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.

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.

TopNEXt: automatic DDA exclusion framework for multi-sample mass spectrometry experiments.

MOTIVATION: Liquid Chromatography Tandem Mass Spectrometry experiments aim to produce high-quality fragmentation spectra, which can be used to annotate metabolites. However, current Data-Dependent Acquisition approaches may fail to collect spectra of sufficient quality and quantity for experimental outcomes, and extend poorly across multiple samples by failing to share information across samples or by requiring manual expert input. RESULTS: We present TopNEXt, a real-time scan prioritization framework that improves data acquisition in multi-sample Liquid Chromatography Tandem Mass Spectrometry metabolomics experiments. TopNEXt extends traditional Data-Dependent Acquisition exclusion methods across multiple samples by using a Region of Interest and intensity-based scoring system. Through both simulated and lab experiments, we show that methods incorporating these novel concepts acquire fragmentation spectra for an additional 10% of our set of target peaks and with an additional 20% of acquisition intensity. By increasing the quality and quantity of fragmentation spectra, TopNEXt can help improve metabolite identification with a potential impact across a variety of experimental contexts. AVAILABILITY AND IMPLEMENTATION: TopNEXt is implemented as part of the ViMMS framework and the latest version can be found at https://github.com/glasgowcompbio/vimms. A stable version used to produce our results can be found at 10.5281/zenodo.7468914.

Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics.

Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their MS/MS spectral annotation performance in untargeted settings due to the lack of ground truth and the costs involved in running a large number of acquisitions. Here, we present a systematic in silico comparison of these two acquisition methods in untargeted metabolomics by extending our Virtual Metabolomics Mass Spectrometer (ViMMS) framework with a DIA module. Our results show that the performance of these methods varies with the average number of co-eluting ions as the most important factor. At low numbers, DIA outperforms DDA, but at higher numbers, DDA has an advantage as DIA can no longer deal with the large amount of overlapping ion chromatograms. Results from simulation were further validated on an actual mass spectrometer, demonstrating that using ViMMS we can draw conclusions from simulation that translate well into the real world. The versatility of the Virtual Metabolomics Mass Spectrometer (ViMMS) framework in simulating different parameters of both Data-Dependent and Data-Independent Acquisition (DDA and DIA) modes is a key advantage of this work. Researchers can easily explore and compare the performance of different acquisition methods within the ViMMS framework, without the need for expensive and time-consuming experiments with real experimental data. By identifying the strengths and limitations of each acquisition method, researchers can optimize their choice and obtain more accurate and robust results. Furthermore, the ability to simulate and validate results using the ViMMS framework can save significant time and resources, as it eliminates the need for numerous experiments. This work not only provides valuable insights into the performance of DDA and DIA, but it also opens the door for further advancements in LC-MS/MS data acquisition methods.

Outcomes of Uterine Rupture in the Setting of the Unscarred Compared With the Scarred Uterus.

Uterine rupture is a rare obstetric complication that is associated with maternal and neonatal morbidity and mortality. The aim of this study was to examine uterine rupture and its outcomes in the setting of the unscarred compared with the scarred uterus. A retrospective observational cohort study was performed examining all cases of uterine rupture in three tertiary care hospitals in Dublin, Ireland, over a 20-year period. The primary outcome was perinatal mortality rate with uterine rupture, which was 11.02% (95% CI 6.5-17.3). There was no significant difference in perinatal mortality between cases of scarred and unscarred uterine rupture. Unscarred uterine rupture was associated with higher maternal morbidity , defined as major obstetric hemorrhage or hysterectomy.

Quantitative monitoring and modelling of retrodialysis drug delivery in a brain phantom.

A vast number of drug molecules are unable to cross the blood-brain barrier, which results in a loss of therapeutic opportunities when these molecules are administered by intravenous infusion. To circumvent the blood-brain barrier, local drug delivery devices have been developed over the past few decades such as reverse microdialysis. Reverse microdialysis (or retrodialysis) offers many advantages, such as a lack of net volume influx to the intracranial cavity and the ability to sample the tumour's micro-environment. However, the translation of this technique to efficient drug delivery has not been systematically studied. In this work, we present an experimental platform to evaluate the performance of microdialysis devices in reverse mode in a brain tissue phantom. The mass of model drug delivered is measured by computing absorbance fields from optical images. Concentration maps are reconstructed using a modern and open-source implementation of the inverse Abel transform. To illustrate our method, we assess the capability of a commercial probe in delivering methylene blue to a gel phantom. We find that the delivery rate can be described by classical microdialysis theory, except at low dialysate flow rates where it is impacted by gravity, and high flow rates where significant convection to the gel occurs. We also show that the flow rate has an important impact not only on the overall size of the drug plume, but also on its shape. The numerical tools developed for this study have been made freely available to ensure that the method presented can be used to rapidly and inexpensively optimise probe design and protocol parameters before proceeding to more in-depth studies.

