are essential to drive the next generation of the design-build-test-learn (DBTL) cycle in synthetic biology. Here, we report the use of active learning in combination with metabolomics for optimising production of surfactin, a complex lipopeptide resulting from a non-ribosomal assembly pathway. We designed a media optimisation algorithm that iteratively learns the yield landscape and steers the media composition toward maximal production. The algorithm led to a 160 % yield increase after three DBTL runs as compared to an M9 baseline. Metabolomics data helped to elucidate the underpinning biochemistry for yield improvement and revealed Pareto-like trade-offs in production of other lipopeptides from related pathways. We found positive associations between organic acids and surfactin, suggesting a key role of central carbon metabolism, as well as system-wide anisotropies in how metabolism reacts to shifts in carbon and nitrogen levels. Our framework offers a novel data-driven approach to improve yield of biological products with complex synthesis pathways that are not amenable to traditional yield optimisation strategies.

On-line targeted metabolomics for real-time monitoring of relevant compounds in fermentation processes.

Fermentation monitoring is a powerful tool for bioprocess development and optimization. On-line metabolomics is a technology that is starting to gain attention as a bioprocess monitoring tool, allowing the direct measurement of many compounds in the fermentation broth at a very high time resolution. In this work, targeted on-line metabolomics was used to monitor 40 metabolites of interest during three Escherichia coli succinate production fermentation experiments every 5 min with a triple quadrupole mass spectrometer. This allowed capturing high-time resolution biological data that can provide critical information for process optimization. For nine of these metabolites, simple univariate regression models were used to model compound concentration from their on-line mass spectrometry peak area. These on-line metabolomics univariate models performed comparably to vibrational spectroscopy multivariate partial least squares regressions models reported in the literature, which typically are much more complex and time consuming to build. In conclusion, this work shows how on-line metabolomics can be used to directly monitor many bioprocess compounds of interest and obtain rich biological and bioprocess data.

Pilot study of a ketogenic diet in bipolar disorder.

BACKGROUND: Recent evidence from case reports suggests that a ketogenic diet may be effective for bipolar disorder. However, no clinical trials have been conducted to date. AIMS: To assess the recruitment and feasibility of a ketogenic diet intervention in bipolar disorder. METHOD: Euthymic individuals with bipolar disorder were recruited to a 6-8 week trial of a modified ketogenic diet, and a range of clinical, economic and functional outcome measures were assessed. Study registration number: ISRCTN61613198. RESULTS: Of 27 recruited participants, 26 commenced and 20 completed the modified ketogenic diet for 6-8 weeks. The outcomes data-set was 95% complete for daily ketone measures, 95% complete for daily glucose measures and 95% complete for daily ecological momentary assessment of symptoms during the intervention period. Mean daily blood ketone readings were 1.3 mmol/L (s.d. = 0.77, median = 1.1) during the intervention period, and 91% of all readings indicated ketosis, suggesting a high degree of adherence to the diet. Over 91% of daily blood glucose readings were within normal range, with 9% indicating mild hypoglycaemia. Eleven minor adverse events were recorded, including fatigue, constipation, drowsiness and hunger. One serious adverse event was reported (euglycemic ketoacidosis in a participant taking SGLT2-inhibitor medication). CONCLUSIONS: The recruitment and retention of euthymic individuals with bipolar disorder to a 6-8 week ketogenic diet intervention was feasible, with high completion rates for outcome measures. The majority of participants reached and maintained ketosis, and adverse events were generally mild and modifiable. A future randomised controlled trial is now warranted.

Streamlined identification of strain engineering targets for bioprocess improvement using metabolic pathway enrichment analysis.

Metabolomics is a powerful tool for the identification of genetic targets for bioprocess optimisation. However, in most cases, only the biosynthetic pathway directed to product formation is analysed, limiting the identification of these targets. Some studies have used untargeted metabolomics, allowing a more unbiased approach, but data interpretation using multivariate analysis is usually not straightforward and requires time and effort. Here we show, for the first time, the application of metabolic pathway enrichment analysis using untargeted and targeted metabolomics data to identify genetic targets for bioprocess improvement in a more streamlined way. The analysis of an Escherichia coli succinate production bioprocess with this methodology revealed three significantly modulated pathways during the product formation phase: the pentose phosphate pathway, pantothenate and CoA biosynthesis and ascorbate and aldarate metabolism. From these, the two former pathways are consistent with previous efforts to improve succinate production in Escherichia coli. Furthermore, to the best of our knowledge, ascorbate and aldarate metabolism is a newly identified target that has so far never been explored for improving succinate production in this microorganism. This methodology therefore represents a powerful tool for the streamlined identification of strain engineering targets that can accelerate bioprocess optimisation.

