Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies.

Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.

Imbalances in TCA, Short Fatty Acids and One-Carbon Metabolisms as Important Features of Homeostatic Disruption Evidenced by a Multi-Omics Integrative Approach of LPS-Induced Chronic Inflammation in Male Wistar Rats.

Chronic inflammation is an important risk factor in a broad variety of physical and mental disorders leading to highly prevalent non-communicable diseases (NCDs). However, there is a need for a deeper understanding of this condition and its progression to the disease state. For this reason, it is important to define metabolic pathways and complementary biomarkers associated with homeostatic disruption in chronic inflammation. To achieve that, male Wistar rats were subjected to intraperitoneal and intermittent injections with saline solution or increasing lipopolysaccharide (LPS) concentrations (0.5, 5 and 7.5 mg/kg) thrice a week for 31 days. Biochemical and inflammatory parameters were measured at the end of the study. To assess the omics profile, GC-qTOF and UHPLC-qTOF were performed to evaluate plasma metabolome; 1H-NMR was used to evaluate urine metabolome; additionally, shotgun metagenomics sequencing was carried out to characterize the cecum microbiome. The chronicity of inflammation in the study was evaluated by the monitoring of monocyte chemoattractant protein-1 (MCP-1) during the different weeks of the experimental process. At the end of the study, together with the increased levels of MCP-1, levels of interleukin-6 (IL-6), tumour necrosis factor alpha (TNF-α) and prostaglandin E2 (PGE2) along with 8-isoprostanes (an indicative of oxidative stress) were significantly increased (p-value < 0.05). The leading features implicated in the current model were tricarboxylic acid (TCA) cycle intermediates (i.e., alpha-ketoglutarate, aconitic acid, malic acid, fumaric acid and succinic acid); lipids such as specific cholesterol esters (ChoEs), lysophospholipids (LPCs) and phosphatidylcholines (PCs); and glycine, as well as N, N-dimethylglycine, which are related to one-carbon (1C) metabolism. These metabolites point towards mitochondrial metabolism through TCA cycle, ß-oxidation of fatty acids and 1C metabolism as interconnected pathways that could reveal the metabolic effects of chronic inflammation induced by LPS administration. These results provide deeper knowledge concerning the impact of chronic inflammation on the disruption of metabolic homeostasis.

Alterations in Metabolome and Microbiome Associated with an Early Stress Stage in Male Wistar Rats: A Multi-Omics Approach.

Stress disorders have dramatically increased in recent decades becoming the most prevalent psychiatric disorder in the United States and Europe. However, the diagnosis of stress disorders is currently based on symptom checklist and psychological questionnaires, thus making the identification of candidate biomarkers necessary to gain better insights into this pathology and its related metabolic alterations. Regarding the identification of potential biomarkers, omic profiling and metabolic footprint arise as promising approaches to recognize early biochemical changes in such disease and provide opportunities for the development of integrative candidate biomarkers. Here, we studied plasma and urine metabolites together with metagenomics in a 3 days Chronic Unpredictable Mild Stress (3d CUMS) animal approach that aims to focus on the early stress period of a well-established depression model. The multi-omics integration showed a profile composed by a signature of eight plasma metabolites, six urine metabolites and five microbes. Specifically, threonic acid, malic acid, alpha-ketoglutarate, succinic acid and cholesterol were proposed as key metabolites that could serve as key potential biomarkers in plasma metabolome of early stages of stress. Such findings targeted the threonic acid metabolism and the tricarboxylic acid (TCA) cycle as important pathways in early stress. Additionally, an increase in opportunistic microbes as virus of the Herpesvirales was observed in the microbiota as an effect of the primary stress stages. Our results provide an experimental biochemical characterization of the early stage of CUMS accompanied by a subsequent omic profiling and a metabolic footprinting that provide potential candidate biomarkers.

TGIF1 homeodomain interacts with Smad MH1 domain and represses TGF-ß signaling.

