Genomic data integration tutorial, a plant case study.

BACKGROUND: The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts. RESULTS: To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration. CONCLUSION: The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.

Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder.

The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.

Improvement of variables interpretability in kernel PCA.

BACKGROUND: Kernel methods have been proven to be a powerful tool for the integration and analysis of high-throughput technologies generated data. Kernels offer a nonlinear version of any linear algorithm solely based on dot products. The kernelized version of principal component analysis is a valid nonlinear alternative to tackle the nonlinearity of biological sample spaces. This paper proposes a novel methodology to obtain a data-driven feature importance based on the kernel PCA representation of the data. RESULTS: The proposed method, kernel PCA Interpretable Gradient (KPCA-IG), provides a data-driven feature importance that is computationally fast and based solely on linear algebra calculations. It has been compared with existing methods on three benchmark datasets. The accuracy obtained using KPCA-IG selected features is equal to or greater than the other methods' average. Also, the computational complexity required demonstrates the high efficiency of the method. An exhaustive literature search has been conducted on the selected genes from a publicly available Hepatocellular carcinoma dataset to validate the retained features from a biological point of view. The results once again remark on the appropriateness of the computed ranking. CONCLUSIONS: The black-box nature of kernel PCA needs new methods to interpret the original features. Our proposed methodology KPCA-IG proved to be a valid alternative to select influential variables in high-dimensional high-throughput datasets, potentially unravelling new biological and medical biomarkers.

Asterics: a simple tool for the ExploRation and Integration of omiCS data.

BACKGROUND: The rapid development of omics acquisition techniques has induced the production of a large volume of heterogeneous and multi-level omics datasets, which require specific and sometimes complex analyses to obtain relevant biological information. Here, we present ASTERICS (version 2.5), a publicly available web interface for the analyses of omics datasets. RESULTS: ASTERICS is designed to make both standard and complex exploratory and integration analysis workflows easily available to biologists and to provide high quality interactive plots. Special care has been taken to provide a comprehensive documentation of the implemented analyses and to guide users toward sound analysis choices regarding some specific omics data. Data and analyses are organized in a comprehensive graphical workflow within ASTERICS workspace to facilitate the understanding of successive data editions and analyses leading to a given result. CONCLUSION: ASTERICS provides an easy to use platform for omics data exploration and integration. The modular organization of its open source code makes it easy to incorporate new workflows and analyses by external contributors. ASTERICS is available at https://asterics.miat.inrae.fr and can also be deployed using provided docker images.

A powerful framework for an integrative study with heterogeneous omics data: from univariate statistics to multi-block analysis.

High-throughput data generated by new biotechnologies require specific and adapted statistical treatment in order to be efficiently used in biological studies. In this article, we propose a powerful framework to manage and analyse multi-omics heterogeneous data to carry out an integrative analysis. We have illustrated this using the mixOmics package for R software as it specifically addresses data integration issues. Our work also aims at applying the most recent functionalities of mixOmics to real datasets. Although multi-block integrative methodologies exist, we hope to encourage a more widespread use of such approaches in an operational framework by biologists. We have used natural populations of the model plant Arabidopsis thaliana in this work, but the framework proposed is not limited to this plant and can be deployed whatever the organisms of interest and the biological question may be. Four omics datasets (phenomics, metabolomics, cell wall proteomics and transcriptomics) were collected, analysed and integrated to study the cell wall plasticity of plants exposed to sub-optimal temperature growth conditions. The methodologies presented here start from basic univariate statistics leading to multi-block integration analysis. We have also highlighted the fact that each method, either unsupervised or supervised, is associated with one biological issue. Using this powerful framework enabled us to arrive at novel conclusions on the biological system, which would not have been possible using standard statistical approaches.

Rv0180c contributes to Mycobacterium tuberculosis cell shape and to infectivity in mice and macrophages.

