eLIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity.

Spatial segmentation is an essential processing method for image analysis aiming to identify the characteristic suborgans or microregions from mass spectrometry imaging (MSI) data, which is critical for understanding the spatial heterogeneity of biological information and function and the underlying molecular signatures. Due to the intrinsic characteristics of MSI data including spectral nonlinearity, high-dimensionality, and large data size, the common segmentation methods lack the capability for capturing the accurate microregions associated with biological functions. Here we proposed an ensemble learning-based spatial segmentation strategy, named eLIMS, that combines a randomized unified manifold approximation and projection (r-UMAP) dimensionality reduction module for extracting significant features and an ensemble pixel clustering module for aggregating the clustering maps from r-UMAP. Three MSI datasets are used to evaluate the performance of eLIMS, including mouse fetus, human adenocarcinoma, and mouse brain. Experimental results demonstrate that the proposed method has potential in partitioning the heterogeneous tissues into several subregions associated with anatomical structure, i.e., the suborgans of the brain region in mouse fetus data are identified as dorsal pallium, midbrain, and brainstem. Furthermore, it effectively discovers critical microregions related to physiological and pathological variations offering new insight into metabolic heterogeneity.

Characterization of wheat dough and Chinese steamed bread using mealworm powder formulated with medium-gluten and whole wheat flour.

BACKGROUND: Mealworm (Tenebrio molitor) larvae are nutritious edible insects and exhibit the potential to act as protein substitutes in food products. In this study, we added mealworm powder as a substitute to medium-gluten wheat and whole wheat flours to enhance the quality of baked products. We compared the pasting, farinograph and extensograph properties of medium-gluten wheat and whole wheat flours replaced with different concentrations of mealworm powder to explore the interactions between flour and mealworm powder. RESULTS: Mealworm powder changed the pasting characteristics of medium-gluten wheat and whole wheat flours. After adding 20% mealworm powder, the pasting temperature of the medium-gluten wheat flour remained unchanged (approximately 89.9 °C), while the pasting temperature of whole wheat flour increased from 88.83 to 90.27 °C. Water absorption of medium-gluten and whole wheat flours exhibited a decreasing trend with increasing mealworm powder concentrations. Mealworm powder substitution resulted in stronger medium-gluten dough but exerted an opposite effect on the farinograph properties of whole wheat dough. Mealworm powder substitution decreased the stretching resistance of medium-gluten dough but increased that of whole wheat dough. With an increase in the concentration of mealworm powder, the specific volume of medium-gluten wheat steamed bread significantly increased from 1.69 mL g-1 (M0) to 3.31 mL g-1 (M10) whereas that of whole wheat steamed bread increased from 1.64 mL g-1 (M0) to 2.34 mL g-1 (M15). The addition of mealworm powder increased the protein, dietary fiber, lipid and sodium contents of steamed bread samples. CONCLUSIONS: This study provides a reference for the rheological properties of medium-gluten wheat and whole wheat flours substituted with mealworm powder and supports the addition of insects as a protein source in food products. © 2023 Society of Chemical Industry.

Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease.

Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.

Concordance-Based Batch Effect Correction for Large-Scale Metabolomics.

For a large-scale metabolomics study, sample collection, preparation, and analysis may last several days, months, or even (intermittently) over years. This may lead to apparent batch effects in the acquired metabolomics data due to variability in instrument status, environmental conditions, or experimental operators. Batch effects may confound the true biological relationships among metabolites and thus obscure real metabolic changes. At present, most of the commonly used batch effect correction (BEC) methods are based on quality control (QC) samples, which require sufficient and stable QC samples. However, the quality of the QC samples may deteriorate if the experiment lasts for a long time. Alternatively, isotope-labeled internal standards have been used, but they generally do not provide good coverage of the metabolome. On the other hand, BEC can also be conducted through a data-driven method, in which no QC sample is needed. Here, we propose a novel data-driven BEC method, namely, CordBat, to achieve concordance between each batch of samples. In the proposed CordBat method, a reference batch is first selected from all batches of data, and the remaining batches are referred to as "other batches." The reference batch serves as the baseline for the batch adjustment by providing a coordinate of correlation between metabolites. Next, a Gaussian graphical model is built on the combined dataset of reference and other batches, and finally, BEC is achieved by optimizing the correction coefficients in the other batches so that the correlation between metabolites of each batch and their combinations are in concordance with that of the reference batch. Three real-world metabolomics datasets are used to evaluate the performance of CordBat by comparing it with five commonly used BEC methods. The present experimental results showed the effectiveness of CordBat in batch effect removal and the concordance of correlation between metabolites after BEC. CordBat was found to be comparable to the QC-based methods and achieved better performance in the preservation of biological effects. The proposed CordBat method may serve as an alternative BEC method for large-scale metabolomics that lack proper QC samples.

iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions.

Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.

Application of Band-Selective HSQC NMR in Species Discrimination and Adulteration Identification of Panax Linn.

The quality of Panax Linn products available in the market is threatened by adulteration with different Panax species, such as Panax quinquefolium (PQ), Panax ginseng (PG), and Panax notoginseng (PN). In this paper, we established a 2D band-selective heteronuclear single quantum coherence (bs-HSQC) NMR method to discriminate species and detect adulteration of Panax Linn. The method involves selective excitation of the anomeric carbon resonance region of saponins and non-uniform sampling (NUS) to obtain high-resolution spectra in less than 10 min. The combined strategy overcomes the signal overlap limitation in 1H NMR and the long acquisition time in traditional HSQC. The present results showed that twelve well-separated resonance peaks can be assigned in the bs-HSQC spectra, which are of high resolution, good repeatability, and precision. Notably, the identification accuracy of species was found to be 100% for all tests conducted in the present study. Furthermore, in combination with multivariate statistical methods, the proposed method can effectively determine the composition proportion of adulterants (from 10% to 90%). Based on the PLS-DA models, the identification accuracy was greater than 80% when composition proportion of adulterants was 10%. Thus, the proposed method may provide a fast, practical, and effective analysis technique for food quality control or authenticity identification.

Sparse PLS-Based Method for Overlapping Metabolite Set Enrichment Analysis.

Metabolite set enrichment analysis (MSEA) has gained increasing research interest for identification of perturbed metabolic pathways in metabolomics. The method incorporates predefined metabolic pathways information in the analysis where metabolite sets are typically assumed to be mutually exclusive to each other. However, metabolic pathways are known to contain common metabolites and intermediates. This situation, along with limitations in metabolite detection or coverage leads to overlapping, incomplete metabolite sets in pathway analysis. For overlapping metabolite sets, MSEA tends to result in high false positives due to improper weights allocated to the overlapping metabolites. Here, we proposed an extended partial least squares (PLS) model with a new sparse scheme for overlapping metabolite set enrichment analysis, named overlapping group PLS (ogPLS) analysis. The weight vector of the ogPLS model was decomposed into pathway-specific subvectors, and then a group lasso penalty was imposed on these subvectors to achieve a proper weight allocation for the overlapping metabolites. Two strategies were adopted in the proposed ogPLS model to identify the perturbed metabolic pathways. The first strategy involves debiasing regularization, which was used to reduce inequalities amongst the predefined metabolic pathways. The second strategy is stable selection, which was used to rank pathways while avoiding the nuisance problems of model parameter optimization. Both simulated and real-world metabolomic datasets were used to evaluate the proposed method and compare with two other MSEA methods including Global-test and the multiblock PLS (MB-PLS)-based pathway importance in projection (PIP) methods. Using a simulated dataset with known perturbed pathways, the average true discovery rate for the ogPLS method was found to be higher than the Global-test and the MB-PLS-based PIP methods. Analysis with a real-world metabolomics dataset also indicated that the developed method was less prone to select pathways with highly overlapped detected metabolite sets. Compared with the two other methods, the proposed method features higher accuracy, lower false-positive rate, and is more robust when applied to overlapping metabolite set analysis. The developed ogPLS method may serve as an alternative MSEA method to facilitate biological interpretation of metabolomics data for overlapping metabolite sets.

