Autologous hematopoietic stem cell transplantation significantly alters circulating ceramides in peripheral blood of relapsing-remitting multiple sclerosis patients.

BACKGROUND: The common inflammatory disease multiple sclerosis (MS) is a disease of the central nervous system. For more than 25 years autologous hematopoietic stem cell transplantation (AHSCT) has been used to treat MS. It has been shown to be highly effective in suppressing inflammatory activity in relapsing-remitting MS (RRMS) patients. This treatment is thought to lead to an immune system reset, inducing a new, more tolerant system; however, the precise mechanism behind the treatment effect in MS patients is unknown. In this study, the effect of AHSCT on the metabolome and lipidome in peripheral blood from RRMS patients was investigated. METHODS: Peripheral blood samples were collected from 16 patients with RRMS at ten-time points over the five months course of AHSCT and 16 MS patients not treated with AHSCT. Metabolomics and lipidomics analysis were performed using liquid-chromatography high-resolution mass spectrometry. Mixed linear models, differential expression analysis, and cluster analysis were used to identify differentially expressed features and groups of features that could be of interest. Finally, in-house and in-silico libraries were used for feature identification, and enrichment analysis was performed. RESULTS: Differential expression analysis found 657 features in the lipidomics dataset and 34 in the metabolomics dataset to be differentially expressed throughout AHSCT. The administration of cyclophosphamide during mobilization and conditioning was associated with decreased concentrations in glycerophosphoinositol species. Thymoglobuline administration was associated with an increase in ceramide and glycerophosphoethanolamine species. After the conditioning regimen, a decrease in glycerosphingoidlipids concentration was observed, and following hematopoietic stem cell reinfusion glycerophosphocholine concentrations decreased for a short period of time. Ceramide concentrations were strongly associated with leukocyte levels during the procedure. The ceramides Cer(d19:1/14:0) and Cer(d20:1/12:0) were found to be increased (P < .05) in concentration at the three-month follow-up compared to baseline. C16 ceramide, Cer(D18:2/16:0), and CerPE(d16:2(4E,6E)/22:0) were found to be significantly increased in concentration after AHSCT compared to prior to treatment as well as compared to newly diagnosed RRMS patients. CONCLUSION: AHSCT had a larger impact on the lipids in peripheral blood compared to metabolites. The variation in lipid concentration reflects the transient changes in the peripheral blood milieu during the treatment, rather than the changes in the immune system that are assumed to be the cause of clinical improvement within RRMS patients treated with AHSCT. Ceramide concentrations were affected by AHSCT and associated with leukocyte counts and were altered three months after treatment, suggesting a long-lasting effect.

Disease phenotype prediction in multiple sclerosis.

Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work toward a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n = 15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC = 0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients developing PMS within three years of sample collection were predicted as PMS at that time point. Finally, this methodology was applied to PMS patients as part of a clinical trial for intrathecal treatment with rituximab. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year after treatment. In conclusion, the inclusion of confidence predictors contributes with more information compared to traditional machine learning, and this information is relevant for disease monitoring.

Metabolic drift in the aging nervous system is reflected in human cerebrospinal fluid.

Chronic diseases affecting the central nervous system (CNS) like Alzheimer's or Parkinson's disease typically develop with advanced chronological age. Yet, aging at the metabolic level has been explored only sporadically in humans using biofluids in close proximity to the CNS such as the cerebrospinal fluid (CSF). We have used an untargeted liquid chromatography high-resolution mass spectrometry (LC-HRMS) based metabolomics approach to measure the levels of metabolites in the CSF of non-neurological control subjects in the age of 20 up to 74. Using a random forest-based feature selection strategy, we extracted 69 features that were strongly related to age (page < 0.001, rage = 0.762, R2Boruta age = 0.764). Combining an in-house library of known substances with in silico chemical classification and functional semantic annotation we successfully assigned putative annotations to 59 out of the 69 CSF metabolites. We found alterations in metabolites related to the Cytochrome P450 system, perturbations in the tryptophan and kynurenine pathways, metabolites associated with cellular energy (NAD+, ADP), mitochondrial and ribosomal metabolisms, neurological dysfunction, and an increase of adverse microbial metabolites. Taken together our results point at a key role for metabolites found in CSF related to the Cytochrome P450 system as most often associated with metabolic aging.

