Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

How Much Storage Precision Can Be Lost: Guidance for Near-Lossless Compression of Untargeted Metabolomics Mass Spectrometry Data.

Several lossy compressors have achieved superior compression rates for mass spectrometry (MS) data at the cost of storage precision. Currently, the impacts of precision losses on MS data processing have not been thoroughly evaluated, which is critical for the future development of lossy compressors. We first evaluated different storage precision (32 bit and 64 bit) in lossless mzML files. We then applied 10 truncation transformations to generate precision-lossy files: five relative errors for intensities and five absolute errors for m/z values. MZmine3 and XCMS were used for feature detection and GNPS for compound annotation. Lastly, we compared Precision, Recall, F1 - score, and file sizes between lossy files and lossless files under different conditions. Overall, we revealed that the discrepancy between 32 and 64 bit precision was under 1%. We proposed an absolute m/z error of 10-4 and a relative intensity error of 2 × 10-2, adhering to a 5% error threshold (F1 - scores above 95%). For a stricter 1% error threshold (F1 - scores above 99%), an absolute m/z error of 2 × 10-5 and a relative intensity error of 2 × 10-3 were advised. This guidance aims to help researchers improve lossy compression algorithms and minimize the negative effects of precision losses on downstream data processing.

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology.

BACKGROUND: Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN). METHODS: Untargeted metabolomic quantification (GC-MS/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (n: FSGS=63, OU=122, A/D/H=109, and RN=86). Lasso regression was used for feature selection, adjusting for clinical covariates. Four methods were then applied to stratify significance: logistic regression, support vector machine, random forest, and extreme gradient boosting. ML training was performed on 80% total cohort subsets and validated on 20% holdout subsets. Important features were selected based on being significant in at least two of the four modeling approaches. We additionally performed pathway enrichment analysis to identify metabolic subpathways associated with CKD cause. RESULTS: ML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome-derived histidine metabolites. CONCLUSION: ML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome-derived histidine metabolites are associated with OU.

The Metabolomic Effects of Tripeptide Gut Hormone Infusion Compared to Roux-en-Y Gastric Bypass and Caloric Restriction.

CONTEXT: The gut-derived peptide hormones glucagon-like peptide-1 (GLP-1), oxyntomodulin (OXM), and peptide YY (PYY) are regulators of energy intake and glucose homeostasis and are thought to contribute to the glucose-lowering effects of bariatric surgery. OBJECTIVE: To establish the metabolomic effects of a combined infusion of GLP-1, OXM, and PYY (tripeptide GOP) in comparison to a placebo infusion, Roux-en-Y gastric bypass (RYGB) surgery, and a very low-calorie diet (VLCD). DESIGN AND SETTING: Subanalysis of a single-blind, randomized, placebo-controlled study of GOP infusion (ClinicalTrials.gov NCT01945840), including VLCD and RYGB comparator groups. PATIENTS AND INTERVENTIONS: Twenty-five obese patients with type 2 diabetes or prediabetes were randomly allocated to receive a 4-week subcutaneous infusion of GOP (n = 14) or 0.9% saline control (n = 11). An additional 22 patients followed a VLCD, and 21 underwent RYGB surgery. MAIN OUTCOME MEASURES: Plasma and urine samples collected at baseline and 4 weeks into each intervention were subjected to cross-platform metabolomic analysis, followed by unsupervised and supervised modeling approaches to identify similarities and differences between the effects of each intervention. RESULTS: Aside from glucose, very few metabolites were affected by GOP, contrasting with major metabolomic changes seen with VLCD and RYGB. CONCLUSIONS: Treatment with GOP provides a powerful glucose-lowering effect but does not replicate the broader metabolomic changes seen with VLCD and RYGB. The contribution of these metabolomic changes to the clinical benefits of RYGB remains to be elucidated.

Metabolic Analysis of Potential Key Genes Associated with Systemic Lupus Erythematosus Using Liquid Chromatography-Mass Spectrometry.

