Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington's disease subjects

Dopaminergic system and neurons: Role in multiple neurological diseases

The dopaminergic system is a complex and powerful neurotransmitter system in the brain. It plays an important regulatory role in motivation, reward, cognition, and motor control. In recent decades, research in the field of the dopaminergic system and neurons has increased exponentially and is gradually becoming a point of intervention in the study and understanding of a wide range of neurological diseases related to human health. Studies have shown that the dopaminergic system and neurons are involved in the development of many neurological diseases (including, but not limited to Parkinson's disease, schizophrenia, depression, attention deficit hyperactivity disorder, etc.) and that dopaminergic neurons either have too much stress or too weak function in the dopaminergic system can lead to disease. Therefore, targeting dopaminergic neurons is considered key to treating these diseases. This article provides a comprehensive review of the dopaminergic system and neurons in terms of brain region distribution, physiological function and subtypes of dopaminergic neurons, as well as the role of the dopaminergic system and neurons in a variety of diseases.

Optimized analytical strategy based on high-resolution mass spectrometry for unveiling associations between long-term chemical exposome in hair and Alzheimer's disease

Exposure to environmental pollutants or contaminants is correlated with detrimental effects on human health, such as neurodegenerative diseases. Adopting hair as a biological matrix for biomonitoring is a significant innovation, since it can reflect the long-term chemical exposome, spanning months to years. However, only a limited number of studies have developed analytical strategies for profiling the chemical exposome in this heterogeneous biological matrix. In this study, a systematic investigation of the chemical extraction procedure from human hair was conducted, using a design of experiments and a high-resolution mass spectrometry (HRMS)-based suspect screening approach. The PlackettBurman (PB) design was applied to identify the significant variables influencing the number of detected features. Then, a central composite design was implemented to optimize the levels of each identified significant variable. Under the optimal conditions-15-minute pulverization, 25 mg of hair weight, 40 min of sonication, and a sonication temperature of 35 °C-approximately 32,000 and 15,000 aligned features were detected in positive and negative ion modes, respectively. This optimized analytical procedure was applied to hair samples from patients with Alzheimer's disease (AD) and individuals with normal cognitive function. Overall, 307 chemicals were identified using the suspect screening approach, with 37 chemicals differentiating patients with AD from controls. This study not only optimized an analytical procedure for characterizing the long-term chemical exposome in human hair but also explored the associations between AD and environmental factors.

Low-Dose Radiation Induces Alterations in Fatty Acid and Tyrosine Metabolism in the Mouse Hippocampus: Insights from Integrated Multiomics

In recent years, there has been a drastic surge in neurological disorders with sporadic cases contributing more than ever to their cause. Radiation exposure through diagnostic or therapeutic routes often results in neurological injuries that may lead to neurodegenerative pathogenesis. However, the underlying mechanisms regulating the neurological impact of exposure to near-low doses of ionizing radiation are not known. In particular, the neurological changes caused by metabolomic reprogramming have not yet been elucidated. Hence, in the present study, C57BL/6 mice were exposed to a single whole-body X-ray dose of 0.5 Gy, and 14 days post-treatment, the hippocampus was subjected to metabolomic analysis. The hippocampus of the irradiated animals showed significant alterations in 15 metabolites, which aligned with altered tyrosine, phenylalanine, and alpha-linolenic acid metabolism and the biosynthesis of unsaturated fatty acids. Furthermore, a multiomics interaction network comprising metabolomics and RNA sequencing data analysis provided insights into gene-metabolite interactions. Tyrosine metabolism was revealed to be the most altered, which was demonstrated by the interaction of several crucial genes and metabolites. The present study revealed the regulation of low-dose radiation-induced neurotoxicity at the metabolomic level and its implications for the pathogenesis of neurological disorders. The present study also provides novel insights into metabolomic pathways altered following near-low-dose IR exposure and its link with neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease.