TGIF1 is a multifunctional protein that represses TGF-ß-activated transcription by interacting with Smad2-Smad4 complexes. We found that the complex structure of TGIF1-HD bound to the TGACA motif revealed a combined binding mode that involves the HD core and the major groove, on the one hand, and the amino-terminal (N-term) arm and the minor groove of the DNA, on the other. We also show that TGIF1-HD interacts with the MH1 domain of Smad proteins, thereby indicating that TGIF1-HD is also a protein-binding domain. Moreover, the formation of the HD-MH1 complex partially hinders the DNA-binding site of the complex, preventing the efficient interaction of TGIF1-HD with DNA. We propose that the binding of the TGIF1 C-term to the Smad2-MH2 domain brings both the HD and MH1 domain into close proximity. This local proximity facilitates the interaction of these DNA-binding domains, thus strengthening the formation of the protein complex versus DNA binding. Once the protein complex has been formed, the TGIF1-Smad system would be released from promoters/enhancers, thereby illustrating one of the mechanisms used by TGIF1 to exert its function as an active repressor of Smad-induced TGF-ß signaling.

Folding kinetics of WW domains with the united residue force field for bridging microscopic motions and experimental measurements.

To demonstrate the utility of the coarse-grained united-residue (UNRES) force field to compare experimental and computed kinetic data for folding proteins, we have performed long-time millisecond-timescale canonical Langevin molecular dynamics simulations of the triple ß-strand from the Formin binding protein 28 WW domain and six nonnatural variants, using UNRES. The results have been compared with available experimental data in both a qualitative and a quantitative manner. Complexities of the folding pathways, which cannot be determined experimentally, were revealed. The folding mechanisms obtained from the simulated folding kinetics are in agreement with experimental results, with a few discrepancies for which we have accounted. The origins of single- and double-exponential kinetics and their correlations with two- and three-state folding scenarios are shown to be related to the relative barrier heights between the various states. The rate constants obtained from time profiles of the fractions of the native, intermediate, and unfolded structures, and the kinetic equations fitted to them, correlate with the experimental values; however, they are about three orders of magnitude larger than the experimental ones for most of the systems. These differences are in agreement with the timescale extension derived by scaling down the friction of water and averaging out the fast degrees of freedom when passing from all-atom to a coarse-grained representation. Our results indicate that the UNRES force field can provide accurate predictions of folding kinetics of these WW domains, often used as models for the study of the mechanisms of proein folding.

Addition of HOBt improves the conversion of thioester-Amine chemical ligation.

The syntheses of large peptides and of those containing non-natural amino acids can be facilitated by the application of convergent approaches, dissecting the native sequence into segments connected through a ligation reaction. We describe an improvement of the ligation protocol used to prepare peptides and proteins without cysteine residues at the ligation junction. We have found that the addition of HOBt to the ligation, improves the conversion of the ligation reaction without affecting the epimerization rate or chemoselectivity, and it can be efficiently used with peptides containing phosphorylated amino acids.

Exploring Metabolic and Gut Microbiome Responses to Paraquat Administration in Male Wistar Rats: Implications for Oxidative Stress.

In this study, we examined the metabolic and gut microbiome responses to paraquat (PQ) in male Wistar rats, focusing on oxidative stress effects. Rats received a single intraperitoneal injection of PQ at 15 and 30 mg/kg, and various oxidative stress parameters (i.e., MDA, SOD, ROS, 8-isoprostanes) were assessed after three days. To explore the omic profile, GC-qTOF and UHPLC-qTOF were performed to assess the plasma metabolome; 1H-NMR was used to assess the urine metabolome; and shotgun metagenomics sequencing was performed to study the gut microbiome. Our results revealed reductions in body weight and tissue changes, particularly in the liver, were observed, suggesting a systemic effect of PQ. Elevated lipid peroxidation and reactive oxygen species levels in the liver and plasma indicated the induction of oxidative stress. Metabolic profiling revealed changes in the tricarboxylic acid cycle, accumulation of ketone body, and altered levels of key metabolites, such as 3-hydroxybutyric acid and serine, suggesting intricate links between energy metabolism and redox reactions. Plasma metabolomic analysis revealed alterations in mitochondrial metabolism, nicotinamide metabolism, and tryptophan degradation. The gut microbiome showed shifts, with higher PQ doses influencing microbial populations (e.g., Escherichia coli and Akkermansia muciniphila) and metagenomic functions (pyruvate metabolism, fermentation, nucleotide and amino acid biosynthesis). Overall, this study provides comprehensive insights into the complex interplay between PQ exposure, metabolic responses, and gut microbiome dynamics. These findings enhance our understanding of the mechanisms behind oxidative stress-induced metabolic alterations and underscore the connections between xenobiotic exposure, gut microbiota, and host metabolism.

