Plasma metabolomics reveals membrane lipids, aspartate/asparagine and nucleotide metabolism pathway differences associated with chloroquine resistance in Plasmodium vivax malaria

Improving insights from metabolomic functional analysis combining multivariate tools

BACKGROUND

Metabolomics is a scientific field that relies on the comprehensive analysis of metabolites to provide direct insights into functional processes in biological systems. Metabolomic data provides valuable insights into the functional processes of biological systems, often analyzed through univariate and multivariate approaches, and well as with functional or pathway analysis using different methods such as mummichog. Yet, the integration of results from these sources to aid the interpretation of their biological significance remains challenging. This represents a significant bottleneck limiting the applicability of multivariate analysis of metabolomic data, despite its potential for providing deep biological insights.

RESULTS

In this work we propose two straightforward methods to facilitate the interpretation of results from multivariate analysis and functional metabolic analysis using: i) p-values from multivariate tests as input in functional analysis, and ii) cluster-CV to assess the impact on the predictive performance of a multivariate model at the pathway level. Four simulated data sets were analyzed including a data set with no class separation, and three data sets with a statistically significant discrimination between classes by including either univariate, multivariate, or both types of discriminant effects. The data sets were analyzed using univariate tests and OPLS-DA. Furthermore, p-values for each feature estimated by univariate analysis and OPLS-DA were used as input for functional analysis in mummichog. Cluster-CV was then used to assess the effect of detected metabolic pathways on the class separation observed by OPLS-DA.

SIGNIFICANCE

Through simulated data, we show how these approaches enhance the interpretation of biological effects driving multivariate models and support the identification of altered pathways not detected by univariate analysis. By providing a deeper understanding of metabolic phenotypes, these methods might improve the biological insights derived from statistical and functional analysis of future or previous studies.

Amyloid beta-correlated plasma metabolite dysregulation in Alzheimer's disease: an untargeted metabolism exploration using high-resolution mass spectrometry toward future clinical diagnosis

Introduction

Alzheimer's disease (AD) is a leading cause of dementia, and it has rapidly become an increasingly burdensome and fatal disease in society. Despite medical research advances, accurate recognition of AD remains challenging. Epidemiological evidence suggests that metabolic abnormalities are tied to higher AD risk.

Methods

This study utilized case-control analyses with plasma samples and identified a panel of 27 metabolites using high-resolution mass spectrometry in both the Alzheimer's disease (AD) and cognitively normal (CN) groups. All identified variables were confirmed using MS/MS with detected fragmented ions and public metabolite databases. To understand the expression of amyloid beta proteins in plasma, ELISA assays were performed for both amyloid beta 42 (Aβ42) and amyloid beta 40 (Aβ40).

Results

The levels of plasma metabolites PAGln and L-arginine were found to significantly fluctuate in the peripheral blood of AD patients. In addition, ELISA results showed a significant increase in amyloid beta 42 (Aβ42) in AD patients compared to those who were cognitively normal (CN), while amyloid beta 40 (Aβ40) did not show any significant changes between the groups. Furthermore, positive correlations were observed between Aβ42/Aβ40 and PAGln or L-arginine, suggesting that both metabolites could play a role in the pathology of amyloid beta proteins. Binary regression analysis with these two metabolites resulted in an optimal model of the ROC (AUC = 0.95, p < 0.001) to effectively discriminate between AD and CN.

Discussion

This study highlights the potential of advanced high-resolution mass spectrometry (HRMS) technology for novel plasma metabolite discovery with high stability and sensitivity, thus paving the way for future clinical studies. The results of this study suggest that the combination of PAGln and L-arginine holds significant potential for improving the diagnosis of Alzheimer's disease (AD) in clinical settings. Overall, these findings have important implications for advancing our understanding of AD and developing effective approaches for its future clinical diagnosis.