The microbiota-gut-brain axis in Huntington's disease: pathogenic mechanisms and therapeutic targets

Huntington's disease (HD) is a currently incurable neurogenerative disorder and is typically characterized by progressive movement disorder (including chorea), cognitive deficits (culminating in dementia), psychiatric abnormalities (the most common of which is depression), and peripheral symptoms (including gastrointestinal dysfunction). There are currently no approved disease-modifying therapies available for HD, with death usually occurring approximately 10-25 years after onset, but some therapies hold promising potential. HD subjects are often burdened by chronic diarrhea, constipation, esophageal and gastric inflammation, and a susceptibility to diabetes. Our understanding of the microbiota-gut-brain axis in HD is in its infancy and growing evidence from preclinical and clinical studies suggests a role of gut microbial population imbalance (gut dysbiosis) in HD pathophysiology. The gut and the brain can communicate through the enteric nervous system, immune system, vagus nerve, and microbiota-derived-metabolites including short-chain fatty acids, bile acids, and branched-chain amino acids. This review summarizes supporting evidence demonstrating the alterations in bacterial and fungal composition that may be associated with HD. We focus on mechanisms through which gut dysbiosis may compromise brain and gut health, thus triggering neuroinflammatory responses, and further highlight outcomes of attempts to modulate the gut microbiota as promising therapeutic strategies for HD. Ultimately, we discuss the dearth of data and the need for more longitudinal and translational studies in this nascent field. We suggest future directions to improve our understanding of the association between gut microbes and the pathogenesis of HD, and other 'brain and body disorders'.

The Role of Amino Acids in Non-Enzymatic Antioxidant Mechanisms in Cancer: A Review

Currently, the antioxidant properties of amino acids and their role in the physicochemical processes accompanying oxidative stress in cancer remain unclear. Cancer cells are known to extensively uptake amino acids, which are used as an energy source, antioxidant precursors that reduce oxidative stress in cancer, and as regulators of inhibiting or inducing tumor cell-associated gene expression. This review examines nine amino acids (Cys, His, Phe, Met, Trp, Tyr, Pro, Arg, Lys), which play a key role in the non-enzymatic oxidative process in various cancers. Conventionally, these amino acids can be divided into two groups, in one of which the activity increases (Cys, Phe, Met, Pro, Arg, Lys) in cancer, and in the other, it decreases (His, Trp, Tyr). The review examines changes in the metabolism of nine amino acids in eleven types of oncology. We have identified the main nonspecific mechanisms of changes in the metabolic activity of amino acids, and described direct and indirect effects on the redox homeostasis of cells. In the future, this will help to understand better the nature of life of a cancer cell and identify therapeutic targets more effectively.

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

Metabolic deregulation associated with aging modulates protein aggregation in the yeast model of Huntington's disease

Huntington's disease is associated with increased CAG repeat resulting in an expanded polyglutamine tract in the protein Huntingtin (HTT) leading to its aggregation resulting in neurodegeneration. Previous studies have shown that N-terminal HTT with 46Q aggregated in the stationary phase but not the logarithmic phase in the yeast model of HD. We carried out a metabolomic analysis of logarithmic and stationary phase yeast model of HD expressing different polyQ lengths attached to N-terminal HTT tagged with enhanced green fluorescent protein (EGFP). The results show significant changes in the metabolic profile and deregulated pathways in stationary phase cells compared to logarithmic phase cells. Comparison of metabolic pathways obtained from logarithmic phase 46Q versus 25Q with those obtained for presymptomatic HD patients from our previous study and drosophila model of HD showed considerable overlap. The arginine biosynthesis pathway emerged as one of the key pathways that is common in stationary phase yeast compared to logarithmic phase and HD patients. Treatment of yeast with arginine led to a significant decrease, while transfer to arginine drop-out media led to a significant increase in the size of protein aggregates in both logarithmic and stationary phase yeast model of HD. Knockout of arginine transporters in the endoplasmic reticulum and vacuole led to a significant decrease in mutant HTT aggregation. Overall our results highlight arginine as a critical metabolite that modulates the aggregation of mutant HTT and disease progression in HD.Communicated by Ramaswamy H. Sarma.

