Is metabolomics reachable? Different purification strategies of human colon cancer cells provide different CE-MS metabolite profiles

Discovery of Synergistic Drug Combinations for Colorectal Cancer Driven by Tumor Barcode Derived from Metabolomics “Big Data”

The accumulation of cancer metabolomics data in the past decade provides exceptional opportunities for deeper investigations into cancer metabolism. However, integrating a large amount of heterogeneous metabolomics data to draw a full picture of the metabolic reprogramming and to discover oncometabolites of certain cancers remains challenging. In this study, a tumor barcode constructed based upon existing metabolomics “big data” using the Bayesian vote-counting method is proposed to identify oncometabolites in colorectal cancer (CRC). Specifically, a panel of oncometabolites of CRC was generated from 39 clinical studies with 3202 blood samples (1332 CRC vs. 1870 controls) and 990 tissue samples (495 CRC vs. 495 controls). Next, an oncometabolite-protein network was constructed by combining the tumor barcode and its involved proteins/enzymes. The effect of anti-cancer drugs or drug combinations was then mapped into this network by the random walk with restart process. Utilizing this network, potential Irinotecan (CPT-11)-sensitizing agents for CRC treatment were discovered by random forest and Xgboost. Finally, a compound named MK-2206 was highlighted and its synergy with CPT-11 was validated on two CRC cell lines. To summarize, we demonstrate in the present study that the metabolomics “big data”-based tumor barcodes and the subsequent network analyses are potentially useful for drug combination discovery or drug repositioning.

Ultrafiltration-based Extraction and LC-MS/MS Quantification of Phenylalanine in Human Blood Sample for Metabolite Target Analysis

Background:

Phenylalanine is a significant biomarker for various diseases like phenylketonuria, gastric cancers, and ischemic stroke according to recent studies.

Methods:

In the present study; a simple, sensitive, selective and novel analytical method was validated by using an ultrafiltration-based extraction and LC-MS/MS quantification of phenylalanine in human plasma using 13C phenylalanine heavy isotope. Amicon® Ultra Centrifugal Filter was used for ultrafiltration. Parameters affecting LC separation and MS/MS detection were investigated and optimized. Chromatographic separation was achieved on a Merck SeQuant ZIC-HILIC (100x4.6 mm, 5 μm) at a column temperature of 40°C using a mobile phase of mixture of acetonitrile containing 0.1% formic acid and water containing 0.1% formic acid (50:50 v/v) at a flow rate of 0.35 mL/min. The transitions m/z 167→121 for 13C phenylalanine, m/z 166→120 for phenylalanine itself were monitored using the MRM mode.

Result:

The assay was linear concentration range of 0.0025 μg/mL to 1.20 μg/mL (R2=0.999). The developed method was validated according to FDA guidelines. The method was found linear, sensitive, precise, accurate, and selective.

Advances of Metabolomics in Fungal Pathogen-Plant Interactions

Plant disease caused by fungus is one of the major threats to global food security, and understanding fungus-plant interactions is important for plant disease control. Research devoted to revealing the mechanisms of fungal pathogen-plant interactions has been conducted using genomics, transcriptomics, proteomics, and metabolomics. Metabolomics research based on mass spectrometric techniques is an important part of systems biology. In the past decade, the emerging field of metabolomics in plant pathogenic fungi has received wide attention. It not only provides a qualitative and quantitative approach for determining the pathogenesis of pathogenic fungi but also helps to elucidate the defense mechanisms of their host plants. This review focuses on the methods and progress of metabolomics research in fungal pathogen-plant interactions. In addition, the prospects and challenges of metabolomics research in plant pathogenic fungi and their hosts are addressed.

Pharmacometabolomics-aided Pharmacogenomics in Autoimmune Disease

Inter-individual variability has been a major hurdle to optimize disease management. Precision medicine holds promise for improving health and healthcare via tailor-made therapeutic strategies. Herein, we outline the paradigm of "pharmacometabolomics-aided pharmacogenomics" in autoimmune diseases. We envisage merging pharmacometabolomic and pharmacogenomic data (to address the interplay of genomic and environmental influences) with information technologies to facilitate data analysis as well as sense- and decision-making on the basis of synergy between artificial and human intelligence. Humans can detect patterns, which computer algorithms may fail to do so, whereas data-intensive and cognitively complex settings and processes limit human ability. We propose that better-informed, rapid and cost-effective omics studies need the implementation of holistic and multidisciplinary approaches.

Separation technique for the determination of highly polar metabolites in biological samples

Metabolomics is a new approach that is based on the systematic study of the full complement of metabolites in a biological sample. Metabolomics has the potential to fundamentally change clinical chemistry and, by extension, the fields of nutrition, toxicology, and medicine. However, it can be difficult to separate highly polar compounds. Mass spectrometry (MS), in combination with capillary electrophoresis (CE), gas chromatography (GC), or high performance liquid chromatography (HPLC) is the key analytical technique on which emerging "omics" technologies, namely, proteomics, metabolomics, and lipidomics, are based. In this review, we introduce various methods for the separation of highly polar metabolites.

Systematic evaluation of extraction methods for multiplatform-based metabotyping: application to the Fasciola hepatica metabolome

Combining data from multiple analytical platforms is essential for comprehensive study of the molecular phenotype (metabotype) of a given biological sample. The metabolite profiles generated are intrinsically dependent on the analytical platforms, each requiring optimization of instrumental parameters, separation conditions, and sample extraction to deliver maximal biological information. An in-depth evaluation of extraction protocols for characterizing the metabolome of the hepatobiliary fluke Fasciola hepatica, using ultra performance liquid chromatography and capillary electrophoresis coupled with mass spectroscopy is presented. The spectrometric methods were characterized by performance, and metrics of merit were established, including precision, mass accuracy, selectivity, sensitivity, and platform stability. Although a core group of molecules was common to all methods, each platform contributed a unique set, whereby 142 metabolites out of 14,724 features were identified. A mixture design revealed that the chloroform:methanol:water proportion of 15:59:26 was globally the best composition for metabolite extraction across UPLC-MS and CE-MS platforms accommodating different columns and ionization modes. Despite the general assumption of the necessity of platform-adapted protocols for achieving effective metabotype characterization, we show that an appropriately designed single extraction procedure is able to fit the requirements of all technologies. This may constitute a paradigm shift in developing efficient protocols for high-throughput metabolite profiling with more-general analytical applicability.

Omics Screening for Pharmaceutical Efficacy and Safety in Clinical Practice

As molecular techniques have improved, investigators have attempted to improve pharmaceutical efficacy and safety by making trait associations with genomic, epigenomic, transcriptomic, proteomic, and metabolomic polymorphisms. The 'omics era has seen screening assays for pharmaceutical efficacy and safety translated into clinical practice. This manuscript will discuss each 'omic field and the screening assays available to the clinician. While success has been demonstrated in each 'omic field, many challenges remain. Assays need wider availability, predictive values remain low, and costs remain high. In order for clinicians to realize improved efficacy and safety due 'omic screens, development of improved techniques, combining of 'omic assays, and increased clinical utilization is necessary. This is an exciting time for investigators and clinicians that desire improved pharmaceutical therapy.