Metabolomics of Cerebrospinal Fluid from Healthy Subjects Reveal Metabolites Associated with Ageing.

To increase our understanding of age-related diseases affecting the central nervous system (CNS) it is important to understand the molecular processes of biological ageing. Metabolomics of cerebrospinal fluid (CSF) is a promising methodology to increase our understanding of naturally occurring processes of ageing of the brain and CNS that could be reflected in CSF. In the present study the CSF metabolomes of healthy subjects aged 30-74 years (n = 23) were studied using liquid chromatography high-resolution mass spectrometry (LC-HRMS), and investigated in relation to age. Ten metabolites were identified with high confidence as significantly associated with ageing, eight with increasing levels with ageing: isoleucine, acetylcarnitine, pipecolate, methionine, glutarylcarnitine, 5-hydroxytryptophan, ketoleucine, and hippurate; and two decreasing with ageing: methylthioadenosine and 3-methyladenine. To our knowledge, this is the first time the CSF metabolomes of healthy subjects are assessed in relation to ageing. The present study contributes to the field of ageing metabolomics by presenting a number of metabolites present in CSF with potential relevance for ageing and the results motivate further studies.

Urokinase, CX3CL1, CCL2, TRAIL and IL-18 induced by interferon-ß treatment.

OBJECTIVE: To identify serum proteins associated with MS and affected by interferon beta treatment. METHODS: Plasma samples from 29 untreated relapsing-remitting MS patients and 15 healthy controls were investigated with a multiplexed panel containing 92 proteins related to inflammation. Follow-up samples were available from 13 patients at 1 and 3 months after initiation of treatment with interferon beta-1a. RESULTS: Ten proteins were differentially expressed in MS patients. Five of these were altered by treatment with IFN-ß 1a: uPA, CX3CL1, CCL2, TRAIL and IL18. CONCLUSION: CCL2 and TRAIL were confirmed to be modulated with interferon beta treatment in MS. As novel findings, we now report that uPA and CX3CL1 were differentially expressed in MS and increased after IFN-beta-1a treatment. Conflicting results have been reported on how interferon beta affects IL-18.

Descriptive Proteome Analysis to Investigate Context-Dependent Treatment Responses to OXPHOS Inhibition in Colon Carcinoma Cells Grown as Monolayer and Multicellular Tumor Spheroids.

We have previously identified selective upregulation of the mevalonate pathway genes upon inhibition of oxidative phosphorylation (OXPHOS) in quiescent cancer cells. Using mass spectrometry-based proteomics, we here investigated whether these responses are corroborated on the protein level and whether proteomics could yield unique insights into context-dependent biology. HCT116 colon carcinoma cells were cultured as monolayer cultures, proliferative multicellular tumor spheroids (P-MCTS), or quiescent (Q-MCTS) multicellular tumor spheroids and exposed to OXPHOS inhibitors: nitazoxanide, FCCP, oligomycin, and salinomycin or the HMG-CoA-reductase inhibitor simvastatin at two different doses for 6 and 24 h. Samples were processed using an in-depth bottom-up proteomics workflow resulting in a total of 9286 identified protein groups. Gene set enrichment analysis showed profound differences between the three cell systems and confirmed differential enrichment of hypoxia, OXPHOS, and cell cycle progression-related protein responses in P-MCTS and Q-MCTS. Treatment experiments showed that the observed drug-induced alterations in gene expression of metabolically challenged cells are not translated directly to the protein level, but the results reaffirmed OXPHOS as a selective vulnerability of quiescent cancer cells. This work provides rationale for the use of deep proteome profiling to identify context-dependent treatment responses and encourages further studies investigating metabolic processes that could be co-targeted together with OXPHOS to eradicate quiescent cancer cells.

