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.

Multiplet-Assisted Peak Alignment for 1H NMR-Based Metabolomics.

NMR-based metabolomics aims at recovering biological information by comparing spectral data from samples of biological interest and appropriate controls. Any statistical analysis performed on the data matrix relies on the proper peak alignment to produce meaningful results. Through the last decades, several peak alignment algorithms have been proposed, as well as alternatives like spectral binning or strategies for annotation and quantification, the latter depending on reference databases. Most of the alignment algorithms, mainly based on segmentation of the spectra, present limitations for regions with peak overlap or cases of frequency order exchange. Here, we present our multiplet-assisted peak alignment algorithm, a new methodology that consists of aligning peaks by matching multiplet profiles of f1 traces from J-resolved spectra. A correspondence matrix with the linked f1 traces is built, and multivariate data analysis can be performed on it to obtain useful information from the data, overcoming the issues of peak overlap and frequency crossovers. Statistical total correlation spectroscopy can be applied on the matrix as well, toward a better identification of molecules of interest. The results can be queried on one-dimensional (1D) 1H databases or can be directly coupled to our previously published Chemical Shift Multiplet Database.

The Assembly of Bacteria Living in Natural Environments Shapes Neuronal Integrity and Behavioral Outputs in Caenorhabditis elegans.

Bacterivore nematodes are the most abundant animals in the biosphere, largely contributing to global biogeochemistry. Thus, the effects of environmental microbes on the nematodes' life-history traits are likely to contribute to the general health of the biosphere. Caenorhabditis elegans is an excellent model to study the behavioral and physiological outputs of microbial diets. However, the effects of complex natural bacterial assemblies have only recently been reported, as most studies have been carried out with monoxenic cultures of laboratory-reared bacteria. Here, we quantified the physiological, phenotypic, and behavioral traits of C. elegans feeding on two bacteria that were coisolated with wild nematodes from a soil sample. These bacteria were identified as a putative novel species of Stenotrophomonas named Stenotrophomonas sp. strain Iso1 and a strain of Bacillus pumilus designated Iso2. The distinctive behaviors and developmental patterns observed in animals fed with individual isolates changed when bacteria were mixed. We studied in more depth the degeneration rate of the touch circuit of C. elegans and show that B. pumilus alone is protective, while the mix with Stenotrophomonas sp. is degenerative. The analysis of the metabolite contents of each isolate and their combination identified NAD+ as being potentially neuroprotective. In vivo supplementation shows that NAD+ restores neuroprotection to the mixes and also to individual nonprotective bacteria. Our results highlight the distinctive physiological effects of bacteria resembling native diets in a multicomponent scenario rather than using single isolates on nematodes. IMPORTANCE Do behavioral choices depend on animals' microbiota? To answer this question, we studied how different bacterial assemblies impact the life-history traits of the bacterivore nematode C. elegans using isolated bacteria found in association with wild nematodes in Chilean soil. We identified the first isolate, Iso1, as a novel species of Stenotrophomonas and isolate Iso2 as Bacillus pumilus. We find that worm traits such as food choice, pharyngeal pumping, and neuroprotection, among others, are dependent on the biota composition. For example, the neurodegeneration of the touch circuit needed to sense and escape from predators in the wild decreases when nematodes are fed on B. pumilus, while its coculture with Stenotrophomonas sp. eliminates neuroprotection. Using metabolomics analysis, we identify metabolites such as NAD+, present in B. pumilus yet lost in the mix, as being neuroprotective and validated their protective effects using in vivo experiments.

Gut colonization by Proteobacteria alters host metabolism and modulates cocaine neurobehavioral responses.

Gut-microbiota membership is associated with diverse neuropsychological outcomes, including substance use disorders (SUDs). Here, we use mice colonized with Citrobacter rodentium or the human γ-Proteobacteria commensal Escherichia coli HS as a model to examine the mechanistic interactions between gut microbes and host responses to cocaine. We find that cocaine exposure increases intestinal norepinephrine levels that are sensed through the bacterial adrenergic receptor QseC to promote intestinal colonization of γ-Proteobacteria. Colonized mice show enhanced host cocaine-induced behaviors. The neuroactive metabolite glycine, a bacterial nitrogen source, is depleted in the gut and cerebrospinal fluid of colonized mice. Systemic glycine repletion reversed, and γ-Proteobacteria mutated for glycine uptake did not alter the host response to cocaine. γ-Proteobacteria modulated glycine levels are linked to cocaine-induced transcriptional plasticity in the nucleus accumbens through glutamatergic transmission. The mechanism outline here could potentially be exploited to modulate reward-related brain circuits that contribute to SUDs.

