Untargeted Metabolomic Analysis of Lactation-Stage-Matched Human and Bovine Milk Samples at 2 Weeks Postnatal.

Epidemiological data demonstrate that bovine whole milk is often substituted for human milk during the first 12 months of life and may be associated with adverse infant outcomes. The objective of this study is to interrogate the human and bovine milk metabolome at 2 weeks of life to identify unique metabolites that may impact infant health outcomes. Human milk (n = 10) was collected at 2 weeks postpartum from normal-weight mothers (pre-pregnant BMI < 25 kg/m2) that vaginally delivered term infants and were exclusively breastfeeding their infant for at least 2 months. Similarly, bovine milk (n = 10) was collected 2 weeks postpartum from normal-weight primiparous Holstein dairy cows. Untargeted data were acquired on all milk samples using high-resolution liquid chromatography-high-resolution tandem mass spectrometry (HR LC-MS/MS). MS data pre-processing from feature calling to metabolite annotation was performed using MS-DIAL and MS-FLO. Our results revealed that more than 80% of the milk metabolome is shared between human and bovine milk samples during early lactation. Unbiased analysis of identified metabolites revealed that nearly 80% of milk metabolites may contribute to microbial metabolism and microbe-host interactions. Collectively, these results highlight untargeted metabolomics as a potential strategy to identify unique and shared metabolites in bovine and human milk that may relate to and impact infant health outcomes.

Metabolic changes induced by Cuscuta campestris Yunck in the host species Artemisia campestris subsp. variabilis (Ten.) Greuter as a strategy for successful parasitisation.

MAIN CONCLUSIONS: C. campestris parasitisation increases internal host defences at the expense of environmentally directed ones in the host species A. campestris, thus limiting plant defence against progressive parasitisation. Cuscuta campestris Yunck is a holoparasitic species that parasitises wild species and crops. Among their hosts, Artemisia campestris subsp. variabilis (Ten.) Greuter is significantly affected in natural ecosystems. Limited information is available on the host recognition mechanism and there are no data on the interactions between these species and the effects on the primary and specialised metabolism in response to parasitisation. The research aims at evaluating the effect of host-parasite interactions, through a GC-MS untargeted metabolomic analysis, chlorophyll a fluorescence, ionomic and δ13C measurements, as well as volatile organic compound (VOC) fingerprint in A. campestris leaves collected in natural environment. C. campestris parasitisation altered plant water status, forcing stomatal opening, stimulating plant transpiration, and inducing physical damages to the host antenna complex, thus reducing the efficiency of its photosynthetic machinery. Untargeted-metabolomics analysis highlighted that the parasitisation significantly perturbed the amino acids and sugar metabolism, inducing an increase in the production of osmoprotectants, which generally accumulate in plants as a protective strategy against oxidative stress. Notably, VOCs analysis highlighted a reduction in sesquiterpenoids and an increase in monoterpenoids levels; involved in plant defence and host recognition, respectively. Moreover, C. campestris induced in the host a reduction in 3-hexenyl-acetate, a metabolite with known repellent activity against Cuscuta spp. We offer evidences that C. campestris parasitisation increases internal host defences via primary metabolites at the expense of more effective defensive compounds (secondary metabolites), thus limiting A. campestris defence against progressive parasitisation.

Mass Spectrometry-Based Proteome Profiling of Extracellular Vesicles Derived from the Cerebrospinal Fluid of Adult Rhesus Monkeys Exposed to Cocaine throughout Gestation.

Cocaine use disorder has been reported to cause transgenerational effects. However, due to the lack of standardized biomarkers, the effects of cocaine use during pregnancy on postnatal development and long-term neurobiological and behavioral outcomes have not been investigated thoroughly. Therefore, in this study, we examined extracellular vesicles (EVs) in adult (~12 years old) female and male rhesus monkeys prenatally exposed to cocaine (n = 11) and controls (n = 9). EVs were isolated from the cerebrospinal fluid (CSF) and characterized for the surface expression of specific tetraspanins, concentration (particles/mL), size distribution, and cargo proteins by mass spectrometry (MS). Transmission electron microscopy following immunogold labeling for tetraspanins (CD63, CD9, and CD81) confirmed the successful isolation of EVs. Nanoparticle tracking analyses showed that the majority of the particles were <200 nm in size, suggesting an enrichment for small EVs (sEV). Interestingly, the prenatally cocaine-exposed group showed ~54% less EV concentration in CSF compared to the control group. For each group, MS analyses identified a number of proteins loaded in CSF-EVs, many of which are commonly listed in the ExoCarta database. Ingenuity pathway analysis (IPA) demonstrated the association of cargo EV proteins with canonical pathways, diseases and disorders, upstream regulators, and top enriched network. Lastly, significantly altered proteins between groups were similarly characterized by IPA, suggesting that prenatal cocaine exposure could be potentially associated with long-term neuroinflammation and risk for neurodegenerative diseases. Overall, these results indicate that CSF-EVs could potentially serve as biomarkers to assess the transgenerational adverse effects due to prenatal cocaine exposure.

