Ten quick tips for building FAIR workflows.

Research data is accumulating rapidly and with it the challenge of fully reproducible science. As a consequence, implementation of high-quality management of scientific data has become a global priority. The FAIR (Findable, Accesible, Interoperable and Reusable) principles provide practical guidelines for maximizing the value of research data; however, processing data using workflows-systematic executions of a series of computational tools-is equally important for good data management. The FAIR principles have recently been adapted to Research Software (FAIR4RS Principles) to promote the reproducibility and reusability of any type of research software. Here, we propose a set of 10 quick tips, drafted by experienced workflow developers that will help researchers to apply FAIR4RS principles to workflows. The tips have been arranged according to the FAIR acronym, clarifying the purpose of each tip with respect to the FAIR4RS principles. Altogether, these tips can be seen as practical guidelines for workflow developers who aim to contribute to more reproducible and sustainable computational science, aiming to positively impact the open science and FAIR community.

N-linked glycosylation of the M-protein variable region: glycoproteogenomics reveals a new layer of personalized complexity in multiple myeloma.

OBJECTIVES: Multiple myeloma (MM) is a plasma cell malignancy characterized by a monoclonal expansion of plasma cells that secrete a characteristic M-protein. This M-protein is crucial for diagnosis and monitoring of MM in the blood of patients. Recent evidence has emerged suggesting that N-glycosylation of the M-protein variable (Fab) region contributes to M-protein pathogenicity, and that it is a risk factor for disease progression of plasma cell disorders. Current methodologies lack the specificity to provide a site-specific glycoprofile of the Fab regions of M-proteins. Here, we introduce a novel glycoproteogenomics method that allows detailed M-protein glycoprofiling by integrating patient specific Fab region sequences (genomics) with glycoprofiling by glycoproteomics. METHODS: Glycoproteogenomics was used for the detailed analysis of de novo N-glycosylation sites of M-proteins. First, Genomic analysis of the M-protein variable region was used to identify de novo N-glycosylation sites. Subsequently glycopeptide analysis with LC-MS/MS was used for detailed analysis of the M-protein glycan sites. RESULTS: Genomic analysis uncovered a more than two-fold increase in the Fab Light Chain N-glycosylation of M-proteins of patients with Multiple Myeloma compared to Fab Light Chain N-glycosylation of polyclonal antibodies from healthy individuals. Subsequent glycoproteogenomics analysis of 41 patients enrolled in the IFM 2009 clinical trial revealed that the majority of the Fab N-glycosylation sites were fully occupied with complex type glycans, distinguishable from Fc region glycans due to high levels of sialylation, fucosylation and bisecting structures. CONCLUSIONS: Together, glycoproteogenomics is a powerful tool to study de novo Fab N-glycosylation in plasma cell dyscrasias.

Application of metabolite set enrichment analysis on untargeted metabolomics data prioritises relevant pathways and detects novel biomarkers for inherited metabolic disorders.

Untargeted metabolomics (UM) allows for the simultaneous measurement of hundreds of metabolites in a single analytical run. The sheer amount of data generated in UM hampers its use in patient diagnostics because manual interpretation of all features is not feasible. Here, we describe the application of a pathway-based metabolite set enrichment analysis method to prioritise relevant biological pathways in UM data. We validate our method on a set of 55 patients with a diagnosed inherited metabolic disorder (IMD) and show that it complements feature-based prioritisation of biomarkers by placing the features in a biological context. In addition, we find that by taking enriched pathways shared across different IMDs, we can identify common drugs and compounds that could otherwise obscure genuine disease biomarkers in an enrichment method. Finally, we demonstrate the potential of this method to identify novel candidate biomarkers for known IMDs. Our results show the added value of pathway-based interpretation of UM data in IMD diagnostics context.

Confirmation of neurometabolic diagnoses using age-dependent cerebrospinal fluid metabolomic profiles.