Bacterial isolates from Argentine Pampas and their ability to degrade glyphosate.

Glyphosate is a synthetic phosphonate compound characterized by a carbon­phosphorus bond. Glyphosate based herbicides (GBH) are widely distributed in most of the economically productive lands in which crop production is mainly based on glyphosate-resistant genetically modified plants. Naturally, glyphosate is remediated by soil microorganisms, which accelerate its degradation. Technology based on microorganisms is considered highly efficient, low-cost and eco-friendly to remediate contaminated environments, denoting the importance of characterizing new bacterial strains able to degrade glyphosate to perform its bioremediation. In this work, 13 different bacterial strains able to grow in GBH as only phosphorous source were isolated from different environmental samples from the Argentine vastly productive glyphosate-resistant soybean crop area. These strains were identified and they belong to the genera Acinetobacter, Achromobacter, Agrobacterium, Ochrobactrum, Pantoea and Pseudomonas. Their ability to grow and consume GBH, glyphosate or the aminomethylphosphonic acid (AMPA), another phosphonate derived from glyphosate degradation, was evaluated. The best degradation performance was observed for bacteria from the genera Achromobacter, Agrobacterium and Ochrobactrum. The genome of the highly efficient GBH degrader Agrobacterium tumefaciens CHLDO was sequenced revealing the presence of a phn cluster, responsible for phosphonate metabolization. Expression analysis of A. tumefaciens CHLDO phn genes in the presence of 1.5 mM GBH compared to inorganic phosphorous showed that most of them are highly expressed during growth in the presence of the herbicide, suggesting a strong participation of phn cluster in GBH degradation. The importance of discovering new bacterial strains and the value of deciphering molecular determinants of GBH degradation give promising tools for bioremediation techniques to be used in glyphosate-contaminated environments is discussed.

Consecutive Queries to Assess Biological Correlation in NMR Metabolomics: Performance of Comprehensive Search of Multiplets over Typical 1D 1H NMR Database Search.

NMR-based metabolomics requires proper identification of metabolites to draw conclusions from the system under study. Normally, multivariate data analysis is performed using 1D 1H NMR spectra, and identification of peaks (and then compounds) relevant to the classification is accomplished using database queries as a first step. 1D 1H NMR spectra of complex mixtures often suffer from peak overlap. To overcome this issue, several studies employed the projections of the (tilted and symmetrized) 2D 1H J-resolved (JRES) spectra, p-JRES, which are similar to 1D 1H decoupled spectra. Nonetheless, there are no public databases available that allow searching for chemical shift spectral data for multiplets. We present the Chemical Shift Multiplet Database (CSMDB), built utilizing JRES spectra obtained from the Birmingham Metabolite Library. The CSMDB provides scoring accounting for both matched and unmatched peaks from a query list and the database hits. This input list is generated from a projection of a 2D statistical correlation analysis on the JRES spectra, p-(JRES-STOCSY), being able to compare the multiplets for the matched peaks, in essence, the f1 traces from the JRES-STOCSY spectrum and from the database hit. The inspection of the unmatched peaks for the database hit allows the retrieval of peaks in the query list that have a decreased correlation coefficient due to low intensities. The CSMDB is coupled to "ConQuer ABC", which permits the assessment of biological correlation by means of consecutive queries with the unmatched peaks in the first and subsequent queries.

Bacterially produced metabolites protect C. elegans neurons from degeneration.