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.

An integrated transcriptomic and metabolomic approach to investigate the heterogeneous Candida albicans biofilm phenotype.

Candida albicans is the most prevalent and notorious of the Candida species involved in bloodstream infections, which is characterised by its capacity to form robust biofilms. Biofilm formation is an important clinical entity shown to be highly variable among clinical isolates. There are various environmental and physiological factors, including nutrient availability which influence the phenotype of Candida species. However, mechanisms underpinning adaptive biofilm heterogeneity have not yet been fully explored. Within this study we have profiled previously characterised and phenotypically distinct C. albicans bloodstream isolates. We assessed the dynamic susceptibility of these differing populations to antifungal treatments using population analysis profiling in addition to assessing biofilm formation and morphological changes. High throughput methodologies of RNA-Seq and LC-MS were employed to map and integrate the transcriptional and metabolic reprogramming undertaken by heterogenous C. albicans isolates in response to biofilm and hyphal inducing serum. We found a significant relationship between biofilm heterogeneity and azole resistance (P < 0.05). In addition, we observed that in response to serum our low biofilm forming (LBF) C. albicans exhibited a significant increase in biofilm formation and hyphal elongation. The transcriptional reprogramming of LBF strains compared to high biofilm forming (HBF) was distinct, indicating a high level of plasticity and variation in stress responses by heterogenous strains. The metabolic responses, although variable between LBF and HBF, shared many of the same responses to serum. Notably, a high upregulation of the arachidonic acid cascade, part of the COX pathway, was observed and this pathway was found to induce biofilm formation in LBF 3-fold. C. albicans is a highly heterogenous bloodstream pathogen with clinical isolates varying in antifungal tolerance and biofilm formation. In addition to this, C. albicans is capable of highly complex and variable regulation of transcription and metabolic pathways and heterogeneity across isolates further increases the complexity of these pathways. Here we have shown with a dual and integrated approach, the importance of studying a diverse panel of C. albicans isolates, which has the potential to reveal distinct pathways that can harnessed for drug discovery.

Metabolic insights into phosphofructokinase inhibition in bloodstream-form trypanosomes.

Previously, we reported the development of novel small molecules that are potent inhibitors of the glycolytic enzyme phosphofructokinase (PFK) of Trypanosoma brucei and related protists responsible for serious diseases in humans and domestic animals. Cultured bloodstream-form trypanosomes, which are fully reliant on glycolysis for their ATP production, are rapidly killed at submicromolar concentrations of these compounds, which have no effect on the activity of human PFKs and human cells. Single-day oral dosing cures stage 1 human trypanosomiasis in an animal model. Here we analyze changes in the metabolome of cultured trypanosomes during the first hour after addition of a selected PFK inhibitor, CTCB405. The ATP level of T. brucei drops quickly followed by a partial increase. Already within the first five minutes after dosing, an increase is observed in the amount of fructose 6-phosphate, the metabolite just upstream of the PFK reaction, while intracellular levels of the downstream glycolytic metabolites phosphoenolpyruvate and pyruvate show an increase and decrease, respectively. Intriguingly, a decrease in the level of O-acetylcarnitine and an increase in the amount of L-carnitine were observed. Likely explanations for these metabolomic changes are provided based on existing knowledge of the trypanosome's compartmentalized metabolic network and kinetic properties of its enzymes. Other major changes in the metabolome concerned glycerophospholipids, however, there was no consistent pattern of increase or decrease upon treatment. CTCB405 treatment caused less prominent changes in the metabolome of bloodstream-form Trypanosoma congolense, a ruminant parasite. This agrees with the fact that it has a more elaborate glucose catabolic network with a considerably lower glucose consumption rate than bloodstream-form T. brucei.

Nanotopography reveals metabolites that maintain the immunomodulatory phenotype of mesenchymal stromal cells.

