Biomolecules

2024

Jump to: 2023, 2022, 2021, 2020

21 pages, 1441 KiB

Open AccessReview

NMR Spectroscopy in Diagnosis and Monitoring of Methylmalonic and Propionic Acidemias

by Calin Deleanu and Alina Nicolescu

Biomolecules 2024, 14(5), 528; https://doi.org/10.3390/biom14050528 - 28 Apr 2024

Viewed by 1308

Abstract

Although both localized nuclear magnetic resonance spectroscopy (MRS) and non-localized nuclear magnetic resonance spectroscopy (NMR) generate the same information, i.e., spectra generated by various groups from the structure of metabolites, they are rarely employed in the same study or by the same research [...] Read more.

Although both localized nuclear magnetic resonance spectroscopy (MRS) and non-localized nuclear magnetic resonance spectroscopy (NMR) generate the same information, i.e., spectra generated by various groups from the structure of metabolites, they are rarely employed in the same study or by the same research group. As our review reveals, these techniques have never been applied in the same study of methylmalonic acidemia (MMA), propionic acidemia (PA) or vitamin B₁₂ deficiency patients. On the other hand, MRS and NMR provide complementary information which is very valuable in the assessment of the severity of disease and efficiency of its treatment. Thus, MRS provides intracellular metabolic information from localized regions of the brain, while NMR provides extracellular metabolic information from biological fluids like urine, blood or cerebrospinal fluid. This paper presents an up-to-date review of the NMR and MRS studies reported to date for methylmalonic and propionic acidemias. Vitamin B₁₂ deficiency, although in most of its cases not inherited, shares similarities in its metabolic effects with MMA and it is also covered in this review. Full article

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2023

Jump to: 2024, 2022, 2021, 2020

15 pages, 3546 KiB

Open AccessArticle

Emergent Functional Organization of Gut Microbiomes in Health and Diseases

by Marcello Seppi, Jacopo Pasqualini, Sonia Facchin, Edoardo Vincenzo Savarino and Samir Suweis

Biomolecules 2024, 14(1), 5; https://doi.org/10.3390/biom14010005 - 20 Dec 2023

Cited by 2 | Viewed by 1656

Abstract

Continuous and significant progress in sequencing technologies and bioinformatics pipelines has revolutionized our comprehension of microbial communities, especially for human microbiomes. However, most studies have focused on studying the taxonomic composition of the microbiomes and we are still not able to characterize dysbiosis [...] Read more.

Continuous and significant progress in sequencing technologies and bioinformatics pipelines has revolutionized our comprehension of microbial communities, especially for human microbiomes. However, most studies have focused on studying the taxonomic composition of the microbiomes and we are still not able to characterize dysbiosis and unveil the underlying ecological consequences. This study explores the emergent organization of functional abundances and correlations of gut microbiomes in health and disease. Leveraging metagenomic sequences, taxonomic and functional tables are constructed, enabling comparative analysis. First, we show that emergent taxonomic and functional patterns are not useful to characterize dysbiosis. Then, through differential abundance analyses applied to functions, we reveal distinct functional compositions in healthy versus unhealthy microbiomes. In addition, we inquire into the functional correlation structure, revealing significant differences between the healthy and unhealthy groups, which may significantly contribute to understanding dysbiosis. Our study demonstrates that scrutinizing the functional organization in the microbiome provides novel insights into the underlying state of the microbiome. The shared data structure underlying the functional and taxonomic compositions allows for a comprehensive macroecological examination. Our findings not only shed light on dysbiosis, but also underscore the importance of studying functional interrelationships for a nuanced understanding of the dynamics of the microbial community. This research proposes a novel approach, bridging the gap between microbial ecology and functional analyses, promising a deeper understanding of the intricate world of the gut microbiota and its implications for human health. Full article

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13 pages, 1469 KiB

Open AccessEditor’s ChoiceArticle

Anti-TNF Biologicals Enhance the Anti-Inflammatory Properties of IgG N-Glycome in Crohn’s Disease

by Maja Hanić, Frano Vučković, Helena Deriš, Claire Bewshea, Simeng Lin, James R. Goodhand, Tariq Ahmad, Irena Trbojević-Akmačić, Nicholas A. Kennedy, Gordan Lauc and PANTS Consortium

Biomolecules 2023, 13(6), 954; https://doi.org/10.3390/biom13060954 - 7 Jun 2023

Cited by 6 | Viewed by 2028

Abstract

Crohn’s disease (CD) is a chronic inflammation of the digestive tract that significantly impairs patients’ quality of life and well-being. Anti-TNF biologicals revolutionised the treatment of CD, yet many patients do not adequately respond to such therapy. Previous studies have demonstrated a pro-inflammatory [...] Read more.

Crohn’s disease (CD) is a chronic inflammation of the digestive tract that significantly impairs patients’ quality of life and well-being. Anti-TNF biologicals revolutionised the treatment of CD, yet many patients do not adequately respond to such therapy. Previous studies have demonstrated a pro-inflammatory pattern in the composition of CD patients’ immunoglobulin G (IgG) N-glycome compared to healthy individuals. Here, we utilised the high-throughput UHPLC method for N-glycan analysis to explore the longitudinal effect of the anti-TNF drugs infliximab and adalimumab on N-glycome composition of total serum IgG in 198 patients, as well as the predictive potential of IgG N-glycans at baseline to detect primary non-responders to anti-TNF therapy in 1315 patients. We discovered a significant decrease in IgG agalactosylation and an increase in monogalactosylation, digalactosylation and sialylation during the 14 weeks of anti-TNF treatment, regardless of therapy response, all of which suggested a diminished inflammatory environment in CD patients treated with anti-TNF therapy. Furthermore, we observed that IgG N-glycome might contain certain information regarding the anti-TNF therapy outcome before initiating the treatment. However, it is impossible to predict future primary non-responders to anti-TNF therapy based solely on IgG N-glycome composition at baseline. Full article

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19 pages, 3723 KiB

Open AccessArticle

Space Environment Impacts Homeostasis: Exposure to Spaceflight Alters Mammary Gland Transportome Genes

by Osman V. Patel, Charlyn Partridge and Karen Plaut

Biomolecules 2023, 13(5), 872; https://doi.org/10.3390/biom13050872 - 22 May 2023

Viewed by 1974

Abstract

Membrane transporters and ion channels that play an indispensable role in metabolite trafficking have evolved to operate in Earth’s gravity. Dysregulation of the transportome expression profile at normogravity not only affects homeostasis along with drug uptake and distribution but also plays a key [...] Read more.

