Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes

Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts

BACKGROUND

Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics.

RESULTS

This study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, 'Low-Level data fusion' (LLDF) and 'Mid-Level data fusion' (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing-encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers.

SIGNIFICANCE

Both methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.

Measurement of chemical penetration in skin using Stimulated Raman scattering microscopy and multivariate curve resolution - alternating least squares

The mechanistic understanding of skin penetration underpins the design, efficacy and risk assessment of many high-value products including functional personal care products, topical and transdermal drugs. Stimulated Raman scattering (SRS) microscopy, a label free chemical imaging tool, combines molecular spectroscopy with submicron spatial information to map the distribution of chemicals as they penetrate the skin. However, the quantification of penetration is hampered by significant interference from Raman signals of skin constituents. This study reports a method for disentangling exogeneous contributions and measuring their permeation profile through human skin combining SRS measurements with chemometrics. We investigated the spectral decomposition capability of multivariate curve resolution - alternating least squares (MCR-ALS) using hyperspectral SRS images of skin dosed with 4-cyanophenol. By performing MCR-ALS on the fingerprint region spectral data, the distribution of 4-cyanophenol in skin was estimated in an attempt to quantify the amount permeated at different depths. The reconstructed distribution was compared with the experimental mapping of CN, a strong vibrational peak in 4-cyanophenol where the skin is spectroscopically silent. The similarity between MCR-ALS resolved and experimental distribution in skin dosed for 4 h was 0.79 which improved to 0.91 for skin dosed for 1 h. The correlation was observed to be lower for deeper layers of skin where SRS signal intensity is low which is an indication of low sensitivity of SRS. This work is the first demonstration, to the best of our knowledge, of combining SRS imaging technique with spectral unmixing methods for direct observation and mapping of the chemical penetration and distribution in biological tissues.

Raman microspectroscopy based TNM staging and grading of breast cancer

The tumor-node-metastasis (TNM) system is the most common way that doctors determine the anatomical extent of cancer on the basis of clinical and pathological criteria. In this study, a spectral histopathological study has been carried out to bridge Raman micro spectroscopy with the breast cancer TNM system. A total of seventy breast tissue samples, including healthy tissue, early, middle, and advanced cancer, were investigated to provide detailed insights into compositional and structural variations that accompany breast malignant evolution. After evaluating the main spectral variations in all tissue types, the generalized discriminant analysis (GDA) pathological diagnostic model was established to discriminate the TNM staging and grading information. Moreover, micro-Raman images were reconstructed by K-means clustering analysis (KCA) for visualizing the lobular acinar in healthy tissue and ductal structures in all early, middle and advanced breast cancer tissue groups. While, univariate imaging techniques were adapted to describe the distribution differences of biochemical components such as tryptophan, β-carotene, proteins, and lipids in the scanned regions. The achieved spectral histopathological results not only established a spectra-structure correlations via tissue biochemical profiles but also provided important data and discriminative model references for in vivo Raman-based breast cancer diagnosis.

Integrated Multi-Omics Analysis Reveals Differential Effects of Fructo-Oligosaccharides (FOS) Supplementation on the Human Gut Ecosystem

Changes in the gut ecosystem, including the microbiome and the metabolome, and the host immune system after fructo-oligosaccharide (FOS) supplementation were evaluated. The supplementation of FOS showed large inter-individual variability in the absolute numbers of fecal bacteria and an increase in Bifidobacterium. The fecal metabolome analysis revealed individual variability in fructose utilization in response to FOS supplementation. In addition, immunoglobulin A(IgA) tended to increase upon FOS intake, and peripheral blood monocytes significantly decreased upon FOS intake and kept decreasing in the post-FOS phase. Further analysis using a metagenomic approach showed that the differences could be at least in part due to the differences in gene expressions of enzymes that are involved in the fructose metabolism pathway. While the study showed individual differences in the expected health benefits of FOS supplementation, the accumulation of “personalized” knowledge of the gut ecosystem with its genetic expression may enable effective instructions on prebiotic consumption to optimize health benefits for individuals in the future.