The biological mechanism underlying the pathogenesis of systemic lupus erythematosus (SLE) remains unclear. In this study, we found 21 proteins upregulated and 38 proteins downregulated by SLE relative to normal protein metabolism in our samples using liquid chromatography-mass spectrometry. By PPI network analysis, we identified 9 key proteins of SLE, including AHSG, VWF, IGF1, ORM2, ORM1, SERPINA1, IGF2, IGFBP3, and LEP. In addition, we identified 4569 differentially expressed metabolites in SLE sera, including 1145 reduced metabolites and 3424 induced metabolites. Bioinformatics analysis showed that protein alterations in SLE were associated with modulation of multiple immune pathways, TP53 signaling, and AMPK signaling. In addition, we found altered metabolites associated with valine, leucine, and isoleucine biosynthesis; one carbon pool by folate; tyrosine metabolism; arginine and proline metabolism; glycine, serine, and threonine metabolism; limonene and pinene degradation; tryptophan metabolism; caffeine metabolism; vitamin B6 metabolism. We also constructed differently expressed protein-metabolite network to reveal the interaction among differently expressed proteins and metabolites in SLE. A total of 481 proteins and 327 metabolites were included in this network. Although the role of altered metabolites and proteins in the diagnosis and therapy of SLE needs to be further investigated, the present study may provide new insights into the role of metabolites in SLE.

Integrating systemic and molecular levels to infer key drivers sustaining metabolic adaptations.

Metabolic adaptations to complex perturbations, like the response to pharmacological treatments in multifactorial diseases such as cancer, can be described through measurements of part of the fluxes and concentrations at the systemic level and individual transporter and enzyme activities at the molecular level. In the framework of Metabolic Control Analysis (MCA), ensembles of linear constraints can be built integrating these measurements at both systemic and molecular levels, which are expressed as relative differences or changes produced in the metabolic adaptation. Here, combining MCA with Linear Programming, an efficient computational strategy is developed to infer additional non-measured changes at the molecular level that are required to satisfy these constraints. An application of this strategy is illustrated by using a set of fluxes, concentrations, and differentially expressed genes that characterize the response to cyclin-dependent kinases 4 and 6 inhibition in colon cancer cells. Decreases and increases in transporter and enzyme individual activities required to reprogram the measured changes in fluxes and concentrations are compared with down-regulated and up-regulated metabolic genes to unveil those that are key molecular drivers of the metabolic response.

Effects of Paired Associative Stimulation on Metabolites in Ischemia Stroke Rats Model as Studied by Nuclear Magnetic Resonance Spectrum.

Paired associated stimulation (PAS) has been confirmed to play a role in motor recovery after stroke, but the underlying mechanism has not been fully elucidated. In this study, we employed a comprehensive battery of measurements, including behavioral test, electrophysiology and 1H-NMR approaches, to investigate the therapeutic effects of PAS in rat model of cerebral ischemia and its underlying mechanism. Rats were randomly divided into a transient middle cerebral artery occlusion group (tMCAO group), a tMCAO + PAS group (PAS group), and a sham group. PAS was applied over 7 consecutive days in PAS group. The behavioral function of rats was evaluated by modified Garcia Scores and Rota-rod test. Electrophysiological changes were measured by motor evoked potentials (MEP). Metabolic changes of ischemic penumbra were detected by 1H-NMR. After PAS intervention, the performances on Rota-rod test and Garcia test improved and the amplitude of MEP increased significantly. The gamma-aminobutyric acid (GABA) in penumbra cortex was decreased significantly, whereas the glutamate showed the opposite changes. The results suggested that post-stroke recovery promoted by PAS may be related to the metabolites alteration in ischemic penumbra and also regulate the excitability of motor cortex.

Comparison of Untargeted Metabolomic Profiling vs Traditional Metabolic Screening to Identify Inborn Errors of Metabolism.

