The remodel of the "central dogma": a metabolomics interaction perspective

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data

BACKGROUND

Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies.

METHOD

In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information.

RESULTS

We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites.

CONCLUSION

The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

Metaboepigenetic regulation of gene expression in obesity and insulin resistance

INTRODUCTION

Variation in DNA methylation (DNAm) in adipose tissue is associated with the pathogenesis of obesity and insulin resistance. The activity of enzymes involved in altering DNAm levels is dependent on several metabolite cofactors.

OBJECTIVES

To understand the role of metabolites as mechanistic regulators of epigenetic marks, we tested the association between selected plasma metabolites and DNAm levels in the adipose tissue of African Americans.

METHODS

In the AAGMEx cohort (N = 256), plasma levels of metabolites were measured by untargeted liquid chromatography-mass spectrometry; adipose tissue DNAm and transcript levels were measured by reduced representation bisulfite sequencing, and expression microarray, respectively.

RESULTS

Among the 21 one-carbon metabolism pathway metabolites evaluated, six were associated with gluco-metabolic traits (PFDR < 0.05, for BMI, SI, or Matsuda index) in AAGMEx. Methylation levels of 196, 116, and 180 CpG-sites were associated (P < 0.0001) with S-adenosylhomocysteine (SAH), cystine, and hypotaurine, respectively. Cis-expression quantitative trait methylation (cis eQTM) analyses suggested the role of metabolite-level-associated CpG sites in regulating the expression of adipose tissue transcripts, including genes in G-protein coupled receptor signaling pathway. Plasma SAH level-associated CpG sites chr19:3403712 and chr19:3403735 were also associated with the expression of G-protein subunit alpha 15 (GNA15) in adipose. The expression of GNA15 was significantly correlated with BMI (β = 1.87, P = 1.9 × 10-16) and SI (β = -1.61, P = 2.49 × 10-5).

CONCLUSION

Our study suggests that a subset of metabolites modulates the methylation levels of CpG sites in specific loci and, in turn, regulates the expression of transcripts involved in obesity and insulin resistance.

Mass spectrometry-based metabolomics for the investigation of antibiotic-bacterial interactions

With the development of analytical technologies especially mass spectrometry, metabolomics is becoming increasingly hot in the field of studying antibiotic-bacterial interactions. On the one hand, metabolomics can reveal metabolic perturbations in bacteria in the presence of antibiotics and expose metabolic mechanisms. On the other hand, through in-depth analysis of bacterial metabolic profiles, biomarkers and bioactive secondary metabolites with great potential as drug precursors can be discovered. This review focuses on the experimental workflow of bacterial metabolomics and its application to study the interaction between bacteria and antibiotics. Metabolomics improves the understanding of antibiotic lethality, reveals metabolic perturbations in antibiotic-resistant bacteria, guides the diagnosis and antibiotic treatment of infectious diseases, and aids in the exploration of antibacterial metabolites in nature. Furthermore, current limitations and directions for future developments in this area are discussed.

Proteomics of the heart

Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.

Ketocarotenoid production in tomato triggers metabolic reprogramming and cellular adaptation: The quest for homeostasis

Plants are sessile and therefore have developed an extraordinary capacity to adapt to external signals. Here, the focus is on the plasticity of the plant cell to respond to new intracellular cues. Ketocarotenoids are high-value natural red pigments with potent antioxidant activity. In the present study, system-level analyses have revealed that the heterologous biosynthesis of ketocarotenoids in tomato initiated a series of cellular and metabolic mechanisms to cope with the formation of metabolites that are non-endogenous to the plant. The broad multilevel changes were linked to, among others, (i) the remodelling of the plastidial membrane, where the synthesis and storage of ketocarotenoids occurs; (ii) the recruiting of core metabolic pathways for the generation of metabolite precursors and energy; and (iii) redox control. The involvement of the metabolites as regulators of cellular processes shown here reinforces their pivotal role suggested in the remodelled 'central dogma' concept. Furthermore, the role of metabolic reprogramming to ensure cellular homeostasis is proposed.