Timely diagnosis is essential for patients with neurometabolic disorders to enable targeted treatment. Next-Generation Metabolic Screening (NGMS) allows for simultaneous screening of multiple diseases and yields a holistic view of disturbed metabolic pathways. We applied this technique to define a cerebrospinal fluid (CSF) reference metabolome and validated our approach with patients with known neurometabolic disorders. Samples were measured using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry followed by (un)targeted analysis. For the reference metabolome, CSF samples from patients with normal general chemistry results and no neurometabolic diagnosis were selected and grouped based on sex and age (0-2/2-5/5-10/10-15 years). We checked the levels of known biomarkers in CSF from seven patients with five different neurometabolic disorders to confirm the suitability of our method for diagnosis. Untargeted analysis of 87 control CSF samples yielded 8036 features for semiquantitative analysis. No sex differences were found, but 1782 features (22%) were different between age groups (q < 0.05). We identified 206 diagnostic metabolites in targeted analysis. In a subset of 20 high-intensity metabolites and 10 biomarkers, 17 (57%) were age-dependent. For each neurometabolic patient, ≥1 specific biomarker(s) could be identified in CSF, thus confirming the diagnosis. In two cases, age-matching was essential for correct interpretation of the metabolomic profile. In conclusion, NGMS in CSF is a powerful tool in defining a diagnosis for neurometabolic disorders. Using our database with many (age-dependent) features in CSF, our untargeted approach will facilitate biomarker discovery and further understanding of mechanisms of neurometabolic disorders.

Secondary ion mass spectrometry imaging and multivariate data analysis reveal co-aggregation patterns of Populus trichocarpa leaf surface compounds on a micrometer scale.

Spatially resolved analysis of a multitude of compound classes has become feasible with the rapid advancement in mass spectrometry imaging strategies. In this study, we present a protocol that combines high lateral resolution time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging with a multivariate data analysis (MVA) approach to probe the complex leaf surface chemistry of Populus trichocarpa. Here, epicuticular waxes (EWs) found on the adaxial leaf surface of P. trichocarpa were blotted on silicon wafers and imaged using TOF-SIMS at 10 µm and 1 µm lateral resolution. Intense M+● and M-● molecular ions were clearly visible, which made it possible to resolve the individual compound classes present in EWs. Series of long-chain aliphatic saturated alcohols (C21 -C30 ), hydrocarbons (C25 -C33 ) and wax esters (WEs; C44 -C48 ) were clearly observed. These data correlated with the 7 Li-chelation matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis, which yielded mostly molecular adduct ions of the analyzed compounds. Subsequently, MVA was used to interrogate the TOF-SIMS dataset for identifying hidden patterns on the leaf's surface based on its chemical profile. After the application of principal component analysis (PCA), a small number of principal components (PCs) were found to be sufficient to explain maximum variance in the data. To further confirm the contributions from pure components, a five-factor multivariate curve resolution (MCR) model was applied. Two distinct patterns of small islets, here termed 'crystals', were apparent from the resulting score plots. Based on PCA and MCR results, the crystals were found to be formed by C23 or C29 alcohols. Other less obvious patterns observed in the PCs revealed that the adaxial leaf surface is coated with a relatively homogenous layer of alcohols, hydrocarbons and WEs. The ultra-high-resolution TOF-SIMS imaging combined with the MVA approach helped to highlight the diverse patterns underlying the leaf's surface. Currently, the methods available to analyze the surface chemistry of waxes in conjunction with the spatial information related to the distribution of compounds are limited. This study uses tools that may provide important biological insights into the composition of the wax layer, how this layer is repaired after mechanical damage or insect feeding, and which transport mechanisms are involved in deploying wax constituents to specific regions on the leaf surface.

LAESI mass spectrometry imaging as a tool to differentiate the root metabolome of native and range-expanding plant species.

MAIN CONCLUSION: LAESI-MSI, an innovative high-throughput technique holds a unique potential for untargeted detection, profiling and spatial localization of metabolites from intact plant samples without need for extraction or extensive sample preparation. Our understanding of chemical diversity in biological samples has greatly improved through recent advances in mass spectrometry (MS). MS-based-imaging (MSI) techniques have further enhanced this by providing spatial information on the distribution of metabolites and their relative abundance. This study aims to employ laser-ablation electrospray ionization (LAESI) MSI as a tool to profile and compare the root metabolome of two pairs of native and range-expanding plant species. It has been proposed that successful range-expanding plant species, like introduced exotic invaders, have a novel, or a more diverse secondary chemistry. Although some tests have been made using aboveground plant materials, tests using root materials are rare. We tested the hypothesis that range-expanding plants possess more diverse root chemistries than native plant species. To examine the root chemistry of the selected plant species, LAESI-MSI was performed in positive ion mode and data were acquired in a mass range of m/z 50-1200 with a spatial resolution of 100 µm. The acquired data were analyzed using in-house scripts, and differences in the spatial profiles were studied for discriminatory mass features. The results revealed clear differences in the metabolite profiles amongst and within both pairs of congeneric plant species, in the form of distinct metabolic fingerprints. The use of ambient conditions and the fact that no sample preparation was required, established LAESI-MSI as an ideal technique for untargeted metabolomics and for direct correlation of the acquired data to the underlying metabolomic complexity present in intact plant samples.