Caenorhabditis elegans and its cognate bacterial diet comprise a reliable, widespread model to study diet and microbiota effects on host physiology. Nonetheless, how diet influences the rate at which neurons die remains largely unknown. A number of models have been used in C. elegans as surrogates for neurodegeneration. One of these is a C. elegans strain expressing a neurotoxic allele of the mechanosensory abnormality protein 4 (MEC-4d) degenerin/epithelial Na+ (DEG/ENaC) channel, which causes the progressive degeneration of the touch receptor neurons (TRNs). Using this model, our study evaluated the effect of various dietary bacteria on neurodegeneration dynamics. Although degeneration of TRNs was steady and completed at adulthood in the strain routinely used for C. elegans maintenance (Escherichia coli OP50), it was significantly reduced in environmental and other laboratory bacterial strains. Strikingly, neuroprotection reached more than 40% in the E. coli HT115 strain. HT115 protection was long lasting well into old age of animals and was not restricted to the TRNs. Small amounts of HT115 on OP50 bacteria as well as UV-killed HT115 were still sufficient to produce neuroprotection. Early growth of worms in HT115 protected neurons from degeneration during later growth in OP50. HT115 diet promoted the nuclear translocation of DAF-16 (ortholog of the FOXO family of transcription factors), a phenomenon previously reported to underlie neuroprotection caused by down-regulation of the insulin receptor in this system. Moreover, a daf-16 loss-of-function mutation abolishes HT115-driven neuroprotection. Comparative genomics, transcriptomics, and metabolomics approaches pinpointed the neurotransmitter γ-aminobutyric acid (GABA) and lactate as metabolites differentially produced between E. coli HT115 and OP50. HT115 mutant lacking glutamate decarboxylase enzyme genes (gad), which catalyze the conversion of GABA from glutamate, lost the ability to produce GABA and also to stop neurodegeneration. Moreover, in situ GABA supplementation or heterologous expression of glutamate decarboxylase in E. coli OP50 conferred neuroprotective activity to this strain. Specific C. elegans GABA transporters and receptors were required for full HT115-mediated neuroprotection. Additionally, lactate supplementation also increased anterior ventral microtubule (AVM) neuron survival in OP50. Together, these results demonstrate that bacterially produced GABA and other metabolites exert an effect of neuroprotection in the host, highlighting the role of neuroactive compounds of the diet in nervous system homeostasis.

Tackling the Peak Overlap Issue in NMR Metabolomics Studies: 1D Projected Correlation Traces from Statistical Correlation Analysis on Nontilted 2D 1H NMR J-Resolved Spectra.

The identification of metabolites in complex biological matrices is a challenging task in 1D 1H-NMR-based metabolomics studies. Statistical total correlation spectroscopy (STOCSY) has emerged for aiding the structural elucidation by revealing the peaks that present a high correlation to a driver peak of interest (which would likely belong to the same molecule). However, in these studies, the signals from metabolites are normally present as a mixture of overlapping resonances, limiting the performance of STOCSY. As an alternative to avoid the overlap issue, 2D 1H homonuclear J-resolved (JRES) spectra were projected, in their usual tilted and symmetrized processed form, and STOCSY was applied on these 1D projections (p-JRES-STOCSY). Nonetheless, this approach suffers in cases where the signals are very close. In addition, STOCSY was applied to the whole JRES spectra (also tilted) to identify correlated multiplets, although the overlap issue in itself was not addressed directly and the subsequent search in databases is complicated in cases of higher order coupling. With these limitations in mind, in the present work, we propose a new methodology based on the application of STOCSY on a set of nontilted JRES spectra, detecting peaks that would overlap in 1D spectra of the same sample set. Correlation comparison analysis for peak overlap detection (COCOA-POD) is able to reconstruct projected 1D STOCSY traces that result in more suitable database queries, as all peaks are summed at their f2 resonances instead of the resonance corresponding to the multiplet center in the tilted JRES spectra. (The peak dispersion and resolution enhancement gained are not sacrificed by the projection.) Besides improving database queries with better peak lists obtained from the projections of the 2D STOCSY analysis, the overlap region is examined, and the multiplet itself is analyzed from the correlation trace at 45° to obtain a cleaner multiplet profile, free from contributions from uncorrelated neighboring peaks.

Integrated Analytical and Statistical Two-Dimensional Spectroscopy Strategy for Metabolite Identification: Application to Dietary Biomarkers.