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.

Multi-omics & pathway analysis identify potential roles for tumor N-acetyl aspartate accumulation in murine models of castration-resistant prostate cancer.

Castration-resistant prostate cancer (CRPC) is incurable and remains a significant worldwide challenge (Oakes and Papa, 2015). Matched untargeted multi-level omic datasets may reveal biological changes driving CRPC, identifying novel biomarkers and/or therapeutic targets. Untargeted RNA sequencing, proteomics, and metabolomics were performed on xenografts derived from three independent sets of hormone naive and matched CRPC human cell line models of local, lymph node, and bone metastasis grown as murine orthografts. Collectively, we tested the feasibility of muti-omics analysis on models of CRPC in revealing pathways of interest for future validation investigation. Untargeted metabolomics revealed NAA and NAAG commonly accumulating in CRPC across three independent models and proteomics showed upregulation of related enzymes, namely N-acetylated alpha-linked acidic dipeptidases (FOLH1/NAALADL2). Based on pathway analysis integrating multiple omic levels, we hypothesize that increased NAA in CRPC may be due to upregulation of NAAG hydrolysis via NAALADLases providing a pool of acetyl Co-A for upregulated sphingolipid metabolism and a pool of glutamate and aspartate for nucleotide synthesis during tumor growth.

GraphOmics: an interactive platform to explore and integrate multi-omics data.

BACKGROUND: An increasing number of studies now produce multiple omics measurements that require using sophisticated computational methods for analysis. While each omics data can be examined separately, jointly integrating multiple omics data allows for deeper understanding and insights to be gained from the study. In particular, data integration can be performed horizontally, where biological entities from multiple omics measurements are mapped to common reactions and pathways. However, data integration remains a challenge due to the complexity of the data and the difficulty in interpreting analysis results. RESULTS: Here we present GraphOmics, a user-friendly platform to explore and integrate multiple omics datasets and support hypothesis generation. Users can upload transcriptomics, proteomics and metabolomics data to GraphOmics. Relevant entities are connected based on their biochemical relationships, and mapped to reactions and pathways from Reactome. From the Data Browser in GraphOmics, mapped entities and pathways can be ranked, sorted and filtered according to their statistical significance (p values) and fold changes. Context-sensitive panels provide information on the currently selected entities, while interactive heatmaps and clustering functionalities are also available. As a case study, we demonstrated how GraphOmics was used to interactively explore multi-omics data and support hypothesis generation using two complex datasets from existing Zebrafish regeneration and Covid-19 human studies. CONCLUSIONS: GraphOmics is fully open-sourced and freely accessible from https://graphomics.glasgowcompbio.org/ . It can be used to integrate multiple omics data horizontally by mapping entities across omics to reactions and pathways. Our demonstration showed that by using interactive explorations from GraphOmics, interesting insights and biological hypotheses could be rapidly revealed.

Direct-writing microporous polymer architectures - print, capture and release.

In the nature-inspired breath figure method, rafts of condensed water droplets self-organise and imprint into a permanent microporous polymer structure. This could have exciting applications in drug delivery, tissue engineering and sensors but it is extremely difficult to control or functionalise the final structure. Here, we show direct-writing of droplets onto fluid surfaces by inkjet printing as a breakthrough to dial-in a required pattern, structure and function into the polymer film.

A monolithic single-chip point-of-care platform for metabolomic prostate cancer detection.

There is a global unmet need for rapid and cost-effective prognostic and diagnostic tools that can be used at the bedside or in the doctor's office to reduce the impact of serious disease. Many cancers are diagnosed late, leading to costly treatment and reduced life expectancy. With prostate cancer, the absence of a reliable test has inhibited the adoption of screening programs. We report a microelectronic point-of-care metabolite biomarker measurement platform and use it for prostate cancer detection. The platform, using an array of photodetectors configured to operate with targeted, multiplexed, colorimetric assays confined in monolithically integrated passive microfluidic channels, completes a combined assay of 4 metabolites in a drop of human plasma in under 2 min. A preliminary clinical study using l-amino acids, glutamate, choline, and sarcosine was used to train a cross-validated random forest algorithm. The system demonstrated sensitivity to prostate cancer of 94% with a specificity of 70% and an area under the curve of 0.78. The technology can implement many similar assay panels and hence has the potential to revolutionize low-cost, rapid, point-of-care testing.

Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions.

Specialised metabolites from microbial sources are well-known for their wide range of biomedical applications, particularly as antibiotics. When mining paired genomic and metabolomic data sets for novel specialised metabolites, establishing links between Biosynthetic Gene Clusters (BGCs) and metabolites represents a promising way of finding such novel chemistry. However, due to the lack of detailed biosynthetic knowledge for the majority of predicted BGCs, and the large number of possible combinations, this is not a simple task. This problem is becoming ever more pressing with the increased availability of paired omics data sets. Current tools are not effective at identifying valid links automatically, and manual verification is a considerable bottleneck in natural product research. We demonstrate that using multiple link-scoring functions together makes it easier to prioritise true links relative to others. Based on standardising a commonly used score, we introduce a new, more effective score, and introduce a novel score using an Input-Output Kernel Regression approach. Finally, we present NPLinker, a software framework to link genomic and metabolomic data. Results are verified using publicly available data sets that include validated links.

2D and 3D inkjet printing of biopharmaceuticals - A review of trends and future perspectives in research and manufacturing.

There is an ongoing global shift in pharmaceutical business models from small molecule drugs to biologics. This increase in complexity is in response to advancements in our diagnoses and understanding of diseases. With the more targeted approach coupled with its inherently more costly development and manufacturing, 2D and 3D printing are being explored as suitable techniques to deliver more personalised and affordable routes to drug discovery and manufacturing. In this review, we explore first the business context underlying this shift to biopharmaceuticals and provide an update on the latest work exploring discovery and pharmaceutics. We then draw on multiple disciplines to help reveal the shared challenges facing researchers and firms aiming to develop biopharmaceuticals, specifically when using the most commonly explored manufacturing routes of drop-on-demand inkjet printing and pneumatic extrusion. This includes separating out how to consider mechanical and chemical influences during manufacturing, the role of the chosen hardware and the challenges of aqueous formulation based on similar challenges being faced by the printing industry. Together, this provides a review of existing work and guidance for researchers and industry to help with the de-risking and rapid development of future biopharmaceutical products.

Capturing the value in printed pharmaceuticals - A study of inkjet droplets released from a polymer matrix.

The future of personalised combination dosages will rely on the programming and delivery of multiple, separate APIs, their carrier fluids and excipients to a stable matrix, where each will remain separate until it is needed to be released. A recent technique has demonstrated how to print, capture and release materials from a polymer matrix using inkjet printing, a low cost and customisable technique. Droplets of a formulation are delivered to a fluid polymer matrix, where they are imbibed and remain pinned just under the upper surface, held in place by a balance of interfacial energies. Once the surrounding matrix cures and solidifies, the coating or matrix has trapped the formulation, but each drop has a small opening or pore to the outside that will allow delivery through diffusion. However, while the trapping mechanism has been explored in detail, to-date the release involved in this delivery has never been studied or quantified to the same level. Here we show a first study to quantify the release of a model system from a polymer matrix. An aqueous fluorescein solution is delivered and trapped, with release demonstrated to an agarose gel and aqueous environments. The work reveals that the balance of interfacial tensions prevents a reliable release until low concentrations of surfactant are included. This provides a route forward to further explore stabilising combinations of drugs within one material using a digitally controlled and affordable technique.

Rapid Development of Improved Data-Dependent Acquisition Strategies.

Tandem mass spectrometry (LC-MS/MS) is widely used to identify unknown ions in untargeted metabolomics. Data-dependent acquisition (DDA) chooses which ions to fragment based upon intensities observed in MS1 survey scans and typically only fragments a small subset of the ions present. Despite this inefficiency, relatively little work has addressed the development of new DDA methods, partly due to the high overhead associated with running the many extracts necessary to optimize approaches in busy MS facilities. In this work, we first provide theoretical results that show how much improvement is possible over current DDA strategies. We then describe an in silico framework for fast and cost-efficient development of new DDA strategies using a previously developed virtual metabolomics mass spectrometer (ViMMS). Additional functionality is added to ViMMS to allow methods to be used both in simulation and on real samples via an Instrument Application Programming Interface (IAPI). We demonstrate this framework through the development and optimization of two new DDA methods that introduce new advanced ion prioritization strategies. Upon application of these developed methods to two complex metabolite mixtures, our results show that they are able to fragment more unique ions than standard DDA strategies.

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.

High sodium intake, glomerular hyperfiltration, and protein catabolism in patients with essential hypertension.