Correcting mass shifts: A lock mass-free recalibration procedure for mass spectrometry imaging data.

Mass spectrometry imaging (MSI) has become widely popular because of its potential to map the spatial distribution of thousands of compounds in a single measurement directly from tissue surfaces. With every MSI experiment, it is important to maintain high mass accuracy for correct identification of the observed ions. Many times this can be compromised due to different experimental factors, leading to erroneous assignment of peaks. This makes recalibration a crucial preprocessing step. We describe a lock mass-free mass spectra recalibration method, which enables to significantly reduce these mass shift effects. The recalibration method is applied in three steps: First, we decide on an order to process all the spectra. Herein, we describe three different methods for ordering the spectra-minimum spanning tree (MST), topological greedy (TG), and crystal growth (CG). Second, we construct a reference (consensus) spectrum, from the ordered spectra, and third, all spectra are individually corrected against this consensus spectrum. The performance of the recalibration method is demonstrated on three imaging datasets acquired from matrix-assisted laser desorptionionization (MALDI) and laser desorption/ionization (LDI) mass spectrometry imaging of whole-body Drosophila melanogaster fly. The applied recalibration method is shown to strongly reduce the observed mass shifts in the imaging datasets. Among the three ordering methods, CG and MST perform comparatively better than TG and highly decrease the overall standard deviation of the mass error distribution. Lock mass correction of MSI data is practically difficult, as not all spectra contain the selected lock mass peak. Our method eliminates this need.

A multi-omics data analysis workflow packaged as a FAIR Digital Object.

BACKGROUND: Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. FINDINGS: We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. CONCLUSIONS: Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.

Plasma glycoproteomics delivers high-specificity disease biomarkers by detecting site-specific glycosylation abnormalities.

INTRODUCTION: The human plasma glycoproteome holds enormous potential to identify personalized biomarkers for diagnostics. Glycoproteomics has matured into a technology for plasma N-glycoproteome analysis but further evolution towards clinical applications depends on the clinical validity and understanding of protein- and site-specific glycosylation changes in disease. OBJECTIVES: Here, we exploited the uniqueness of a patient cohort of genetic defects in well-defined glycosylation pathways to assess the clinical applicability of plasma N-glycoproteomics. METHODS: Comparative glycoproteomics was performed of blood plasma from 40 controls and 74 patients with 13 different genetic diseases that impact the protein N-glycosylation pathway. Baseline glycosylation in healthy individuals was compared to reference glycome and intact transferrin protein mass spectrometry data. Use of glycoproteomics data for biomarker discovery and sample stratification was evaluated by multivariate chemometrics and supervised machine learning. Clinical relevance of site-specific glycosylation changes were evaluated in the context of genetic defects that lead to distinct accumulation or loss of specific glycans. Integrated analysis of site-specific glycoproteome changes in disease was performed using chord diagrams and correlated with intact transferrin protein mass spectrometry data. RESULTS: Glycoproteomics identified 191 unique glycoforms from 58 unique peptide sequences of 34 plasma glycoproteins that span over 3 magnitudes of abundance in plasma. Chemometrics identified high-specificity biomarker signatures for each of the individual genetic defects with better stratification performance than the current diagnostic standard method. Bioinformatic analyses revealed site-specific glycosylation differences that could be explained by underlying glycobiology and protein-intrinsic factors. CONCLUSION: Our work illustrates the strong potential of plasma glycoproteomics to significantly increase specificity of glycoprotein biomarkers with direct insights in site-specific glycosylation changes to better understand the glycobiological mechanisms underlying human disease.

Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data.