A single-blinded, randomized, parallel intervention to evaluate genetics and omics-based personalized nutrition in general population via an e-commerce tool: The PREVENTOMICS e-commerce study.

BACKGROUND: Personalized nutrition (PN) has been proposed as a strategy to increase the effectiveness of dietary recommendations and ultimately improve health status. OBJECTIVES: We aimed to assess whether including omics-based PN in an e-commerce tool improves dietary behavior and metabolic profile in general population. METHODS: A 21-wk parallel, single-blinded, randomized intervention involved 193 adults assigned to a control group following Mediterranean diet recommendations (n = 57, completers = 36), PN (n = 70, completers = 45), or personalized plan (PP, n = 68, completers = 53) integrating a behavioral change program with PN recommendations. The intervention used metabolomics, proteomics, and genetic data to assist participants in creating personalized shopping lists in a simulated e-commerce retailer portal. The primary outcome was the Mediterranean diet adherence screener (MEDAS) score; secondary outcomes included biometric and metabolic markers and dietary habits. RESULTS: Volunteers were categorized with a scoring system based on biomarkers of lipid, carbohydrate metabolism, inflammation, oxidative stress, and microbiota, and dietary recommendations delivered accordingly in the PN and PP groups. The intervention significantly increased MEDAS scores in all volunteers (control-3 points; 95% confidence interval [CI]: 2.2, 3.8; PN-2.7 points; 95% CI: 2.0, 3.3; and PP-2.8 points; 95% CI: 2.1, 3.4; q < 0.001). No significant differences were observed in dietary habits or health parameters between PN and control groups after adjustment for multiple comparisons. Nevertheless, personalized recommendations significantly (false discovery rate < 0.05) and selectively enhanced the scores calculated with biomarkers of carbohydrate metabolism (ß: -0.37; 95% CI: -0.56, -0.18), oxidative stress (ß: -0.37; 95% CI: -0.60, -0.15), microbiota (ß: -0.38; 95% CI: -0.63, -0.15), and inflammation (ß: -0.78; 95% CI: -1.24, -0.31) compared with control diet. CONCLUSIONS: Integration of personalized strategies within an e-commerce-like tool did not enhance adherence to Mediterranean diet or improved health markers compared with general recommendations. The metabotyping approach showed promising results and more research is guaranteed to further promote its application in PN. This trial was registered at clinicaltrials.gov as NCT04641559 (https://clinicaltrials.gov/study/NCT04641559?cond=NCT04641559&rank=1).

Characterization of a proteomic profile associated with organ dysfunction and mortality of sepsis and septic shock.

INTRODUCTION: The search for new biomarkers that allow an early diagnosis in sepsis and predict its evolution has become a necessity in medicine. The objective of this study is to identify, through omics techniques, potential protein biomarkers that are expressed in patients with sepsis and their relationship with organ dysfunction and mortality. METHODS: Prospective, observational and single-center study that included adult patients (≥ 18 years) who were admitted to a tertiary hospital and who met the criteria for sepsis. A mass spectrometry-based approach was used to analyze the plasma proteins in the enrolled subjects. Subsequently, using recursive feature elimination classification and cross-validation with a vector classifier, an association of these proteins with mortality and organ dysfunction was established. The protein-protein interaction network was analyzed with String software. RESULTS: 141 patients were enrolled in this study. Mass spectrometry identified 177 proteins. Of all of them, and by recursive feature elimination, nine proteins (GPX3, APOB, ORM1, SERPINF1, LYZ, C8A, CD14, APOC3 and C1QC) were associated with organ dysfunction (SOFA > 6) with an accuracy of 0.82 ± 0.06, precision of 0.85 ± 0.093, sensitivity 0.81 ± 0.10, specificity 0.84 ± 0.10 and AUC 0.82 ± 0.06. Twenty-two proteins (CLU, LUM, APOL1, SAA1, CLEBC3B, C8A, ITIH4, KNG1, AGT, C7, SAA2, APOH, HRG, AFM, APOE, APOC1, C1S, SERPINC1, IGFALS, KLKB1, CFB and BTD) were associated with mortality with an accuracy of 0.86 ± 0.05, a precision of 0.91 ± 0.05, a sensitivity of 0.91 ± 0.05, a specificity of 0.72 ± 0.17, and an area under the curve (AUC) of 0.81 ± 0.08 with a confidence interval of 95%. CONCLUSION: In sepsis there are proteomic patterns associated with organ dysfunction and mortality.