Membrane transporters and ion channels that play an indispensable role in metabolite trafficking have evolved to operate in Earth’s gravity. Dysregulation of the transportome expression profile at normogravity not only affects homeostasis along with drug uptake and distribution but also plays a key role in the pathogenesis of diverse localized to systemic diseases including cancer. The profound physiological and biochemical perturbations experienced by astronauts during space expeditions are well-documented. However, there is a paucity of information on the effect of the space environment on the transportome profile at an organ level. Thus, the goal of this study was to analyze the effect of spaceflight on ion channels and membrane substrate transporter genes in the periparturient rat mammary gland. Comparative gene expression analysis revealed an upregulation (p < 0.01) of amino acid, Ca²⁺, K⁺, Na⁺, Zn²⁺, Cl⁻, PO₄³⁻, glucose, citrate, pyruvate, succinate, cholesterol, and water transporter genes in rats exposed to spaceflight. Genes associated with the trafficking of proton-coupled amino acids, Mg²⁺, Fe²⁺, voltage-gated K⁺-Na⁺, cation-coupled chloride, as well as Na⁺/Ca²⁺ and ATP-Mg/Pi exchangers were suppressed (p < 0.01) in these spaceflight-exposed rats. These findings suggest that an altered transportome profile contributes to the metabolic modulations observed in the rats exposed to the space environment. Full article

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27 pages, 1949 KiB

Open AccessFeature PaperReview

Orchestration of Mitochondrial Function and Remodeling by Post-Translational Modifications Provide Insight into Mechanisms of Viral Infection

by Ji Woo Park, Matthew D. Tyl and Ileana M. Cristea

Biomolecules 2023, 13(5), 869; https://doi.org/10.3390/biom13050869 - 20 May 2023

Cited by 1 | Viewed by 2597

Abstract

The regulation of mitochondria structure and function is at the core of numerous viral infections. Acting in support of the host or of virus replication, mitochondria regulation facilitates control of energy metabolism, apoptosis, and immune signaling. Accumulating studies have pointed to post-translational modification [...] Read more.

The regulation of mitochondria structure and function is at the core of numerous viral infections. Acting in support of the host or of virus replication, mitochondria regulation facilitates control of energy metabolism, apoptosis, and immune signaling. Accumulating studies have pointed to post-translational modification (PTM) of mitochondrial proteins as a critical component of such regulatory mechanisms. Mitochondrial PTMs have been implicated in the pathology of several diseases and emerging evidence is starting to highlight essential roles in the context of viral infections. Here, we provide an overview of the growing arsenal of PTMs decorating mitochondrial proteins and their possible contribution to the infection-induced modulation of bioenergetics, apoptosis, and immune responses. We further consider links between PTM changes and mitochondrial structure remodeling, as well as the enzymatic and non-enzymatic mechanisms underlying mitochondrial PTM regulation. Finally, we highlight some of the methods, including mass spectrometry-based analyses, available for the identification, prioritization, and mechanistic interrogation of PTMs. Full article

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16 pages, 1289 KiB

Open AccessArticle

The Effects of Two Kinds of Dietary Interventions on Serum Metabolic Profiles in Haemodialysis Patients

by Lucyna Kozlowska, Karolina Jagiello, Krzesimir Ciura, Anita Sosnowska, Rafal Zwiech, Zbigniew Zbrog, Wojciech Wasowicz and Jolanta Gromadzinska

Biomolecules 2023, 13(5), 854; https://doi.org/10.3390/biom13050854 - 18 May 2023

Viewed by 2226

Abstract

The goal of this study was to evaluate the effects of two kinds of 24-week dietary interventions in haemodialysis patients, a traditional nutritional intervention without a meal before dialysis (HG1) and implementation of a nutritional intervention with a meal served just before dialysis [...] Read more.

The goal of this study was to evaluate the effects of two kinds of 24-week dietary interventions in haemodialysis patients, a traditional nutritional intervention without a meal before dialysis (HG1) and implementation of a nutritional intervention with a meal served just before dialysis (HG2), in terms of analysing the differences in the serum metabolic profiles and finding biomarkers of dietary efficacy. These studies were performed in two homogenous groups of patients (n = 35 in both groups). Among the metabolites with the highest statistical significance between HG1 and HG2 after the end of the study, 21 substances were putatively annotated, which had potential significance in both of the most relevant metabolic pathways and those related to diet. After the 24 weeks of the dietary intervention, the main differences between the metabolomic profiles in the HG2 vs. HG1 groups were related to the higher signal intensities from amino acid metabolites: indole-3-carboxaldehyde, 5-(hydroxymethyl-2-furoyl)glycine, homocitrulline, 4-(glutamylamino)butanoate, tryptophol, gamma-glutamylthreonine, and isovalerylglycine. These metabolites are intermediates in the metabolic pathways of the necessary amino acids (Trp, Tyr, Phe, Leu, Ile, Val, Liz, and amino acids of the urea cycle) and are also diet-related intermediates (4-guanidinobutanoic acid, indole-3-carboxyaldehyde, homocitrulline, and isovalerylglycine). Full article

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14 pages, 2231 KiB

Open AccessArticle

The Association between Infant Colic and the Multi-Omic Composition of Human Milk

by Desirae Chandran, Kaitlyn Warren, Daniel McKeone and Steven D. Hicks

Biomolecules 2023, 13(3), 559; https://doi.org/10.3390/biom13030559 - 18 Mar 2023

Cited by 4 | Viewed by 3246

Abstract

Infant colic is a common condition with unclear biologic underpinnings and limited treatment options. We hypothesized that complex molecular networks within human milk (i.e., microbes, micro-ribonucleic acids (miRNAs), cytokines) would contribute to colic risk, while controlling for medical, social, and nutritional variables. This [...] Read more.