Untargeted metabolomics (UM) is increasingly being deployed as a strategy for screening patients that are suspected of having an inborn error of metabolism (IEM). In this study, we examined the potential of existing outlier detection methods to detect IEM patient profiles. We benchmarked 30 different outlier detection methods when applied to three untargeted metabolomics datasets. Our results show great differences in IEM detection performances across the various methods. The methods DeepSVDD and R-graph performed most consistently across the three metabolomics datasets. For datasets with a more balanced number of samples-to-features ratio, we found that AE reconstruction error, Mahalanobis and PCA reconstruction error also performed well. Furthermore, we demonstrated the importance of a PCA transform prior to applying an outlier detection method since we observed that this increases the performance of several outlier detection methods. For only one of the three metabolomics datasets, we observed clinically satisfying performances for some outlier detection methods, where we were able to detect 90% of the IEM patient samples while detecting no false positives. These results suggest that outlier detection methods have the potential to aid the clinical investigator in routine screening for IEM using untargeted metabolomics data, but also show that further improvements are needed to ensure clinically satisfying performances.

Exploring the Interspecific Interactions and the Metabolome of the Soil Isolate Hylemonella gracilis.

Microbial community analysis of aquatic environments showed that an important component of its microbial diversity consists of bacteria with cell sizes of ~0.1 µm. Such small bacteria can show genomic reductions and metabolic dependencies with other bacteria. However, so far, no study has investigated if such bacteria exist in terrestrial environments like soil. Here, we isolated soil bacteria that passed through a 0.1-µm filter. The complete genome of one of the isolates was sequenced and the bacterium was identified as Hylemonella gracilis. A set of coculture assays with phylogenetically distant soil bacteria with different cell and genome sizes was performed. The coculture assays revealed that H. gracilis grows better when interacting with other soil bacteria like Paenibacillus sp. AD87 and Serratia plymuthica. Transcriptomics and metabolomics showed that H. gracilis was able to change gene expression, behavior, and biochemistry of the interacting bacteria without direct cell-cell contact. Our study indicates that in soil there are bacteria that can pass through a 0.1-µm filter. These bacteria may have been overlooked in previous research on soil microbial communities. Such small bacteria, exemplified here by H. gracilis, can induce transcriptional and metabolomic changes in other bacteria upon their interactions in soil. In vitro, the studied interspecific interactions allowed utilization of growth substrates that could not be utilized by monocultures, suggesting that biochemical interactions between substantially different sized soil bacteria may contribute to the symbiosis of soil bacterial communities. IMPORTANCE Analysis of aquatic microbial communities revealed that parts of its diversity consist of bacteria with cell sizes of ~0.1 µm. Such bacteria can show genomic reductions and metabolic dependencies with other bacteria. So far, no study investigated if such bacteria exist in terrestrial environments such as soil. Here, we show that such bacteria also exist in soil. The isolated bacteria were identified as Hylemonella gracilis. Coculture assays with phylogenetically different soil bacteria revealed that H. gracilis grows better when cocultured with other soil bacteria. Transcriptomics and metabolomics showed that H. gracilis was able to change gene expression, behavior, and biochemistry of the interacting bacteria without direct contact. Our study revealed that bacteria are present in soil that can pass through 0.1-µm filters. Such bacteria may have been overlooked in previous research on soil microbial communities and may contribute to the symbiosis of soil bacterial communities.

Evaluation and comparison of silorane resin composite to glass ionomer in occluso-proximal restorations of primary molars: A randomized controlled trial.

Objective: In general, proximal restorations of primary molars fracture, so it is vital to study the new materials that could solve this problem. Hence, the present trial assessed the success of occluso-proximal atraumatic restorative treatment (ART) restorations using silorane and glass ionomer cement (GIC) in carious primary molars for a period of 2 years. Materials and Methods: One hundred and ninety-two children between the age group of 4 and 9 years were randomly allocated to GIC or silorane. In the clinical set up, they were treated by a pediatric dentist, and their restorations were evaluated at 3, 6, 12, 18, and 24 months. The primary outcome was the survival of restoration, which was analyzed using the Kaplan-Meier and superior Cox regression models. As a sensitivity analysis, intention-to-treat (ITT) was executed. Sex, age, molar, jaw, cavity volume, and caries incidence were the independent variables. Results: The restoration survival after 24 months for GIC and silorane was 82.75% and 88.88%, respectively, whereas ITT analysis showed a success of 84.37% and 89.58% for GIC and silorane, respectively. Conclusion: With regard to longevity, there was no statistically significant difference between silorane and GIC in primary molar occlusoproximal ART restorations.