Automated Hyperspectral 2D/3D Raman Analysis Using the Learner-Predictor Strategy: Machine Learning-Based Inline Raman Data Analytics

Synchronously detecting multiple Raman spectral signatures in two-dimensional/three-dimensional (2D/3D) hyperspectral Raman analysis is a daunting challenge. The underlying reasons notwithstanding the enormous volume of the data and also the complexities involved in the end-to-end Raman analytics pipeline: baseline removal, cosmic noise elimination, and extraction of trusted spectral signatures and abundance maps. Elimination of cosmic noise is the bottleneck in the entire Raman analytics pipeline. Unless this issue is addressed, the realization of autonomous Raman analytics is impractical. Here, we present a learner-predictor strategy-based "automated hyperspectral Raman analysis framework" to rapidly fingerprint the molecular variations in the hyperspectral 2D/3D Raman dataset. We introduce the spectrum angle mapper (SAM) technique to eradicate the cosmic noise from the hyperspectral Raman dataset. The learner-predictor strategy eludes the necessity of human inference, and analytics can be done in autonomous mode. The learner owns the ability to learn; it automatically eliminates the baseline and cosmic noise from the Raman dataset, extracts the predominant spectral signatures, and renders the respective abundance maps. In a nutshell, the learner precisely learned the spectral features space during the hyperspectral Raman analysis. Afterward, the learned spectral features space was translated into a neural network (LNN) model. In the predictor, machine-learned intelligence (LNN) is utilized to predict the alternate batch specimen's abundance maps in real time. The qualitative/quantitative evaluation of abundance maps implicitly lays the foundation for monitoring the offline/inline industrial qualitative/quantitative quality control (QA/QC) process. The present strategy is best suited for 2D/3D/four-dimensional (4D) hyperspectral Raman spectroscopic techniques. The proposed ML framework is intuitive because it obviates human intelligence, sophisticated computational hardware, and solely a personal computer is enough for the end-to-end pipeline.

Oral Pathobiont-Induced Changes in Gut Microbiota Aggravate the Pathology of Nonalcoholic Fatty Liver Disease in Mice

Background & Aims

Periodontitis increases the risk of nonalcoholic fatty liver disease (NAFLD); however, the underlying mechanisms are unclear. Here, we show that gut dysbiosis induced by oral administration of Porphyromonas gingivalis, a representative periodontopathic bacterium, is involved in the aggravation of NAFLD pathology.

Methods

C57BL/6N mice were administered either vehicle, P. gingivalis, or Prevotella intermedia, another periodontopathic bacterium with weaker periodontal pathogenicity, followed by feeding on a choline-deficient, l-amino acid-defined, high-fat diet with 60 kcal% fat and 0.1% methionine (CDAHFD60). The gut microbial communities were analyzed by pyrosequencing the 16S ribosomal RNA genes. Metagenomic analysis was used to determine the relative abundance of the Kyoto Encyclopedia of Genes and Genomes pathways encoded in the gut microbiota. Serum metabolites were analyzed using nuclear magnetic resonance-based metabolomics coupled with multivariate statistical analyses. Hepatic gene expression profiles were analyzed via DNA microarray and quantitative polymerase chain reaction.

Results

CDAHFD60 feeding induced hepatic steatosis, and in combination with bacterial administration, it further aggravated NAFLD pathology, thereby increasing fibrosis. Gene expression analysis of liver samples revealed that genes involved in NAFLD pathology were perturbed, and the two bacteria induced distinct expression profiles. This might be due to quantitative and qualitative differences in the influx of bacterial products in the gut because the serum endotoxin levels, compositions of the gut microbiota, and serum metabolite profiles induced by the ingested P. intermedia and P. gingivalis were different.

Conclusions

Swallowed periodontopathic bacteria aggravate NAFLD pathology, likely due to dysregulation of gene expression by inducing gut dysbiosis and subsequent influx of gut bacteria and/or bacterial products.

