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.

Monitoring of cellulose-rich biowaste co-digestion with 3D fluorescence spectroscopy and mass spectrometry-based metabolomics.

Anaerobic digestion (AD) is a promising waste management strategy that reduces landfilling while generating biogas. Anaerobic co-digestion involves mixing two or more substrates to enhance the nutrient balance required for microorganism growth and thus improve the degradation. Monitoring AD is crucial for comprehending the biological process, optimizing process stability, and achieving efficient biogas production. In this work, we have used three dimensional excitation emission fluorescence spectroscopy and mass spectrometry metabolomics, two complementary techniques, to monitor the anaerobic co-digestion (AcoD) of cellulose, ash wood or oak wood with food waste. The two approaches were compared together and to the biogas production records. Results of this experiment demonstrated the complementarity of both analytical techniques with the measurement of the biogas production since 3D fluorescence spectroscopy and MS metabolomics revealed the earlier molecular changes occurring in the bioreactors, mainly associated with the hydrolysis step, whereas the biogas production data reflected the biological activity in the last step of the digestion. Moreover, in all cases, the three data sets effectively delineated the differences among the substrates. While the two wood substrates were poorly degradable as they were richer in aromatic compounds, cellulose was highly degradable and was characterized by the production of several glycolipids. Then, the three tested AcoDs resulted in a similar 3D EEM fluorescence and metabolomics profiles, close to the one observed for the AD of food waste alone, indicating that the incorporation of the food waste drove the molecular degradation events in the AcoDs. Substrate-specific differences were appreciated from the biogas production data. The overall results of this research are expected to provide insight into the design of guidelines for monitoring AcoD.

Evidence of a causal and modifiable relationship between kidney function and circulating trimethylamine N-oxide.

The host-microbiota co-metabolite trimethylamine N-oxide (TMAO) is linked to increased cardiovascular risk but how its circulating levels are regulated remains unclear. We applied "explainable" machine learning, univariate, multivariate and mediation analyses of fasting plasma TMAO concentration and a multitude of phenotypes in 1,741 adult Europeans of the MetaCardis study. Here we show that next to age, kidney function is the primary variable predicting circulating TMAO, with microbiota composition and diet playing minor, albeit significant, roles. Mediation analysis suggests a causal relationship between TMAO and kidney function that we corroborate in preclinical models where TMAO exposure increases kidney scarring. Consistent with our findings, patients receiving glucose-lowering drugs with reno-protective properties have significantly lower circulating TMAO when compared to propensity-score matched control individuals. Our analyses uncover a bidirectional relationship between kidney function and TMAO that can potentially be modified by reno-protective anti-diabetic drugs and suggest a clinically actionable intervention for decreasing TMAO-associated excess cardiovascular risk.

A longitudinal study of the effect of temperature modification in full-scale anaerobic digesters - dataset combining 16S rDNA gene sequencing, metagenomics, and metabolomics data.

Data in this article provides detailed information on the microbial dynamics and degradation performances in two full-scale anaerobic digesters operated in parallel for 476 days. One of them was kept at 35 °C for the whole experiment, while the other was submitted to sub-mesophilic (25 °C) conditions between days 123 and 373. Sludge samples were collected from both digesters at days 0, 80, 177, 218, 281, 353, and 462. The provided data include the operational conditions of the digesters and the characterization of the sludge samples at the physicochemical level, indicative of the digesters' degradation performance. It also includes the characterization of the sludge samples at the multiomics level (16S rRNA gene sequencing, metagenomics, and metabolomics profiling), to decipher the changes in the microbial structure and molecular activity. The 16S rDNA gene sequencing, metagenomics, and metabolomics data were generated using an IonTorrent PGM sequencer, an Illumina NextSeq 500 sequencer, and LTQ-Orbitrap XL mass spectrometer respectively. The 16S rDNA gene raw data and the metagenomics data have been deposited in the BioProject PRJEB49115, in the ENA database (https://www.ebi.ac.uk/ena/browser/view/PRJEB49115). The metabolomics data has been deposited at the Metabolomics Workbench, with study id ST002004 (DOI: 10.21228/M8JM6B). The data can be used as a source for comparisons with other studies working with data from full-scale anaerobic digesters, especially for those investigating the effect of the temperature modification. The data is associated with the research article "Metataxonomics, metagenomics, and metabolomics analysis of the influence of temperature modification in full-scale anaerobic digesters" (Puig-Castellví et al [1]).