Importance: Recent advances in newborn screening (NBS) have improved the diagnosis of inborn errors of metabolism (IEMs); however, many potentially treatable IEMs are not included on NBS panels, nor are they covered in standard, first-line biochemical testing. Objective: To examine the utility of untargeted metabolomics as a primary screening tool for IEMs by comparing the diagnostic rate of clinical metabolomics with the recommended traditional metabolic screening approach. Design, Setting, and Participants: This cross-sectional study compares data from 4464 clinical samples received from 1483 unrelated families referred for trio testing of plasma amino acids, plasma acylcarnitine profiling, and urine organic acids (June 2014 to October 2018) and 2000 consecutive plasma samples from 1807 unrelated families (July 2014 to February 2019) received for clinical metabolomic screening at a College of American Pathologists and Clinical Laboratory Improvement Amendments-certified biochemical genetics laboratory. Data analysis was performed from September 2019 to August 2020. Exposures: Metabolic and molecular tests performed at a genetic testing reference laboratory in the US and available clinical information for each patient were assessed to determine diagnostic rate. Main Outcomes and Measures: The diagnostic rate of traditional metabolic screening compared with clinical metabolomic profiling was assessed in the context of expanded NBS. Results: Of 1483 cases screened by the traditional approach, 912 patients (61.5%) were male and 1465 (98.8%) were pediatric (mean [SD] age, 4.1 [6.0] years; range, 0-65 years). A total of 19 families were identified with IEMs, resulting in a 1.3% diagnostic rate. A total of 14 IEMs were detected, including 3 conditions not included in the Recommended Uniform Screening Panel for NBS. Of the 1807 unrelated families undergoing plasma metabolomic profiling, 1059 patients (58.6%) were male, and 1665 (92.1%) were pediatric (mean [SD] age, 8.1 [10.4] years; range, 0-80 years). Screening identified 128 unique cases with IEMs, giving an overall diagnostic rate of 7.1%. In total, 70 different metabolic conditions were identified, including 49 conditions not presently included on the Recommended Uniform Screening Panel for NBS. Conclusions and Relevance: These findings suggest that untargeted metabolomics provided a 6-fold higher diagnostic yield compared with the conventional screening approach and identified a broader spectrum of IEMs. Notably, with the expansion of NBS programs, traditional metabolic testing approaches identify few disorders beyond those covered on the NBS. These data support the capability of clinical untargeted metabolomics in screening for IEMs and suggest that broader screening approaches should be considered in the initial evaluation for metabolic disorders.

Urine NMR-based TB metabolic fingerprinting for the diagnosis of TB in children.

Tuberculosis (TB) is a major cause of morbidity and mortality in children, and early diagnosis and treatment are crucial to reduce long-term morbidity and mortality. In this study, we explore whether urine nuclear magnetic resonance (NMR)-based metabolomics could be used to identify differences in the metabolic response of children with different diagnostic certainty of TB. We included 62 children with signs and symptoms of TB and 55 apparently healthy children. Six of the children with presumptive TB had bacteriologically confirmed TB, 52 children with unconfirmed TB, and 4 children with unlikely TB. Urine metabolic fingerprints were identified using high- and low-field proton NMR platforms and assessed with pattern recognition techniques such as principal components analysis and partial least squares discriminant analysis. We observed differences in the metabolic fingerprint of children with bacteriologically confirmed and unconfirmed TB compared to children with unlikely TB (p = 0.041 and p = 0.013, respectively). Moreover, children with unconfirmed TB with X-rays compatible with TB showed differences in the metabolic fingerprint compared to children with non-pathological X-rays (p = 0.009). Differences in the metabolic fingerprint in children with different diagnostic certainty of TB could contribute to a more accurate characterisation of TB in the paediatric population. The use of metabolomics could be useful to improve the prediction of TB progression and diagnosis in children.

Identification and Validation of Plasma Metabolomic Signatures in Precancerous Gastric Lesions That Progress to Cancer.