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery.

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.

Untargeted metabolomics in primary murine bone marrow stromal cells reveals distinct profile throughout osteoblast differentiation.

INTRODUCTION: Skeletal homeostasis is an exquisitely regulated process most directly influenced by bone resorbing osteoclasts, bone forming osteoblasts, and the mechano-sensing osteocytes. These cells work together to constantly remodel bone as a mechanism to prevent from skeletal fragility. As such, when an individual experiences a disconnect in these tightly coupled processes, fracture incidence increases, such as during ageing, gonadal hormone deficiency, weightlessness, and diabetes. While therapeutic options have significantly aided in the treatment of low bone mineral density (BMD) or osteoporosis, limited options remain for anabolic or bone forming agents. Therefore, it is of interest to continue to understand how osteoblasts regulate their metabolism to support the energy expensive process of bone formation. OBJECTIVE: The current project sought to rigorously characterize the distinct metabolic processes and intracellular metabolite profiles in stromal cells throughout osteoblast differentiation using untargeted metabolomics. METHODS: Primary, murine bone marrow stromal cells (BMSCs) were characterized throughout osteoblast differentiation using standard staining protocols, Seahorse XFe metabolic flux analyses, and untargeted metabolomics. RESULTS: We demonstrate here that the metabolic footprint of stromal cells undergoing osteoblast differentiation are distinct, and while oxidative phosphorylation drives adenosine triphosphate (ATP) generation early in the differentiation process, mature osteoblasts depend on glycolysis. Importantly, the intracellular metabolite profile supports these findings while also suggesting additional pathways critical for proper osteoblast function. CONCLUSION: These data are the first of their kind to characterize these metabolites in conjunction with the bioenergetic profile in primary, murine stromal cells throughout osteoblast differentiation and provide provocative targets for future investigation.

New software tools, databases, and resources in metabolomics: updates from 2020.

BACKGROUND: Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success. AIM OF THE REVIEW: In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community. KEY SCIENTIFIC CONCEPTS OF REVIEW: In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.

Advances in high resolution GC-MS technology: a focus on the application of GC-Orbitrap-MS in metabolomics and exposomics for FAIR practices.

Gas chromatography-mass spectrometry (GC-MS) provides a complementary analytical platform for capturing volatiles, non-polar and (derivatized) polar metabolites and exposures from a diverse array of matrixes. High resolution (HR) GC-MS as a data generation platform can capture data on analytes that are usually not detectable/quantifiable in liquid chromatography mass-spectrometry-based solutions. With the rise of high-resolution accurate mass (HRAM) GC-MS systems such as GC-Orbitrap-MS in the last decade after the time-of-flight (ToF) renaissance, numerous applications have been found in the fields of metabolomics and exposomics. In a short span of time, a multitude of studies have used GC-Orbitrap-MS to generate exciting new high throughput data spanning from diverse basic to applied research areas. The GC-Orbitrap-MS has found application in both targeted and untargeted efforts for capturing metabolomes and exposomes across diverse studies. In this review, I capture and summarize all the reported studies to date, and provide a snapshot of the milieu of commercial and open-source software solutions, spectral libraries, and informatics solutions available to a GC-Orbitrap-MS system instrument user or a data analyst dealing with these datasets. Lastly, but importantly, I provide an account on data sharing and meta-data capturing solutions that are available to make HRAM GC-MS based metabolomics and exposomics studies findable, accessible, interoperable, and reproducible (FAIR). These FAIR practices would allow data generators and users of GC-HRMS instruments to help the community of GC-MS researchers to collaborate and co-develop exciting tools and algorithms in the future.