Metataxonomics, metagenomics and metabolomics analysis of the influence of temperature modification in full-scale anaerobic digesters.

Full-scale anaerobic digesters' performance is regulated by modifying their operational conditions, but little is known about how these modifications affect their microbiome. In this work, we monitored two originally mesophilic (35 °C) full-scale anaerobic digesters during 476 days. One digester was submitted to sub-mesophilic (25 °C) conditions between days 123 and 373. We characterized the effect of temperature modification using a multi-omics (metataxonomics, metagenomics, and metabolomics) approach. The metataxonomics and metagenomics results revealed that the lower temperature allowed a substantial increase of the sub-dominant bacterial population, destabilizing the microbial community equilibrium and reducing the biogas production. After restoring the initial mesophilic temperature, the bacterial community manifested resilience in terms of microbial structure and functional activity. The metabolomic signature of the sub-mesophilic acclimation was characterized by a rise of amino acids and short peptides, suggesting a protein degradation activity not directed towards biogas production.

Integrative Analyses to Investigate the Link between Microbial Activity and Metabolite Degradation during Anaerobic Digestion.

Anaerobic digestion (AD) is a promising biological process that converts waste into sustainable energy. To fully exploit AD's capability, we need to deepen our knowledge of the microbiota involved in this complex bioprocess. High-throughput methodologies open new perspectives to investigate the AD process at the molecular level, supported by recent data integration methodologies to extract relevant information. In this study, we investigated the link between microbial activity and substrate degradation in a lab-scale anaerobic codigestion experiment, where digesters were fed with nine different mixtures of three cosubstrates (fish waste, sewage sludge, and grass). Samples were profiled using 16S rRNA sequencing and untargeted metabolomics. In this article, we propose a suite of multivariate tools to statistically integrate these data and identify coordinated patterns between groups of microbial and metabolic profiles specific of each cosubstrate. Five main groups of features were successfully evidenced, including cadaverine degradation found to be associated with the activity of microorganisms from the order Clostridiales and the genus Methanosarcina. This study highlights the potential of data integration toward a comprehensive understanding of AD microbiota.

MCR-ALS analysis of 1H NMR spectra by segments to study the zebrafish exposure to acrylamide.

Metabolomics is currently an important field within bioanalytical science and NMR has become a key technique for drawing the full metabolic picture. However, the analysis of 1H NMR spectra of metabolomics samples is often very challenging, as resonances usually overlap in crowded regions, hindering the steps of metabolite profiling and resonance integration. In this context, a pre-processing method for the analysis of 1D 1H NMR data from metabolomics samples is proposed, consisting of the blind resolution and integration of all resonances of the spectral dataset by multivariate curve resolution-alternating least squares (MCR-ALS). The resulting concentration estimates can then be examined with traditional chemometric methods such as principal component analysis (PCA), ANOVA-simultaneous component analysis (ASCA), and partial least squares-discriminant analysis (PLS-DA). Since MCR-ALS does not require the use of spectral templates, the concentration estimates for all resonances are obtained even before being assigned. Consequently, the metabolomics study can be performed without neglecting any relevant resonance. In this work, the proposed pipeline performance was validated with 1D 1H NMR spectra from a metabolomics study of zebrafish upon acrylamide (ACR) exposure. Remarkably, this method represents a framework for the high-throughput analysis of NMR metabolomics data that opens the way for truly untargeted NMR metabolomics analyses. Graphical abstract.

Assessment of the microbial interplay during anaerobic co-digestion of wastewater sludge using common components analysis.