Potential roles of HSYA in attenuating sepsis-induced liver injury through multi-omics analysis

Liver injury is a strong independent predictor of mortality in patients with sepsis, in which gut dysbiosis plays a crucial role. Hydroxyl safflower yellow A (HSYA), an important component of safflower, has been used to treat liver injury in animal models. However, its role in sepsis-induced liver dysfunction and the specific molecular mechanisms remain unclear. In the current study, we first discussed the discrepancy in the gut microbiota between the cecal ligation puncture (CLP) and HSYA groups using 16 S RNA sequencing. Our data demonstrated that HSYA supplementation significantly decreased the relative abundance of Proteobacteria, Firmicutes, and Campylobacterota, and further decreased the abundance of Bacteroidota, suggesting that the protective effects of HSYA against sepsis-induced liver injury may be partially attributed to the alteration of these bacteria. In addition, the metabolomic data identified 823 differentially expressed metabolites associated with sepsis-induced liver injury. After HSYA supplementation, the levels of 56 metabolites were restored to sham-like levels. Transcriptomic analysis revealed 4990 differentially expressed genes (DEGs) between the sham and CLP groups, and after HSYA injection, 1613 genes were modulated. Comprehensive analysis demonstrated that the enrichment pathways of the 903 DEGs mainly focused on inflammatory responses, amino acid metabolism, and Lipid reactions. In conclusion, our study revealed the potential mechanism of action of HSYA in sepsis-induced liver injury through a comprehensive analysis of 16 S RNA sequencing, metabolomics, and transcriptomics, thus providing a theoretical basis for further clinical applications of HSYA.

An omics review and perspective of researches on intrahepatic cholestasis of pregnancy

Intrahepatic cholestasis of pregnancy (ICP) is one of the common pregnancy complications that may threaten the health of both pregnant women and their fetuses. Hence, it is of vital importance to identify key moleculars and the associated functional pathways of ICP, which will help us to better understand the pathological mechanisms as well as to develop precise clinical biomarkers. The emerging and developing of multiple omics approaches enable comprehensive studies of the genome, transcriptome, proteome and metabolome of clinical samples. The present review collected and summarized the omics based studies of ICP, aiming to provide an overview of the current progress, limitations and future directions. Briefly, these studies covered a broad range of research contents by the comparing of different experimental groups including ICP patients, ICP subtypes, ICP fetuses, ICP models and other complications. Correspondingly, the studied samples contain various types of clinical samples, in vitro cultured tissues, cell lines and the samples from animal models. According to the main research objectives, we further categorized these studies into two groups: pathogenesis and diagnosis analyses. The pathogenesis studies identified tens of functional pathways that may represent the key regulatory events for the occurrence, progression, treatment and fetal effects of ICP. On the other hand, the diagnosis studies tested more than 40 potential models for the early-prediction, diagnosis, grading, prognosis or differential diagnosis of ICP. Apart from these achievements, we also evaluated the limitations of current studies, and emphasized that many aspects of clinical characteristics, sample processing, and analytical method can greatly affect the reliability and repeatability of omics results. Finally, we also pointed out several new directions for the omics based analyses of ICP and other perinatal associated conditions in the future.

Analysis of Metabolic Changes in Endogenous Metabolites and Diagnostic Biomarkers for Various Diseases Using Liquid Chromatography and Mass Spectrometry