Workflow for the Supervised Learning of Chemical Data: Efficient Data Reduction-Multivariate Curve Resolution (EDR-MCR)

A new method termed efficient data reduction-multivariate curve resolution (EDR-MCR) has been devised for classification of high-dimensional data. The method introduces the coupling of EDR and MCR as a new strategy for data splitting, variable selection, and supervised classification of high dimensionality data. The method reduces data dimensionality and selects the training set using principal component analysis (PCA) and convex geometry prior to data classification. Then, the reduced data are categorized using an MCR model, in which numerical constraints are imposed to resolve the data into classes and readily interpretable pure component signal weights. The performance of the EDR and supervised MCR methods were tested for their ability to enable discrimination between the constituents of two benchmark and two high-dimensional data sets. The results were compared with the output of the application of different data splitting methods including iterative random selection (IRS), Kennard-Stone (KS), and discrimination methods including partial least-squares-discriminant analysis (PLS-DA) and the ensemble-learning frameworks of linear discriminant analysis (LDA), k-nearest neighbors (KNN), classification and regression trees (CART), and support vector machine (SVM). Overall, EDR resulted in comparable results with other data splitting methods despite the small size of the training set samples that it created. The proposed MCR approach, in comparison with other commonly used supervised techniques, has the advantages of speed in implementation, tuning of fewer parameters, flexibility in the analysis of data characterized by low sample numbers and class imbalances, improved accuracy from the inclusion of additional system information in the form of numerical constraints, and the ability to resolve pure components signal weights.

Autonomous adaptive data acquisition for scanning hyperspectral imaging

Non-invasive and label-free spectral microscopy (spectromicroscopy) techniques can provide quantitative biochemical information complementary to genomic sequencing, transcriptomic profiling, and proteomic analyses. However, spectromicroscopy techniques generate high-dimensional data; acquisition of a single spectral image can range from tens of minutes to hours, depending on the desired spatial resolution and the image size. This substantially limits the timescales of observable transient biological processes. To address this challenge and move spectromicroscopy towards efficient real-time spatiochemical imaging, we developed a grid-less autonomous adaptive sampling method. Our method substantially decreases image acquisition time while increasing sampling density in regions of steeper physico-chemical gradients. When implemented with scanning Fourier Transform infrared spectromicroscopy experiments, this grid-less adaptive sampling approach outperformed standard uniform grid sampling in a two-component chemical model system and in a complex biological sample, Caenorhabditis elegans. We quantitatively and qualitatively assess the efficiency of data acquisition using performance metrics and multivariate infrared spectral analysis, respectively.

Holman et al. develop a grid-less autonomous adaptive sampling method to explore high-dimensional spatiochemical experimental systems. Their method greatly decreases image acquisition time while improving spatial resolution, and when implemented with FTIR, it outperforms existing standard grid sampling approaches. They further show its utility for a complex biological sample, C. elegans.

Deep phenotyping of myalgic encephalomyelitis/chronic fatigue syndrome in Japanese population

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating disease with no molecular diagnostics and no treatment options. To identify potential markers of this illness, we profiled 48 patients and 52 controls for standard laboratory tests, plasma metabolomics, blood immuno-phenotyping and transcriptomics, and fecal microbiome analysis. Here, we identified a set of 26 potential molecular markers that distinguished ME/CFS patients from healthy controls. Monocyte number, microbiome abundance, and lipoprotein profiles appeared to be the most informative markers. When we correlated these molecular changes to sleep and cognitive measurements of fatigue, we found that lipoprotein and microbiome profiles most closely correlated with sleep disruption while a different set of markers correlated with a cognitive parameter. Sleep, lipoprotein, and microbiome changes occur early during the course of illness suggesting that these markers can be examined in a larger cohort for potential biomarker application. Our study points to a cluster of sleep-related molecular changes as a prominent feature of ME/CFS in our Japanese cohort.

Factors determining formation efficiencies of one-electron-reduced species of redox photosensitizers