Recent metabolomic developments for antimalarial drug discovery

Malaria is a parasitic disease that remains a global health issue, responsible for a significant death and morbidity toll. Various factors have impacted the use and delayed the development of antimalarial therapies, such as the associated financial cost and parasitic resistance. In order to discover new drugs and validate parasitic targets, a powerful omics tool, metabolomics, emerged as a reliable approach. However, as a fairly recent method in malaria, new findings are timely and original practices emerge frequently. This review aims to discuss recent research towards the development of new metabolomic methods in the context of uncovering antiplasmodial mechanisms of action in vitro and to point out innovative metabolic pathways that can revitalize the antimalarial pipeline.

Contribution of Transcriptome to Elucidate the Biology of Plasmodium spp

In the present review, we discuss some of the new technologies that have been applied to elucidate how Plasmodium spp escape from the immune system and subvert the host physiology to orchestrate the regulation of its biological pathways. Our manuscript describes how techniques such as microarray approaches, RNA-Seq and single-cell RNA sequencing have contributed to the discovery of transcripts and changed the concept of gene expression regulation in closely related malaria parasite species. Moreover, the text highlights the contributions of high-throughput RNA sequencing for the current knowledge of malaria parasite biology, physiology, vaccine target and the revelation of new players in parasite signaling.

Multi-Omics Advancements towards Plasmodium vivax Malaria Diagnosis

Plasmodium vivax malaria is one of the most lethal infectious diseases, with 7 million infections annually. One of the roadblocks to global malaria elimination is the lack of highly sensitive, specific, and accurate diagnostic tools. The absence of diagnostic tools in particular has led to poor differentiation among parasite species, poor prognosis, and delayed treatment. The improvement necessary in diagnostic tools can be broadly grouped into two categories: technologies-driven and omics-driven progress over time. This article discusses the recent advancement in omics-based malaria for identifying the next generation biomarkers for a highly sensitive and specific assay with a rapid and antecedent prognosis of the disease. We summarize the state-of-the-art diagnostic technologies, the key challenges, opportunities, and emerging prospects of multi-omics-based sensors.

From Metabolite to Metabolome: Metabolomics Applications in Plasmodium Research

Advances in research over the past few decades have greatly improved metabolomics-based approaches in studying parasite biology and disease etiology. This improves the investigation of varied metabolic requirements during life stages or when following transmission to their hosts, and fulfills the demand for improved diagnostics and precise therapeutics. Therefore, this review highlights the progress of metabolomics in malaria research, including metabolic mapping of Plasmodium vertebrate life cycle stages to investigate antimalarials mode of actions and underlying complex host-parasite interactions. Also, we discuss current limitations as well as make several practical suggestions for methodological improvements which could drive metabolomics progress for malaria from a comprehensive perspective.

Lipid hijacking: a unifying theme in vector-borne diseases

Vector-borne illnesses comprise a significant portion of human maladies, representing 17% of global infections. Transmission of vector-borne pathogens to mammals primarily occurs by hematophagous arthropods. It is speculated that blood may provide a unique environment that aids in the replication and pathogenesis of these microbes. Lipids and their derivatives are one component enriched in blood and are essential for microbial survival. For instance, the malarial parasite Plasmodium falciparum and the Lyme disease spirochete Borrelia burgdorferi, among others, have been shown to scavenge and manipulate host lipids for structural support, metabolism, replication, immune evasion, and disease severity. In this Review, we will explore the importance of lipid hijacking for the growth and persistence of these microbes in both mammalian hosts and arthropod vectors.

Predicting human health from biofluid-based metabolomics using machine learning

Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified—where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categories, despite disparate experimental and disease contexts. Using only non-significant features it is still often possible to train models and achieve high predictive performance, suggesting useful predictive signal. This work highlights the potential for health state diagnostics using all metabolomics features with data-driven analysis.

Untargeted metabolomics reveal dysregulations in sugar, methionine, and tyrosine pathways in the prodromal state of AD

INTRODUCTION

Altered metabolism may occur years before clinical manifestations of Alzheimer's disease (AD). We used untargeted metabolomics on the cerebrospinal fluid of patients with mild cognitive impairment (MCI) to uncover metabolomic derangements.