Fabrication and characterization of fast dissolving glycerol monolaurate microemulsion encapsulated gelatin nanofibers with antimicrobial activity.

BACKGROUND: Electrospun fibers are a good candidate for the delivery of bioactive compounds in the food industry because of their advantages that include a tunable diameter, high porosity and a high specific surface area. In the present study, we fabricated gelatin/glycerol monolaurate (GML) microemulsion nanofibers by solubilizing GML in Tween-80 followed by mixing with gelatin solution for electrospinning. We hypothesized that the addition of GML microemulsions affects the properties of the gelatin solution and modifies the physical and antimicrobial properties of the resulting nanofibers. RESULTS: Both pure gelatin solution and gelatin/GML microemulsions showed shear-thinning behavior. However, electrospinnability was not affected by the addition of GML microemulsions. A significantly higher average diameter of nanofibers (1147 nm) with 5% GML was observed compared to the gelatin fiber diameter of 560 nm. Fourier transform infrared spectroscopy showed hydrogen bonding between gelatin molecules and GML microemulsions. Thermal analysis and X-ray diffraction indicated an amorphous structure of gelatin/GML microemulsion nanofibers, although a small amount of crystalline GML existed in the nanofibers with high GML content. Gelatin/GML microemulsion nanofibers showed high thermal stability and improved hydrophilicity. Nanofibers with 5% GML (weight with respect to nanofiber) (D64 nanofibers) showed effective antimicrobial activity against Escherichia coli and Staphylococcus aureus. CONCLUSION: Gelatin/GML microemulsion nanofibrous films demonstrate superhydrophilicity and fast dissolution properties as a result of the high surface-to-volume ratio, amorphous structure and improved hydrophilicity of the nanofiber surface. The results indicate the potential application of gelatin/GML microemulsion nanofibrous films as edible antimicrobial food packaging. © 2021 Society of Chemical Industry.

Probiotics-fermented blueberry juices as potential antidiabetic product: antioxidant, antimicrobial and antidiabetic potentials.

BACKGROUND: Fermentation is a traditional food-preserving technique. It is an effective process, widely used to enrich the nutrients diversity and bioactivity of the fermented foods since ancient times. This study aimed at investigating the effects of various fermentation starters on the physicochemical, antioxidant, antimicrobial, and antidiabetic properties of blueberry juices. The blueberry juices were fermented by natural fermentation (NFBJ), self-made starters fermentation (SFBJ), and commercial starters fermentation (CFBJ); fresh blueberry juice (BBJ) was processed without fermentation for comparison. RESULTS: Probiotics-fermented blueberry juices (SFBJ and CFBJ) showed less total and reducing sugars, higher titratable acidity, and a wider variety and higher amounts of organic acids than non-fermented blueberry juice (BBJ) did. All the fermented blueberry juices (NFBJ, SFBJ, and CFBJ) showed significantly (P < 0.05) higher antioxidant potentials than that of BBJ measured by 2,2'-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid, cupric-reducing antioxidant capacity, and ferric-reducing ability power assays. The SFBJ exhibited the highest antibacterial activities against Escherichia coli, Staphylococcus aureus, and Salmonella Typhimurium, with inhibition zone diameters of 38.84 ± 1.74 mm, 34.91 ± 1.53 mm, and 36.18 ± 3.16 mm respectively. Compared with BBJ, the α-glucosidase inhibitory activity of the SFBJ and CFBJ increased by two-to threefold. The α-amylase inhibitory activity of the SFBJ and CFBJ increased by 600%, whereas the spontaneous fermentation showed no improvement. The SFBJ and CFBJ promoted glucose consumption of HepG2 cell lines, indicating the promising potential for a higher glucose bio-utilization. CONCLUSIONS: The SFBJ and CFBJ showed remarkable improvements in the antioxidant, antimicrobial, and antidiabetic activities compared with non-fermented and spontaneous fermented juices, indicating their promising potentials as an antihyperglycemic agent. © 2021 Society of Chemical Industry.

Fabrication and characterization of dextran/zein hybrid electrospun fibers with tailored properties for controlled release of curcumin.