Anaerobic digestion (AD) is used to minimize solid waste while producing biogas by the action of microorganisms. To give an insight into the underlying microbial dynamics in anaerobic digesters, we investigated two different AD systems (wastewater sludge mixed with either fish or grass waste). The microbial activity was characterized by 16S RNA sequencing. 16S data is sparse and dispersed, and existent data analysis methods do not take into account this complexity nor the potential microbial interactions. In this line, we proposed a data pre-processing pipeline addressing these issues while not restricting only to the most abundant microorganisms. The data were analyzed by Common Components Analysis (CCA) to decipher the effect of substrate composition on the microorganisms. CCA results hinted the relationships between the microorganisms responding similarly to the AD physicochemical parameters. Thus, in overall, CCA allowed a better understanding of the inter-species interactions within microbial communities.

Assessment of substrate biodegradability improvement in anaerobic Co-digestion using a chemometrics-based metabolomic approach.

Anaerobic co-digestion (AcoD) can increase methane production of anaerobic digesters in plants treating wastewater sludge by improving the nutrient balance needed for the microorganisms to grow in the digesters, resulting in a faster process stabilization. Substrate mixture proportions are usually optimized in terms of biogas production, while the metabolic biodegradability of the whole mixture is neglected in this optimisation. In this aim, we developed a strategy to assess AcoD using metabolomics data. This strategy was explored in two different systems. Specifically, we investigated the co-digestion of wastewater sludge with different proportions of either grass or fish waste using untargeted High Performance Liquid Chromatography coupled to Mass Spectrometry (HPLC-MS) metabolomics and chemometrics methods. The analysis of these data revealed that adding grass waste did not improve the metabolic biodegradability of wastewater sludge. Conversely, a synergistic effect in the metabolic biodegradability was observed when fish waste was used, this effect being the highest for 25% of fish waste. In conclusion, metabolomics can be regarded as a promising tool both for characterizing the biochemical processes occurring during anaerobic digestion, and for providing a better understanding of the anaerobic digestion processes.

Targeting redox metabolism: the perfect storm induced by acrylamide poisoning in the brain.

Exposure to acrylamide may lead to different neurotoxic effects in humans and in experimental animals. To gain insights into this poorly understood type of neurotoxicological damage, we used a multi-omic approach to characterize the molecular changes occurring in the zebrafish brain exposed to acrylamide at metabolite, transcript and protein levels. We detected the formation of acrylamide adducts with thiol groups from both metabolites and protein residues, leading to a quasi-complete depletion of glutathione and to the inactivation of different components of the thioredoxin system. We propose that the combined loss-of-function of both redox metabolism-related systems configure a perfect storm that explains many acrylamide neurotoxic effects, like the dysregulation of genes related to microtubules, presynaptic vesicle alteration, and behavioral alterations. We consider that our mechanistical approach may help developing new treatments against the neurotoxic effects of acrylamide and of other neurotoxicants that may share its toxic mode of action.

Comparative analysis of 1H NMR and 1H-13C HSQC NMR metabolomics to understand the effects of medium composition in yeast growth.

In nuclear magnetic resonance (NMR) metabolomics, most of the studies have been focused on the analysis of one-dimensional proton (1D 1H) NMR, whereas the analysis of other nuclei, such as 13C, or other NMR experiments are still underrepresented. The preference of 1D 1H NMR metabolomics lies on the fact that it has good sensitivity and a short acquisition time, but it lacks spectral resolution because it presents a high degree of overlap. In this study, the growth metabolism of yeast ( Saccharomyces cerevisiae) was analyzed by 1D 1H NMR and by two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) NMR spectroscopy, leading to the detection of more than 50 metabolites with both analytical approaches. These two analyses allow for a better understanding of the strengths and intrinsic limitations of the two types of NMR approaches. The two data sets (1D and 2D NMR) were investigated with PCA, ASCA, and PLS DA chemometric methods, and similar results were obtained regardless of the data type used. However, data-analysis time for the 2D NMR data set was substantially reduced when compared with the data analysis of the corresponding 1H NMR data set because, for the 2D NMR data, signal overlap was not a major problem and deconvolution was not required. The comparative study described in this work can be useful for the future design of metabolomics workflows, to assist in the selection of the most convenient NMR platform and to guide the posterior data analysis of biomarker selection.

Deciphering the Underlying Metabolomic and Lipidomic Patterns Linked to Thermal Acclimation in Saccharomyces cerevisiae.