METHODS

CSF from 92 normal controls and 93 MCI underwent untargeted metabolomics using high-resolution mass spectrometry with liquid chromatography. Partial least squares discriminant analysis was used followed by metabolite annotation and pathway enrichment analysis (PES). Significant features were correlated with disease phenotypes.

RESULTS

We identified 294 features differentially expressed between the two groups and 94 were annotated. PES showed that sugar regulation (N-glycan, P = .0007; sialic acid, P = .0014; aminosugars, P = .0042; galactose, P = .0054), methionine regulation (P = .0081), and tyrosine metabolism (P = .019) pathways were differentially activated and significant features within these pathways correlated with multiple disease phenotypes.

CONCLUSION

There is a metabolic signature characterized by impairments in sugars, methionine, and tyrosine regulation in MCI. Targeting these pathways may offer new therapeutic approaches to AD.

Mining the Human Host Metabolome Toward an Improved Understanding of Malaria Transmission

The big data movement has led to major advances in our ability to assess vast and complex datasets related to the host and parasite during malaria infection. While host and parasite genomics and transcriptomics are often the focus of many computational efforts in malaria research, metabolomics represents another big data type that has great promise for aiding our understanding of complex host-parasite interactions that lead to the transmission of malaria. Recent analyses of the complement of metabolites present in human blood, skin and breath suggest that host metabolites play a critical role in the transmission cycle of malaria. Volatile compounds released through breath and skin serve as attractants to mosquitoes, with malaria-infected hosts appearing to have unique profiles that further increase host attractiveness. Inside the host, fluctuations in the levels of certain metabolites in blood may trigger increased production of transmission-competent sexual stages (gametocytes), setting the stage for enhanced transmission of malaria from human to mosquito. Together, these recent discoveries suggest that metabolites of human blood, skin and breath play critical roles in malaria transmission. This review discusses recent advances in this area, with a focus on metabolites that have been identified to play a role in malaria transmission and methods that may lead to an improved understanding of malaria transmission.

Arginine and Carnitine Metabolites Are Altered in Diabetic Retinopathy

Purpose

To determine plasma metabolite and metabolic pathway differences between patients with type 2 diabetes with diabetic retinopathy (DR) and without retinopathy (diabetic controls), and between patients with proliferative DR (PDR) and nonproliferative DR (NPDR).

Methods

Using high-resolution mass spectrometry with liquid chromatography, untargeted metabolomics was performed on plasma samples from 83 DR patients and 90 diabetic controls. Discriminatory metabolic features were identified through partial least squares discriminant analysis, and linear regression was used to adjust for age, sex, diabetes duration, and hemoglobin A1c. Pathway analysis was performed using Mummichog 2.0.

Results

In the adjusted analysis, 126 metabolic features differed significantly between DR patients and diabetic controls. Pathway analysis revealed alterations in the metabolism of amino acids, leukotrienes, niacin, pyrimidine, and purine. Arginine, citrulline, glutamic γ-semialdehyde, and dehydroxycarnitine were key contributors to these pathway differences. A total of 151 features distinguished PDR patients from NPDR patients, and pathway analysis revealed alterations in the β-oxidation of saturated fatty acids, fatty acid metabolism, and vitamin D3 metabolism. Carnitine was a major contributor to the pathway differences.

Conclusions

This study demonstrates that arginine and citrulline-related pathways are dysregulated in DR, and fatty acid metabolism is altered in PDR patients compared with NPDR patients.

Comparative serum metabolomics between SCID mice and BALB/c mice with or without Schistosoma japonicum infection: Clues to the abnormal growth and development of schistosome in SCID mice