Loss of function of lysosomal acid lipase (LAL) profoundly impacts osteoblastogenesis and increases fracture risk in humans.

Lysosomal acid lipase (LAL) is essential for cholesteryl ester (CE) and triacylglycerol (TAG) hydrolysis in the lysosome. Clinically, an autosomal recessive LIPA mutation causes LAL deficiency (LALD), previously described as Wolman Disease or Cholesteryl Ester Storage Disease (CESD). LAL-D is associated with ectopic lipid accumulation in the liver, small intestine, spleen, adrenal glands, and blood. Considering the importance of unesterified cholesterol and fatty acids in bone metabolism, we hypothesized that LAL is essential for bone formation, and ultimately, skeletal health. To investigate the role of LAL in skeletal homeostasis, we used LAL-deficient (-/-) mice, in vitro osteoblast cultures, and novel clinical data from LAL-D patients. Both male and female LAL-/- mice demonstarted lower trabecular and cortical bone parameters , which translated to reduced biomechanical properties. Further histological analyses revealed that LAL-/- mice had fewer osteoblasts, with no change in osteoclast or marrow adipocyte numbers. In studying the cell-autonomous role of LAL, we observed impaired differentiation of LAL-/- calvarial osteoblasts and in bone marrow stromal cells treated with the LAL inhibitor lalistat. Consistent with LAL's role in other tissues, lalistat resulted in profound lipid puncta accumulation and an altered intracellular lipid profile. Finally, we analyzed a large de-identified national insurance database (i.e. 2016/2017 Optum Clinformatics®) which revealed that adults (≥18 years) with CESD (n = 3076) had a higher odds ratio (OR = 1.21; 95% CI = 1.03-1.41) of all-cause fracture at any location compared to adults without CESD (n = 13.7 M) after adjusting for demographic variables and osteoporosis. These data demonstrate that alterations in LAL have significant clinical implications related to fracture risk and that LAL's modulation of lipid metabolism is a critical for osteoblast function.

Time-course analysis of Streptococcus sanguinis after manganese depletion reveals changes in glycolytic and nucleic acid metabolites.

INTRODUCTION: Manganese is important for the endocarditis pathogen Streptococcus sanguinis. Little is known about why manganese is required for virulence or how it impacts the metabolome of streptococci. OBJECTIVES: We applied untargeted metabolomics to cells and media to understand temporal changes resulting from manganese depletion. METHODS: EDTA was added to a S. sanguinis manganese-transporter mutant in aerobic fermentor conditions. Cell and media samples were collected pre- and post-EDTA treatment. Metabolomics data were generated using positive and negative modes of data acquisition on an LC-MS/MS system. Data were subjected to statistical processing using MetaboAnalyst and time-course analysis using Short Time series Expression Miner (STEM). Recombinant enzymes were assayed for metal dependence. RESULTS: We observed quantitative changes in 534 and 422 metabolites in cells and media, respectively, after EDTA addition. The 173 cellular metabolites identified as significantly different indicated enrichment of purine and pyrimidine metabolism. Further multivariate analysis revealed that the top 15 cellular metabolites belonged primarily to lipids and redox metabolites. The STEM analysis revealed global changes in cells and media in comparable metabolic pathways. Glycolytic intermediates such as fructose-1,6-bisphosphate increased, suggesting that enzymes that utilize them require manganese for activity or expression. Recombinant enzymes were confirmed to utilize manganese in vitro. Nucleosides accumulated, possibly due to a blockage in conversion to nucleobases resulting from manganese-dependent regulation. CONCLUSION: Differential analysis of metabolites revealed the activation of a number of metabolic pathways in response to manganese depletion, many of which are connected to carbon catabolite repression.

Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data.

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

The Chemical Exposome of Human Aging.