Importance: Metabolic deregulation plays an important role in gastric cancer (GC) development. To date, no studies have comprehensively explored the metabolomic profiles along the cascade of gastric lesions toward GC. Objective: To draw a metabolic landscape and define metabolomic signatures associated with the progression of gastric lesions and risk of early GC. Design, Setting, and Participants: A 2-stage, population-based cohort study was initiated in 2017 in Linqu County, Shandong Province, China, a high-risk area for GC. Prospective follow-up was conducted during the validation stage (June 20, 2017, to May 27, 2020). A total of 400 individuals were included based on the National Upper Gastrointestinal Cancer Early Detection Program in China. The discovery stage involved 200 individuals with different gastric lesions or GC (high-grade intraepithelial neoplasia or invasive GC). The validation stage prospectively enrolled 152 individuals with gastric lesions who were followed up for 118 to 1063 days and 48 individuals with GC. Exposures: Metabolomic profiles and metabolite signatures were examined based on untargeted plasma metabolomics assay. Main Outcomes and Measures: The risk of GC overall and early GC (high-grade intraepithelial neoplasia), and progression of gastric lesions. Results: Of the 400 participants, 124 of 200 (62.0%) in the discovery set were men; mean (SD) age was 56.8 (7.5) years. In the validation set, 136 of 200 (68.0%) were men; mean (SD) age was 57.5 (8.1) years. Distinct metabolomic profiles were noted for gastric lesions and GC. Six metabolites, including α-linolenic acid, linoleic acid, palmitic acid, arachidonic acid, sn-1 lysophosphatidylcholine (LysoPC)(18:3), and sn-2 LysoPC(20:3) were significantly inversely associated with risk of GC overall and early GC (high-grade intraepithelial neoplasia). Among these metabolites, the first 3 were significantly inversely associated with gastric lesion progression, especially for the progression of intestinal metaplasia (α-linolenic acid: OR, 0.42; 95% CI, 0.18-0.98; linoleic acid: OR, 0.43; 95% CI, 0.19-1.00; and palmitic acid: OR, 0.32; 95% CI, 0.13-0.78). Compared with models including only age, sex, Helicobacter pylori infection, and gastric histopathologic findings, integrating these metabolites significantly improved the performance for predicting the progression of gastric lesions (area under the curve [AUC], 0.86; 95% CI, 0.70-1.00 vs AUC, 0.69; 95% CI, 0.50-0.88; P = .02) and risk of early GC (AUC, 0.83; 95% CI, 0.58-1.00 vs AUC, 0.61; 95% CI, 0.31-0.91; P = .03). Conclusions and Relevance: This study defined metabolite signatures that might serve as meaningful biomarkers for assessing high-risk populations and early diagnosis of GC, possibly advancing targeted GC prevention and control.

Admission Urinary and Serum Metabolites Predict Renal Outcomes in Hospitalized Patients With Cirrhosis.

BACKGROUND AND AIMS: Acute kidney injury (AKI) has a poor prognosis in cirrhosis. Given the variability of creatinine, the prediction of AKI and dialysis by other markers is needed. The aim of this study is to determine the role of serum and urine metabolomics in the prediction of AKI and dialysis in an inpatient cirrhosis cohort. APPROACH AND RESULTS: Inpatients with cirrhosis from 11 North American Consortium of End-stage Liver Disease centers who provided admission serum/urine when they were AKI and dialysis-free were included. Analysis of covariance adjusted for demographics, infection, and cirrhosis severity was performed to identify metabolites that differed among patients (1) who developed AKI or not; (2) required dialysis or not; and/pr (3) within AKI subgroups who needed dialysis or not. We performed random forest and AUC analyses to identify specific metabolite(s) associated with outcomes. Logistic regression with clinical variables with/without metabolites was performed. A total of 602 patients gave serum (218 developed AKI, 80 needed dialysis) and 435 gave urine (164 developed AKI, 61 needed dialysis). For AKI prediction, clinical factor-adjusted AUC was 0.91 for serum and 0.88 for urine. Major metabolites such as uremic toxins (2,3-dihydroxy-5-methylthio-4-pentenoic acid [DMTPA], N2N2dimethylguanosine, uridine/pseudouridine) and tryptophan/tyrosine metabolites (kynunerate, 8-methoxykyunerate, quinolinate) were higher in patients who developed AKI. For dialysis prediction, clinical factor-adjusted AUC was 0.93 for serum and 0.91 for urine. Similar metabolites as AKI were altered here. For dialysis prediction in those with AKI, the AUC was 0.81 and 0.79 for serum/urine. Lower branched-chain amino-acid (BCAA) metabolites but higher cysteine, tryptophan, glutamate, and DMTPA were seen in patients with AKI needing dialysis. Serum/urine metabolites were additive to clinical variables for all outcomes. CONCLUSIONS: Specific admission urinary and serum metabolites were significantly additive to clinical variables to predict AKI development and dialysis initiation in inpatients with cirrhosis. These observations can potentially facilitate earlier initiation of renoprotective measures.