Integrative Analysis Unveils the Correlation of Aminoacyl-tRNA Biosynthesis Metabolites with the Methylation of the SEPSECS Gene in Huntington's Disease Brain Tissue

The impact of environmental factors on epigenetic changes is well established, and cellular function is determined not only by the genome but also by interacting partners such as metabolites. Given the significant impact of metabolism on disease progression, exploring the interaction between the metabolome and epigenome may offer new insights into Huntington's disease (HD) diagnosis and treatment. Using fourteen post-mortem HD cases and fourteen control subjects, we performed metabolomic profiling of human postmortem brain tissue (striatum and frontal lobe), and we performed DNA methylome profiling using the same frontal lobe tissue. Along with finding several perturbed metabolites and differentially methylated loci, Aminoacyl-tRNA biosynthesis (adj p-value = 0.0098) was the most significantly perturbed metabolic pathway with which two CpGs of the SEPSECS gene were correlated. This study improves our understanding of molecular biomarker connections and, importantly, increases our knowledge of metabolic alterations driving HD progression.

Vitamin B6, B12 and folate modulate deregulated pathways and protein aggregation in yeast model of Huntington disease

Huntington's disease (HD) is an incurable and progressive neurodegenerative disease affecting the basal ganglia of the brain. HD is caused due to expansion of the polyglutamine tract in the protein Huntingtin resulting in aggregates. The increased PolyQ length results in aggregation of protein Huntingtin leading to neuronal cell death. Vitamin B6, B12 and folate are deficient in many neurodegenerative diseases. We performed an integrated analysis of transcriptomic, metabolomic and cofactor-protein network of vitamin B6, B12 and folate was performed. Our results show considerable overlap of pathways modulated by Vitamin B6, B12 and folate with those obtained from transcriptomic and metabolomic data of HD patients and model systems. Further, in yeast model of HD we showed treatment of B6, B12 or folate either alone or in combination showed impaired aggregate formation. Transcriptomic analysis of yeast model treated with B6, B12 and folate showed upregulation of pathways like ubiquitin mediated proteolysis, autophagy, peroxisome, fatty acid, lipid and nitrogen metabolism. Metabolomic analysis of yeast model shows deregulation of pathways like aminoacyl-tRNA biosynthesis, metabolism of various amino acids, nitrogen metabolism and glutathione metabolism. Integrated transcriptomic and metabolomic analysis of yeast model showed concordance in the pathways obtained. Knockout of Peroxisomal (PXP1 and PEX7) and Autophagy (ATG5) genes in yeast increased aggregates which is mitigated by vitamin B6, B12 and folate treatment. Taken together our results show a role for Vitamin B6, B12 and folate mediated modulation of pathways important for preventing protein aggregation with potential implications for HD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03525-y.

Impaired Extracellular Proteostasis in Patients with Heart Failure

BACKGROUND

Proteostasis impairment and the consequent increase of amyloid burden in the myocardium have been associated with heart failure (HF) development and poor prognosis. A better knowledge of the protein aggregation process in biofluids could assist the development and monitoring of tailored interventions.

AIM

To compare the proteostasis status and protein's secondary structures in plasma samples of patients with HF with preserved ejection fraction (HFpEF), patients with HF with reduced ejection fraction (HFrEF), and age-matched individuals.

METHODS

A total of 42 participants were enrolled in 3 groups: 14 patients with HFpEF, 14 patients with HFrEF, and 14 age-matched individuals. Proteostasis-related markers were analyzed by immunoblotting techniques. Fourier Transform Infrared (FTIR) Spectroscopy in Attenuated Total Reflectance (ATR) was applied to assess changes in the protein's conformational profile.