Improvement in the photochemical formation efficiency of one-electron-reduced species (OERS) of a photoredox photosensitizer (a redox catalyst) is directly linked to the improvement in efficiencies of the various photocatalytic reactions themselves. We investigated the primary processes of a photochemical reduction of two series [Ru(diimine)₃]²⁺ and [Os(diimine)₃]²⁺ as frequently used redox photosensitizers (PS²⁺), by 1,3-dimethyl-2-phenyl-2,3-dihydro-1H-benzo[d]imidazole (BIH) as a typical reductant in detail using steady-irradiation and time-resolved spectroscopies. The rate constants of all elementary processes of the photochemical reduction of PS²⁺ by BIH to give the free PS•⁺ were obtained or estimated. The most important process for determining the formation efficiency of the free PS•⁺ was the escape yield from the solvated ion pair [PS•⁺-BIH•⁺], which was strongly dependent on both the central metal ion and the ligands. In cases with the same central metal ion, the system with larger -ΔG_bet, which is the free energy change in the back-electron transfer from the OERS of PS•⁺ to BIH•⁺, tended to lower the escape yield of the free OERS of PS²⁺. On the other hand, different central metal ions drastically affected the escape yield even in cases with similar -ΔG_bet; the escape yield in the case of RuH²⁺ (-ΔG_bet = 1.68 eV) was 5-11 times higher compared to those of OsH²⁺ (-ΔG_bet = 1.60 eV) and OsMe²⁺ (-ΔG_bet = 1.71 eV). The back-electron transfer process from the free PS•⁺ to the free BIH•⁺ could not compete against the further reaction of the free BIH•⁺, which is the deprotonation process giving BI•, in DMA for all examples. The produced BI• gave one electron to PS²⁺ in the ground state to give another PS•⁺, quantitatively. Based on these findings and investigations, it is clarified that the photochemical formation efficiency of the free PS•⁺ should be affected not only by -ΔG_bet but also by the heavy-atom effect of the central metal ion, and/or the oxidation power of the excited PS²⁺, which should determine the distance between the excited PS and BIH at the moment of the electron transfer.

Large-Scale Evaluation of Major Soluble Macromolecular Components of Fish Muscle from a Conventional 1H-NMR Spectral Database

Conventional proton nuclear magnetic resonance (¹H-NMR) has been widely used for identification and quantification of small molecular components in food. However, identification of major soluble macromolecular components from conventional ¹H-NMR spectra is difficult. This is because the baseline appearance is masked by the dense and high-intensity signals from small molecular components present in the sample mixtures. In this study, we introduced an integrated analytical strategy based on the combination of additional measurement using a diffusion filter, covariation peak separation, and matrix decomposition in a small-scale training dataset. This strategy is aimed to extract signal profiles of soluble macromolecular components from conventional ¹H-NMR spectral data in a large-scale dataset without the requirement of re-measurement. We applied this method to the conventional ¹H-NMR spectra of water-soluble fish muscle extracts and investigated the distribution characteristics of fish diversity and muscle soluble macromolecular components, such as lipids and collagens. We identified a cluster of fish species with low content of lipids and high content of collagens in muscle, which showed great potential for the development of functional foods. Because this mechanical data processing method requires additional measurement of only a small-scale training dataset without special sample pretreatment, it should be immediately applicable to extract macromolecular signals from accumulated conventional ¹H-NMR databases of other complex gelatinous mixtures in foods.

Machine Learning based Analytical Framework for Automatic Hyperspectral Raman Analysis of Lithium-ion Battery Electrodes

The intelligence to synchronously identify multiple spectral signatures in a lithium-ion battery electrode (LIB) would facilitate the usage of analytical technique for inline quality control and product development. Here, we present an analytical framework (AF) to automatically identify the existing spectral signatures in the hyperspectral Raman dataset of LIB electrodes. The AF is entirely automated and requires fewer or almost no human assistance. The end-to-end pipeline of AF own the following features; (i) intelligently pre-processing the hyperspectral Raman dataset to eliminate the cosmic noise and baseline, (ii) extract all the reliable spectral signatures from the hyperspectral dataset and assign the class labels, (iii) training a neural network (NN) on to the precisely “labelled” spectral signature, and finally, examined the interoperability/reusability of already trained NN on to the newly measured dataset taken from the same LIB specimen or completely different LIB specimen for inline real-time analytics. Furthermore, we demonstrate that it is possible to quantitatively assess the capacity degradation of LIB via a capacity retention coefficient that can be calculated by comparing the LMO signatures extracted by the analytical framework (AF). The present approach is suited for real-time vibrational spectroscopy based industrial applications; multicomponent chemical reactions, chromatographic, spectroscopic mixtures, and environmental monitoring.