Novel Technique for the Surgical Management of Pleomorphic Adenoma of the Upper Lip.

Pleomorphic adenoma (PA) is one of the most common benign lesions of the salivary glands, with a majority of them occurring in the parotid gland. PA has origin from the epithelial and mesenchymal elements and can arise from both major and minor salivary glands. Among minor salivary glands, the palate is the most commonly affected site, followed by lips, cheeks, gingiva, the floor of the mouth, and tongue. PA of the upper lip without intraoral mucosal involvement is a rare entity. In this article, we report a case of PA of the upper lip in a middle-aged female patient and its surgical management with a novel technique. During six months' postoperative review, the patient showed excellent wound healing with very minimal scar formation.

Low-Level Laser Therapy Role in Surgical Extractions: An Original Research.

Introduction: Low-level laser therapy (LLLT) has been of interest in the clinical application in dentistry. Hence, we evaluated the LLLT on the wound healing in the mandibular surgical extraction. Materials and Methods: We conducted a prospective clinical study with 50 male subjects, equally distributed as controls and cases. Routine surgical extraction was done for control group, and in case group, LLLT was applied after the extraction. Evaluation was done for various parameters and after comparison the P value was less than 0.05 which was considered as significant. Results: Significant difference was seen between the groups for the socket depth on 7th day, swelling on 1st, 3rd, and 5th days, and pain on all the days. Conclusion: LLLT can be successfully used for the surgical extractions.

Management of chyle leak in right side neck dissection: a rare case and review of literature (a case report).

Chyle leak is a well-recognized iatrogenic thoracic duct injury but a rare and serious complication of head and neck surgery affecting 1-2.5% of head and neck surgery dissections. It is potentially a life-threatening condition and management may be problematic and prolonged. Here we presented a rare case report of right sided chyle leak with its surgical management and review of literature. A 56-year-old patient with a complain of non-healing ulcer in the right buccal vestibule in the last 1-2 months reported to the outpatient department (OPD). After complete preoperative profile and counseling patient's consent was taken and wide local excision of lesion was done with bite composite resection with right hemimandibulectomy and maxillary alveolectomy till pterygoid plates, with right side selective neck dissection, level I-III followed by reconstruction with right side pectoralis major myofascial flap. Then the patient was on 5 days octreotide therapy. Regular post-operative follow-up was taken and no leak was noted further. In case of a chyle leak early diagnosis and aggressive treatment is essential to avoid local and systemic complications that prolong hospitalization.

Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline.

Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects.

Untargeted metabolomics and infrared ion spectroscopy identify biomarkers for pyridoxine-dependent epilepsy.

BackgroundPyridoxine-dependent epilepsy (PDE-ALDH7A1) is an inborn error of lysine catabolism that presents with refractory epilepsy in newborns. Biallelic ALDH7A1 variants lead to deficiency of α-aminoadipic semialdehyde dehydrogenase/antiquitin, resulting in accumulation of piperideine-6-carboxylate (P6C), and secondary deficiency of the important cofactor pyridoxal-5'-phosphate (PLP, active vitamin B6) through its complexation with P6C. Vitamin B6 supplementation resolves epilepsy in patients, but intellectual disability may still develop. Early diagnosis and treatment, preferably based on newborn screening, could optimize long-term clinical outcome. However, no suitable PDE-ALDH7A1 newborn screening biomarkers are currently available.MethodsWe combined the innovative analytical methods untargeted metabolomics and infrared ion spectroscopy to discover and identify biomarkers in plasma that would allow for PDE-ALDH7A1 diagnosis in newborn screening.ResultsWe identified 2S,6S-/2S,6R-oxopropylpiperidine-2-carboxylic acid (2-OPP) as a PDE-ALDH7A1 biomarker, and confirmed 6-oxopiperidine-2-carboxylic acid (6-oxoPIP) as a biomarker. The suitability of 2-OPP as a potential PDE-ALDH7A1 newborn screening biomarker in dried bloodspots was shown. Additionally, we found that 2-OPP accumulates in brain tissue of patients and Aldh7a1-knockout mice, and induced epilepsy-like behavior in a zebrafish model system.ConclusionThis study has opened the way to newborn screening for PDE-ALDH7A1. We speculate that 2-OPP may contribute to ongoing neurotoxicity, also in treated PDE-ALDH7A1 patients. As 2-OPP formation appears to increase upon ketosis, we emphasize the importance of avoiding catabolism in PDE-ALDH7A1 patients.FundingSociety for Inborn Errors of Metabolism for Netherlands and Belgium (ESN), United for Metabolic Diseases (UMD), Stofwisselkracht, Radboud University, Canadian Institutes of Health Research, Dutch Research Council (NWO), and the European Research Council (ERC).