RESULTS

Patients with HFrEF showed an elevated concentration of oligomeric proteic species and reduced clusterin levels. ATR-FTIR spectroscopy coupled with multivariate analysis allowed the discrimination of HF patients from age-matched individuals in the protein amide I absorption region (1700-1600 cm^-1), reflecting changes in protein conformation, with a sensitivity of 73 and a specificity of 81%. Further analysis of FTIR spectra showed significantly reduced random coils levels in both HF phenotypes. Also, compared to the age-matched group, the levels of structures related to fibril formation were significantly increased in patients with HFrEF, whereas the β-turns were significantly increased in patients with HFpEF.

CONCLUSION

Both HF phenotypes showed a compromised extracellular proteostasis and different protein conformational changes, suggesting a less efficient protein quality control system.

Application of Metabolic Reprogramming to Cancer Imaging and Diagnosis

Cellular metabolism governs the signaling that supports physiological mechanisms and homeostasis in an individual, including neuronal transmission, wound healing, and circadian clock manipulation. Various factors have been linked to abnormal metabolic reprogramming, including gene mutations, epigenetic modifications, altered protein epitopes, and their involvement in the development of disease, including cancer. The presence of multiple distinct hallmarks and the resulting cellular reprogramming process have gradually revealed that these metabolism-related molecules may be able to be used to track or prevent the progression of cancer. Consequently, translational medicines have been developed using metabolic substrates, precursors, and other products depending on their biochemical mechanism of action. It is important to note that these metabolic analogs can also be used for imaging and therapeutic purposes in addition to competing for metabolic functions. In particular, due to their isotopic labeling, these compounds may also be used to localize and visualize tumor cells after uptake. In this review, the current development status, applicability, and limitations of compounds targeting metabolic reprogramming are described, as well as the imaging platforms that are most suitable for each compound and the types of cancer to which they are most appropriate.

Identification and validation of a tyrosine metabolism-related prognostic prediction model and characterization of the tumor microenvironment infiltration in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is an aggressive and heterogeneous disease characterized by high morbidity and mortality. The liver is the vital organ that participates in tyrosine catabolism, and abnormal tyrosine metabolism could cause various diseases, including HCC. Besides, the tumor immune microenvironment is involved in carcinogenesis and can influence the patients' clinical outcomes. However, the potential role of tyrosine metabolism pattern and immune molecular signature is poorly understood in HCC.

METHODS

Gene expression, somatic mutations, copy number variation data, and clinicopathological information of HCC were downloaded from The Cancer Genome Atlas (TCGA) database. GSE14520 from the Gene Expression Omnibus (GEO) databases was used as a validation dataset. We performed unsupervised consensus clustering of tyrosine metabolism-related genes (TRGs) and classified patients into distinct molecular subtypes. We used ESTIMATE algorithms to evaluate the immune infiltration. We then applied LASSO Cox regression to establish the TRGs risk model and validated its predictive performance.

RESULTS

In this study, we first described the alterations of 42 TRGs in HCC cohorts and characterized the clinicopathological characteristics and tumor microenvironmental landscape of the two distinct subtypes. We then established a tyrosine metabolism-related scoring system and identified five TRGs, which were highly correlated with prognosis and representative of this gene set, namely METTL6, GSTZ1, ADH4, ADH1A, and LCMT1. Patients in the high-risk group had an inferior prognosis. Univariate and multivariate Cox proportional hazards regression analysis also showed that the tyrosine metabolism-related signature was an independent prognostic indicator. Besides, receiver operating characteristic curve (ROC) analysis demonstrated the predictive accuracy of the TRGs signature that could reliably predict 1-, 3-, and 5-year survival in both TCGA and GEO cohorts. We also got consistent results by performing clone formation and invasion analysis, and immunohistochemical (IHC) assays. Moreover, we also discovered that the TRGs signature was significantly associated with the different immune landscapes and therapeutic drug sensitivity.