Analysis of endogenous metabolites in various diseases is useful for searching diagnostic biomarkers and elucidating the molecular mechanisms of pathophysiology. The author and collaborators have developed some LC/tandem mass spectrometry (LC/MS/MS) methods for metabolites and applied them to disease-related samples. First, we identified urinary conjugated cholesterol metabolites and serum N-palmitoyl-O-phosphocholine serine as useful biomarkers for Niemann-Pick disease type C (NPC). For the purpose of intraoperative diagnosis of glioma patients, we developed the LC/MS/MS analysis methods for 2-hydroxyglutaric acid or cystine and found that they could be good differential biomarkers. For renal cell carcinoma, we searched for various biomarkers for early diagnosis, malignancy evaluation and recurrence prediction by global metabolome analysis and targeted LC/MS/MS analysis. In pathological analysis, we developed a simultaneous LC/MS/MS analysis method for 13 steroid hormones and applied it to NPC cells, we found 6 types of reductions in NPC model cells. For non-alcoholic steatohepatitis (NASH), model mice were prepared with special diet and plasma bile acids were measured, and as a result, hydrophilic bile acids were significantly increased. In addition, we developed an LC/MS/MS method for 17 sterols and analyzed liver cholesterol metabolites and found a decrease in phytosterols and cholesterol synthetic markers and an increase in non-enzymatic oxidative sterols in the pre-onset stage of NASH. We will continue to challenge themselves to add value to clinical practice based on cutting-edge analytical chemistry methodology.

Exploring the interplay between metabolomics and genetics in Parkinson's disease: Insights from ongoing research and future avenues

Parkinson's disease (PD) is a widespread neurodegenerative disorder, whose complex aetiology remains under construction. While rare variants have been associated with the monogenic PD form, most PD cases are influenced by multiple genetic and environmental aspects. Nonetheless, the pathophysiological pathways and molecular networks involved in monogenic/idiopathic PD overlap, and genetic variants are decisive in elucidating the convergent underlying mechanisms of PD. In this scenario, metabolomics has furnished a dynamic and systematic picture of the synergy between the genetic background and environmental influences that impact PD, making it a valuable tool for investigating PD-related metabolic dysfunctions. In this review, we performed a brief overview of metabolomics current research in PD, focusing on significant metabolic alterations observed in idiopathic PD from different biofluids and strata and exploring how they relate to genetic factors associated with monogenic PD. Dysregulated amino acid metabolism, lipid metabolism, and oxidative stress are the critical metabolic pathways implicated in both genetic and idiopathic PD. By merging metabolomics and genetics data, it is possible to distinguish metabolic signatures of specific genetic backgrounds and to pinpoint subgroups of PD patients who could derive personalized therapeutic benefits. This approach holds great promise for advancing PD research and developing innovative, cost-effective treatments.

Glucose metabolism sustains heme-induced Trypanosoma cruzi epimastigote growth in vitro

Chagas disease is caused by the protozoan parasite, Trypanosoma cruzi. This parasite alternates between an insect vector and a mammalian host. T. cruzi epimastigotes reside in the insect vector and coexist with the blood components of the vertebrate host. The metabolic profile of T. cruzi has been extensively studied; however, changes in its metabolism in response to signaling molecules present in the vector are poorly understood. Heme acts as a physiological oxidant that triggers intense epimastigote proliferation and upregulates the expression of genes related to glycolysis and aerobic fermentation in vitro. Here, heme-cultured epimastigotes increased D-glucose consumption. In fact, heme-cultured parasites secreted more succinate (the end product of the so-called succinic fermentation) followed by glucose intake. Increased succinate levels reduced the extracellular pH, leading to acidification of the supernatant. However, the acidification and proliferation stimulated by heme was impaired when glycolysis was inhibited. Otherwise, when glucose amount is enhanced in supernatant, heme-cultured parasites increased its growth whereas the glucose depletion caused a delay in proliferation. Heme supplementation increased epimastigote electron transport system-related O2 consumption rates, while glucose addition reduced both the electron transport system-related O2 consumption rates and spare respiratory capacity, indicating a Crabtree-like effect. These results show that glycolysis predominated in heme-cultured epimastigotes over oxidative phosphorylation for energy supply when glucose is present to sustain its high proliferation in vitro. Furthermore, it provided an insight into the parasite biology in the vector environment that supply glucose and the digestion of blood generates free heme that can lead to the growth of T. cruzi epimastigotes.