Targeted metabolomics of CSF in healthy individuals and patients with secondary progressive multiple sclerosis using high-resolution mass spectrometry.

INTRODUCTION: Standardized commercial kits enable targeted metabolomics analysis and may thus provide an attractive complement to the more explorative approaches. The kits are typically developed for triple quadrupole mass spectrometers using serum and plasma. OBJECTIVES: Here we measure the concentrations of preselected metabolites in cerebrospinal fluid (CSF) using a kit developed for high-resolution mass spectrometry (HRMS). Secondarily, the study aimed to investigate metabolite alterations in patients with secondary progressive multiple sclerosis (SPMS) compared to controls. METHODS: We performed targeted metabolomics in human CSF on twelve SPMS patients and twelve age and sex-matched healthy controls using the Absolute IDQ-p400 kit (Biocrates Life Sciences AG) developed for HRMS. The extracts were analysed using two methods; liquid chromatography-mass spectrometry (LC-HRMS) and flow injection analysis-MS (FIA-HRMS). RESULTS: Out of 408 targeted metabolites, 196 (48%) were detected above limit of detection and 35 were absolutely quantified. Metabolites analyzed using LC-HRMS had a median coefficient of variation (CV) of 3% and 2.5% between reinjections the same day and after prolonged storage, respectively. The corresponding results for the FIA-HRMS were a median CV of 27% and 21%, respectively. We found significantly (p < 0.05) elevated levels of glycine, asymmetric dimethylarginine (ADMA), glycerophospholipid PC-O (34:0) and sum of hexoses in SPMS patients compared to controls. CONCLUSION: The Absolute IDQ-p400 kit could successfully be used for quantifying targeted metabolites in the CSF. Metabolites quantified using LC-HRMS showed superior reproducibility compared to FIA-HRMS.

Profound but Transient Changes in the Inflammatory Milieu of the Blood During Autologous Hematopoietic Stem Cell Transplantation.

Little is known about the inflammatory milieu in the blood during autologous hematopoietic stem cell transplantation (AHSCT) and how it is affected by the stem cell mobilization, collection, and reinfusion and conditioning regimen. In this study, we analyzed 92 proteins connected to inflammation at 10 time points during and after AHSCT in 16 patients with multiple sclerosis (MS). Serum from 29 patients with newly diagnosed MS and 15 healthy controls were included for comparative analysis. There were no significant differences in inflammatory serum protein levels between patients with newly diagnosed MS and healthy controls, but 29 out of 73 detectable proteins were significantly altered between at least 2 adjacent sampling time points during AHSCT. The predominant changes occurred after the conditioning regimen had been administered, whereas stem cell mobilization, collection, and reinfusion appeared to have less impact. Two distinct response patterns could be discerned, likely representing loss of basal cytokine production and homeostasis. The analyzed serum proteins gradually returned to baseline levels after treatment, with no remaining differences at 3 months after AHSCT. We conclude that treatment with AHSCT has a major but transient impact on the inflammatory milieu of peripheral blood.

Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington's disease subjects.

Huntington's disease (HD) is a severe neurological disease leading to psychiatric symptoms, motor impairment and cognitive decline. The disease is caused by a CAG expansion in the huntingtin (HTT) gene, but how this translates into the clinical phenotype of HD remains elusive. Using liquid chromatography mass spectrometry, we analyzed the metabolome of cerebrospinal fluid (CSF) from premanifest and manifest HD subjects as well as control subjects. Inter-group differences revealed that the tyrosine metabolism, including tyrosine, thyroxine, L-DOPA and dopamine, was significantly altered in manifest compared with premanifest HD. These metabolites demonstrated moderate to strong associations to measures of disease severity and symptoms. Thyroxine and dopamine also correlated with the five year risk of onset in premanifest HD subjects. The phenylalanine and the purine metabolisms were also significantly altered, but associated less to disease severity. Decreased levels of lumichrome were commonly found in mutated HTT carriers and the levels correlated with the five year risk of disease onset in premanifest carriers. These biochemical findings demonstrates that the CSF metabolome can be used to characterize molecular pathogenesis occurring in HD, which may be essential for future development of novel HD therapies.