Mycobacterium tuberculosis, the main causative agent of human tuberculosis, is transmitted from person to person via small droplets containing very few bacteria. Optimizing the chance to seed in the lungs is therefore a major adaptation to favor survival and dissemination in the human population. Here we used TnSeq to identify genes important for the early events leading to bacterial seeding in the lungs. Beside several genes encoding known virulence factors, we found three new candidates not previously described: rv0180c, rv1779c and rv1592c. We focused on the gene, rv0180c, of unknown function. First, we found that deletion of rv0180c in M. tuberculosis substantially reduced the initiation of infection in the lungs of mice. Next, we established that Rv0180c enhances entry into macrophages through the use of complement-receptor 3 (CR3), a major phagocytic receptor for M. tuberculosis. Silencing CR3 or blocking the CR3 lectin site abolished the difference in entry between the wild-type parental strain and the Δrv0180c::km mutant. However, we detected no difference in the production of both CR3-known carbohydrate ligands (glucan, arabinomannan, mannan), CR3-modulating lipids (phthiocerol dimycocerosate), or proteins in the capsule of the Δrv0180c::km mutant in comparison to the wild-type or complemented strains. By contrast, we established that Rv0180c contributes to the functionality of the bacterial cell envelope regarding resistance to toxic molecule attack and cell shape. This alteration of bacterial shape could impair the engagement of membrane receptors that M. tuberculosis uses to invade host cells, and open a new perspective on the modulation of bacterial infectivity.

Integrative Multi-omics Analysis of Childhood Aggressive Behavior.

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 277, cases = 42%) and independent clinical data (N = 142, cases = 45%) ranged from 43 to 57%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.

Carbon sources and XlnR-dependent transcriptional landscape of CAZymes in the industrial fungus Talaromyces versatilis: when exception seems to be the rule.

BACKGROUND: Research on filamentous fungi emphasized the remarkable redundancy in genes encoding hydrolytic enzymes, the similarities but also the large differences in their expression, especially through the role of the XlnR/XYR1 transcriptional activator. The purpose of this study was to evaluate the specificities of the industrial fungus Talaromyces versatilis, getting clues into the role of XlnR and the importance of glucose repression at the transcriptional level, to provide further levers for cocktail production. RESULTS: By studying a set of 62 redundant genes representative of several categories of enzymes, our results underlined the huge plasticity of transcriptional responses when changing nutritional status. As a general trend, the more heterogeneous the substrate, the more efficient to trigger activation. Genetic modifications of xlnR led to significant reorganisation of transcriptional patterns. Just a minimal set of genes actually fitted in a simplistic model of regulation by a transcriptional activator, and this under specific substrates. On the contrary, the diversity of xlnR+ versus ΔxlnR responses illustrated the existence of complex and unpredicted patterns of co-regulated genes that were highly dependent on the culture condition, even between genes that encode members of a functional category of enzymes. They notably revealed a dual, substrate-dependant repressor-activator role of XlnR, with counter-intuitive transcripts regulations that targeted specific genes. About glucose, it appeared as a formal repressive sugar as we observed a massive repression of most genes upon glucose addition to the mycelium grown on wheat straw. However, we also noticed a positive role of this sugar on the basal expression of a few genes, (notably those encoding cellulases), showing again the strong dependence of these regulatory mechanisms upon promoter and nutritional contexts. CONCLUSIONS: The diversity of transcriptional patterns appeared to be the rule, while common and stable behaviour, both within gene families and with fungal literature, the exception. The setup of a new biotechnological process to reach optimized, if not customized expression patterns of enzymes, hence appeared tricky just relying on published data that can lead, in the best scenario, to approximate trends. We instead encourage preliminary experimental assays, carried out in the context of interest to reassess gene responses, as a mandatory step before thinking in (genetic) strategies for the improvement of enzyme production in fungi.

Long non-coding RNA expression profile in cytogenetically normal acute myeloid leukemia identifies a distinct signature and a new biomarker in NPM1-mutated patients.

