An evaluation of the National Institutes of Health grants portfolio: identifying opportunities and challenges for multi-omics research that leverage metabolomics data

Data Interoperability and Harmonization in Cardiovascular Genomic and Precision Medicine

Despite advances in cardiovascular care and improved outcomes, fragmented healthcare systems, nonequitable access to health care, and nonuniform and unbiased collection and access to healthcare data have exacerbated disparities in healthcare provision and further delayed the technological-enabled implementation of precision medicine. Precision medicine relies on a foundation of accurate and valid omics and phenomics that can be harnessed at scale from electronic health records. Big data approaches in noncardiovascular healthcare domains have helped improve efficiency and expedite the development of novel therapeutics; therefore, applying such an approach to cardiovascular precision medicine is an opportunity to further advance the field. Several endeavors, including the American Heart Association Precision Medicine platform and public-private partnerships (such as BigData@Heart in Europe), as well as cloud-based platforms, such as Terra used for the National Institutes of Health All of Us, are attempting to temporally and ontologically harmonize data. This state-of-the-art review summarizes best practices used in cardiovascular genomic and precision medicine and provides recommendations for systems' requirements that could enhance and accelerate the integration of these platforms.

The Inflammatory Mechanism of Parkinson's Disease: Gut Microbiota Metabolites Affect the Development of the Disease Through the Gut-Brain Axis

Parkinson's disease is recognized as the second most prevalent neurodegenerative disorder globally, with its incidence rate projected to increase alongside ongoing population growth. However, the precise etiology of Parkinson's disease remains elusive. This article explores the inflammatory mechanisms linking gut microbiota to Parkinson's disease, emphasizing alterations in gut microbiota and their metabolites that influence the disease's progression through the bidirectional transmission of inflammatory signals along the gut-brain axis. Building on this mechanistic framework, this article further discusses research methodologies and treatment strategies focused on gut microbiota metabolites, including metabolomics detection techniques, animal model investigations, and therapeutic approaches such as dietary interventions, probiotic treatments, and fecal transplantation. Ultimately, this article aims to elucidate the relationship between gut microbiota metabolites and the inflammatory mechanisms underlying Parkinson's disease, thereby paving the way for novel avenues in the research and treatment of this condition.

Unveiling the distribution of chemical constituents at different body parts and maturity stages of Ganoderma lingzhi by combining metabolomics with desorption electrospray ionization mass spectrometry imaging (DESI)

Ganoderma lingzhi is an important medicinal fungus, which is widely used as dietary supplement and for pharmaceutical industries. However, the spatial distribution and dynamic accumulation pattern of active components such as ganoderic acids (GAs) among different parts of G. lingzhi fruiting body are still unclear. In this study, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) with untargeted metabolomics analysis was applied to investigate the metabolites distribution within G. lingzhi fruiting body at four different maturity stages (squaring, opening, maturation and harvesting stage). A total of 132 metabolites were characterized from G. lingzhi, including 115 triterpenoids, 11 fatty acids and other component. Most of the GAs content in the cap was significantly higher than that in the stipe, with six components such as ganoderic acid B being extremely significant. GAs in the cap was mainly present in the bottom edge of the mediostratum layer, such as ganoderic A-I and ganoderic GS-1, while in the stipe, they were mainly distributed in the shell layer and the context layer, such as ganoderic A-F. Most ganoderic acids content in both the stipe and the cap of G. lingzhi was gradually decreased with the development of G. lingzhi. The GAs in the stipe was gradually transferred from the shell layer to the content layer, while the distribution of GAs among different tissues of the cap was not significantly changed. In addition, linoleic acid, 9-HODE, 9-KODE and other fatty acids were mainly accumulated in the opening and maturing stage of the caps. This study further clarifies the spatial dynamic distribution of GAs in G. lingzhi fruiting body at four different maturity stages (squaring, opening, maturation and harvesting stage), which provides a basis for the rational utilization of the medicinal parts of G. lingzhi. Furthermore, mass spectrometry imaging combined with non-target metabolome analysis provides a powerful tool for the spatial distribution of active substances in the different regions of the medicinal edible fungi.

A High-Throughput PIXUL-Matrix-Based Toolbox to Profile Frozen and Formalin-Fixed Paraffin-Embedded Tissues Multiomes

Large-scale high-dimensional multiomics studies are essential to unravel molecular complexity in health and disease. We developed an integrated system for tissue sampling (CryoGrid), analytes preparation (PIXUL), and downstream multiomic analysis in a 96-well plate format (Matrix), MultiomicsTracks96, which we used to interrogate matched frozen and formalin-fixed paraffin-embedded (FFPE) mouse organs. Using this system, we generated 8-dimensional omics data sets encompassing 4 molecular layers of intracellular organization: epigenome (H3K27Ac, H3K4m3, RNA polymerase II, and 5mC levels), transcriptome (messenger RNA levels), epitranscriptome (m6A levels), and proteome (protein levels) in brain, heart, kidney, and liver. There was a high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles confirmed known organ-specific superenhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic profiles, known to be poorly correlated with transcriptomic data, can be more accurately predicted by the full suite of multiomics data, compared with using epigenomic, transcriptomic, or epitranscriptomic measurements individually.