Mesenchymal stromal cells (MSCs) are multipotent progenitor cells that are of considerable clinical potential in transplantation and anti-inflammatory therapies due to their capacity for tissue repair and immunomodulation. However, MSCs rapidly differentiate once in culture, making their large-scale expansion for use in immunomodulatory therapies challenging. Although the differentiation mechanisms of MSCs have been extensively investigated using materials, little is known about how materials can influence paracrine activities of MSCs. Here, we show that nanotopography can control the immunomodulatory capacity of MSCs through decreased intracellular tension and increasing oxidative glycolysis. We use nanotopography to identify bioactive metabolites that modulate intracellular tension, growth and immunomodulatory phenotype of MSCs in standard culture and during larger scale cell manufacture. Our findings demonstrate an effective route to support large-scale expansion of functional MSCs for therapeutic purposes.

Metabolomic Profiling of End-Stage Heart Failure Secondary to Chronic Chagas Cardiomyopathy.

Chronic Chagas cardiomyopathy (CCC) is the most frequent and severe clinical form of chronic Chagas disease, representing one of the leading causes of morbidity and mortality in Latin America, and a growing global public health problem. There is currently no approved treatment for CCC; however, omics technologies have enabled significant progress to be made in the search for new therapeutic targets. The metabolic alterations associated with pathogenic mechanisms of CCC and their relationship to cellular and immunopathogenic processes in cardiac tissue remain largely unknown. This exploratory study aimed to evaluate the potential underlying pathogenic mechanisms in the failing myocardium of patients with end-stage heart failure (ESHF) secondary to CCC by applying an untargeted metabolomic profiling approach. Cardiac tissue samples from the left ventricle of patients with ESHF of CCC etiology (n = 7) and healthy donors (n = 7) were analyzed using liquid chromatography-mass spectrometry. Metabolite profiles showed altered branched-chain amino acid and acylcarnitine levels, decreased fatty acid uptake and oxidation, increased activity of the pentose phosphate pathway, dysregulation of the TCA cycle, and alterations in critical cellular antioxidant systems. These findings suggest processes of energy deficit, alterations in substrate availability, and enhanced production of reactive oxygen species in the affected myocardium. This profile potentially contributes to the development and maintenance of a chronic inflammatory state that leads to progression and severity of CCC. Further studies involving larger sample sizes and comparisons with heart failure patients without CCC are needed to validate these results, opening an avenue to investigate new therapeutic approaches for the treatment and prevention of progression of this unique and severe cardiomyopathy.

On-line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process.

The real-time monitoring of metabolites (RTMet) is instrumental for the industrial production of biobased fermentation products. This study shows the first application of untargeted on-line metabolomics for the monitoring of undiluted fermentation broth samples taken automatically from a 5 L bioreactor every 5 min via flow injection mass spectrometry. The travel time from the bioreactor to the mass spectrometer was 30 s. Using mass spectrometry allows, on the one hand, the direct monitoring of targeted key process compounds of interest and, on the other hand, provides information on hundreds of additional untargeted compounds without requiring previous calibration data. In this study, this technology was applied in an Escherichia coli succinate fermentation process and 886 different m/z signals were monitored, including key process compounds (glucose, succinate, and pyruvate), potential biomarkers of biomass formation such as (R)-2,3-dihydroxy-isovalerate and (R)-2,3-dihydroxy-3-methylpentanoate and compounds from the pentose phosphate pathway and nucleotide metabolism, among others. The main advantage of the RTMet technology is that it allows the monitoring of hundreds of signals without the requirement of developing partial least squares regression models, making it a perfect tool for bioprocess monitoring and for testing many different strains and process conditions for bioprocess development.

Mass Spectral Molecular Networking to Profile the Metabolome of Biostimulant Bacillus Strains.

Beneficial soil microbes like plant growth-promoting rhizobacteria (PGPR) significantly contribute to plant growth and development through various mechanisms activated by plant-PGPR interactions. However, a complete understanding of the biochemistry of the PGPR and microbial intraspecific interactions within the consortia is still enigmatic. Such complexities constrain the design and use of PGPR formulations for sustainable agriculture. Therefore, we report the application of mass spectrometry (MS)-based untargeted metabolomics and molecular networking (MN) to interrogate and profile the intracellular chemical space of PGPR Bacillus strains: B. laterosporus, B. amyloliquefaciens, B. licheniformis 1001, and B. licheniformis M017 and their consortium. The results revealed differential and diverse chemistries in the four Bacillus strains when grown separately, and also differing from when grown as a consortium. MolNetEnhancer networks revealed 11 differential molecular families that are comprised of lipids and lipid-like molecules, benzenoids, nucleotide-like molecules, and organic acids and derivatives. Consortium and B. amyloliquefaciens metabolite profiles were characterized by the high abundance of surfactins, whereas B. licheniformis strains were characterized by the unique presence of lichenysins. Thus, this work, applying metabolome mining tools, maps the microbial chemical space of isolates and their consortium, thus providing valuable insights into molecular information of microbial systems. Such fundamental knowledge is essential for the innovative design and use of PGPR-based biostimulants.