Infant colic is a common condition with unclear biologic underpinnings and limited treatment options. We hypothesized that complex molecular networks within human milk (i.e., microbes, micro-ribonucleic acids (miRNAs), cytokines) would contribute to colic risk, while controlling for medical, social, and nutritional variables. This hypothesis was tested in a cohort of 182 breastfed infants, assessed with a modified Infant Colic Scale at 1 month. RNA sequencing was used to interrogate microbial and miRNA features. Luminex assays were used to measure growth factors and cytokines. Milk from mothers of infants with colic (n = 28) displayed higher levels of Staphylococcus (adj. p = 0.038, d = 0.30), miR-224-3p (adj. p = 0.023, d = 0.33), miR-125b-5p (adj. p = 0.028, d = 0.29), let-7a-5p (adj. p = 0.028, d = 0.27), and miR-205-5p (adj. p = 0.029, d = 0.26) compared to milk from non-colic mother–infant dyads (n = 154). Colic symptom severity was directly associated with milk hepatocyte growth factor levels (R = 0.21, p = 0.025). A regression model involving let-7a-5p, miR-29a-3p, and Lactobacillus accurately modeled colic risk (X² = 16.7, p = 0.001). Molecular factors within human milk may impact colic risk, and provide support for a dysbiotic/inflammatory model of colic pathophysiology. Full article

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33 pages, 778 KiB

Open AccessArticle

An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images

by Lisa M. Duff, Andrew F. Scarsbrook, Nishant Ravikumar, Russell Frood, Gijs D. van Praagh, Sarah L. Mackie, Marc A. Bailey, Jason M. Tarkin, Justin C. Mason, Kornelis S. M. van der Geest, Riemer H. J. A. Slart, Ann W. Morgan and Charalampos Tsoumpas

Biomolecules 2023, 13(2), 343; https://doi.org/10.3390/biom13020343 - 9 Feb 2023

Cited by 9 | Viewed by 2850

Abstract

The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural [...] Read more.

The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts. Radiomic features (RF), including SUV metrics, were extracted from the segmented data and harmonized. Three radiomic fingerprints were constructed: A—RFs with high diagnostic utility removing highly correlated RFs; B used principal component analysis (PCA); C—Random Forest intrinsic feature selection. The diagnostic utility was evaluated with accuracy and area under the receiver operating characteristic curve (AUC). Several RFs and Fingerprints had high AUC values (AUC > 0.8), confirmed by balanced accuracy, across training, test and external validation datasets. Good diagnostic performance achieved across several multi-centre datasets suggests that a radiomic pipeline can be generalizable. These findings could be used to build an automated clinical decision tool to facilitate objective and standardized assessment regardless of observer experience. Full article

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18 pages, 5522 KiB

Open AccessFeature PaperArticle

Identification of Lipid Biomarkers for Chronic Joint Pain Associated with Different Joint Diseases

by Spiro Khoury, Jenny Colas, Véronique Breuil, Eva Kosek, Aisha S. Ahmed, Camilla I. Svensson, Fabien Marchand, Emmanuel Deval and Thierry Ferreira

Biomolecules 2023, 13(2), 342; https://doi.org/10.3390/biom13020342 - 9 Feb 2023

Cited by 4 | Viewed by 2171

Abstract

Lipids, especially lysophosphatidylcholine LPC16:0, have been shown to be involved in chronic joint pain through the activation of acid-sensing ion channels (ASIC3). The aim of the present study was to investigate the lipid contents of the synovial fluids from controls and patients suffering [...] Read more.

Lipids, especially lysophosphatidylcholine LPC16:0, have been shown to be involved in chronic joint pain through the activation of acid-sensing ion channels (ASIC3). The aim of the present study was to investigate the lipid contents of the synovial fluids from controls and patients suffering from chronic joint pain in order to identify characteristic lipid signatures associated with specific joint diseases. For this purpose, lipids were extracted from the synovial fluids and analyzed by mass spectrometry. Lipidomic analyses identified certain choline-containing lipid classes and molecular species as biomarkers of chronic joint pain, regardless of the pathology, with significantly higher levels detected in the patient samples. Moreover, correlations were observed between certain lipid levels and the type of joint pathologies. Interestingly, LPC16:0 levels appeared to correlate with the metabolic status of patients while other choline-containing lipids were more specifically associated with the inflammatory state. Overall, these data point at selective lipid species in synovial fluid as being strong predictors of specific joint pathologies which could help in the selection of the most adapted treatment. Full article

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14 pages, 2704 KiB

Open AccessEditor’s ChoiceArticle

Metabolic Pathway Analysis: Advantages and Pitfalls for the Functional Interpretation of Metabolomics and Lipidomics Data

by Sofia Tsouka and Mojgan Masoodi

Biomolecules 2023, 13(2), 244; https://doi.org/10.3390/biom13020244 - 27 Jan 2023

Cited by 9 | Viewed by 5126

Abstract

Over the past decades, pathway analysis has become one of the most commonly used approaches for the functional interpretation of metabolomics data. Although the approach is widely used, it is not well standardized and the impact of different methodologies on the functional outcome [...] Read more.

Over the past decades, pathway analysis has become one of the most commonly used approaches for the functional interpretation of metabolomics data. Although the approach is widely used, it is not well standardized and the impact of different methodologies on the functional outcome is not well understood. Using four publicly available datasets, we investigated two main aspects of topological pathway analysis, namely the consideration of non-human native enzymatic reactions (e.g., from microbiota) and the interconnectivity of individual pathways. The exclusion of non-human native reactions led to detached and poorly represented reaction networks and to loss of information. The consideration of connectivity between pathways led to better emphasis of certain central metabolites in the network; however, it occasionally overemphasized the hub compounds. We proposed and examined a penalization scheme to diminish the effect of such compounds in the pathway evaluation. In order to compare and assess the results between different methodologies, we also performed over-representation analysis of the same datasets. We believe that our findings will raise awareness on both the capabilities and shortcomings of the currently used pathway analysis practices in metabolomics. Additionally, it will provide insights on various methodologies and strategies that should be considered for the analysis and interpretation of metabolomics data. Full article

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2022

Jump to: 2024, 2023, 2021, 2020

17 pages, 2831 KiB

Open AccessArticle

Combining Dietary Intervention with Metformin Treatment Enhances Non-Alcoholic Steatohepatitis Remission in Mice Fed a High-Fat High-Sucrose Diet

by Gerard Baiges-Gaya, Elisabet Rodríguez-Tomàs, Helena Castañé, Andrea Jiménez-Franco, Núria Amigó, Jordi Camps and Jorge Joven

Biomolecules 2022, 12(12), 1787; https://doi.org/10.3390/biom12121787 - 30 Nov 2022

Viewed by 1775

Abstract

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are serious health concerns for which lifestyle interventions are the only effective first-line treatment. Dietary interventions are effective in body weight reduction, but not in improving insulin sensitivity and hepatic lipid mobilization. Conversely, metformin [...] Read more.