Vitamin A deficiency from maternal gestation may contribute to autistic-like behaviors and gastrointestinal dysfunction in rats through the disrupted purine and tryptophan metabolism

Vitamin A deficiency (VAD) has been linked to autism spectrum disorder (ASD) in multiple studies, and autistic children with gastrointestinal (GI) symptoms have been found to have lower VA levels than those without GI symptoms. However, the exact mechanism by which VAD causes both core symptoms and GI symptoms in ASD is ill defined. We constructed VAD and vitamin A normal (VAN) rat models from maternal gestation onwards. Autism-related behaviors were tested using the open-field test and the three-chamber test, and GI function was assessed with the GI transit time, the colonic transit time and fecal water content. Untargeted metabolomic analysis on the prefrontal cortex (PFC) and fecal samples was performed. VAD rats displayed autistic-like behaviors and impaired GI function compared to VAN rats. Metabolic profiles of both PFC and feces from VAD and VAN rats were significantly different. The differential metabolites in both PFC and feces between the VAN and VAD rats were mostly enriched in the purine metabolic pathway. Moreover, the most significantly affected metabolic pathway in PFC of VAD rats was the phenylalanine, tyrosine and tryptophan biosynthesis pathway, and the most remarkably altered metabolic pathway in the feces of VAD rats was the tryptophan metabolism pathway. These results indicate that VAD starting from maternal gestation might be linked to core symptoms of ASD and its GI co-occurring disorders through the purine and tryptophan-related metabolism disorders.

Recent advances in mass spectrometry-based computational metabolomics

The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".

MultiomicsTracks96: A high throughput PIXUL-Matrix-based toolbox to profile frozen and FFPE tissues multiomes

Background

The multiome is an integrated assembly of distinct classes of molecules and molecular properties, or "omes," measured in the same biospecimen. Freezing and formalin-fixed paraffin-embedding (FFPE) are two common ways to store tissues, and these practices have generated vast biospecimen repositories. However, these biospecimens have been underutilized for multi-omic analysis due to the low throughput of current analytical technologies that impede large-scale studies.

Methods

Tissue sampling, preparation, and downstream analysis were integrated into a 96-well format multi-omics workflow, MultiomicsTracks96. Frozen mouse organs were sampled using the CryoGrid system, and matched FFPE samples were processed using a microtome. The 96-well format sonicator, PIXUL, was adapted to extract DNA, RNA, chromatin, and protein from tissues. The 96-well format analytical platform, Matrix, was used for chromatin immunoprecipitation (ChIP), methylated DNA immunoprecipitation (MeDIP), methylated RNA immunoprecipitation (MeRIP), and RNA reverse transcription (RT) assays followed by qPCR and sequencing. LC-MS/MS was used for protein analysis. The Segway genome segmentation algorithm was used to identify functional genomic regions, and linear regressors based on the multi-omics data were trained to predict protein expression.

Results

MultiomicsTracks96 was used to generate 8-dimensional datasets including RNA-seq measurements of mRNA expression; MeRIP-seq measurements of m6A and m5C; ChIP-seq measurements of H3K27Ac, H3K4m3, and Pol II; MeDIP-seq measurements of 5mC; and LC-MS/MS measurements of proteins. We observed high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles (ChIP-seq: H3K27Ac, H3K4m3, Pol II; MeDIP-seq: 5mC) was able to recapitulate and predict organ-specific super-enhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic expression profiles can be more accurately predicted by the full suite of multi-omics data, compared to using epigenomic, transcriptomic, or epitranscriptomic measurements individually.

Conclusions

The MultiomicsTracks96 workflow is well suited for high dimensional multi-omics studies - for instance, multiorgan animal models of disease, drug toxicities, environmental exposure, and aging as well as large-scale clinical investigations involving the use of biospecimens from existing tissue repositories.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

NMR-Based Chromatography Readouts: Indispensable Tools to “Translate” Analytical Features into Molecular Structures

Gaining structural information is a must to allow the unequivocal structural characterization of analytes from natural sources. In liquid state, NMR spectroscopy is almost the only possible alternative to HPLC-MS and hyphenating the effluent of an analyte separation device to the probe head of an NMR spectrometer has therefore been pursued for more than three decades. The purpose of this review article was to demonstrate that, while it is possible to use mass spectrometry and similar methods to differentiate, group, and often assign the differentiating variables to entities that can be recognized as single molecules, the structural characterization of these putative biomarkers usually requires the use of NMR spectroscopy.