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

INTRODUCTION: Recent advances in high-throughput methodologies in the 'omics' and synthetic biology fields call for rapid and sensitive workflows in the metabolic phenotyping of complex biological samples. OBJECTIVE: The objective of this research was to evaluate a straightforward to implement LC-MS metabolomics method using a commercially available chromatography column that provides increased throughput. Reducing run time can potentially impact chromatography and therefore the effects of ion mobility spectrometry to expand peak capacity were also evaluated. Additional confidence provided via collision cross section measurements for detected features was also explored. METHODS: A rapid untargeted metabolomics workflow was developed with broad metabolome coverage, combining zwitterionic-phase hydrophilic interaction chromatography (HILIC-Z) with drift tube ion mobility-quadrupole time-of-flight (DTIM-qTOF) mass spectrometry. The analytical performance of our method was explored using extracts from complex biological samples, including a reproducibility study on chicken serum and a simple comparative study on a bacterial metabolome. RESULTS: The method is acronymised RHIMMS for rapid HILIC-Z ion mobility mass spectrometry. We present the RHIMMS workflow starting with data acquisition, followed by data processing and analysis. RHIMMS demonstrates improved chromatographic separation for a selection of metabolites with wide physicochemical properties while maintaining reproducibility at better than 20% over 200 injections at 3.5 min per sample for the selected metabolites, and a mean of 13.9% for the top 50 metabolites by intensity. Additionally, the combination of rapid chromatographic separation with ion mobility allows improved annotation and the ability to distinguish isobaric compounds. CONCLUSION: Our results demonstrate RHIMMS to be a rapid, reproducible, sensitive and high-resolution analytical platform that is highly applicable to the untargeted metabolomics analysis of complex samples.

Materials-driven fibronectin assembly on nanoscale topography enhances mesenchymal stem cell adhesion, protecting cells from bacterial virulence factors and preventing biofilm formation.

Post-operative infection is a major complication in patients recovering from orthopaedic surgery. As such, there is a clinical need to develop biomaterials for use in regenerative surgery that can promote mesenchymal stem cell (MSC) osteospecific differentiation and that can prevent infection caused by biofilm-forming pathogens. Nanotopographical approaches to pathogen control are being identified, including in orthopaedic materials such as titanium and its alloys. These topographies use high aspect ratio nanospikes or nanowires to prevent bacterial adhesion but these features also significantly reduce MSC adhesion and activity. Here, we use a poly (ethyl acrylate) (PEA) polymer coating on titanium nanowires to spontaneously organise fibronectin (FN) and to deliver bone morphogenetic protein 2 (BMP2) to enhance MSC adhesion and osteospecific signalling. Using a novel MSC-Pseudomonas aeruginosa co-culture, we show that the coated nanotopographies protect MSCs from cytotoxic quorum sensing and signalling molecules, enhance MSC adhesion and osteoblast differentiation and reduce biofilm formation. We conclude that the PEA polymer-coated nanotopography can both support MSCs and prevent pathogens from adhering to a biomaterial surface, thus protecting from biofilm formation and bacterial infection, and supporting osteogenic repair.

A Metabolomic Landscape of Maize Plants Treated With a Microbial Biostimulant Under Well-Watered and Drought Conditions.

Microbial plant biostimulants have been successfully applied to improve plant growth, stress resilience and productivity. However, the mechanisms of action of biostimulants are still enigmatic, which is the main bottleneck for the fully realization and implementation of biostimulants into the agricultural industry. Here, we report the elucidation of a global metabolic landscape of maize (Zea mays L) leaves in response to a microbial biostimulant, under well-watered and drought conditions. The study reveals that the increased pool of tricarboxylic acid (TCA) intermediates, alterations in amino acid levels and differential changes in phenolics and lipids are key metabolic signatures induced by the application of the microbial-based biostimulant. These reconfigurations of metabolism gravitate toward growth-promotion and defense preconditioning of the plant. Furthermore, the application of microbial biostimulant conferred enhanced drought resilience to maize plants via altering key metabolic pathways involved in drought resistance mechanisms such as the redox homeostasis, strengthening of the plant cell wall, osmoregulation, energy production and membrane remodeling. For the first time, we show key molecular events, metabolic reprogramming, activated by a microbial biostimulant for plant growth promotion and defense priming. Thus, these elucidated metabolomic insights contribute to ongoing efforts in decoding modes of action of biostimulants and generating fundamental scientific knowledgebase that is necessary for the development of the plant biostimulants industry, for sustainable food security.