The small blood flukes of genus Schistosoma, which cause one of the most prevalent and serious parasitic zoonosis schistosomiasis, are dependent on immune-related factors of their mammalian host to facilitate their growth and development, and the formation of granulomatous pathology caused by eggs deposited in host's liver and intestinal wall. Schistosome development is hampered in the mice lacking just T cells, and is even more heavily retarded in the severe combined immunodeficient (SCID) mice lacking both T and B lymphocytes. Nevertheless, it's still not clear about the underlying regulatory molecular mechanisms of schistosome growth and development by host's immune system. This study, therefore, detected and compared the serum metabolic profiles between the immunodeficient mice and immunocompetent mice (SCID mice vs. BALB/c mice) before and after S. japonicum infection (on the thirty-fifth day post infection using liquid chromatography-mass spectrometry (LC-MS). Totally, 705 ion features in electrospray ionization in positive-ion mode (ESI+) and 242 ion features in ESI- mode were identified, respectively. First, distinct serum metabolic profiles were identified between SCID mice and BALB/c mice without S. japonicum worms infection. Second, uniquely perturbed serum metabolites and their enriched pathways were also obtained between SCID mice and BALB/c mice after S. japonicum infection, which included differential metabolites due to both species differences and differential responses to S. japonicum infection. The metabolic pathways analysis revealed that arachidonic acid metabolism, biosynthesis of unsaturated fatty acids, linoleic acid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, alpha-linolenic acid metabolism, glycerophospholipid metabolism, sphingolipid metabolism and purine metabolism were enriched based on the differential serum metabolites between SCID mice and BALB/c mice after S. japonicum infection, which was addressed to be related to the retarded growth and development of S. japonicum in SCID mice. These findings provide new clues to the underlying molecular events of host's systemic metabolic changes on the growth and development of S. japonicum worms, and also provide quite promising candidates for exploitation of drugs or vaccines against schistosome and schistosomiasis.

Plasma Eicosanoid Profile in Plasmodium vivax Malaria: Clinical Analysis and Impacts of Self-Medication

The participation of cytokines and chemokines in Plasmodium vivax malaria (Pv-malaria) activates the immune response and thus causes the production of several inflammatory mediators. This process is already well-established, but little is known about eicosanoids in malaria physiopathology, especially in regards to inflammation and immunity. Malaria is an acute febrile syndrome similar to any other less important infectious disease and people may self-medicate with any anti-inflammatory drugs in order to cease the recurrent symptoms of the disease. Based on this information, the study describes the eicosanoid profile and its possible influence on the production of cytokines and chemokines in P. vivax infections. In addition, we investigated the influence of self-medication with anti-inflammatory drugs in this immune profile. Twenty-three patients were included in the study, with or without self-medication by anti-inflammatory drugs prior to diagnosis. A total 12 individuals were selected for the control group. Eicosanoid profiles were quantified by HPLC-MS/MS, and cytokines and chemokines by flow cytometry and ELISA. The Pv-malaria infection significantly reduces the production of several lipid mediators, and its action is increased by self-medication. We observed that the eicosanoids we found derive from the lipoxygenase and cyclooxygenase pathways, and present positive and negative correlations with chemokines and cytokines in the follow-up of patients. Our data suggest that self-medication may interfere in the immunological characteristics in P. vivax infection and may modify the follow-up of the disease.

Distinct amino acid and lipid perturbations characterize acute versus chronic malaria

Chronic malaria is a major public health problem and significant challenge for disease eradication efforts. Despite its importance, the biological factors underpinning chronic malaria are not fully understood. Recent studies have shown that host metabolic state can influence malaria pathogenesis and transmission, but its role in chronicity is not known. Here, with the goal of identifying distinct modifications in the metabolite profiles of acute versus chronic malaria, metabolomics was performed on plasma from Plasmodium-infected humans and nonhuman primates with a range of parasitemias and clinical signs. In rhesus macaques infected with Plasmodium coatneyi, significant alterations in amines, carnitines, and lipids were detected during a high parasitemic acute phase and many of these reverted to baseline levels once a low parasitemic chronic phase was established. Plasmodium gene expression, studied in parallel in the macaques, revealed transcriptional changes in amine, fatty acid, lipid and energy metabolism genes, as well as variant antigen genes. Furthermore, a common set of amines, carnitines, and lipids distinguished acute from chronic malaria in plasma from human Plasmodium falciparum cases. In summary, distinct host-parasite metabolic environments have been uncovered that characterize acute versus chronic malaria, providing insights into the underlying host-parasite biology of malaria disease progression.