CONCLUSION

Our comprehensive analysis revealed the potential molecular signature and clinical utilities of TRGs in HCC. The model based on five TRGs can accurately predict the survival outcomes of HCC, improving our knowledge of TRGs in HCC and paving a new path for guiding risk stratification and treatment strategy development for HCC patients.

Integrated multi-omics analysis of Huntington disease identifies pathways that modulate protein aggregation

Huntington disease (HD) is a neurodegenerative disease associated with polyglutamine expansion in the protein huntingtin (HTT). Although the length of the polyglutamine repeat correlates with age at disease onset and severity, psychological, cognitive and behavioral complications point to the existence of disease modifiers. Mitochondrial dysfunction and metabolic deregulation are both associated with the HD but, despite multi-omics characterization of patients and model systems, their mechanisms have remained elusive. Systems analysis of multi-omics data and its validation by using a yeast model could help to elucidate pathways that modulate protein aggregation. Metabolomics analysis of HD patients and of a yeast model of HD was, therefore, carried out. Our analysis showed a considerable overlap of deregulated metabolic pathways. Further, the multi-omics analysis showed deregulated pathways common in human, mice and yeast model systems, and those that are unique to them. The deregulated pathways include metabolic pathways of various amino acids, glutathione metabolism, longevity, autophagy and mitophagy. The addition of certain metabolites as well as gene knockouts targeting the deregulated metabolic and autophagy pathways in the yeast model system showed that these pathways do modulate protein aggregation. Taken together, our results showed that the modulation of deregulated pathways influences protein aggregation in HD, and has implications for progression and prognosis. This article has an associated First Person interview with the first author of the paper.

Gene Expression Analysis Reveals Prognostic Biomarkers of the Tyrosine Metabolism Reprogramming Pathway for Prostate Cancer

Background

Tyrosine metabolism pathway-related genes were related to prostate cancer progression, which may be used as potential prognostic markers.

Aims

To dissect the dysregulation of tyrosine metabolism in prostate cancer and build a prognostic signature based on tyrosine metabolism-related genes for prostate cancer. Materials and Method. Cross-platform gene expression data of prostate cancer cohorts were collected from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Based on the expression of tyrosine metabolism-related enzymes (TMREs), an unsupervised consensus clustering method was used to classify prostate cancer patients into different molecular subtypes. We employed the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to evaluate prognostic characteristics based on TMREs to obtain a prognostic effect. The nomogram model was established and used to synthesize molecular subtypes, prognostic characteristics, and clinicopathological features. Kaplan–Meier plots and logrank analysis were used to clarify survival differences between subtypes.

Results

Based on the hierarchical clustering method and the expression profiles of TMREs, prostate cancer samples were assigned into two subgroups (S1, subgroup 1; S2, subgroup 2), and the Kaplan–Meier plot and logrank analysis showed distinct survival outcomes between S1 and S2 subgroups. We further established a four-gene-based prognostic signature, and both in-group testing dataset and out-group testing dataset indicated the robustness of this model. By combining the four gene-based signatures and clinicopathological features, the nomogram model achieved better survival outcomes than any single classifier. Interestingly, we found that immune-related pathways were significantly concentrated on S1-upregulated genes, and the abundance of memory B cells, CD4+ resting memory T cells, M0 macrophages, resting dendritic cells, and resting mast cells were significantly different between S1 and S2 subgroups.

Conclusions

Our results indicate the prognostic value of genes related to tyrosine metabolism in prostate cancer and provide inspiration for treatment and prevention strategies.

MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data

BACKGROUND

Modern mass spectrometry has revolutionized the detection and analysis of metabolites but likewise, let the data skyrocket with repositories for metabolomics data filling up with thousands of datasets. While there are many software tools for the analysis of individual experiments with a few to dozens of chromatograms, we see a demand for a contemporary software solution capable of processing and analyzing hundreds or even thousands of experiments in an integrative manner with standardized workflows.