Interoperable and scalable data analysis with microservices: applications in metabolomics.

MOTIVATION: Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. RESULTS: We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science. AVAILABILITY AND IMPLEMENTATION: The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing⁻Remitting Multiple Sclerosis.

To better understand the pathophysiological differences between secondary progressive multiple sclerosis (SPMS) and relapsing-remitting multiple sclerosis (RRMS), and to identify potential biomarkers of disease progression, we applied high-resolution mass spectrometry (HRMS) to investigate the metabolome of cerebrospinal fluid (CSF). The biochemical differences were determined using partial least squares discriminant analysis (PLS-DA) and connected to biochemical pathways as well as associated to clinical and radiological measures. Tryptophan metabolism was significantly altered, with perturbed levels of kynurenate, 5-hydroxytryptophan, 5-hydroxyindoleacetate, and N-acetylserotonin in SPMS patients compared with RRMS and controls. SPMS patients had altered kynurenine compared with RRMS patients, and altered indole-3-acetate compared with controls. Regarding the pyrimidine metabolism, SPMS patients had altered levels of uridine and deoxyuridine compared with RRMS and controls, and altered thymine and glutamine compared with RRMS patients. Metabolites from the pyrimidine metabolism were significantly associated with disability, disease activity and brain atrophy, making them of particular interest for understanding the disease mechanisms and as markers of disease progression. Overall, these findings are of importance for the characterization of the molecular pathogenesis of SPMS and support the hypothesis that the CSF metabolome may be used to explore changes that occur in the transition between the RRMS and SPMS pathologies.

Improved Differential Diagnosis of Alzheimer's Disease by Integrating ELISA and Mass Spectrometry-Based Cerebrospinal Fluid Biomarkers.

BACKGROUND: Alzheimer's disease (AD) is diagnosed based on a clinical evaluation as well as analyses of classical biomarkers: Aß42, total tau (t-tau), and phosphorylated tau (p-tau) in cerebrospinal fluid (CSF). Although the sensitivities and specificities of the classical biomarkers are fairly good for detection of AD, there is still a need to develop novel biochemical markers for early detection of AD. OBJECTIVE: We explored if integration of novel proteins with classical biomarkers in CSF can better discriminate AD from non-AD subjects. METHODS: We applied ELISA, mass spectrometry, and multivariate modeling to investigate classical biomarkers and the CSF proteome in subjects (n = 206) with 76 AD patients, 74 mild cognitive impairment (MCI) patients, 11 frontotemporal dementia (FTD) patients, and 45 non-dementia controls. The MCI patients were followed for 4-9 years and 21 of these converted to AD, whereas 53 remained stable. RESULTS: By combining classical CSF biomarkers with twelve novel markers, the area of the ROC curves (AUROCS) of distinguishing AD and MCI/AD converters from non-AD were 93% and 96%, respectively. The FTDs and non-dementia controls were identified versus all other groups with AUROCS of 96% and 87%, respectively. CONCLUSIONS: Integration of new and classical CSF biomarkers in a model-based approach can improve the identification of AD, FTD, and non-dementia control subjects.

Systematic analysis of the cerebrospinal fluid proteome of fibromyalgia patients.