Integrated NMR and MS Analysis of the Plasma Metabolome Reveals Major Changes in One-Carbon, Lipid, and Amino Acid Metabolism in Severe and Fatal Cases of COVID-19

Brazil has the second-highest COVID-19 death rate worldwide, and Rio de Janeiro is among the states with the highest rate in the country. Although vaccine coverage has been achieved, it is anticipated that COVID-19 will transition into an endemic disease. It is concerning that the molecular mechanisms underlying clinical evolution from mild to severe disease, as well as the mechanisms leading to long COVID-19, are not yet fully understood. NMR and MS-based metabolomics were used to identify metabolites associated with COVID-19 pathophysiology and disease outcome. Severe COVID-19 cases (n = 35) were enrolled in two reference centers in Rio de Janeiro within 72 h of ICU admission, alongside 12 non-infected control subjects. COVID-19 patients were grouped into survivors (n = 18) and non-survivors (n = 17). Choline-related metabolites, serine, glycine, and betaine, were reduced in severe COVID-19, indicating dysregulation in methyl donors. Non-survivors had higher levels of creatine/creatinine, 4-hydroxyproline, gluconic acid, and N-acetylserine, indicating liver and kidney dysfunction. Several changes were greater in women; thus, patients' sex should be considered in pandemic surveillance to achieve better disease stratification and improve outcomes. These metabolic alterations may be useful to monitor organ (dys) function and to understand the pathophysiology of acute and possibly post-acute COVID-19 syndromes.

Clustering single-cell multimodal omics data with jrSiCKLSNMF

Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise. Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") that extracts latent factors shared across omics modalities within the same set of single cells. Results: We compare our clustering algorithm to several existing methods on four sets of data simulated from third party software. We also apply our algorithm to a real set of cell line data. Discussion: We show overwhelmingly better clustering performance than several existing methods on the simulated data. On a real multimodal omics dataset, we also find our method to produce scientifically accurate clustering results.

A revised central dogma for the 21st century:all biology is cognitive information processing

Crick's Central Dogma has been a foundational aspect of 20th century biology, describing an implicit relationship governing the flow of information in biological systems in biomolecular terms. Accumulating scientific discoveries support the need for a revised Central Dogma to buttress evolutionary biology's still-fledgling migration from a Neodarwinian canon. A reformulated Central Dogma to meet contemporary biology is proposed: all biology is cognitive information processing. Central to this contention is the recognition that life is the self-referential state, instantiated within the cellular form. Self-referential cells act to sustain themselves and to do so, cells must be in consistent harmony with their environment. That consonance is achieved by the continuous assimilation of environmental cues and stresses as information to self-referential observers. All received cellular information must be analyzed to be deployed as cellular problem-solving to maintain homeorhetic equipoise. However, the effective implementation of information is definitively a function of orderly information management. Consequently, effective cellular problem-solving is information processing and management. The epicenter of that cellular information processing is its self-referential internal measurement. All further biological self-organization initiates from this obligate activity. As the internal measurement by cells of information is self-referential by definition, self-reference is biological self-organization, underpinning 21st century Cognition-Based Biology.

Metabolic Adaptations Correlated with Antibody Response after Immunization with Inactivated SARS-CoV-2 in Brazilian Subjects

The adsorbed vaccine SARS-CoV-2 (inactivated) produced by Sinovac (SV) was the first vaccine against COVID-19 to be used in Brazil. To understand the metabolic effects of SV in Brazilian subjects, NMR-based metabolomics was used, and the immune response was studied in Brazilian subjects. Forty adults without (group^-, n = 23) and with previous COVID-19 infection (group⁺, n = 17) were followed-up for 90 days postcompletion of the vaccine regimen. After 90 days, our results showed that subjects had increased levels of lipoproteins, lipids, and N-acetylation of glycoproteins (NAG) as well as decreased levels of amino acids, lactate, citrate, and 3-hydroxypropionate. NAG and threonine were the highest correlated metabolites with N and S proteins, and neutralizing Ab levels. This study sheds light on the immunometabolism associated with the use of SV in Brazilian subjects from Rio de Janeiro and identifies potential metabolic markers associated with the immune status.