Long non-coding RNAs are defined as transcripts larger than 200 nucleotides but without protein-coding potential. There is growing evidence of the important role of long non-coding RNAs in cancer initiation, development and progression. In this study, we sought to evaluate the long non-coding RNA expression profile of patients with cytogenetically normal acute myeloid leukemia (AML). RNA-sequencing of 40 cytogenetically normal AML patients allowed us to quantify 11,036 long non-coding RNAs. Among these, more than 8000 were previously undescribed long non-coding RNAs. Using unsupervised analysis, we observed a specific long non-coding RNA expression profile dependent on the mutational status of the NPM1 gene. Statistical analysis allowed us to identify a minimal set of 12 long non-coding RNAs capable of discriminating NPM1-mutated from NPM1-wild-type patients. These results were validated by qRT-PCR on an independent cohort composed of 134 cytogenetically normal AML patients. Furthermore, we have identified one putative biomarker, the long non-coding RNA XLOC_109948 whose expression pattern predicts clinical outcome. Interestingly, low XLOC_109948 expression indicates a good prognosis especially for NPM1-mutated patients. Transient transfection of GapmeR against XLOC_109948 in NPM1-mutated OCI-AML3 cell line treated with Ara-C or ATRA enhances apoptosis suggesting XLOC_109948 plays a role in drug sensitivity. This study improves our knowledge of the long non-coding RNA transcriptome in cytogenetically normal AML patients. We observed a distinct long non-coding RNA expression profile in patients with the NPM1 mutation. The newly identified XLOC_109948 long non-coding RNA emerged as a strong prognostic factor able to better stratify NPM1-mutated patients.

First-in-man phase 1 clinical trial of gene therapy for advanced pancreatic cancer: safety, biodistribution, and preliminary clinical findings.

This phase 1 trial was aimed to determine the safety, pharmacokinetics, and preliminary clinical activity of CYL-02, a nonviral gene therapy product that sensitizes pancreatic cancer cells to chemotherapy. CYL-02 was administrated using endoscopic ultrasound in 22 patients with pancreatic cancer that concomitantly received chemotherapy (gemcitabine). The maximum-tolerated dose (MTD) exceeded the maximal feasible dose of CYL-02 and was not identified. Treatment-related toxicities were mild, without serious adverse events. Pharmacokinetic analysis revealed a dose-dependent increase in CYL-02 DNA exposure in blood and tumors, while therapeutic RNAs were detected in tumors. No objective response was observed, but nine patients showed stable disease up to 6 months following treatment and two of these patients experienced long-term survival. Panels of plasmatic microRNAs and proteins were identified as predictive of gene therapy efficacy. We demonstrate that CYL-02 nonviral gene therapy has a favorable safety profile and is well tolerated in patients. We characterize CYL-02 biodistribution and demonstrate therapeutic gene expression in tumors. Treated patients experienced stability of disease and predictive biomarkers of response to treatment were identified. These promising results warrant further evaluation in phase 2 clinical trial.

Pancreatic preneoplastic lesions plasma signatures and biomarkers based on proteome profiling of mouse models.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with a mortality that is almost identical to incidence. Because early detected PDAC is potentially curable, blood-based biomarkers that could detect currently developing neoplasia would improve patient survival and management. PDAC develops from pancreatic intraepithelial neoplasia (PanIN) lesions, graded from low grade (PanIN1) to high grade (PanIN3). We made the hypothesis that specific proteomic signatures from each precancerous stage exist and are detectable in plasma. METHODS: We explored the peptide profiles of microdissected PanIN cells and of plasma samples corresponding to the different PanIN grade from genetically engineered mouse models of PDAC using capillary electrophoresis coupled to mass spectrometry (CE-MS) and Chip-MS/MS. RESULTS: We successfully characterised differential peptides profiles from PanIN microdissected cells. We found that plasma from tumor-bearing mice and age-matched controls exhibit discriminative peptide signatures. We also determined plasma peptide signatures corresponding to low- and high-grade precancerous step present in the mice pancreas using the two mass spectrometry technologies. Importantly, we identified biomarkers specific of PanIN3. CONCLUSIONS: We demonstrate that benign and advanced PanIN lesions display distinct plasma peptide patterns. This strongly supports the perspectives of developing a non-invasive screening test for prediction and early detection of PDAC.