A novel multivariate curve resolution-alternating least squares (MCR-ALS) methodology for application in hyperspectral Raman imaging analysis

Multivariate curve resolution-alternating least squares (MCR-ALS) applied to hyperspectral Raman imaging is extensively used to spatially and spectrally resolve the individual, pure chemical species within complex, heterogeneous samples. A critical aspect of performing MCR-ALS with hyperspectral Raman imaging is the selection of the number of chemical components within the experimental data. Several methods have previously been proposed to determine the number of chemical components, but it remains a challenging task that if done incorrectly, can lead to the loss of chemical information. In this work, we show that the choice of 'optimal' number of factors in the MCR-ALS model may vary depending on the relative contribution of the targeted species to the overall spectral intensity. In a data set consisting of 27 hyperspectral Raman images of TiO2 polymorphs, it was observed that the more dominant species were best resolved with a parsimonious model. However, species with intensities near the noise level often needed more factors to be resolved than was predicted by standard methods. Based on the observations in this data set, we propose a new method that employs approximate reference spectra for determining optimal model complexity for identifying minor constituents with MCR-ALS.

Imaging of dehydration in particulate matter using Raman line-focus microscopy

Crystalline solids can incorporate water molecules into their crystal lattice causing a dramatic impact on their properties. This explains the increasing interest in understanding the dehydration pathways of these solids. However, the classical thermal analytical techniques cannot spatially resolve the dehydration pathway of organic hydrates at the single particle level. We have developed a new method for imaging the dehydration of organic hydrates using Raman line-focus microscopy during heating of a particle. Based on this approach, we propose a new metastable intermediate of theophylline monohydrate during the three-step dehydration process of this system and further, we visualize the complex nature of the three-step dehydration pathway of nitrofurantoin monohydrate to its stable anhydrous form. A Raman line-focus mapping option was applied for fast simultaneous mapping of differently sized and shaped particles of nitrofurantoin monohydrate, revealing the appearance of multiple solid-state forms and the non-uniformity of this particle system during the complex dehydration process. This method provides an in-depth understanding of phase transformations and can be used to explain practical industrial challenges related to variations in the quality of particulate materials.

Band target entropy minimization and target partial least squares for spectral recovery and quantitation

The resolution and quantitation of pure spectra of minority components in measurements of chemical mixtures without prior knowledge of the mixture is a challenging problem. In this work, a combination of band target entropy minimization (BTEM) and target partial least squares (T-PLS) was used to obtain estimates for single pure component spectra and to calibrate those estimates in a true, one-at-a-time fashion. This approach allows for minor components to be targeted and their relative amounts estimated in the presence of other varying components in spectral data. The use of T-PLS estimation is an improvement to the BTEM method because it overcomes the need to identify all of the pure components prior to estimation. Estimated amounts from this combination were found to be similar to those obtained from a standard method, multivariate curve resolution-alternating least squares (MCR-ALS), on a simple, three component mixture dataset. Studies from two experimental datasets demonstrate where the combination of BTEM and T-PLS was used to model the pure component spectra and to obtain concentration profiles of minor components, but MCR-ALS could not.

Oral Administration of Porphyromonas gingivalis Alters the Gut Microbiome and Serum Metabolome