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.

A Prospective Investigation of Circulating Metabolome Identifies Potential Biomarkers for Gastric Cancer Risk.

BACKGROUND: Metabolomics is widely used to identify potential novel biomarkers for cancer risk. No investigation, however, has been conducted to prospectively evaluate the role of perturbation of metabolome in gastric cancer development. METHODS: 250 incident cases diagnosed with primary gastric cancer were selected from the Shanghai Women's Health and the Shanghai Men's Health Study, and each was individually matched to one control by incidence density sampling. An untargeted global profiling platform was used to measure approximately 1,000 metabolites in prediagnostic plasma. Conditional logistic regression was utilized to generate ORs and P values. RESULTS: Eighteen metabolites were associated with gastric cancer risk at P < 0.01. Among them, 11 metabolites were lysophospholipids or lipids of other classes; for example, 1-(1-enyl-palmitoyl)-GPE (P-16:0) (OR = 1.56; P = 1.89 × 10-4). Levels of methylmalonate, a suggested biomarker of vitamin B12 deficiency, was correlated with increased gastric cancer risk (OR = 1.42; P = 0.004). Inverse associations were found for three biomarkers for coffee/tea consumption (3-hydroxypyridine sulfate, quinate and N-(2-furoyl) glycine), although the associations were only significant when comparing cases that were diagnosed within 5 years after the blood collection to matched controls. Most of the identified associations were more profound in women and never smokers than their male or ever smoking counterparts and some with notable significant interactions. CONCLUSIONS: Our study identified multiple potential risk biomarkers for gastric cancer independent of Helicobacter pylori infection and other major risk factors. IMPACT: New risk-assessment tools to identify high-risk population could be developed to improve prevention of gastric cancer.See related commentary by Drew et al., p. 1601.

GC-FID-MS Based Metabolomics to Access Plum Brandy Quality.

Plum brandy (Slivovitz (en); Sljivovica(sr)) is an alcoholic beverage that is increasingly consumed all over the world. Its quality assessment has become of great importance. In our study, the main volatiles and aroma compounds of 108 non-aged plum brandies originating from three plum cultivars, and fermented using different conditions, were investigated. The chemical profiles obtained after two-step GC-FID-MS analysis were subjected to multivariate data analysis to reveal the peculiarity in different cultivars and fermentation process. Correlation of plum brandy chemical composition with its sensory characteristics obtained by expert commission was also performed. The utilization of PCA and OPLS-DA multivariate analysis methods on GC-FID-MS, enabled discrimination of brandy samples based on differences in plum varieties, pH of plum mash, and addition of selected yeast or enzymes during fermentation. The correlation of brandy GC-FID-MS profiles with their sensory properties was achieved by OPLS multivariate analysis. Proposed workflow confirmed the potential of GC-FID-MS in combination with multivariate data analysis that can be applied to assess the plum brandy quality.

Noninvasive screening for congenital heart defects using a serum metabolomics approach.