Size control of the Drosophila hematopoietic niche by bone morphogenetic protein signaling reveals parallels with mammals.

The Drosophila melanogaster larval hematopoietic organ, the lymph gland, is a model to study in vivo the function of the hematopoietic niche. A small cluster of cells in the lymph gland, the posterior signaling center (PSC), maintains the balance between hematopoietic progenitors (prohemocytes) and their differentiation into specialized blood cells (hemocytes). Here, we show that Decapentaplegic/bone morphogenetic protein (Dpp/BMP) signaling activity in PSC cells controls niche size. In the absence of BMP signaling, the number of PSC cells increases. Correlatively, no hemocytes differentiate. Controlling PSC size is, thus, essential for normal blood cell homeostasis. Activation of BMP signaling in the PSC requires expression of the Dally-like heparan-sulfate proteoglycan, under the control of the Collier/early B-cell factor (EBF) transcription factor. A Dpp > dpp autoregulatory loop maintains BMP signaling, which limits PSC cell proliferation by repressing the protooncogene dmyc. Dpp antagonizes activity of wingless (Wg)/Wnt signaling, which positively regulates the number of PSC cells via the control of Dmyc expression. Together, our data show that Collier controls hemocyte homeostasis via coordinate regulation of PSC cell number and PSC signaling to prohemocytes. In mouse, EBF2, BMP, and Wnt signaling in osteoblasts is required for the proper number of niche and hematopoietic stem cells. Our findings bring insights to niche size control and draw parallels between Drosophila and mammalian hematopoiesis.

Peptidomics analysis of in vitro digested wheat breads: Effect of genotype and environment on protein digestibility and release of celiac disease and wheat allergy related epitopes.

Wheat proteins can trigger immunogenic reactions due to their resistance to digestion and immunostimulatory epitopes. Here, we investigated the peptidomic map of partially digested bread samples and the fingerprint of epitope diversity from 16 wheat genotypes grown in two environmental conditions. Flour protein content and composition were characterized; gastric and jejunal peptides were quantified using LC-MS/MS, and genotypes were classified into high or low bread protein digestibility. Differences in flour protein content and peptide composition distinguish high from low digestibility genotypes in both growing environments. No common peptide signature was found between high- and low-digestible genotypes; however, the celiac or allergen epitopes were noted not to be higher in low-digestible genotypes. Overall, this study established a peptidomic and epitope diversity map of digested wheat bread and provided new insights and correlations between weather conditions, genotypes, digestibility and wheat sensitivities such as celiac disease and wheat allergy.

PENDAH program for parents with children with attention deficit hyperactivity disorder. French adaptation of a behavioral parent training group: pilot study.

Behavioral parent training (BPT) is recognized as an effective part of the care offered to children with attention deficit hyperactivity disorder (ADHD). The aim of this pilot study was to objectively examine the effect that this intervention may have on motor activity, in addition to the measures classically found in this type of study. Parents of 24 school-aged children (6-12 year) with ADHD who met eligibility criteria were enrolled in the study. Before, after and five months after the intervention, we used three-dimensional accelerometers over one-week periods to measure the children's motor activity, and questionnaires for parental stress, quality of life, ADHD symptoms, anxiety and sensory disorders. To measure motor activity, a control group of normotypic children matched for age, sex and socio-professional category was set up. The experimental group showed slight decreases in motor activity compared with the control group, particularly in the classroom. The intervention showed improvements for parents in average stress and quality of life, and for children in average intensity global ADHD symptom, inattention, opposition and aggression, in line with previous studies on the effectiveness of BPT. The trial is the first clinical study to assess the effects of BPT on motor activity in children with ADHD.