AIMS: A blood pressure (BP)-independent metabolic shift towards a catabolic state upon high sodium (Na+) diet, ultimately favouring body fluid preservation, has recently been described in pre-clinical controlled settings. We sought to investigate the real-life impact of high Na+ intake on measures of renal Na+/water handling and metabolic signatures, as surrogates for cardiovascular risk, in hypertensive patients. METHODS AND RESULTS: We analysed clinical and biochemical data from 766 consecutive patients with essential hypertension, collected at the time of screening for secondary causes. The systematic screening protocol included 24 h urine (24 h-u-) collection on usual diet and avoidance of renin-angiotensin-aldosterone system-confounding medications. Urinary 24 h-Na+ excretion, used to define classes of Na+ intake (low ≤2.3 g/day; medium 2.3-5 g/day; high >5 g/day), was an independent predictor of glomerular filtration rate after correction for age, sex, BP, BMI, aldosterone, and potassium excretion [P = 0.001; low: 94.1 (69.9-118.8) vs. high: 127.5 (108.3-147.8) mL/min/1.73 m2]. Renal Na+ and water handling diverged, with higher fractional excretion of Na+ and lower fractional excretion of water in those with evidence of high Na+ intake [FENa: low 0.39% (0.30-0.47) vs. high 0.81% (0.73-0.98), P < 0.001; FEwater: low 1.13% (0.73-1.72) vs. high 0.89% (0.69-1.12), P = 0.015]. Despite higher FENa, these patients showed higher absolute 24 h Na+ reabsorption and higher associated tubular energy expenditure, estimated by tubular Na+/ATP stoichiometry, accordingly [Δhigh-low = 18 (12-24) kcal/day, P < 0.001]. At non-targeted liquid chromatography/mass spectrometry plasma metabolomics in an unselected subcohort (n = 67), metabolites which were more abundant in high versus low Na+ intake (P < 0.05) mostly entailed intermediates or end products of protein catabolism/urea cycle. CONCLUSION: When exposed to high Na+ intake, kidneys dissociate Na+ and water handling. In hypertensive patients, this comes at the cost of higher glomerular filtration rate, increased tubular energy expenditure, and protein catabolism from endogenous (muscle) or excess exogenous (dietary) sources. Glomerular hyperfiltration and the metabolic shift may have broad implications on global cardiovascular risk independent of BP.

Paper-Based Electrochemical Sensors Using Paper as a Scaffold to Create Porous Carbon Nanotube Electrodes.

Paper-based sensors and assays have evolved rapidly due to the conversion of paper-based microfluidics, functional paper coatings, and new electrical and optical readout techniques. Nanomaterials have gained substantial attraction as key components in paper-based sensors, as they can be coated or printed relatively easily on paper to locally control the device functionality. Here, we report a new combination of methods to fabricate carbon nanotube-based (CNT) electrodes for paper-based electrochemical sensors using a combination of laser cutting, drop-casting, and origami. We applied this process to a range of filter papers with different porosities and used their differences in three-dimensional cellulose networks to study the influence of the cellulose scaffold on the final CNT network and the resulting electrochemical detection of glucose. We found that an optimal porosity exists, which balances the benefits of surface enhancement and electrical connectivity within the cellulose scaffold of the paper-based device and demonstrates a cost-effective process for the fabrication of device arrays.

Changes in Plasma Itaconate Elevation in Early Rheumatoid Arthritis Patients Elucidates Disease Activity Associated Macrophage Activation.

Changes in the plasma metabolic profile were characterised in newly diagnosed rheumatoid arthritis (RA) patients upon commencement of conventional disease-modifying anti-rheumatic drug (cDMARD) therapy. Plasma samples collected in an early RA randomised strategy study (NCT00920478) that compared clinical (DAS) disease activity assessment with musculoskeletal ultrasound assessment (MSUS) to drive treatment decisions were subjected to untargeted metabolomic analysis. Metabolic profiles were collected at pre- and three months post-commencement of nonbiologic cDMARD. Metabolites that changed in association with changes in the DAS44 score were identified at the three-month timepoint. A total of nine metabolites exhibited a clear correlation with a reduction in DAS44 score following cDMARD commencement, particularly itaconate, its derived anhydride and a derivative of itaconate CoA. Increasing itaconate correlated with improved DAS44 score and decreasing levels of C-reactive protein (CRP). cDMARD treatment effects invoke consistent changes in plasma detectable metabolites, that in turn implicate clinical disease activity with macrophages. Such changes inform RA pathogenesis and reveal for the first time a link between itaconate production and resolution of inflammatory disease in humans. Quantitative metabolic biomarker-based tests of clinical change in state are feasible and should be developed around the itaconate pathway.