BACKGROUND: In recent years, there has been considerable interest in the use of biopolymer electrospun nanofibers for various food applications due to the biocompatibility, biodegradability, and high loading capacity. Herein, we fabricated and characterized novel hybrid electrospun fibers from dextran (50%, w/v) and zein (0-30%, w/v) solutions, and the effects of various zein concentrations on the properties of the hybrid electrospun fibers were investigated. RESULTS: When zein was added at low concentrations (5% and 10%), dextran and zein showed poor miscibility, as reflected by significantly decreased viscosity of the solutions, and the poor mechanical properties of the derived fiber membranes. When zein was added at medium concentrations (15-25%), hydrogen bonds were formed between dextran and zein molecules, as indicated by the red shift of Fourier-transform infrared bands and ß-sheet to α-helix structural transformations. The fiber membranes electrospun from a solution with 25% zein showed the most hydrophobic surface, with a water contact angle of 116.9°. The homogenous dispersion of dextran and zein resulted in improved mechanical properties for fibers electrospun from a solution with 30% zein. Curcumin encapsulating dextran/zein electrospun fibers exhibited effective radical scavenging activity and ferric reducing power, along with the desired controlled release behavior for curcumin delivery. CONCLUSION: Food grade dextran/zein hybrid electrospun fibers demonstrated tunable properties, and appear to be promising as delivery systems for bioactive and edible antimicrobial food packaging. © 2021 Society of Chemical Industry.

Identifying Significant Metabolic Pathways Using Multi-Block Partial Least-Squares Analysis.

In metabolomics, identification of metabolic pathways altered by disease, genetics, or environmental perturbations is crucial to uncover the underlying biological mechanisms. A number of pathway analysis methods are currently available, which are generally based on equal-probability, topological-centrality, or model-separability methods. In brief, prior identification of significant metabolites is needed for the first two types of methods, while each pathway is modeled separately in the model-separability-based methods. In these methods, interactions between metabolic pathways are not taken into consideration. The current study aims to develop a novel metabolic pathway identification method based on multi-block partial least squares (MB-PLS) analysis by including all pathways into a global model to facilitate biological interpretation. The detected metabolites are first assigned to pathway blocks based on their roles in metabolism as defined by the KEGG pathway database. The metabolite intensity or concentration data matrix is then reconstructed as data blocks according to the metabolite subsets. Then, a MB-PLS model is built on these data blocks. A new metric, named the pathway importance in projection (PIP), is proposed for evaluation of the significance of each metabolic pathway for group separation. A simulated dataset was generated by imposing artificial perturbation on four pre-defined pathways of the healthy control group of a colorectal cancer study. Performance of the proposed method was evaluated and compared with seven other commonly used methods using both an actual metabolomics dataset and the simulated dataset. For the real metabolomics dataset, most of the significant pathways identified by the proposed method were found to be consistent with the published literature. For the simulated dataset, the significant pathways identified by the proposed method are highly consistent with the pre-defined pathways. The experimental results demonstrate that the proposed method is effective for identification of significant metabolic pathways, which may facilitate biological interpretation of metabolomics data.

Dietary glycerol monolaurate supplementation for the modification of functional properties of egg white protein.

BACKGROUND: Understanding the interactions between feed additives and the functional properties of egg white protein (EWP) may offer novel insights into the effects of feed additives on laying hens and may provide an alternative for modification of the functional properties of EWP by using laying hens as bioreactors. Glycerol monolaurate (GML) is widely used in the food industry as an effective antibacterial emulsifier. In this work, the effects of three doses of dietary GML supplementation (150, 300, and 450 mg kg-1 hen) on the functional properties of EWP were investigated. RESULTS: The hardness of EWP gels was significantly improved by 300 and 450 mg kg-1 GML supplementation. Foaming capacity (FC) and foaming stability (FS) were increased after GML treatment; 450 mg kg-1 GML supplementation showed the most significant improvements, with 44.82% in FC and 23.39% in FS. Stabilization of EWP-oil emulsions was also improved, supported by a slowed creaming process and the formation of smaller oil droplets. The heat denaturation temperature and rheological properties were also modified by dietary GML supplementation, implying improved thermal stability. CONCLUSION: Our study demonstrated that GML supplementation has the potential to modify the functional properties of EWP, broadening the application of GML and providing a new perspective for evaluation of the efficacy of feed additives. © 2019 Society of Chemical Industry.

Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis.

Introduction: Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications. Objectives: To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking. Methods: A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRC patients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyp patients relative to healthy controls. Results: The results showed that demographic covariates, such as gender, BMI, BMI2, and smoking status, exhibit significant confounding effects on metabolite levels, which can be modeled effectively. Conclusion: These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease.

Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls.

Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.

Exploring Metabolic Profile Differences between Colorectal Polyp Patients and Controls Using Seemingly Unrelated Regression.

Despite the fact that colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world, the development of improved and robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC continues to be evasive. In particular, patients with colon polyps are at higher risk of developing colon cancer; however, noninvasive methods to identify these patients suffer from poor performance. In consideration of the challenges involved in identifying metabolite biomarkers in individuals with high risk for colon cancer, we have investigated NMR-based metabolite profiling in combination with numerous demographic parameters to investigate the ability of serum metabolites to differentiate polyp patients from healthy subjects. We also investigated the effect of disease risk on different groups of biologically related metabolites. A powerful statistical approach, seemingly unrelated regression (SUR), was used to model the correlated levels of metabolites in the same biological group. The metabolites were found to be significantly affected by demographic covariates such as gender, BMI, BMI(2), and smoking status. After accounting for the effects of the confounding factors, we then investigated potential of metabolites from serum to differentiate patients with polyps and age matched healthy controls. Our results showed that while only valine was slightly associated, individually, with polyp patients, a number of biologically related groups of metabolites were significantly associated with polyps. These results may explain some of the challenges and promise a novel avenue for future metabolite profiling methodologies.

Repeated range expansions and inter-/postglacial recolonization routes of Sargentodoxa cuneata (Oliv.) Rehd. et Wils. (Lardizabalaceae) in subtropical China revealed by chloroplast phylogeography.

Most plant phylogeographic studies in subtropical China have stressed the importance of multiple refugia and limited admixture among refugia. Little attention has been paid to range expansion and recolonization routes in this region. In this study, we implemented a phylogeographic survey on Sargentodoxa cuneata, a widespread woody deciduous climber in subtropical China to determine if it conforms to the expansion-contraction (EC) model during the Pleistocene. Sequence variation of two chloroplast intergenic spacers (IGSs) in 369 individuals from 54 populations of S. cuneata was examined. Twenty-six chloroplast haplotypes were recovered. One of these (H5) occurred across the range of S. cuneata and was absent from only 13 populations. Sixteen of the 26 haplotypes were connected to H5 by one mutation and displayed a star-like pattern in the haplotype network. All chloroplast haplotypes clustered into two lineages (A and B) in a Bayesian tree, and most haplotypes (18 out of 26) originated during the mid-Pleistocene (0.63-1.07Ma). Demographic analyses detected a recent range expansion that occurred at 95.98ka (CI: 61.7-112.53ka) for Lineage A. The genetic signature of an ancient range expansion after the Middle Pleistocene Transition (MPT) was also evident. Three recolonization routes were identified in subtropical China. The results suggest that temperate plants in subtropical China may conform to the EC model to some extent. However, the genetic signature from multiple historical processes may complicate the phylogeographic patterns of organisms in the region due to the mild Pleistocene climate. This study provides a new perspective for understanding the evolutionary history of temperate plants in subtropical China.

Targeted metabolic profiling of wounds in diabetic and nondiabetic mice.