The Repertoire of RNA Modifications Orchestrates a Plethora of Cellular Responses

Although a plethora of DNA modifications have been extensively investigated in the last decade, recent breakthroughs in molecular biology, including high throughput sequencing techniques, have enabled the identification of post-transcriptional marks that decorate RNAs; hence, epitranscriptomics has arisen. This recent scientific field aims to decode the regulatory layer of the transcriptome and set the ground for the detection of modifications in ribose nucleotides. Until now, more than 170 RNA modifications have been reported in diverse types of RNA that contribute to various biological processes, such as RNA biogenesis, stability, and transcriptional and translational accuracy. However, dysfunctions in the RNA-modifying enzymes that regulate their dynamic level can lead to human diseases and cancer. The present review aims to highlight the epitranscriptomic landscape in human RNAs and match the catalytic proteins with the deposition or deletion of a specific mark. In the current review, the most abundant RNA modifications, such as N6-methyladenosine (m⁶A), N5-methylcytosine (m⁵C), pseudouridine (Ψ) and inosine (I), are thoroughly described, their functional and regulatory roles are discussed and their contributions to cellular homeostasis are stated. Ultimately, the involvement of the RNA modifications and their writers, erasers, and readers in human diseases and cancer is also discussed.

EpisomiR, a New Family of miRNAs, and Its Possible Roles in Human Diseases

MicroRNAs (miRNAs) are synthesized through a canonical pathway and play a role in human diseases, such as cancers and cardiovascular, neurodegenerative, psychiatric, and chronic inflammatory diseases. The development of sequencing technologies has enabled the identification of variations in noncoding miRNAs. These miRNA variants, called isomiRs, are generated through a non-canonical pathway, by several enzymes that alter the length and sequence of miRNAs. The isomiR family is, now, expanding further to include episomiRs, which are miRNAs with different modifications. Since recent findings have shown that isomiRs reflect the cell-specific biological function of miRNAs, knowledge about episomiRs and isomiRs can, possibly, contribute to the optimization of diagnosis and therapeutic technology for precision medicine.

Limited Mechanistic Link Between the Monod Equation and Methanogen Growth: a Perspective from Metabolic Modeling

The Monod equation has been widely applied as the general rate law of microbial growth, but its applications are not always successful. By drawing on the frameworks of kinetic and stoichiometric metabolic models and metabolic control analysis, the modeling reported here simulated the growth kinetics of a methanogenic microorganism and illustrated that different enzymes and metabolites control growth rate to various extents and that their controls peak at either very low, intermediate, or very high substrate concentrations. In comparison, with a single term and two parameters, the Monod equation only approximately accounts for the controls of rate-determining enzymes and metabolites at very high and very low substrate concentrations, but neglects the enzymes and metabolites whose controls are most notable at intermediate concentrations. These findings support a limited link between the Monod equation and methanogen growth, and unify the competing views regarding enzyme roles in shaping growth kinetics. The results also preclude a mechanistic derivation of the Monod equation from methanogen metabolic networks and highlight a fundamental challenge in microbiology: single-term expressions may not be sufficient for accurate prediction of microbial growth. IMPORTANCE The Monod equation has been widely applied to predict the rate of microbial growth, but its application is not always successful. Using a novel metabolic modeling approach, we simulated the growth of a methanogen and uncovered a limited mechanistic link between the Monod equation and the methanogen's metabolic network. Specifically, the equation provides an approximation to the controls by rate-determining metabolites and enzymes at very low and very high substrate concentrations, but it is missing the remaining enzymes and metabolites whose controls are most notable at intermediate concentrations. These results support the Monod equation as a useful approximation of growth rates and highlight a fundamental challenge in microbial kinetics: single-term rate expressions may not be sufficient for accurate prediction of microbial growth.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.