OBJECTIVE: Heart anomalies represent nearly one-third of all congenital anomalies. They are currently diagnosed using ultrasound. However, there is a strong need for a more accurate and less operator-dependent screening method. Here we report a metabolomics characterization of maternal serum in order to describe a metabolomic fingerprint representative of heart congenital anomalies. METHODS: Metabolomic profiles were obtained from serum of 350 mothers (280 controls and 70 cases). Nine classification models were built and optimized. An ensemble model was built based on the results from the individual models. RESULTS: The ensemble machine learning model correctly classified all cases and controls. Malonic, 3-hydroxybutyric and methyl glutaric acid, urea, androstenedione, fructose, tocopherol, leucine, and putrescine were determined as the most relevant metabolites in class separation. CONCLUSION: The metabolomic signature of second trimester maternal serum from pregnancies affected by a fetal heart anomaly is quantifiably different from that of a normal pregnancy. Maternal serum metabolomics is a promising tool for the accurate and sensitive screening of such congenital defects. Moreover, the revelation of the associated metabolites and their respective biochemical pathways allows a better understanding of the overall pathophysiology of affected pregnancies.

A Nuclear Magnetic Resonance-Based Metabolomic Study to Identify Metabolite Differences between Iranian Isolates of Leishmania major and Leishmania tropica.

Background: Cutaneous leishmaniasis caused by Leishmania species (L. spp) is one of the most important parasitic diseases in humans. To gain information on the metabolite variations and biochemical pathways between L. spp, we used the comparative metabolome of metacyclic promastigotes in the Iranian isolates of L. major and L. tropica by proton nuclear magnetic resonance (1H-NMR). Methods: L. tropica and L. major were collected from three areas of Iran, namely Gonbad, Mashhad, and Bam, between 2017 and 2018, and were cultured. The metacyclic promastigote of each species was separated, and cell metabolites were extracted. 1H-NMR spectroscopy was applied, and the data were processed using ProMatab in MATLAB (version 7.8.0.347). Multivariate statistical analyses, including the principal component analysis and the orthogonal projections to latent structures discriminant analysis, were performed to identify the discriminative metabolites between the two L. spp. Metabolites with variable influences in projection values of more than one and a P value of less than 0.05 were marked as significant differences. Results: A set of metabolites were detected, and 24 significantly differentially expressed metabolites were found between the metacyclic forms of L. major and L. tropica isolates. The top differential metabolites were methionine, aspartate, betaine, and acetylcarnitine, which were increased more in L. tropica than L. major (P<0.005), whereas asparagine, 3-hydroxybutyrate, L-proline, and kynurenine were increased significantly in L. major (P<0.01). The significantly altered metabolites were involved in eight metabolic pathways. Conclusion: Metabolomics, as an invaluable technique, yielded significant metabolites, and their biochemical pathways related to the metacyclic promastigotes of L. major and L. tropica. The findings offer greater insights into parasite biology and how pathogens adapt to their hosts.

NMR-based clinical metabolomics revealed distinctive serum metabolic profiles in patients with spondyloarthritis.

Spondyloarthritis (SpA) is a common rheumatic disorder of the young, marred by delay in diagnosis, and paucity of biomarkers of disease activity. The present study aimed to explore the potential of serum metabolic profiling of patients with SpA to identify biomarker for the diagnosis and assessment of disease activity. The serum metabolic profiles of 81 patients with SpA were compared with that of 86 healthy controls (HCs) using nuclear magnetic resonance (NMR)-based metabolomics approach. Seventeen patients were followed up after 3 months of standard treatment, and paired sera were analyzed for effects of therapy. Comparisons were done using the multivariate partial least squares discriminant analysis (PLS-DA), and the discriminatory metabolic entities were identified based on variable importance in projection (VIP) statistics and further evaluated for statistical significance (p value < 0.05). We found that the serum metabolic profiles differed significantly in SpA as compared with HCs. Compared with HC, the SpA patients were characterized by increased serum levels of amino acids, acetate, choline, N-acetyl glycoproteins, Nα-acetyl lysine, creatine/creatinine, and so forth and decreased levels of low-/very low-density lipoproteins and polyunsaturated lipids. PLS-DA analysis also revealed metabolic differences between axial and peripheral SpA patients. Further metabolite profiles were found to differ with disease activity and treatment in responding patients. The results presented in this study demonstrate the potential of serum metabolic profiling of axial SpA as a useful tool for diagnosis, prediction of peripheral disease, assessment of disease activity, and treatment response.