Inhibition of glycolysis and mitochondrial respiration promotes radiosensitisation of neuroblastoma and glioma cells.

BACKGROUND: Neuroblastoma accounts for 7% of paediatric malignancies but is responsible for 15% of all childhood cancer deaths. Despite rigorous treatment involving chemotherapy, surgery, radiotherapy and immunotherapy, the 5-year overall survival rate of high-risk disease remains < 40%, highlighting the need for improved therapy. Since neuroblastoma cells exhibit aberrant metabolism, we determined whether their sensitivity to radiotherapy could be enhanced by drugs affecting cancer cell metabolism. METHODS: Using a panel of neuroblastoma and glioma cells, we determined the radiosensitising effects of inhibitors of glycolysis (2-DG) and mitochondrial function (metformin). Mechanisms underlying radiosensitisation were determined by metabolomic and bioenergetic profiling, flow cytometry and live cell imaging and by evaluating different treatment schedules. RESULTS: The radiosensitising effects of 2-DG were greatly enhanced by combination with the antidiabetic biguanide, metformin. Metabolomic analysis and cellular bioenergetic profiling revealed this combination to elicit severe disruption of key glycolytic and mitochondrial metabolites, causing significant reductions in ATP generation and enhancing radiosensitivity. Combination treatment induced G2/M arrest that persisted for at least 24 h post-irradiation, promoting apoptotic cell death in a large proportion of cells. CONCLUSION: Our findings demonstrate that the radiosensitising effect of 2-DG was significantly enhanced by its combination with metformin. This clearly demonstrates that dual metabolic targeting has potential to improve clinical outcomes in children with high-risk neuroblastoma by overcoming radioresistance.

Ranking Metabolite Sets by Their Activity Levels.

Related metabolites can be grouped into sets in many ways, e.g., by their participation in series of chemical reactions (forming metabolic pathways), or based on fragmentation spectral similarities or shared chemical substructures. Understanding how such metabolite sets change in relation to experimental factors can be incredibly useful in the interpretation and understanding of complex metabolomics data sets. However, many of the available tools that are used to perform this analysis are not entirely suitable for the analysis of untargeted metabolomics measurements. Here, we present PALS (Pathway Activity Level Scoring), a Python library, command line tool, and Web application that performs the ranking of significantly changing metabolite sets over different experimental conditions. The main algorithm in PALS is based on the pathway level analysis of gene expression (PLAGE) factorisation method and is denoted as mPLAGE (PLAGE for metabolomics). As an example of an application, PALS is used to analyse metabolites grouped as metabolic pathways and by shared tandem mass spectrometry fragmentation patterns. A comparison of mPLAGE with two other commonly used methods (overrepresentation analysis (ORA) and gene set enrichment analysis (GSEA)) is also given and reveals that mPLAGE is more robust to missing features and noisy data than the alternatives. As further examples, PALS is also applied to human African trypanosomiasis, Rhamnaceae, and American Gut Project data. In addition, normalisation can have a significant impact on pathway analysis results, and PALS offers a framework to further investigate this. PALS is freely available from our project Web site.

The use of nanovibration to discover specific and potent bioactive metabolites that stimulate osteogenic differentiation in mesenchymal stem cells.

Bioactive metabolites have wide-ranging biological activities and are a potential source of future research and therapeutic tools. Here, we use nanovibrational stimulation to induce osteogenic differentiation of mesenchymal stem cells, in the absence of off-target, nonosteogenic differentiation. We show that this differentiation method, which does not rely on the addition of exogenous growth factors to culture media, provides an artifact-free approach to identifying bioactive metabolites that specifically and potently induce osteogenesis. We first identify a highly specific metabolite, cholesterol sulfate, an endogenous steroid. Next, a screen of other small molecules with a similar steroid scaffold identified fludrocortisone acetate with both specific and highly potent osteogenic-inducing activity. Further, we implicate cytoskeletal contractility as a measure of osteogenic potency and cell stiffness as a measure of specificity. These findings demonstrate that physical principles can be used to identify bioactive metabolites and then enable optimization of metabolite potency can be optimized by examining structure-function relationships.