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are serious health concerns for which lifestyle interventions are the only effective first-line treatment. Dietary interventions are effective in body weight reduction, but not in improving insulin sensitivity and hepatic lipid mobilization. Conversely, metformin increases insulin sensitivity and promotes the inhibition of de novo hepatic lipogenesis. In this study, we evaluated the metformin effectiveness in NASH prevention and treatment, when combined with dietary intervention in male mice fed a high-fat high-sucrose diet (HFHSD). Eighty 5-week-old C57BL/6J male mice were fed a chow or HFHSD diet and sacrificed at 20 or 40 weeks. The HFHSD-fed mice developed NASH after 20 weeks. Lipoprotein and lipidomic analyses showed that the changes associated with diet were not prevented by metformin administration. HFHSD-fed mice subject to dietary intervention combined with metformin showed a 19.6% body weight reduction compared to 9.8% in those mice subjected to dietary intervention alone. Lower hepatic steatosis scores were induced. We conclude that metformin should not be considered a preventive option for NAFLD, but it is effective in the treatment of this disorder when combined with dietary intervention. Full article

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24 pages, 2658 KiB

Open AccessArticle

Hub Genes in Non-Small Cell Lung Cancer Regulatory Networks

by Qing Ye and Nancy Lan Guo

Biomolecules 2022, 12(12), 1782; https://doi.org/10.3390/biom12121782 - 29 Nov 2022

Cited by 3 | Viewed by 2714

Abstract

There are currently no accurate biomarkers for optimal treatment selection in early-stage non-small cell lung cancer (NSCLC). Novel therapeutic targets are needed to improve NSCLC survival outcomes. This study systematically evaluated the association between genome-scale regulatory network centralities and NSCLC tumorigenesis, proliferation, and [...] Read more.

There are currently no accurate biomarkers for optimal treatment selection in early-stage non-small cell lung cancer (NSCLC). Novel therapeutic targets are needed to improve NSCLC survival outcomes. This study systematically evaluated the association between genome-scale regulatory network centralities and NSCLC tumorigenesis, proliferation, and survival in early-stage NSCLC patients. Boolean implication networks were used to construct multimodal networks using patient DNA copy number variation, mRNA, and protein expression profiles. T statistics of differential gene/protein expression in tumors versus non-cancerous adjacent tissues, dependency scores in in vitro CRISPR-Cas9/RNA interference (RNAi) screening of human NSCLC cell lines, and hazard ratios in univariate Cox modeling of the Cancer Genome Atlas (TCGA) NSCLC patients were correlated with graph theory centrality metrics. Hub genes in multi-omics networks involving gene/protein expression were associated with oncogenic, proliferative potentials and poor patient survival outcomes (p < 0.05, Pearson’s correlation). Immunotherapy targets PD1, PDL1, CTLA4, and CD27 were ranked as top hub genes within the 10th percentile in most constructed multi-omics networks. BUB3, DNM1L, EIF2S1, KPNB1, NMT1, PGAM1, and STRAP were discovered as important hub genes in NSCLC proliferation with oncogenic potential. These results support the importance of hub genes in NSCLC tumorigenesis, proliferation, and prognosis, with implications in prioritizing therapeutic targets to improve patient survival outcomes. Full article

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12 pages, 2193 KiB

Open AccessArticle

Metabolomic Profile of Indonesian Betel Quids

by Pangzhen Zhang, Elizabeth Fitriana Sari, Michael J. McCullough and Nicola Cirillo

Biomolecules 2022, 12(10), 1469; https://doi.org/10.3390/biom12101469 - 13 Oct 2022

Cited by 13 | Viewed by 3096

Abstract

Consumption of areca nut alone, or in the form of betel quid (BQ), has negative health effects and is carcinogenic to humans. Indonesia is one of the largest producers of areca nuts worldwide, yet little is known about the biomolecular composition of Indonesian [...] Read more.

Consumption of areca nut alone, or in the form of betel quid (BQ), has negative health effects and is carcinogenic to humans. Indonesia is one of the largest producers of areca nuts worldwide, yet little is known about the biomolecular composition of Indonesian areca nuts and BQs. We have recently shown that phenolic and alkaloid content of Indonesian BQs exhibits distinct geographical differences. Here, we profiled for the first time the metabolomics of BQ constituents from four regions of Indonesia using non-targeted gas chromatography–mass spectrometry (GC–MS) analysis. In addition to well-known alkaloids, the analysis of small-molecule profiles tentatively identified 92 phytochemicals in BQ. These included mainly benzenoids and terpenes, as well as acids, aldehydes, alcohols, and esters. Safrole, a potentially genotoxic benzenoid, was found abundantly in betel (Piper betle) inflorescence from West Papua and was not detected in areca nut samples from any Indonesian region except West Papua. Terpenes were mostly detected in betel leaves and inflorescence/stem. Areca nut, husk, betel leaf, the inflorescence stem, and BQ mixture expressed distinctive metabolite patterns, and a significant variation in the content and concentration of metabolites was found across different geographical regions. In summary, this was the first metabolomic study of BQs using GC–MS. The results demonstrate that the molecular constituents of BQs vary geographically and suggest that the differential disease-inducing capacity of BQs may reflect their distinct chemical composition. Full article

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21 pages, 2286 KiB

Open AccessArticle

Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets

by Oscar Alcazar, Mitsunori Ogihara, Gang Ren, Peter Buchwald and Midhat H. Abdulreda

Biomolecules 2022, 12(10), 1444; https://doi.org/10.3390/biom12101444 - 9 Oct 2022

Cited by 4 | Viewed by 2501

Abstract

Background: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed [...] Read more.