Periodontal disease induced by periodontopathic bacteria like Porphyromonas gingivalis is demonstrated to increase the risk of metabolic, inflammatory, and autoimmune disorders. Although precise mechanisms for this connection have not been elucidated, we have proposed mechanisms by which orally administered periodontopathic bacteria might induce changes in gut microbiota composition, barrier function, and immune system, resulting in an increased risk of diseases characterized by low-grade systemic inflammation. Accumulating evidence suggests a profound effect of altered gut metabolite profiles on overall host health. Therefore, it is possible that P. gingivalis can affect these metabolites. To test this, C57BL/6 mice were administered with P. gingivalis W83 orally twice a week for 5 weeks and compared with sham-inoculated mice. The gut microbial communities were analyzed by pyrosequencing the 16S rRNA genes. Inferred metagenomic analysis was used to determine the relative abundance of KEGG pathways encoded in the gut microbiota. Serum metabolites were analyzed using nuclear magnetic resonance (NMR)-based metabolomics coupled with multivariate statistical analyses. Oral administration of P. gingivalis induced a change in gut microbiota composition. The distributions of metabolic pathways differed between the two groups, including those related to amino acid metabolism and, in particular, the genes for phenylalanine, tyrosine, and tryptophan biosynthesis. Also, alanine, glutamine, histidine, tyrosine, and phenylalanine were significantly increased in the serum of P. gingivalis-administered mice. In addition to altering immune modulation and gut barrier function, oral administration of P. gingivalis affects the host's metabolic profile. This supports our hypothesis regarding a gut-mediated systemic pathology resulting from periodontal disease.IMPORTANCE Increasing evidence suggest that alterations of the gut microbiome underlie metabolic disease pathology by modulating gut metabolite profiles. We have shown that orally administered Porphyromonas gingivalis, a representative periodontopathic bacterium, alters the gut microbiome; that may be a novel mechanism by which periodontitis increases the risk of various diseases. Given the association between periodontal disease and metabolic diseases, it is possible that P. gingivalis can affect the metabolites. Metabolite profiling analysis demonstrated that several amino acids related to a risk of developing diabetes and obesity were elevated in P. gingivalis-administered mice. Our results revealed that the increased risk of various diseases by P. gingivalis might be mediated at least in part by alteration of metabolic profiles. The findings should add new insights into potential links between periodontal disease and systemic disease for investigators in periodontal disease and also for investigators in the field of other diseases, such as metabolic diseases.

Nuclear magnetic resonance spectroscopy as a new approach for improvement of early diagnosis and risk stratification of prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide and the fifth leading cause of death from cancer in men. Early detection and risk stratification is the most effective way to improve the survival of PCa patients. Current PCa biomarkers lack sufficient sensitivity and specificity to cancer. Metabolite biomarkers are evolving as a new diagnostic tool. This review is aimed to evaluate the potential of metabolite biomarkers for early detection, risk assessment, and monitoring of PCa. Of the 154 identified publications, 27 and 38 were original papers on urine and serum metabolomics, respectively. Nuclear magnetic resonance (NMR) is a promising method for measuring concentrations of metabolites in complex samples with good reproducibility, high sensitivity, and simple sample processing. Especially urine-based NMR metabolomics has the potential to be a cost-efficient method for the early detection of PCa, risk stratification, and monitoring treatment efficacy.

Intestinal microbiota composition is altered according to nutritional biorhythms in the leopard coral grouper (Plectropomus leopardus)

Aquaculture is currently a major source of fish and has the potential to become a major source of protein in the future. These demands require efficient aquaculture. The intestinal microbiota plays an integral role that benefits the host, providing nutrition and modulating the immune system. Although our understanding of microbiota in fish gut has increased, comprehensive studies examining fish microbiota and host metabolism remain limited. Here, we investigated the microbiota and host metabolism in the coral leopard grouper, which is traded in Asian markets as a superior fish and has begun to be produced via aquaculture. We initially examined the structural changes of the gut microbiota using next-generation sequencing and found that the composition of microbiota changed between fasting and feeding conditions. The dominant phyla were Proteobacteria in fasting and Firmicutes in feeding; interchanging the dominant bacteria required 12 hours. Moreover, microbiota diversity was higher under feeding conditions than under fasting conditions. Multivariate analysis revealed that Proteobacteria are the key bacteria in fasting and Firmicutes and Fusobacteria are the key bacteria in feeding. Subsequently, we estimated microbiota functional capacity. Microbiota functional structure was relatively stable throughout the experiment; however, individual function activity changed according to feeding conditions. Taken together, these findings indicate that the gut microbiota could be a key factor to understanding fish feeding conditions and play a role in interactions with host metabolism. In addition, the composition of microbiota in ambient seawater directly affects the fish; therefore, it is important to monitor the microbiota in rearing tanks and seawater circulating systems.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

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

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

Meta-Analysis of Fecal Microbiota and Metabolites in Experimental Colitic Mice during the Inflammatory and Healing Phases