Fibromyalgia (FM) is a syndrome characterized by widespread muscular pain, fatigue and functional symptoms, which is known to be difficult to diagnose as the various symptoms overlap with many other conditions. Currently, there are no biomarkers for FM, and the diagnosis is made subjectively by the clinicians. We have performed shotgun proteomics on cerebrospinal fluid (CSF) from FM patients and non-pain controls to find potential biomarker candidates for this syndrome. Based on our multivariate and univariate analyses, we found that the relative differences in the CSF proteome between FM patients and controls were moderate. Four proteins, important to discriminate FM patients from non-pain controls, were found: Apolipoprotein C-III, Galectin-3-binding protein, Malate dehydrogenase cytoplasmic and the neuropeptide precursor protein ProSAAS. These proteins are involved in lipoprotein lipase (LPL) activity, inflammatory signaling, energy metabolism and neuropeptide signaling. SIGNIFICANCE: Fibromyalgia is present in as much as 2% of the population, causing pain, stiffness, and tenderness of the muscles. Upon accurate diagnostic, nonpharmacological and pharmacological therapies can be used to alleviate pain and manage other symptoms. However, lack of objective, universal applicable diagnostic criteria as well as vague and diffused symptoms, have made diagnosis difficult. In this context, our findings can shed light on potential value of CSF proteome for objectively diagnosing FM.

Container-based bioinformatics with Pachyderm.

MOTIVATION: Computational biologists face many challenges related to data size, and they need to manage complicated analyses often including multiple stages and multiple tools, all of which must be deployed to modern infrastructures. To address these challenges and maintain reproducibility of results, researchers need (i) a reliable way to run processing stages in any computational environment, (ii) a well-defined way to orchestrate those processing stages and (iii) a data management layer that tracks data as it moves through the processing pipeline. RESULTS: Pachyderm is an open-source workflow system and data management framework that fulfils these needs by creating a data pipelining and data versioning layer on top of projects from the container ecosystem, having Kubernetes as the backbone for container orchestration. We adapted Pachyderm and demonstrated its attractive properties in bioinformatics. A Helm Chart was created so that researchers can use Pachyderm in multiple scenarios. The Pachyderm File System was extended to support block storage. A wrapper for initiating Pachyderm on cloud-agnostic virtual infrastructures was created. The benefits of Pachyderm are illustrated via a large metabolomics workflow, demonstrating that Pachyderm enables efficient and sustainable data science workflows while maintaining reproducibility and scalability. AVAILABILITY AND IMPLEMENTATION: Pachyderm is available from https://github.com/pachyderm/pachyderm. The Pachyderm Helm Chart is available from https://github.com/kubernetes/charts/tree/master/stable/pachyderm. Pachyderm is available out-of-the-box from the PhenoMeNal VRE (https://github.com/phnmnl/KubeNow-plugin) and general Kubernetes environments instantiated via KubeNow. The code of the workflow used for the analysis is available on GitHub (https://github.com/pharmbio/LC-MS-Pachyderm). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PhenoMeNal: processing and analysis of metabolomics data in the cloud.

BACKGROUND: Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. FINDINGS: PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. CONCLUSIONS: PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains.

Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis.

Molecular networks in neurological diseases are complex. Despite this fact, contemporary biomarkers are in most cases interpreted in isolation, leading to a significant loss of information and power. We present an analytical approach to scrutinize and combine information from biomarkers originating from multiple sources with the aim of discovering a condensed set of biomarkers that in combination could distinguish the progressive degenerative phenotype of multiple sclerosis (SPMS) from the relapsing-remitting phenotype (RRMS). Methods: Clinical and magnetic resonance imaging (MRI) data were integrated with data from protein and metabolite measurements of cerebrospinal fluid, and a method was developed to sift through all the variables to establish a small set of highly informative measurements. This prospective study included 16 SPMS patients, 30 RRMS patients and 10 controls. Protein concentrations were quantitated with multiplexed fluorescent bead-based immunoassays and ELISA. The metabolome was recorded using liquid chromatography-mass spectrometry. Clinical follow-up data of the SPMS patients were used to assess disease progression and development of disability. Results: Eleven variables were in combination able to distinguish SPMS from RRMS patients with high confidence superior to any single measurement. The identified variables consisted of three MRI variables: the size of the spinal cord and the third ventricle and the total number of T1 hypointense lesions; six proteins: galectin-9, monocyte chemoattractant protein-1 (MCP-1), transforming growth factor alpha (TGF-α), tumor necrosis factor alpha (TNF-α), soluble CD40L (sCD40L) and platelet-derived growth factor AA (PDGF-AA); and two metabolites: 20ß-dihydrocortisol (20ß-DHF) and indolepyruvate. The proteins myelin basic protein (MBP) and macrophage-derived chemokine (MDC), as well as the metabolites 20ß-DHF and 5,6-dihydroxyprostaglandin F1a (5,6-DH-PGF1), were identified as potential biomarkers of disability progression. Conclusion: Our study demonstrates, in a limited but well-defined and data-rich cohort, the importance and value of combining multiple biomarkers to aid diagnostics and track disease progression.