The gene expression profile of phosphoantigen-specific human γδ T lymphocytes is a blend of αß T-cell and NK-cell signatures.

Global transcriptional technologies have revolutionised the study of lymphoid cell populations, but human γδ T lymphocytes specific for phosphoantigens remain far less deeply characterised by these methods despite the great therapeutic potential of these cells. Here we analyse the transcriptome of circulating TCRVγ(+) γδ T cells isolated from healthy individuals, and their relation with those from other lymphoid cell subsets. We report that the gene signature of phosphoantigen-specific TCRVγ(+) γδ T cells is a hybrid of those from αß T and NK cells, with more 'NK-cell' genes than αß T cells have and more 'T-cell' genes than NK cells. The expression profile of TCRVγ(+) γδ T cells stimulated with phosphoantigen recapitulates their immediate physiological functions: Th1 cytokine, chemokine and cytotoxic activities reflect their high mitotic activity at later time points and do not indicate antigen-presenting functions. Finally, such hallmarks make the transcriptome of γδ T cells, whether resting or clonally expanding, clearly distinctive from that of NK/T or peripheral T-cell lymphomas of the γδ subtype.

Multi-omics Data Integration in the Context of Plant Abiotic Stress Signaling.

In order to answer new biological questions, high-throughput data generated by new biotechnologies can be very meaningful but require specific and adapted statistical treatments. Thus, in the context of abiotic stress signaling studies, understanding the integration of cascading mechanisms from stress perception to biochemical and physiological adjustments necessarily entails efficient and valid analysis of multilevel and heterogeneous data. In this chapter, we propose examples to manage, analyze, and integrate multi-omics heterogeneous data. This workflow suggests and follows different general biological questions or issues answered with detailed code, data analysis, multiple visualizations, and always followed by brief interpretations. We illustrated this using the mixOmics package for the R software, as it specifically provides tools to address vertical and horizontal data integration issues. In order to illustrate this workflow, we used the usual omics datasets biologists can generate (phenomics, metabolomics, proteomics, and transcriptomics). These data were collected from two organs (leaf rosettes, floral stems) of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. They are available in the R package WallOmicsData. The workflow presented here is not limited to Arabidopsis thaliana and can be applied to any plant species. It can even be largely deployed to whatever the organisms of interest and the biological questions may be.

Multivariate Analysis with the R Package mixOmics.

The high-dimensional nature of proteomics data presents challenges for statistical analysis and biological interpretation. Multivariate analysis, combined with insightful visualization can help to reveal the underlying patterns in complex biological data. This chapter introduces the R package mixOmics which focuses on data exploration and integration. We first introduce methods for single data sets: both Principal Component Analysis, which can identify the patterns of variance present in data, and sparse Partial Least Squares Discriminant Analysis, which aims to identify variables that can classify samples into known groups. We then present integrative methods with Projection to Latent Structures and further extensions for discriminant analysis. We illustrate each technique on a breast cancer multi-omics study and provide the R code and data as online supplementary material for readers interested in reproducing these analyses.

Multicolor flow cytometry in clinical samples for platelet signaling assessment.