A major purpose of exploratory metabolic profiling is for the identification of molecular species that are statistically associated with specific biological or medical outcomes; unfortunately, the structure elucidation process of unknowns is often a major bottleneck in this process. We present here new holistic strategies that combine different statistical spectroscopic and analytical techniques to improve and simplify the process of metabolite identification. We exemplify these strategies using study data collected as part of a dietary intervention to improve health and which elicits a relatively subtle suite of changes from complex molecular profiles. We identify three new dietary biomarkers related to the consumption of peas (N-methyl nicotinic acid), apples (rhamnitol), and onions (N-acetyl-S-(1Z)-propenyl-cysteine-sulfoxide) that can be used to enhance dietary assessment and assess adherence to diet. As part of the strategy, we introduce a new probabilistic statistical spectroscopy tool, RED-STORM (Resolution EnhanceD SubseT Optimization by Reference Matching), that uses 2D J-resolved 1H NMR spectra for enhanced information recovery using the Bayesian paradigm to extract a subset of spectra with similar spectral signatures to a reference. RED-STORM provided new information for subsequent experiments (e.g., 2D-NMR spectroscopy, solid-phase extraction, liquid chromatography prefaced mass spectrometry) used to ultimately identify an unknown compound. In summary, we illustrate the benefit of acquiring J-resolved experiments alongside conventional 1D 1H NMR as part of routine metabolic profiling in large data sets and show that application of complementary statistical and analytical techniques for the identification of unknown metabolites can be used to save valuable time and resources.

Induced folding in RNA recognition by Arabidopsis thaliana DCL1.

DCL1 is the ribonuclease that carries out miRNA biogenesis in plants. The enzyme has two tandem double stranded RNA binding domains (dsRBDs) in its C-terminus. Here we show that the first of these domains binds precursor RNA fragments when isolated and cooperates with the second domain in the recognition of substrate RNA. Remarkably, despite showing RNA binding activity, this domain is intrinsically disordered. We found that it acquires a folded conformation when bound to its substrate, being the first report of a complete dsRBD folding upon binding. The free unfolded form shows tendency to adopt folded conformations, and goes through an unfolded bound state prior to the folding event. The significance of these results is discussed by comparison with the behavior of other dsRBDs.

Structural determinants of Arabidopsis thaliana Hyponastic leaves 1 function in vivo.

MicroRNAs have turned out to be important regulators of gene expression. These molecules originate from longer transcripts that are processed by ribonuclease III (RNAse III) enzymes. Dicer proteins are essential RNAse III enzymes that are involved in the generation of microRNAs (miRNAs) and other small RNAs. The correct function of Dicer relies on the participation of accessory dsRNA binding proteins, the exact function of which is not well-understood so far. In plants, the double stranded RNA binding protein Hyponastic Leaves 1 (HYL1) helps Dicer Like protein (DCL1) to achieve an efficient and precise excision of the miRNAs from their primary precursors. Here we dissected the regions of HYL1 that are essential for its function in Arabidopsis thaliana plant model. We generated mutant forms of the protein that retain their structure but affect its RNA-binding properties. The mutant versions of HYL1 were studied both in vitro and in vivo, and we were able to identify essential aminoacids/residues for its activity. Remarkably, mutation and even ablation of one of the purportedly main RNA binding determinants does not give rise to any major disturbances in the function of the protein. We studied the function of the mutant forms in vivo, establishing a direct correlation between affinity for the pri-miRNA precursors and protein activity.

Second double-stranded RNA binding domain of dicer-like ribonuclease 1: structural and biochemical characterization.

Dicer-like ribonuclease III enzymes are involved in different paths related to RNA silencing in plants. Little is known about the structural aspects of these processes. Here we present a structural characterization of the second double-stranded RNA binding domain (dsRBD) of DCL1, which is presumed to participate in pri-micro-RNA recognition and subcellular localization of this protein. We determined the solution structure and found that it has a canonical fold but bears some variation with respect to other homologous domains. We also found that this domain binds both double-stranded RNA and double-stranded DNA, in contrast to most dsRBDs. Our characterization shows that this domain likely has functions other than substrate recognition and binding.

Structure and RNA interactions of the plant MicroRNA processing-associated protein HYL1.

HYL1 is a double-stranded RNA binding protein involved in microRNA processing in plants. HYL1 enhances the efficiency and precision of the RNase III protein DCL1 and participates in microRNA strand selection. In this work, we dissect the contributions of the domains of HYL1 to the binding of RNA targets. We found that the first domain is the main contributor to RNA binding. Mapping of the interaction regions by nuclear magnetic resonance on the structure of HYL1 RNA-binding domains showed that the difference in binding capabilities can be traced to sequence divergence in ß2-ß3 loop. The possible role of each domain is discussed in light of previous experimental data.