Aging is an inevitable biological phenomenon displayed by single cells and organs to entire organismal systems. Aging as a biological process is characterized as a progressive decline in intrinsic biological function. Understanding the causative mechanisms of aging has always captured the imagination of researchers since time immemorial. Although both biological and chronological aging are well defined and studied in terms of genetic, epigenetic, and lifestyle predispositions, the hallmarks of aging in terms of small molecules (i.e., endogenous metabolites to chemical exposures) are limited to obscure. On top of the endogenous metabolites leading to the onset and progression of healthy aging, human beings are constantly exposed to a natural and anthropogenic "chemical" environment round the clock, from conception till death, affecting one's physiology, health and well-being, and disease predisposition. The research community has started gaining sizeable insights into deciphering the aging factors such as immunosenescence, nutrition, frailty, inflamm-aging, and diseases till date, without much input from their interaction with exogenous chemical exposures. The "exposome" around us, mostly, accelerates the process of aging by affecting the internal biological pathways and signaling mechanisms that result in the deterioration of human health. However, the entirety of exposome on human aging is far from established. This review intends to catalog the known and established associations of the exposome from past studies focusing on aging in humans and other model organisms. Further discussed are the current technologies and informatics tools that enable the study of aging exposotypes, and thus, provide a window of opportunities and challenges to study the "aging exposome" in granular details.

State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing.

Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets to aid in data analysis, visualization and interpretation to determine the mechanism of a biological process. Multi-omics efforts have taken center stage in biomedical research leading to the development of new insights into biological events and processes. However, the mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field. The aims of this review are to provide an overview of the current state of the field, inform on available reliable resources, discuss the application of statistics and machine/deep learning in multi-omics analyses, discuss findable, accessible, interoperable, reusable (FAIR) research, and point to best practices in benchmarking. Thus, we provide guidance to interested users of the domain by addressing challenges of the underlying biology, giving an overview of the available toolset, addressing common pitfalls, and acknowledging current methods' limitations. We conclude with practical advice and recommendations on software engineering and reproducibility practices to share a comprehensive awareness with new researchers in multi-omics for end-to-end workflow.

Short-term effects of the allelochemical umbelliferone on Triticum durum L. metabolism through GC-MS based untargeted metabolomics.

The present study used untargeted metabolomics to investigate the short-term metabolic changes induced in wheat seedlings by the specialized metabolite umbelliferone, an allelochemical. We used 10 day-old wheat seedlings treated with 104 µM umbelliferone over a time course experiment covering 6 time points (0 h, 6 h, 12 h, 24 h, 48 h, and 96 h), and compared the metabolomic changes to control (mock-treated) plants. Using gas chromatography mass spectrometry (GCMS)-based metabolomics, we obtained quantitative data on 177 metabolites that were derivatized (either derivatized singly or multiple times) or not, representing 139 non-redundant (unique) metabolites. Of these 139 metabolites, 118 were associated with a unique Human Metabolome Database (HMDB) identifier, while 113 were associated with a Kyoto Encyclopedia of Genes and Genomes (KEGG) identifier. Relative quantification of these metabolites across the time-course of umbelliferone treatment revealed 22 compounds (sugars, fatty acids, secondary metabolites, organic acids, and amino acids) that changed significantly (repeated measures ANOVA, P-value < 0.05) over time. Using multivariate partial least squares discriminant analysis (PLS-DA), we showed the grouping of samples based on time-course across the control and umbelliferone-treated plants, whereas the metabolite-metabolite Pearson correlations revealed tightly formed clusters of umbelliferone-derived metabolites, fatty acids, amino acids, and carbohydrates. Also, the time-course umbelliferone treatment revealed that phospho-l-serine, maltose, and dehydroquinic acid were the top three metabolites showing highest importance in discrimination among the time-points. Overall, the biochemical changes converge towards a mechanistic explanation of the plant metabolic responses induced by umbelliferone. In particular, the perturbation of metabolites involved in tryptophan metabolism, as well as the imbalance of the shikimate pathways, which are strictly interconnected, were significantly altered by the treatment, suggesting a possible mechanism of action of this natural compound.

Solute Carrier Family 37 Member 2 (SLC37A2) Negatively Regulates Murine Macrophage Inflammation by Controlling Glycolysis.

Increased flux of glucose through glycolysis is a hallmark of inflammatory macrophages and is essential for optimal effector functions. Solute carrier (SLC) 37A2 is an endoplasmic reticulum-anchored phosphate-linked glucose-6-phosphate transporter that is highly expressed in macrophages and neutrophils. We demonstrate that SLC37A2 plays a pivotal role in murine macrophage inflammatory activation and cellular metabolic rewiring. Toll-like receptor (TLR) 4 stimulation by lipopolysaccharide (LPS) rapidly increases macrophage SLC37A2 protein expression. SLC37A2 deletion reprograms macrophages to a hyper-glycolytic process and accelerates LPS-induced inflammatory cytokine production, which partially depends on nicotinamide adenine dinucleotide (NAD+) biosynthesis. Blockade of glycolysis normalizes the differential expression of pro-inflammatory cytokines between control and SLC37A2 deficient macrophages. Conversely, overexpression of SLC37A2 lowers macrophage glycolysis and significantly reduces LPS-induced pro-inflammatory cytokine expression. In conclusion, our study suggests that SLC37A2 dampens murine macrophage inflammation by down-regulating glycolytic reprogramming as a part of macrophage negative feedback system to curtail acute innate activation.