Background: Availability of multichannel cytometers and specific commercial antibodies makes flow cytometry a new option to simultaneously assess multiple intracellular platelet signaling pathways for clinical purposes, in small volume of blood or low platelet count. Objectives: To describe a multicolor flow cytometry with fluorescent barcoding technique for screening signaling pathways downstream membrane receptors of major platelet agonists (adenosine diphosphate, thrombin, thromboxane, and collagen). Methods: By comparison with immunoblotting, we first selected the target phosphoproteins, AKT, P38MAPK, LIMK, and SPL76; the times of stimulation; and phosphoflow barcoding conditions. We then performed a clinical study on whole blood of patients without evidence of blood platelet disorder on standard biological screening, consulting for trivial or occasionally provoked bleeds without familial antecedent (bleeding of unknown origin, n = 23) or type-1 von Willebrand disease (n = 9). In addition, we included a small group of patients with definite platelet disorders (Glanzmann thrombasthenia, δ-storage pool deficiency, and immune glycoprotein VI-related disease with granule secretion defect). Results: The range, kinetics, and distribution of fluorescence intensity were established for each agonist-target protein combination. Principal component analysis indicates a correlation in response to a target phosphoprotein (AKT and P38MAPK) to different agonists but no correlation in the response of different target phosphoproteins to the same agonist. The heterogeneity of individual responses in the whole population displayed was analyzed using clustering algorithm. Patients with platelet storage pool deficiency were positioned as lowest responders on the heatmap. Conclusion: In complement of functional tests, this study introduces a new approach for rapid platelet signaling profiling in clinical practice.

Autonomic Nervous System Adaptation and Circadian Rhythm Disturbances of the Cardiovascular System in a Ground-Based Murine Model of Spaceflight.

Whether in real or simulated microgravity, Humans or animals, the kinetics of cardiovascular adaptation and its regulation by the autonomic nervous system (ANS) remain controversial. In this study, we used hindlimb unloading (HU) in 10 conscious mice. Blood pressure (BP), heart rate (HR), temperature, and locomotor activity were continuously monitored with radio-telemetry, during 3 days of control, 5 days of HU, and 2 days of recovery. Six additional mice were used to assess core temperature. ANS activity was indirectly determined by analyzing both heart rate variability (HRV) and baroreflex sensitivity (BRS). Our study showed that HU induced an initial bradycardia, accompanied by an increase in vagal activity markers of HRV and BRS, together with a decrease in water intake, indicating the early adaptation to fluid redistribution. During HU, BRS was reduced; temperature and BP circadian rhythms were altered, showing a loss in day/night differences, a decrease in cycle amplitude, a drop in core body temperature, and an increase in day BP suggestive of a rise in sympathetic activity. Reloading induced resting tachycardia and a decrease in BP, vagal activity, and BRS. In addition to cardiovascular deconditioning, HU induces disruption in day/night rhythmicity of locomotor activity, temperature, and BP.

Quality assessment of commercial Magnoliae officinalis Cortex by ¹H-NMR-based metabolomics and HPLC methods.

INTRODUCTION: The quality control of Magnoliae officinalis Cortex, a commonly used traditional Chinese medicine, is currently based on the assay of the two active compounds, honokiol and magnolol, by TLC or HPLC. OBJECTIVE: To compare ¹H-NMR-based metabolomics with the HPLC method for controlling the quality of Magnoliae officinalis Cortex. To identify the metabolites contributing to the differences between the samples and to discriminate different medicinal parts and geographic origins of these samples by ¹H-NMR-based metabolomics. METHODOLOGY: ¹H-NMR and several multivariate analysis techniques were applied to analyse the extracts of 18 batches of Magnoliae officinalis Cortex commercial samples, and the contents of honokiol and magnolol in these samples were determined by HPLC. The correlation analysis between the data from ¹H-NMR and HPLC was performed with the mixOmics software based on an unsupervised method. RESULTS: Honokiol and magnolol were the main compounds responsible for the discrimination of samples from different batches, thus proving that the choice of these two compounds as markers for quality assessment by HPLC is relevant. The two sources of Magnoliae officinalis Cortex recorded in the Chinese Pharmacopoeia, Magnolia officinalis and Magnolia officinalis var. biloba, could be differentiated from ¹H-NMR data, but the pattern recognition analysis by PLS-DA was unsuccessful in discriminating samples from various geographical origins. CONCLUSION: The combination of ¹H-NMR that gives a comprehensive profile of the metabolites and HPLC that targets two biomarkers is an efficient means for a better quality control of Magnoliae officinalis Cortex.