A non-invasive soil-based setup to study tomato root volatiles released by healthy and infected roots.

The role of root exudates in mediating plant-microbe interactions has been well documented. However, the function of volatile organic compounds (VOCs) emitted by plant roots has only recently begun to attract attention. This newly recognized relevance of belowground VOCs has so far mostly been tested using systems limited to a two-compartment Petri-dish design. Furthermore, many of the plant-microbe interaction studies have only investigated the effects of microbial VOCs on plant growth. Here, we go two steps further. First we investigated the volatile profile of healthy and pathogen (Fusarium oxysporum) infected tomato roots grown in soil. We then used a unique soil-based olfactometer-choice assay to compare the migration pattern of four beneficial bacteria (Bacillus spp.) towards the roots of the tomato plants. We demonstrate that the blend of root-emitted VOCs differs between healthy and diseased plants. Our results show that VOCs are involved in attracting bacteria to plant roots.

Variable Selection in Untargeted Metabolomics and the Danger of Sparsity.

The goal of metabolomics is to measure as many metabolites as possible in order to capture biomarkers that may indicate disease mechanisms. Variable selection in chemometric methods can be divided into the following two groups: (1) sparse methods that find the minimal set of variables to discriminate between groups and (2) methods that find all variables important for discrimination. Such important variables can be summarized into metabolic pathways using pathway analysis tools like Mummichog. As a test case, we studied the metabolic effects of treatment with nicotinamide riboside, a form of vitamin B3, in a cohort of patients with ataxia-telangiectasia. Vitamin B3 is an important co-factor for many enzymatic reactions in the human body. Thus, the variable selection method was expected to find vitamin B3 metabolites and also other secondary metabolic changes during treatment. However, sparse methods did not select any vitamin B3 metabolites despite the fact that these metabolites showed a large difference when comparing intensity before and during treatment. Univariate analysis or significance multivariate correlation (sMC) in combination with pathway analysis using Mummichog were able to select vitamin B3 metabolites. Moreover, sMC analysis found additional metabolites. Therefore, in our comparative study, sMC displayed the best performance for selection of relevant variables.

Proteomic profiling of striatal tissue of a rat model of Parkinson's disease after implantation of collagen-encapsulated human umbilical cord mesenchymal stem cells.

Parkinson's disease (PD) is the most common neurodegenerative disorder of movement worldwide. To date, only symptomatic treatments are available. Implantation of collagen-encapsulated human umbilical cord mesenchymal stem cells (hUC-MSCs) is being developed as a novel therapeutic approach to potentially modify PD progression. However, implanted collagen scaffolds may induce a host tissue response. To gain insight into such response, hUC-MSCs were encapsulated into collagen hydrogels and implanted into the striatum of hemi-Parkinsonian male Sprague-Dawley rats. One or 14 days after implantation, the area of interest was dissected using a cryostat. Total protein extracts were subjected to tryptic digestion and subsequent LC-MS/MS analyses for protein expression profiling. Univariate and multivariate analyses were performed to identify differentially expressed protein profiles with subsequent gene ontology and pathway analysis for biological interpretation of the data; 2,219 proteins were identified by MaxQuant at 1% false discovery rate. A high correlation of label-free quantification (LFQ) protein values between biological replicates (r = .95) was observed. No significant differences were observed between brains treated with encapsulated hUC-MSCs compared to appropriate controls. Proteomic data were highly robust and reproducible, indicating the suitability of this approach to map differential protein expression caused by the implants. The lack of differences between conditions suggests that the effects of implantation may be minimal. Alternatively, effects may only have been focal and/or could have been masked by nonrelevant high-abundant proteins. For follow-up assessment of local changes, a more accurate dissection technique, such as laser micro dissection, and analysis method are recommended.