Background: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges. Methods: Here, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies. Results: Using proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using Ingenuity Pathway Analysis (IPA) software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the “golden ratio” of 38.2%:61.8%. Specifically, the Canonical Pathway and Diseases and Functions modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The Biomarker Prediction module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis. Conclusions: These preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited. Full article

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27 pages, 26874 KiB

Open AccessEditor’s ChoiceArticle

Phosphatidylethanolamine N-Methyltransferase Knockout Modulates Metabolic Changes in Aging Mice

by Qishun Zhou, Fangrong Zhang, Jakob Kerbl-Knapp, Melanie Korbelius, Katharina Barbara Kuentzel, Nemanja Vujić, Alena Akhmetshina, Gerd Hörl, Margret Paar, Ernst Steyrer, Dagmar Kratky and Tobias Madl

Biomolecules 2022, 12(9), 1270; https://doi.org/10.3390/biom12091270 - 9 Sep 2022

Cited by 7 | Viewed by 3246

Abstract

Phospholipid metabolism, including phosphatidylcholine (PC) biosynthesis, is crucial for various biological functions and is associated with longevity. Phosphatidylethanolamine N-methyltransferase (PEMT) is a protein that catalyzes the biosynthesis of PC, the levels of which change in various organs such as the brain and [...] Read more.

Phospholipid metabolism, including phosphatidylcholine (PC) biosynthesis, is crucial for various biological functions and is associated with longevity. Phosphatidylethanolamine N-methyltransferase (PEMT) is a protein that catalyzes the biosynthesis of PC, the levels of which change in various organs such as the brain and kidneys during aging. However, the role of PEMT for systemic PC supply is not fully understood. To address how PEMT affects aging-associated energy metabolism in tissues responsible for nutrient absorption, lipid storage, and energy consumption, we employed NMR-based metabolomics to study the liver, plasma, intestine (duodenum, jejunum, and ileum), brown/white adipose tissues (BAT and WAT), and skeletal muscle of young (9–10 weeks) and old (91–132 weeks) wild-type (WT) and PEMT knockout (KO) mice. We found that the effect of PEMT-knockout was tissue-specific and age-dependent. A deficiency of PEMT affected the metabolome of all tissues examined, among which the metabolome of BAT from both young and aged KO mice was dramatically changed in comparison to the WT mice, whereas the metabolome of the jejunum was only slightly affected. As for aging, the absence of PEMT increased the divergence of the metabolome during the aging of the liver, WAT, duodenum, and ileum and decreased the impact on skeletal muscle. Overall, our results suggest that PEMT plays a previously underexplored, critical role in both aging and energy metabolism. Full article

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14 pages, 1564 KiB

Open AccessArticle

Metabolomic Analysis of Human Astrocytes in Lipotoxic Condition: Potential Biomarker Identification by Machine Learning Modeling

by Daniel Báez Castellanos, Cynthia A. Martín-Jiménez, Andrés Pinzón, George E. Barreto, Guillermo Federico Padilla-González, Andrés Aristizábal, Martha Zuluaga and Janneth González Santos

Biomolecules 2022, 12(7), 986; https://doi.org/10.3390/biom12070986 - 15 Jul 2022

Cited by 3 | Viewed by 3307

Abstract

The association between neurodegenerative diseases (NDs) and obesity has been well studied in recent years. Obesity is a syndrome of multifactorial etiology characterized by an excessive accumulation and release of fatty acids (FA) in adipose and non-adipose tissue. An excess of FA generates [...] Read more.

The association between neurodegenerative diseases (NDs) and obesity has been well studied in recent years. Obesity is a syndrome of multifactorial etiology characterized by an excessive accumulation and release of fatty acids (FA) in adipose and non-adipose tissue. An excess of FA generates a metabolic condition known as lipotoxicity, which triggers pathological cellular and molecular responses, causing dysregulation of homeostasis and a decrease in cell viability. This condition is a hallmark of NDs, and astrocytes are particularly sensitive to it, given their crucial role in energy production and oxidative stress management in the brain. However, analyzing cellular mechanisms associated with these conditions represents a challenge. In this regard, metabolomics is an approach that allows biochemical analysis from the comprehensive perspective of cell physiology. This technique allows cellular metabolic profiles to be determined in different biological contexts, such as those of NDs and specific metabolic insults, including lipotoxicity. Since data provided by metabolomics can be complex and difficult to interpret, alternative data analysis techniques such as machine learning (ML) have grown exponentially in areas related to omics data. Here, we developed an ML model yielding a 93% area under the receiving operating characteristic (ROC) curve, with sensibility and specificity values of 80% and 93%, respectively. This study aimed to analyze the metabolomic profiles of human astrocytes under lipotoxic conditions to provide powerful insights, such as potential biomarkers for scenarios of lipotoxicity induced by palmitic acid (PA). In this work, we propose that dysregulation in seleno-amino acid metabolism, urea cycle, and glutamate metabolism pathways are major triggers in astrocyte lipotoxic scenarios, while increased metabolites such as alanine, adenosine, and glutamate are suggested as potential biomarkers, which, to our knowledge, have not been identified in human astrocytes and are proposed as candidates for further research and validation. Full article

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2021

Jump to: 2024, 2023, 2022, 2020

20 pages, 7257 KiB

Open AccessArticle

Metabolomics Coupled with Pathway Analysis Provides Insights into Sarco-Osteoporosis Metabolic Alterations and Estrogen Therapeutic Effects in Mice

by Ziheng Wei, Fei Ge, Yanting Che, Si Wu, Xin Dong and Dianwen Song

Biomolecules 2022, 12(1), 41; https://doi.org/10.3390/biom12010041 - 28 Dec 2021

Cited by 19 | Viewed by 3393

Abstract

Postmenopausal osteoporosis (PMOP) and sarcopenia are common diseases that predominantly affect postmenopausal women. In the occurrence and development of these two diseases, they are potentially pathologically connected with each other at various molecular levels. However, the application of metabolomics in sarco-osteoporosis and the [...] Read more.

Postmenopausal osteoporosis (PMOP) and sarcopenia are common diseases that predominantly affect postmenopausal women. In the occurrence and development of these two diseases, they are potentially pathologically connected with each other at various molecular levels. However, the application of metabolomics in sarco-osteoporosis and the metabolic rewiring happening throughout the estrogen loss-replenish process have not been reported. To investigate the metabolic alteration of sarco-osteoporosis and the possible therapeutical effects of estradiol, 24 mice were randomly divided into sham surgery, ovariectomy (OVX), and estradiol-treated groups. Three-dimensional reconstructions and histopathology examination showed significant bone loss after ovariectomy. Estrogen can well protect against OVX-induced bone loss deterioration. UHPLC-Q-TOF/MS was preformed to profile semi- polar metabolites of skeletal muscle samples from all groups. Metabolomics analysis revealed metabolic rewiring occurred in OVX group, most of which can be reversed by estrogen supplementation. In total, 65 differential metabolites were identified, and pathway analysis revealed that sarco-osteoporosis was related to the alterations in purine metabolism, glycerophospholipid metabolism, arginine biosynthesis, tryptophan metabolism, histidine metabolism, oxidative phosphorylation, and thermogenesis, which provided possible explanations for the metabolic mechanism of sarco-osteoporosis. This study indicates that an UHPLC-Q-TOF/MS-based metabolomics approach can elucidate the metabolic reprogramming mechanisms of sarco-osteoporosis and provide biological evidence of the therapeutical effects of estrogen on sarco-osteoporosis. Full article