Data normalization strategies in metabolomics: Current challenges, approaches, and tools.

Data normalization is a big challenge in quantitative metabolomics approaches, whether targeted or untargeted. Without proper normalization, the mass-spectrometry and spectroscopy data can provide erroneous, sub-optimal data, which can lead to misleading and confusing biological results and thereby result in failed application to human healthcare, clinical, and other research avenues. To address this issue, a number of statistical approaches and software tools have been proposed in the literature and implemented over the years, thereby providing a multitude of approaches to choose from - either sample-based or data-based normalization strategies. In recent years, new dedicated software tools for metabolomics data normalization have surfaced as well. In this account article, I summarize the existing approaches and the new discoveries and research findings in this area of metabolomics data normalization, and I introduce some recent tools that aid in data normalization.

Software tools, databases and resources in metabolomics: updates from 2018 to 2019.

Metabolomics has evolved as a discipline from a discovery and functional genomics tool, and is now a cornerstone in the era of big data-driven precision medicine. Sample preparation strategies and analytical technologies have seen enormous growth, and keeping pace with data analytics is challenging, to say the least. This review introduces and briefly presents around 100 metabolomics software resources, tools, databases, and other utilities that have surfaced or have improved in 2019. Table 1 provides the computational dependencies of the tools, categorizes the resources based on utility and ease of use, and provides hyperlinks to webpages where the tools can be downloaded or used. This review intends to keep the community of metabolomics researchers up to date with all the software tools, resources, and databases developed in 2019, in one place.

The Connection and Disconnection Between Microbiome and Metabolome: A Critical Appraisal in Clinical Research.

Big data-driven omics research has led to a steep rise in investigations involving two of the most functional omes, the metabolome and microbiome. The former is touted as the closest to the phenotype, and the latter is implicated in general well-being and a plethora of human diseases. Although some research publications have integrated the concepts of the two domains, most focus their analyses on evidence solely originating from one or the other. With a growing interest in connecting the microbiome and metabolome in the context of disease, researchers must also appreciate the disconnect between the two domains. In the present review, drawing examples from the current literature, tools, and resources, I discuss the connections between the microbiome and metabolome and highlight challenges and opportunities in linking them together for the basic, translational, clinical, and nursing research communities.

High Resolution GC-Orbitrap-MS Metabolomics Using Both Electron Ionization and Chemical Ionization for Analysis of Human Plasma.

Gas chromatography-mass spectrometry (GC-MS) platforms are typically run in electron ionization (EI) mode for mass spectral matching and metabolite annotation. With the advent of high resolution mass spectrometry (HRMS), soft ionization techniques such as chemical ionization (CI) may provide additional coverage for compound identification. We evaluated NIST SRM 1950 pooled plasma reference sample using a HRGC-MS instrument [GC-Orbitrap-MS with electron ionization (EI), positive chemical ionization (PCI), and negative CI (NCI) capabilities] for metabolite annotation and quantification to assess the suitability of the platform for routine discovery metabolomics. Using both open source and vendor workflows, we validated the spectral matches with an in-house spectral library (Wake Forest CPM GC-MS spectral and retention time libraries) of EI-MS and CI-MS/MS spectra obtained from chemical standards. We confidently [metabolomics standards initiative (MSI) confidence level 2] identified 263, 93, and 65 metabolites using EI, PCI, and NCI modes, respectively, of which 270 metabolites (64%) were validated using our Wake Forest CPM GC-MS spectral libraries. When compared to published LC-MS-based efforts using the same NIST SRM 1950 plasma sample, there was only 17% overlap between the two platforms. In addition, the metabolomics analysis of community approved standard human plasma demonstrated the ability of EI- and CI-MS modes of analysis using a HRGC-MS platform to enable reproducible and interoperable spectral matching.