RESULTS

Here, we introduce MetHoS as an automated web-based software platform for the processing, storage and analysis of great amounts of mass spectrometry-based metabolomics data sets originating from different metabolomics studies. MetHoS is based on Big Data frameworks to enable parallel processing, distributed storage and distributed analysis of even larger data sets across clusters of computers in a highly scalable manner. It has been designed to allow the processing and analysis of any amount of experiments and samples in an integrative manner. In order to demonstrate the capabilities of MetHoS, thousands of experiments were downloaded from the MetaboLights database and used to perform a large-scale processing, storage and statistical analysis in a proof-of-concept study.

CONCLUSIONS

MetHoS is suitable for large-scale processing, storage and analysis of metabolomics data aiming at untargeted metabolomic analyses. It is freely available at: https://methos.cebitec.uni-bielefeld.de/ . Users interested in analyzing their own data are encouraged to apply for an account.

Metabolomics in degenerative brain diseases

Among the most studied diseases that affect the central nervous system are Parkinson's, Alzheimer's, and Huntington's diseases, but the lack of effective biomarkers, accurate diagnosis, and precise treatment for each of them is currently an issue. Due to the contribution of biomarkers in supporting diagnosis, many recent efforts have focused on their identification and validation at the beginning or during the progression of the mental illness. Metabolome reveals the metabolic processes that result from protein activities under the guided gene expression and environmental factors, either in healthy or pathological conditions. In this context, metabolomics has proven to be a valuable approach. Currently, magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the most commonly used bioanalytical techniques for metabolomics. MS-assisted profiling is considered the most versatile technique, and the NMR is the most reproductive. However, each one of them has its drawbacks. In this review, we summarized several alterations in metabolites that have been reported for these three classic brain diseases using MS and NMR-based research, which might suggest some possible biomarkers to support the diagnosis and/or new targets for their treatment.

Metabolic drift in the aging nervous system is reflected in human cerebrospinal fluid

Chronic diseases affecting the central nervous system (CNS) like Alzheimer's or Parkinson's disease typically develop with advanced chronological age. Yet, aging at the metabolic level has been explored only sporadically in humans using biofluids in close proximity to the CNS such as the cerebrospinal fluid (CSF). We have used an untargeted liquid chromatography high-resolution mass spectrometry (LC-HRMS) based metabolomics approach to measure the levels of metabolites in the CSF of non-neurological control subjects in the age of 20 up to 74. Using a random forest-based feature selection strategy, we extracted 69 features that were strongly related to age (p_age < 0.001, r_age = 0.762, R²_{Boruta age} = 0.764). Combining an in-house library of known substances with in silico chemical classification and functional semantic annotation we successfully assigned putative annotations to 59 out of the 69 CSF metabolites. We found alterations in metabolites related to the Cytochrome P450 system, perturbations in the tryptophan and kynurenine pathways, metabolites associated with cellular energy (NAD+, ADP), mitochondrial and ribosomal metabolisms, neurological dysfunction, and an increase of adverse microbial metabolites. Taken together our results point at a key role for metabolites found in CSF related to the Cytochrome P450 system as most often associated with metabolic aging.

Emerging role of metabolomics in protein conformational disorders

Introduction: Metabolomics focuses on interactions among different metabolites associated with various cellular functions in cells, tissues, and organs. In recent years, metabolomics has emerged as a powerful tool to identify perturbed metabolites, pathways influenced by the environment, for protein conformational diseases (PCDs) and also offers wide clinical application.Area Covered: This review provides a brief overview of recent advances in metabolomics as applied to identify metabolic variations in PCDs, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, prion disease, and cardiac amyloidosis. The 'PubMed' and 'Google Scholar' database search methods have been used to screen the published reports with key search terms: metabolomics, biomarkers, and protein conformational disorders.Expert opinion: Metabolomics is the large-scale study of metabolites and is deemed to overwhelm other omics. It plays a crucial role in finding variations in diseases due to protein conformational changes. However, many PCDs are yet to be identified. Metabolomics is still an emerging field; there is a need for new high-resolution analytical techniques and more studies need to be carried out to generate new information.