The imbalance of gut microbiota is known to be associated with inflammatory bowel disease, but it remains unknown whether dysbiosis is a cause or consequence of chronic gut inflammation. In order to investigate the effects of gut inflammation on microbiota and metabolome, the sequential changes in gut microbiota and metabolites from the onset of colitis to the recovery in dextran sulfate sodium-induced colitic mice were characterized by using meta 16S rRNA sequencing and proton nuclear magnetic resonance (¹H-NMR) analysis. Mice in the colitis progression phase showed the transient expansions of two bacterial families including Bacteroidaceae and Enterobacteriaceae and the depletion of major gut commensal bacteria belonging to the uncultured Bacteroidales family S24-7, Rikenellaceae, Lachnospiraceae, and Ruminococcaceae. After the initiation of the recovery, commensal Lactobacillus members promptly predominated in gut while other normally abundant bacteria excluding the Erysipelotrichaceae remained diminished. Furthermore, ¹H-NMR analysis revealed characteristic fluctuations in fecal levels of organic acids (lactate and succinate) associated with the disease states. In conclusion, acute intestinal inflammation is a perturbation factor of gut microbiota but alters the intestinal environments suitable for Lactobacillus members.

Trans-omics approaches used to characterise fish nutritional biorhythms in leopard coral grouper (Plectropomus leopardus)

Aquaculture is now a major supplier of fish, and has the potential to be a major source of protein in the future. Leopard coral groupers are traded in Asian markets as superior fish, and production via aquaculture has commenced. As feeding efficiency is of great concern in aquaculture, we sought to examine the metabolism of leopard coral groupers using trans-omics approaches. Metabolic mechanisms were comprehensively analysed using transcriptomic and metabolomic techniques. This study focused on the dynamics of muscular metabolites and gene expression. The omics data were discussed in light of circadian rhythms and fasting/feeding. The obtained data suggest that branched-chain amino acids played a role in energy generation in the fish muscle tissues during fasting. Moreover, glycolysis, TCA cycles, and purine metabolic substances exhibited circadian patterns, and gene expression also varied. This study is the first step to understanding the metabolic mechanisms of the leopard coral grouper.

Raman Microspectroscopic Mapping with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) Applied to the High-Pressure Polymorph of Titanium Dioxide, TiO2-II

The high-pressure, α-PbO₂-structured polymorph of titanium dioxide (TiO₂-II) was recently identified in micrometer-sized grains recovered from four Neoarchean spherule layers deposited between ∼2.65 and ∼2.54 billion years ago. Several lines of evidence support the interpretation that these layers represent distal impact ejecta layers. The presence of shock-induced TiO₂-II provides physical evidence to further support an impact origin for these spherule layers. Detailed characterization of the distribution of TiO₂-II in these grains may be useful for correlating the layers, estimating the paleodistances of the layers from their source craters, and providing insight into the formation of the TiO₂-II. Here we report the investigation of TiO₂-II-bearing grains from these four spherule layers using multivariate curve resolution-alternating least squares (MCR-ALS) applied to Raman microspectroscopic mapping. Raman spectra provide evidence of grains consisting primarily of rutile (TiO₂) and TiO₂-II, as shown by Raman bands at 174 cm^-1 (TiO₂-II), 426 cm^-1 (TiO₂-II), 443 cm^-1 (rutile), and 610 cm^-1 (rutile). Principal component analysis (PCA) yielded a predominantly three-phase system comprised of rutile, TiO₂-II, and substrate-adhesive epoxy. Scanning electron microscopy (SEM) suggests heterogeneous grains containing polydispersed micrometer- and submicrometer-sized particles. Multivariate curve resolution-alternating least squares applied to the Raman microspectroscopic mapping yielded up to five distinct chemical components: three phases of TiO₂ (rutile, TiO₂-II, and anatase), quartz (SiO₂), and substrate-adhesive epoxy. Spectral profiles and spatially resolved chemical maps of the pure chemical components were generated using MCR-ALS applied to the Raman microspectroscopic maps. The spatial resolution of the Raman microspectroscopic maps was enhanced in comparable, cost-effective analysis times by limiting spectral resolution and optimizing spectral acquisition parameters. Using the resolved spectra of TiO₂-II generated from MCR-ALS analysis, a Raman spectrum for pure TiO₂-II was estimated to further facilitate its identification.