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30 pages, 1922 KiB

Open AccessReview

The Emerging Role of Long Non-Coding RNAs and MicroRNAs in Neurodegenerative Diseases: A Perspective of Machine Learning

by Ángela García-Fonseca, Cynthia Martin-Jimenez, George E. Barreto, Andres Felipe Aristizábal Pachón and Janneth González

Biomolecules 2021, 11(8), 1132; https://doi.org/10.3390/biom11081132 - 31 Jul 2021

Cited by 28 | Viewed by 5892

Abstract

Neurodegenerative diseases (NDs) are characterized by progressive neuronal dysfunction and death of brain cells population. As the early manifestations of NDs are similar, their symptoms are difficult to distinguish, making the timely detection and discrimination of each neurodegenerative disorder a priority. Several investigations [...] Read more.

Neurodegenerative diseases (NDs) are characterized by progressive neuronal dysfunction and death of brain cells population. As the early manifestations of NDs are similar, their symptoms are difficult to distinguish, making the timely detection and discrimination of each neurodegenerative disorder a priority. Several investigations have revealed the importance of microRNAs and long non-coding RNAs in neurodevelopment, brain function, maturation, and neuronal activity, as well as its dysregulation involved in many types of neurological diseases. Therefore, the expression pattern of these molecules in the different NDs have gained significant attention to improve the diagnostic and treatment at earlier stages. In this sense, we gather the different microRNAs and long non-coding RNAs that have been reported as dysregulated in each disorder. Since there are a vast number of non-coding RNAs altered in NDs, some sort of synthesis, filtering and organization method should be applied to extract the most relevant information. Hence, machine learning is considered as an important tool for this purpose since it can classify expression profiles of non-coding RNAs between healthy and sick people. Therefore, we deepen in this branch of computer science, its different methods, and its meaningful application in the diagnosis of NDs from the dysregulated non-coding RNAs. In addition, we demonstrate the relevance of machine learning in NDs from the description of different investigations that showed an accuracy between 85% to 95% in the detection of the disease with this tool. All of these denote that artificial intelligence could be an excellent alternative to help the clinical diagnosis and facilitate the identification diseases in early stages based on non-coding RNAs. Full article

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18 pages, 1329 KiB

Open AccessArticle

Multiomic Approach to Analyze Infant Gut Microbiota: Experimental and Analytical Method Optimization

by Helena Torrell, Adrià Cereto-Massagué, Polina Kazakova, Lorena García, Héctor Palacios and Núria Canela

Biomolecules 2021, 11(7), 999; https://doi.org/10.3390/biom11070999 - 7 Jul 2021

Cited by 5 | Viewed by 3880

Abstract

Background: The human intestinal microbiome plays a central role in overall health status, especially in early life stages. 16S rRNA amplicon sequencing is used to profile its taxonomic composition; however, multiomic approaches have been proposed as the most accurate methods for study of [...] Read more.

Background: The human intestinal microbiome plays a central role in overall health status, especially in early life stages. 16S rRNA amplicon sequencing is used to profile its taxonomic composition; however, multiomic approaches have been proposed as the most accurate methods for study of the complexity of the gut microbiota. In this study, we propose an optimized method for bacterial diversity analysis that we validated and complemented with metabolomics by analyzing fecal samples. Methods: Forty-eight different analytical combinations regarding (1) 16S rRNA variable region sequencing, (2) a feature selection approach, and (3) taxonomy assignment methods were tested. A total of 18 infant fecal samples grouped depending on the type of feeding were analyzed by the proposed 16S rRNA workflow and by metabolomic analysis. Results: The results showed that the sole use of V4 region sequencing with ASV identification and VSEARCH for taxonomy assignment produced the most accurate results. The application of this workflow showed clear differences between fecal samples according to the type of feeding, which correlated with changes in the fecal metabolic profile. Conclusion: A multiomic approach using real fecal samples from 18 infants with different types of feeding demonstrated the effectiveness of the proposed 16S rRNA-amplicon sequencing workflow. Full article

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22 pages, 2230 KiB

Open AccessReview

An Introduction to Next Generation Sequencing Bioinformatic Analysis in Gut Microbiome Studies

by Bei Gao, Liang Chi, Yixin Zhu, Xiaochun Shi, Pengcheng Tu, Bing Li, Jun Yin, Nan Gao, Weishou Shen and Bernd Schnabl

Biomolecules 2021, 11(4), 530; https://doi.org/10.3390/biom11040530 - 2 Apr 2021

Cited by 72 | Viewed by 14532

Abstract

The gut microbiome is a microbial ecosystem which expresses 100 times more genes than the human host and plays an essential role in human health and disease pathogenesis. Since most intestinal microbial species are difficult to culture, next generation sequencing technologies have been [...] Read more.

The gut microbiome is a microbial ecosystem which expresses 100 times more genes than the human host and plays an essential role in human health and disease pathogenesis. Since most intestinal microbial species are difficult to culture, next generation sequencing technologies have been widely applied to study the gut microbiome, including 16S rRNA, 18S rRNA, internal transcribed spacer (ITS) sequencing, shotgun metagenomic sequencing, metatranscriptomic sequencing and viromic sequencing. Various software tools were developed to analyze different sequencing data. In this review, we summarize commonly used computational tools for gut microbiome data analysis, which extended our understanding of the gut microbiome in health and diseases. Full article

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21 pages, 5353 KiB

Open AccessFeature PaperReview

Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview

by Helena Castañé, Gerard Baiges-Gaya, Anna Hernández-Aguilera, Elisabet Rodríguez-Tomàs, Salvador Fernández-Arroyo, Pol Herrero, Antoni Delpino-Rius, Nuria Canela, Javier A. Menendez, Jordi Camps and Jorge Joven

Biomolecules 2021, 11(3), 473; https://doi.org/10.3390/biom11030473 - 22 Mar 2021

Cited by 16 | Viewed by 5359

Abstract

Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until [...] Read more.

Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods. Full article

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12 pages, 1938 KiB

Open AccessArticle

Multi-Omics Data Analysis Uncovers Molecular Networks and Gene Regulators for Metabolic Biomarkers

by Su Yon Jung

Biomolecules 2021, 11(3), 406; https://doi.org/10.3390/biom11030406 - 10 Mar 2021

Cited by 1 | Viewed by 2744

Abstract

The insulin-like growth factors (IGFs)/insulin resistance (IR) axis is the major metabolic hormonal pathway mediating the biologic mechanism of several complex human diseases, including type 2 diabetes (T2DM) and cancers. The genomewide association study (GWAS)-based approach has neither fully characterized the phenotype variation [...] Read more.

The insulin-like growth factors (IGFs)/insulin resistance (IR) axis is the major metabolic hormonal pathway mediating the biologic mechanism of several complex human diseases, including type 2 diabetes (T2DM) and cancers. The genomewide association study (GWAS)-based approach has neither fully characterized the phenotype variation nor provided a comprehensive understanding of the regulatory biologic mechanisms. We applied systematic genomics to integrate our previous GWAS data for IGF-I and IR with multi-omics datasets, e.g., whole-blood expression quantitative loci, molecular pathways, and gene network, to capture the full range of genetic functionalities associated with IGF-I/IR and key drivers (KDs) in gene-regulatory networks. We identified both shared (e.g., T2DM, lipid metabolism, and estimated glomerular filtration signaling) and IR-specific (e.g., mechanistic target of rapamycin, phosphoinositide 3-kinases, and erb-b2 receptor tyrosine kinase 4 signaling) molecular biologic processes of IGF-I/IR axis regulation. Next, by using tissue-specific gene–gene interaction networks, we identified both well-established (e.g., IRS1 and IGF1R) and novel (e.g., AKT1, HRAS, and JAK1) KDs in the IGF-I/IR-associated subnetworks. Our results, if validated in additional genomic studies, may provide robust, comprehensive insights into the mechanisms of IGF-I/IR regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for the associated diseases, e.g., T2DM and cancers. Full article

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25 pages, 3118 KiB

Open AccessArticle

Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes

by Oscar Alcazar, Luis F. Hernandez, Ernesto S. Nakayasu, Carrie D. Nicora, Charles Ansong, Michael J. Muehlbauer, James R. Bain, Ciara J. Myer, Sanjoy K. Bhattacharya, Peter Buchwald and Midhat H. Abdulreda

Biomolecules 2021, 11(3), 383; https://doi.org/10.3390/biom11030383 - 4 Mar 2021

Cited by 16 | Viewed by 4878

Abstract

Background: Biomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant β-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics. Methods: Blood from [...] Read more.

Background: Biomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant β-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics. Methods: Blood from human subjects at high risk for T1D (and healthy controls; n = 4 + 4) was subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and transcriptomics. The integrated dataset was analyzed using Ingenuity Pathway Analysis (IPA) software for disturbances in the at-risk subjects compared to controls. Results: The final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples without exception. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of CD4 T-lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF-κB, TGF-β, VEGF, arachidonic acid, and arginase, and inhibition of miRNA Let-7a-5p. IPA-predicted candidate biomarkers were used to construct a putative integrated signature containing several miRNAs and metabolite/lipid features in the at-risk subjects. Conclusions: Preliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors. Full article

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22 pages, 2003 KiB

Open AccessArticle

A Pilot Study for Metabolic Profiling of Obesity-Associated Microbial Gut Dysbiosis in Male Wistar Rats

by Julia Hernandez-Baixauli, Pere Puigbò, Helena Torrell, Hector Palacios-Jordan, Vicent J. Ribas Ripoll, Antoni Caimari, Josep M Del Bas, Laura Baselga-Escudero and Miquel Mulero

Biomolecules 2021, 11(2), 303; https://doi.org/10.3390/biom11020303 - 18 Feb 2021

Cited by 4 | Viewed by 3522

Abstract

Obesity is one of the most incident and concerning disease worldwide. Definite strategies to prevent obesity and related complications remain elusive. Among the risk factors of the onset of obesity, gut microbiota might play an important role in the pathogenesis of the disease, [...] Read more.

Obesity is one of the most incident and concerning disease worldwide. Definite strategies to prevent obesity and related complications remain elusive. Among the risk factors of the onset of obesity, gut microbiota might play an important role in the pathogenesis of the disease, and it has received extensive attention because it affects the host metabolism. In this study, we aimed to define a metabolic profile of the segregated obesity-associated gut dysbiosis risk factor. The study of the metabolome, in an obesity-associated gut dysbiosis model, provides a relevant way for the discrimination on the different biomarkers in the obesity onset. Thus, we developed a model of this obesity risk factors through the transference of gut microbiota from obese to non-obese male Wistar rats and performed a subsequent metabolic analysis in the receptor rats. Our results showed alterations in the lipid metabolism in plasma and in the phenylalanine metabolism in urine. In consequence, we have identified metabolic changes characterized by: (1) an increase in DG:34:2 in plasma, a decrease in hippurate, (2) an increase in 3-HPPA, and (3) an increase in o-coumaric acid. Hereby, we propose these metabolites as a metabolic profile associated to a segregated dysbiosis state related to obesity disease. Full article

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10 pages, 828 KiB

Open AccessReview

Challenges and Perspective in Integrated Multi-Omics in Gut Microbiota Studies

by Eric Banan-Mwine Daliri, Fred Kwame Ofosu, Ramachandran Chelliah, Byong H. Lee and Deog-Hwan Oh

Biomolecules 2021, 11(2), 300; https://doi.org/10.3390/biom11020300 - 17 Feb 2021

Cited by 33 | Viewed by 5834

Abstract

The advent of omic technology has made it possible to identify viable but unculturable micro-organisms in the gut. Therefore, application of multi-omic technologies in gut microbiome studies has become invaluable for unveiling a comprehensive interaction between these commensals in health and disease. Meanwhile, [...] Read more.