A double-blinded, randomized, parallel intervention to evaluate biomarker-based nutrition plans for weight loss: The PREVENTOMICS study.

BACKGROUND & AIMS: Growing evidence suggests that biomarker-guided dietary interventions can optimize response to treatment. In this study, we evaluated the efficacy of the PREVENTOMCIS platform-which uses metabolomic and genetic information to classify individuals into different 'metabolic clusters' and create personalized dietary plans-for improving health outcomes in subjects with overweight or obesity. METHODS: A 10-week parallel, double-blinded, randomized intervention was conducted in 100 adults (82 completers) aged 18-65 years, with body mass index ≥27 but <40 kg/m2, who were allocated into either a personalized diet group (n = 49) or a control diet group (n = 51). About 60% of all food was provided free-of-charge. No specific instruction to restrict energy intake was given. The primary outcome was change in fat mass from baseline, evaluated by dual energy X-ray absorptiometry. Other endpoints included body weight, waist circumference, lipid profile, glucose homeostasis markers, inflammatory markers, blood pressure, physical activity, stress and eating behavior. RESULTS: There were significant main effects of time (P < 0.01), but no group main effects, or time-by-group interactions, for the change in fat mass (personalized: -2.1 [95% CI -2.9, -1.4] kg; control: -2.0 [95% CI -2.7, -1.3] kg) and body weight (personalized: -3.1 [95% CI -4.1, -2.1] kg; control: -3.3 [95% CI -4.2, -2.4] kg). The difference between groups in fat mass change was -0.1 kg (95% CI -1.2, 0.9 kg, P = 0.77). Both diets resulted in significant improvements in insulin resistance and lipid profile, but there were no significant differences between groups. CONCLUSION: Personalized dietary plans did not result in greater benefits over a generic, but generally healthy diet, in this 10-week clinical trial. Further studies are required to establish the soundness of different precision nutrition approaches, and translate this science into clinically relevant dietary advice to reduce the burden of obesity and its comorbidities. CLINICAL TRIAL REGISTRY: ClinicalTrials.gov registry (NCT04590989).

Structural Analysis of the Pin1-CPEB1 interaction and its potential role in CPEB1 degradation.

The Cytoplasmic Polyadenylation Element Binding proteins are RNA binding proteins involved in the translational regulation of mRNA. During cell cycle progression, CPEB1 is labeled for degradation by phosphorylation-dependent ubiquitination by the SCF(ß-TrCP) ligase. The peptidyl-prolyl isomerase Pin1 plays a key role in CPEB1 degradation. Conditioned by the cell cycle stage, CPEB1 and Pin1 interactions occur in a phosphorylation-independent or -dependent manner. CPEB1 contains six potential phosphorylatable Pin1 binding sites. Using a set of biophysical techniques, we discovered that the pS210 site is unique, since it displays binding activity not only to the WW domain but also to the prolyl-isomerase domain of Pin1. The NMR structure of the Pin1 WW-CPEB1 pS210 (PDB ID: 2n1o) reveals that the pSerPro motif is bound in trans configuration through contacts with amino acids located in the first turn of the WW domain and the conserved tryptophan in the ß3-strand. NMR relaxation analyses of Pin1 suggest that inter-domain flexibility is conferred by the modulation of the interaction with peptides containing the pS210 site, which is essential for degradation.