Identification of trace additives in polymer materials by attenuated total reflection Fourier transform infrared mapping coupled with multivariate curve resolution

Although multivariate curve resolution (MCR) has been applied to the analysis of Fourier transform infrared (FTIR) imaging, it is still problematic to determine the number of components. The reported methods at present tend to cause the components of low concentration missed. In this paper a new idea was proposed to resolve this problem. First, MCR calculation was repeated by increasing the number of components sequentially, then each retrieved pure spectrum of as-resulted MCR component was directly compared with a real-world pixel spectrum of the local high concentration in the corresponding MCR map. One component was affirmed only if the characteristic bands of the MCR component had been included in its pixel spectrum. This idea was applied to attenuated total reflection (ATR)/FTIR mapping for identifying the trace additives in blind polymer materials and satisfactory results were acquired. The successful demonstration of this novel approach opens up new possibilities for analyzing additives in polymer materials.

Reconstructing diffusion ordered NMR spectroscopy by simultaneous inversion of Laplace transform

2D diffusion-ordered NMR spectroscopy (DOSY) has been widely recognized as a powerful tool for analyzing mixtures and probing inter-molecular interactions in situ. But it is difficult to differentiate molecules with similar diffusion coefficients in presence of overlapped spectra. Its performance is susceptible to the number of chemical components, and usually gets worse when the number of components increases. Here, to alleviate the problem, numerical simultaneous inversion of Laplace transform (SILT) of many related variables is proposed for reconstructing DOSY spectrum (SILT-DOSY). The advantage of the proposed method in comparison to other methods is that it is capable of estimating the number of analytes more accurately and deriving corresponding component spectra, which in turn leads to the more reliable identification of the components.

Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach

In this article, we propose the use of the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometrics method to resolve the ¹H NMR spectra and concentration of the individual metabolites in their mixtures in untargeted metabolomics studies. A decision tree-based strategy is presented to optimally select and implement spectra estimates and equality constraints during MCR-ALS optimization. The proposed method has been satisfactorily evaluated using different ¹H NMR metabolomics datasets. In a first study, ¹H NMR spectra of the metabolites in a simulated mixture were successfully recovered and assigned. In a second study, more than 30 metabolites were characterized and quantified from an experimental unknown mixture analyzed by ¹H NMR. In this work, MCR-ALS is shown to be a convenient tool for metabolite investigation and sample screening using ¹H NMR, and it opens a new path for performing metabolomics studies with this chemometric technique.

Spatial and spectral resolution of carbonaceous material from hematite (α-Fe2O3) using multivariate curve resolution-alternating least squares (MCR-ALS) with Raman microspectroscopic mapping: implications for the search for life on Mars

We report a novel application of multivariate analysis with Raman microspectroscopic mapping to enhance the search for life on Mars.

The Effect of Molecular Conformation on the Accuracy of Theoretical (1)H and (13)C Chemical Shifts Calculated by Ab Initio Methods for Metabolic Mixture Analysis

NMR spectroscopy is a powerful method for analyzing metabolic mixtures. The information obtained from an NMR spectrum is in the form of physical parameters, such as chemical shifts, and construction of databases for many metabolites will be useful for data interpretation. To increase the accuracy of theoretical chemical shifts for development of a database for a variety of metabolites, the effects of sets of conformations (structural ensembles) and the levels of theory on computations of theoretical chemical shifts were systematically investigated for a set of 29 small molecules in the present study. For each of the 29 compounds, 101 structures were generated by classical molecular dynamics at 298.15 K, and then theoretical chemical shifts for 164 (1)H and 123 (13)C atoms were calculated by ab initio quantum chemical methods. Six levels of theory were used by pairing Hartree-Fock, B3LYP (density functional theory), or second order Møller-Plesset perturbation with 6-31G or aug-cc-pVDZ basis set. The six average fluctuations in the (1)H chemical shift were ±0.63, ± 0.59, ± 0.70, ± 0.62, ± 0.75, and ±0.66 ppm for the structural ensembles, and the six average errors were ±0.34, ± 0.27, ± 0.32, ± 0.25, ± 0.32, and ±0.25 ppm. The results showed that chemical shift fluctuations with changes in the conformation because of molecular motion were larger than the differences between computed and experimental chemical shifts for all six levels of theory. In conclusion, selection of an appropriate structural ensemble should be performed before theoretical chemical shift calculations for development of an accurate database for a variety of metabolites.