Preanalytical Sample Handling Conditions and Their Effects on the Human Serum Metabolome in Epidemiologic Studies.

Many epidemiologic studies use metabolomics for discovery-based research. The degree to which sample handling may influence findings, however, is poorly understood. In 2016, serum samples from 13 volunteers from the US Department of Agriculture's Beltsville Human Nutrition Research Center were subjected to different clotting (30 minutes/120 minutes) and refrigeration (0 minutes/24 hours) conditions, as well as different numbers (0/1/4) and temperatures (ice/refrigerator/room temperature) of thaws. The median absolute percent difference (APD) between metabolite levels and correlations between levels across conditions were estimated for 628 metabolites. The potential for handling artifacts to induce false-positive associations was estimated using variable hypothetical scenarios in which 1%-100% of case samples had different handling than control samples. All handling conditions influenced metabolite levels. Across metabolites, the median APD when extending clotting time was 9.08%. When increasing the number of thaws from 0 to 4, the median APD was 10.05% for ice and 5.54% for room temperature. Metabolite levels were correlated highly across conditions (all r's ≥ 0.84), indicating that relative ranks were preserved. However, if handling varied even modestly by case status, our hypotheticals showed that results can be biased and can result in false-positive findings. Sample handling affects levels of metabolites, and special care should be taken to minimize effects. Shorter room-temperature thaws should be preferred over longer ice thaws, and handling should be meticulously matched by case status.

Interpretation of omics data analyses.

Omics studies attempt to extract meaningful messages from large-scale and high-dimensional data sets by treating the data sets as a whole. The concept of treating data sets as a whole is important in every step of the data-handling procedures: the pre-processing step of data records, the step of statistical analyses and machine learning, translation of the outputs into human natural perceptions, and acceptance of the messages with uncertainty. In the pre-processing, the method by which to control the data quality and batch effects are discussed. For the main analyses, the approaches are divided into two types and their basic concepts are discussed. The first type is the evaluation of many items individually, followed by interpretation of individual items in the context of multiple testing and combination. The second type is the extraction of fewer important aspects from the whole data records. The outputs of the main analyses are translated into natural languages with techniques, such as annotation and ontology. The other technique for making the outputs perceptible is visualization. At the end of this review, one of the most important issues in the interpretation of omics data analyses is discussed. Omics studies have a large amount of information in their data sets, and every approach reveals only a very restricted aspect of the whole data sets. The understandable messages from these studies have unavoidable uncertainty.

An Efficient Algorithm for the Detection of Outliers in Mislabeled Omics Data.

High dimensionality and noise have made it difficult to detect related biomarkers in omics data. Through previous study, penalized maximum trimmed likelihood estimation is effective in identifying mislabeled samples in high-dimensional data with mislabeled error. However, the algorithm commonly used in these studies is the concentration step (C-step), and the C-step algorithm that is applied to robust penalized regression does not ensure that the criterion function is gradually optimized iteratively, because the regularized parameters change during the iteration. This makes the C-step algorithm runs very slowly, especially when dealing with high-dimensional omics data. The AR-Cstep (C-step combined with an acceptance-rejection scheme) algorithm is proposed. In simulation experiments, the AR-Cstep algorithm converged faster (the average computation time was only 2% of that of the C-step algorithm) and was more accurate in terms of variable selection and outlier identification than the C-step algorithm. The two algorithms were further compared on triple negative breast cancer (TNBC) RNA-seq data. AR-Cstep can solve the problem of the C-step not converging and ensures that the iterative process is in the direction that improves criterion function. As an improvement of the C-step algorithm, the AR-Cstep algorithm can be extended to other robust models with regularized parameters.