Effect of acute muscle contusion injury, with and without dietary fish oil, on adult and aged male rats: contractile and biochemical responses.

AIM: Contusion injury in aging muscle has not been studied in detail, but older adults are at risk for such injuries due to increased risk of falls. As falls in older populations are unlikely to be eliminated, interventions to minimize the negative impact of falls, including contusion injury should be pursued. Dietary fish oil (FO) is a common often supplement in older adults, which is associated with factors that might reduce or worsen the negative impact of contusion. METHODS: Here, we investigate whether 8 weeks of FO can blunt the impact of contusion injury in adult (n = 14) and aged (n = 12) rats. We assessed contractility and several biochemical markers in adult and aged gastrocnemius muscles 48 h post-contusion injury, using the uninjured muscles as controls. RESULTS: Injury reduced force production ~40% (P < 0.001), sarcoplasmic reticulum calcium release by ~20% (P = 0.003) and significantly increased several markers of muscle damage (i.e., protein carbonyls, Grp78 abundance (P = 0.022, 0.006, respectively)), and these injury-related changes were not affected by aging. The effects of FO were limited. A main effect (P = 0.018) for FO to increase the myogenic factor Myf5 was observed. In addition FO reduced the injury-associated decline in the mitophagy factor DRP1 (P = 0.027). CONCLUSION: Although age-related differences in certain protein markers differed, aged muscles exhibited no greater acute functional deficits following injury. Similarly, while FO did not reduce functional deficits, it did not worsen them. However, changes in Myf5 and DRP1 with dietary FO suggest the potential to improve recovery from contusion injury, which should be investigated in future studies.

Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions.

INTRODUCTION: Mass spectrometry based metabolomics has become a promising complement and alternative to transcriptomics and proteomics in many fields including in vitro systems pharmacology. Despite several merits, metabolomics based on liquid chromatography mass spectrometry (LC-MS) is a developing area that is yet attached to several pitfalls and challenges. To reach a level of high reliability and robustness, these issues need to be tackled by implementation of refined experimental and computational protocols. OBJECTIVES: This study illustrates some key pitfalls in LC-MS based metabolomics and introduces an automated computational procedure to compensate for them. METHOD: Non-cancerous mammary gland derived cells were exposed to 27 chemicals from four pharmacological classes plus a set of six pesticides. Changes in the metabolome of cell lysates were assessed after 24 h using LC-MS. A data processing pipeline was established and evaluated to handle issues including contaminants, carry over effects, intensity decay and inherent methodology variability and biases. A key component in this pipeline is a latent variable method called OOS-DA (optimal orthonormal system for discriminant analysis), being theoretically more easily motivated than PLS-DA in this context, as it is rooted in pattern classification rather than regression modeling. RESULT: The pipeline is shown to reduce experimental variability/biases and is used to confirm that LC-MS spectra hold drug class specific information. CONCLUSION: LC-MS based metabolomics is a promising methodology, but comes with pitfalls and challenges. Key difficulties can be largely overcome by means of a computational procedure of the kind introduced and demonstrated here. The pipeline is freely available on www.github.com/stephanieherman/MS-data-processing.