While cellular metabolism is known to regulate a number of key biological processes such as cell growth and proliferation, its role in wound healing is unknown. We hypothesized that cutaneous injury would induce significant metabolic changes and that the impaired wound healing seen in diabetes would be associated with a dysfunctional metabolic response to injury. We used a targeted metabolomics approach to characterize the metabolic profile of uninjured skin and full-thickness wounds at day 7 postinjury in nondiabetic (db/-) and diabetic (db/db) mice. By liquid chromatography mass spectrometry, we identified 129 metabolites among all tissue samples. Principal component analysis demonstrated that uninjured skin and wounds have distinct metabolic profiles and that diabetes alters the metabolic profile of both uninjured skin and wounds. Examining individual metabolites, we identified 62 with a significantly altered response to injury in the diabetic mice, with many of these, including glycine, kynurenate, and OH-phenylpyruvate, implicated in wound healing for the first time. Thus, we report the first comprehensive analysis of wound metabolic profiles, and our results highlight the potential for metabolomics to identify novel biomarkers and therapeutic targets for improved wound healing outcomes.

Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring.

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and a major cause of human morbidity and mortality. In addition to early detection, close monitoring of disease progression in CRC can be critical for patient prognosis and treatment decisions. Efforts have been made to develop new methods for improved early detection and patient monitoring; however, research focused on CRC surveillance for treatment response and disease recurrence using metabolomics has yet to be reported. In this proof of concept study, we applied a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolic profiling approach focused on sequential metabolite ratio analysis of serial serum samples to monitor disease progression from 20 CRC patients. The use of serial samples reduces patient to patient metabolic variability. A partial least squares-discriminant analysis (PLS-DA) model using a panel of five metabolites (succinate, N2, N2-dimethylguanosine, adenine, citraconic acid, and 1-methylguanosine) was established, and excellent model performance (sensitivity = 0.83, specificity = 0.94, area under the receiver operator characteristic curve (AUROC) = 0.91 was obtained, which is superior to the traditional CRC monitoring marker carcinoembryonic antigen (sensitivity = 0.75, specificity = 0.76, AUROC = 0.80). Monte Carlo cross validation was applied, and the robustness of our model was clearly observed by the separation of true classification models from the random permutation models. Our results suggest the potential utility of metabolic profiling for CRC disease monitoring.

Colorectal cancer detection using targeted serum metabolic profiling.

Colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world. Despite an expanding knowledge of its molecular pathogenesis during the past two decades, robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC are still lacking. In this study, we present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying biomarker candidates that could enable highly sensitive and specific CRC detection using human serum samples. In this targeted approach, 158 metabolites from 25 metabolic pathways of potential significance were monitored in 234 serum samples from three groups of patients (66 CRC patients, 76 polyp patients, and 92 healthy controls). Partial least-squares-discriminant analysis (PLS-DA) models were established, which proved to be powerful for distinguishing CRC patients from both healthy controls and polyp patients. Receiver operating characteristic curves generated based on these PLS-DA models showed high sensitivities (0.96 and 0.89, respectively, for differentiating CRC patients from healthy controls or polyp patients), good specificities (0.80 and 0.88), and excellent areas under the curve (0.93 and 0.95). Monte Carlo cross validation was also applied, demonstrating the robust diagnostic power of this metabolic profiling approach.

[A novel metabolomic data scaling method based on K-L divergence].

A new scaling method in the current study based on Kullback-Leibler (K-L) divergence is proposed for NMR metabolomic data. The proposed method (called K-L scaling) is a supervised scaling method as group information is incorporated in the scaling procedure. Notably, K-L divergence measures the difference between two different datasets by their probability distributions, it can be used for the analysis of data that either follows Gaussian or non-Gaussian distributions. In K-L scaling, all variables were first standardized to unit variance, then their variance was adjusted using Kullback-Leibler divergence to highlight the significant variables. K-L scaling can tell effectively the difference in spectral data points between two experimental groups, and then enhances the weights of biological-relevant variables, and at the same time reduces the weight of noise and uninformative variables. The developed method was applied to a H-NMR metabolomic dataset acquired from human urine. Analysis results of the dataset showed that this new scaling method is efficient in suppressing the contribution of noise in the resulting multivariate model In addition, it can increase the weights of important variables, and improve the interpretability and predictability of subsequent principal component regression (PCR) and partial least squares discriminant analysis (PLS-DA). Furthermore, the scaling method facilitated the identification of metabolic signatures. The current result suggested that the developed K-L scaling method may become a useful alternative for the preprocessing of NMR-based metabolomic data.