Metabolic perturbations in classic galactosemia beyond the Leloir pathway: Insights from an untargeted metabolomic study

Classic galactosemia (CG) is an autosomal recessive disorder that impacts close to 1/50000 live births in the United States, with varying prevalence in other countries. Following exposure to milk, which contains high levels of galactose, affected infants may experience rapid onset and progression of potentially lethal symptoms. With the benefit of early diagnosis, generally by newborn screening, and immediate and lifelong dietary restriction of galactose, the acute sequelae of disease can be prevented or resolved. However, long-term complications are common, and despite many decades of research, the bases of these complications remain unexplained. As a step toward defining the underlying pathophysiology of long-term outcomes in CG, we applied an untargeted metabolomic approach with mass spectrometry and dual liquid chromatography, comparing thousands of small molecules in plasma samples from 183 patients and 31 controls. All patients were on galactose-restricted diets. Using both univariate and multivariate statistical methods, we identified 252 differentially abundant features from anion exchange chromatography and 167 differentially abundant features from C₁₈ chromatography. Mapping these discriminatory features to putative metabolites and biochemical pathways revealed 14 significantly perturbed pathways; these included multiple redox, amino acid, and mitochondrial pathways, among others. Finally, we tested whether any discriminatory features also distinguished cases with mild vs more severe long-term outcomes and found multiple candidates, of which one achieved false discovery rate-adjusted q < 0.1. These results extend substantially from prior targeted studies of metabolic perturbation in CG and offer a new approach to identifying candidate modifiers and targets for intervention.

Metabolomic changes in vertebrate host during malaria disease progression

Metabolomics refers to top-down systems biological analysis of metabolites in biological specimens. Phenotypic proximity of metabolites makes them interesting candidates for studying biomarkers of environmental stressors such as parasitic infections. Moreover, the host-parasite interaction directly impinges upon metabolic pathways since the parasite uses the host metabolite pool as a biosynthetic resource. Malarial infection, although not recognized as a classic metabolic disorder, often leads to severe metabolic changes such as hypoglycemia and lactic acidosis. Thus, metabolomic analysis of the infection has become an invaluable tool for promoting a better understanding of the host-parasite interaction and for the development of novel therapeutics. In this review, we summarize the current knowledge obtained from metabolomic studies of malarial infection in rodent models and human patients. Metabolomic analysis of experimental rodent malaria has provided significant insights into the mechanisms of disease progression including utilization of host resources by the parasite, sexual dimorphism in metabolic phenotypes, and cellular changes in host metabolism. Moreover, these studies also provide proof of concept for prediction of cerebral malaria. On the other hand, metabolite analysis of patient biofluids generates extensive data that could be of use in identifying biomarkers of infection severity and in monitoring disease progression. Through the use of metabolomic datasets one hopes to assess crucial infection-specific issues such as clinical severity, drug resistance, therapeutic targets, and biomarkers. Also discussed are nascent or newly emerging areas of metabolomics such as pre-erythrocytic stages of the infection and the host immune response. This review is organized in four broad sections-methodologies for metabolomic analysis, rodent infection models, studies of human clinical specimens, and potential of immunometabolomics. Data summarized in this review should serve as a springboard for novel hypothesis testing and lead to a better understanding of malarial infection and parasite biology.

Malaria in India: The Need for New Targets for Diagnosis and Detection of Plasmodium vivax

Plasmodium vivax is a protozoan parasite that is one of the causative agents of human malaria. Due to several occult features of its life cycle, P. vivax threatens to be a problem for the recent efforts toward elimination of malaria globally. With an emphasis on malaria elimination goals, the authors summarize the major gaps in P. vivax diagnosis and describe how proteomics technologies have begun to contribute toward the discovery of antigens that could be used for various technology platforms and applications. The authors suggest areas where, in the future, proteomics technologies could fill in gaps in P. vivax diagnosis that have proved difficult. The discovery of new parasite antigens, host responses, and immune signatures using proteomics technologies will be a key part of the global malaria elimination efforts.