Andrographolide Against Lung Cancer-New Pharmacological Insights Based on High-Throughput Metabolomics Analysis Combined with Network Pharmacology

Andrographolide (Andro) has known to treat various illnesses such as colds, diarrhea, fever and infectious diseases. However, the effect mechanism of Andro is still unclear. Therefore, we used high-throughput metabolomics analysis to discover biomarkers, metabolic profiles and pathways to reveal the pharmacological action and effective mechanism of Andro against lung cancer. The metabolic effects of Andro on lung cancer animal was explored by ultra-performance liquid chromatography-triple-time of flight/mass spectrometry (UPLC-TOF/MS) analysis. Our results showed that Andro exhibited significant protective effects against lung cancer. Compared with control group, a total of 25 metabolites biomarkers was identified in urine of model animals, which 18 of them were regulated toward the normal direction after Andro treatment, and network pharmacology analysis showed that they were related with 570 proteins. Biological pathways analysis showed that the 11 metabolism pathways were regulated by Andro treatment in lung cancer mouse, and amino acid metabolism and arachidonic acid metabolism have great potential as target pathways for Andro against lung cancer. It revealed that high-throughput metabolomics combined with network pharmacology analysis provides deeply insight into the therapeutic mechanisms of natural product for promoting medicine development and disease treatment.

Clinical Insights into Mitochondrial Neurodevelopmental and Neurodegenerative Disorders: Their Biosignatures from Mass Spectrometry-Based Metabolomics

Mitochondria are dynamic multitask organelles that function as hubs for many metabolic pathways. They produce most ATP via the oxidative phosphorylation pathway, a critical pathway that the brain relies on its energy need associated with its numerous functions, such as synaptic homeostasis and plasticity. Therefore, mitochondrial dysfunction is a prevalent pathological hallmark of many neurodevelopmental and neurodegenerative disorders resulting in altered neurometabolic coupling. With the advent of mass spectrometry (MS) technology, MS-based metabolomics provides an emerging mechanistic understanding of their global and dynamic metabolic signatures. In this review, we discuss the pathogenetic causes of mitochondrial metabolic disorders and the recent MS-based metabolomic advances on their metabolomic remodeling. We conclude by exploring the MS-based metabolomic functional insights into their biosignatures to improve diagnostic platforms, stratify patients, and design novel targeted therapeutic strategies.

Metabolomics of Cerebrospinal Fluid from Healthy Subjects Reveal Metabolites Associated with Ageing

To increase our understanding of age-related diseases affecting the central nervous system (CNS) it is important to understand the molecular processes of biological ageing. Metabolomics of cerebrospinal fluid (CSF) is a promising methodology to increase our understanding of naturally occurring processes of ageing of the brain and CNS that could be reflected in CSF. In the present study the CSF metabolomes of healthy subjects aged 30-74 years (n = 23) were studied using liquid chromatography high-resolution mass spectrometry (LC-HRMS), and investigated in relation to age. Ten metabolites were identified with high confidence as significantly associated with ageing, eight with increasing levels with ageing: isoleucine, acetylcarnitine, pipecolate, methionine, glutarylcarnitine, 5-hydroxytryptophan, ketoleucine, and hippurate; and two decreasing with ageing: methylthioadenosine and 3-methyladenine. To our knowledge, this is the first time the CSF metabolomes of healthy subjects are assessed in relation to ageing. The present study contributes to the field of ageing metabolomics by presenting a number of metabolites present in CSF with potential relevance for ageing and the results motivate further studies.

Proenkephalin Decreases in Cerebrospinal Fluid with Symptom Progression of Huntington's Disease

OBJECTIVE

Identifying molecular changes that contribute to the onset and progression of Huntington's disease (HD) is of importance for the development and evaluation of potential therapies.