Temperature is one of the most critical parameters for yeast growth, and it has deep consequences in many industrial processes where yeast is involved. Nevertheless, the metabolic changes required to accommodate yeast cells at high or low temperatures are still poorly understood. In this work, the ultimate responses of these induced transcriptomic effects have been examined using metabolomics-derived strategies. The yeast metabolome and lipidome have been characterized by 1D proton nuclear magnetic resonance spectroscopy and ultra-high-performance liquid chromatography-mass spectrometry at four temperatures, corresponding to low, optimal, high, and extreme thermal conditions. The underlying pathways that drive the acclimation response of yeast to these nonoptimal temperatures were evaluated using multivariate curve resolution-alternating least-squares. The analysis revealed three different thermal profiles (cold, optimal, and high temperature), which include changes in the lipid composition, secondary metabolic pathways, and energy metabolism, and we propose that they reflect the acclimation strategy of yeast cells to low and high temperatures. The data suggest that yeast adjusts membrane fluidity by changing the relative proportions of the different lipid families (acylglycerides, phospholipids, and ceramides, among others) rather than modifying the average length and unsaturation levels of the corresponding fatty acids.

Compression of multidimensional NMR spectra allows a faster and more accurate analysis of complex samples.

We propose an approach to efficiently compress and denoise multidimensional NMR spectral data, improving their corresponding storage, handling, and analysis. This method has been tested with 2D homonuclear, 2D and 3D heteronuclear, and 2D phase-sensitive NMR spectral data and shown to be especially powerful for 2D NMR metabolomics studies.

Unraveling the Multistimuli Responses of a Complex Dynamic System of Pseudopeptidic Macrocycles.

Dynamic combinatorial libraries (DCLs) are excellent benchmark models to study the stimuli-responsiveness of chemical networks. However, increasingly complex systems are difficult to analyze with simple data analysis methods, because many variables and connections must be considered for their full understanding. Here we propose the use of multivariate data analysis methods to bisect the evolution of a complex synthetic dynamic library of pseudopeptidic macrocycles, containing side chains with charges of different sign. Several stimuli (ionic strength, pH and the presence of a biogenic polyamine) were applied to the same dynamic chemical mixture, and the adaptation of the whole system was characterized by HPLC and analyzed with principal component analysis (PCA) and multivariate curve resolution-alternating least squares (MCR-ALS) methods. Both multivariate data analysis chemometric approaches are an excellent combination to extract both qualitative and semi-quantitative information about the adaptive process of the library upon the action of each stimulus. The resolution of the system with these chemometric tools proved to be especially useful when two inter-connected stimuli were combined in the same dynamic system. Our results demonstrate the utility of these two approaches for the analysis of complex dynamic chemical systems and open the way toward the application of these powerful tools in the emergent field of systems chemistry.

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.

(1)H NMR metabolomic study of auxotrophic starvation in yeast using Multivariate Curve Resolution-Alternating Least Squares for Pathway Analysis.

Disruption of specific metabolic pathways constitutes the mode of action of many known toxicants and it is responsible for the adverse phenotypes associated to human genetic defects. Conversely, many industrial applications rely on metabolic alterations of diverse microorganisms, whereas many therapeutic drugs aim to selectively disrupt pathogens' metabolism. In this work we analyzed metabolic changes induced by auxotrophic starvation conditions in yeast in a non-targeted approach, using one-dimensional proton Nuclear Magnetic Resonance spectroscopy ((1)H NMR) and chemometric analyses. Analysis of the raw spectral datasets showed specific changes linked to the different stages during unrestricted yeast growth, as well as specific changes linked to each of the four tested starvation conditions (L-methionine, L-histidine, L-leucine and uracil). Analysis of changes in concentrations of more than 40 metabolites by Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) showed the normal progression of key metabolites during lag, exponential and stationary unrestricted growth phases, while reflecting the metabolic blockage induced by the starvation conditions. In this case, different metabolic intermediates accumulated over time, allowing identification of the different metabolic pathways specifically affected by each gene disruption. This synergy between NMR metabolomics and molecular biology may have clear implications for both genetic diagnostics and drug development.