METHODS

We conducted an unbiased mass-spectrometry proteomic analysis on the cerebrospinal fluid of 12 manifest HD patients (ManHD), 13 pre-manifest (preHD), and 38 controls. A biologically plausible and significant possible biomarker was validated in samples from a separate cohort of patients and controls consisting of 23 ManHD patients and 23 controls.

RESULTS

In ManHD compared to preHD, 10 proteins were downregulated and 43 upregulated. Decreased levels of proenkephalin (PENK) and transthyretin were closely linked to HD symptom severity, whereas levels of 15 upregulated proteins were associated with symptom severity. The decreased PENK levels were replicated in the separate cohort where absolute quantitation was performed.

CONCLUSIONS

We hypothesize that declining PENK levels reflect the degeneration of medium spiny neurons (MSNs) that produce PENK and that assays for PENK may serve as a surrogate marker for the state of MSNs in HD. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Unveiling prognostics biomarkers of tyrosine metabolism reprogramming in liver cancer by cross-platform gene expression analyses

Tyrosine is mainly degraded in the liver by a series of enzymatic reactions. Abnormal expression of the tyrosine catabolic enzyme tyrosine aminotransferase (TAT) has been reported in patients with hepatocellular carcinoma (HCC). Despite this, aberration in tyrosine metabolism has not been investigated in cancer development. In this work, we conduct comprehensive cross-platform study to obtain foundation for discoveries of potential therapeutics and preventative biomarkers of HCC. We explore data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine and Kaplan Meier plotter (KM plotter) and performed integrated analyses to evaluate the clinical significance and prognostic values of the tyrosine catabolic genes in HCC. We find that five tyrosine catabolic enzymes are downregulated in HCC compared to normal liver at mRNA and protein level. Moreover, low expression of these enzymes correlates with poorer survival in patients with HCC. Notably, we identify pathways and upstream regulators that might involve in tyrosine catabolic reprogramming and further drive HCC development. In total, our results underscore tyrosine metabolism alteration in HCC and lay foundation for incorporating these pathway components in therapeutics and preventative strategies.

Targeted metabolomics of CSF in healthy individuals and patients with secondary progressive multiple sclerosis using high-resolution mass spectrometry

INTRODUCTION

Standardized commercial kits enable targeted metabolomics analysis and may thus provide an attractive complement to the more explorative approaches. The kits are typically developed for triple quadrupole mass spectrometers using serum and plasma.

OBJECTIVES

Here we measure the concentrations of preselected metabolites in cerebrospinal fluid (CSF) using a kit developed for high-resolution mass spectrometry (HRMS). Secondarily, the study aimed to investigate metabolite alterations in patients with secondary progressive multiple sclerosis (SPMS) compared to controls.

METHODS

We performed targeted metabolomics in human CSF on twelve SPMS patients and twelve age and sex-matched healthy controls using the Absolute IDQ-p400 kit (Biocrates Life Sciences AG) developed for HRMS. The extracts were analysed using two methods; liquid chromatography-mass spectrometry (LC-HRMS) and flow injection analysis-MS (FIA-HRMS).

RESULTS

Out of 408 targeted metabolites, 196 (48%) were detected above limit of detection and 35 were absolutely quantified. Metabolites analyzed using LC-HRMS had a median coefficient of variation (CV) of 3% and 2.5% between reinjections the same day and after prolonged storage, respectively. The corresponding results for the FIA-HRMS were a median CV of 27% and 21%, respectively. We found significantly (p < 0.05) elevated levels of glycine, asymmetric dimethylarginine (ADMA), glycerophospholipid PC-O (34:0) and sum of hexoses in SPMS patients compared to controls.

CONCLUSION

The Absolute IDQ-p400 kit could successfully be used for quantifying targeted metabolites in the CSF. Metabolites quantified using LC-HRMS showed superior reproducibility compared to FIA-HRMS.