The advent of omic technology has made it possible to identify viable but unculturable micro-organisms in the gut. Therefore, application of multi-omic technologies in gut microbiome studies has become invaluable for unveiling a comprehensive interaction between these commensals in health and disease. Meanwhile, despite the successful identification of many microbial and host–microbial cometabolites that have been reported so far, it remains difficult to clearly identify the origin and function of some proteins and metabolites that are detected in gut samples. However, the application of single omic techniques for studying the gut microbiome comes with its own challenges which may be overcome if a number of different omics techniques are combined. In this review, we discuss our current knowledge about multi-omic techniques, their challenges and future perspective in this field of gut microbiome studies. Full article

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14 pages, 5825 KiB

Open AccessArticle

Serum Acylcarnitines Associated with High Short-Term Mortality in Patients with Alcoholic Hepatitis

by Bei Gao, Josepmaria Argemi, Ramon Bataller and Bernd Schnabl

Biomolecules 2021, 11(2), 281; https://doi.org/10.3390/biom11020281 - 14 Feb 2021

Cited by 7 | Viewed by 2579

Abstract

Alcohol-related liver disease is one of the most prevalent liver diseases in the United States. Early stages of alcohol-related liver disease are characterized by accumulation of triglycerides in hepatocytes. Alcoholic hepatitis is a severe form of alcohol-related liver disease associated with significant morbidity [...] Read more.

Alcohol-related liver disease is one of the most prevalent liver diseases in the United States. Early stages of alcohol-related liver disease are characterized by accumulation of triglycerides in hepatocytes. Alcoholic hepatitis is a severe form of alcohol-related liver disease associated with significant morbidity and mortality. We sought to identify patients who are at greatest risk of death using serum lipids. First, we performed lipidomics analysis on serum samples collected from 118 patients with alcoholic hepatitis to identify lipid markers that are associated with high risk of death. Next, we performed gene set enrichment analysis on liver transcriptomics data to identify dysregulated lipid metabolism in patients who received liver transplantation. Finally, we built a random forest model to predict 30-day mortality using serum lipids. A total of 277 lipids were annotated in the serum of patients with alcoholic hepatitis, among which 25 were significantly different between patients in the deceased and alive groups. Five chemical clusters were significantly altered between the two groups. In particular, acylcarnitine cluster was enriched in the deceased group. Several hepatic lipid metabolism pathways were dysregulated in patients with alcoholic hepatitis who received liver transplantation. The mRNA expression of genes involved in the fatty acid transport into mitochondria and β-oxidation were also dysregulated. When predicting 30-day mortality in alcoholic hepatitis patients using serum lipids, we found that the area under the curve achieved 0.95. Serum lipids such as acylcarnitines may serve as biomarkers to identify alcoholic hepatitis patients at the greatest risk of death. Full article

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18 pages, 3689 KiB

Open AccessArticle

Tissue-Specific Landscape of Metabolic Dysregulation during Ageing

by Fangrong Zhang, Jakob Kerbl-Knapp, Alena Akhmetshina, Melanie Korbelius, Katharina Barbara Kuentzel, Nemanja Vujić, Gerd Hörl, Margret Paar, Dagmar Kratky, Ernst Steyrer and Tobias Madl

Biomolecules 2021, 11(2), 235; https://doi.org/10.3390/biom11020235 - 7 Feb 2021

Cited by 32 | Viewed by 4443

Abstract

The dysregulation of cellular metabolism is a hallmark of ageing. To understand the metabolic changes that occur as a consequence of the ageing process and to find biomarkers for age-related diseases, we conducted metabolomic analyses of the brain, heart, kidney, liver, lung and [...] Read more.

The dysregulation of cellular metabolism is a hallmark of ageing. To understand the metabolic changes that occur as a consequence of the ageing process and to find biomarkers for age-related diseases, we conducted metabolomic analyses of the brain, heart, kidney, liver, lung and spleen in young (9–10 weeks) and old (96–104 weeks) wild-type mice [mixed genetic background of 129/J and C57BL/6] using NMR spectroscopy. We found differences in the metabolic fingerprints of all tissues and distinguished several metabolites to be altered in most tissues, suggesting that they may be universal biomarkers of ageing. In addition, we found distinct tissue-clustered sets of metabolites throughout the organism. The associated metabolic changes may reveal novel therapeutic targets for the treatment of ageing and age-related diseases. Moreover, the identified metabolite biomarkers could provide a sensitive molecular read-out to determine the age of biologic tissues and organs and to validate the effectiveness and potential off-target effects of senolytic drug candidates on both a systemic and tissue-specific level. Full article

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2020

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10 pages, 679 KiB

Open AccessArticle

IL-37 Gene and Cholesterol Metabolism: Association of Polymorphisms with the Presence of Hypercholesterolemia and Cardiovascular Risk Factors. The GEA Mexican Study

by Fabiola López-Bautista, Rosalinda Posadas-Sánchez, Christian Vázquez-Vázquez, José Manuel Fragoso, José Manuel Rodríguez-Pérez and Gilberto Vargas-Alarcón

Biomolecules 2020, 10(10), 1409; https://doi.org/10.3390/biom10101409 - 5 Oct 2020

Cited by 13 | Viewed by 2604

Abstract

Interleukin 37 (IL-37) is an anti-inflammatory cytokine involved in the regulation of cholesterol homeostasis, reducing the levels of plasma cholesterol, fatty acids, and triglycerides. The aim of the present study was to evaluate the association of the IL-37 polymorphisms with the [...] Read more.

Interleukin 37 (IL-37) is an anti-inflammatory cytokine involved in the regulation of cholesterol homeostasis, reducing the levels of plasma cholesterol, fatty acids, and triglycerides. The aim of the present study was to evaluate the association of the IL-37 polymorphisms with the presence of hypercholesterolemia (HC), and with cardiovascular risk factors. Nine IL-37 polymorphisms (rs2708965, rs2708962, rs6717710, rs2708961, rs2708960, rs2708958, rs2723187, rs2708947, and rs2723192) were determined by TaqMan assays in a group of 1292 individuals (514 with and 778 without hypercholesterolemia) belonging to the cohort of the GEA Mexican Study. The associations were evaluated by logistic regression, using inheritance models adjusted by confounding variables. Under codominant 1 model, the rs2708961 (OR = 0.51, p = 0.02), rs2723187 (OR = 0.35, p = 0.005), and rs2708947 (OR = 0.49, p = 0.02) polymorphisms were associated with low risk of HC. The association of the polymorphisms with cardiovascular risk factors was evaluated independently in HC and non-HC individuals. In non-HC individuals, some polymorphisms were associated with the risk of having high levels of LDL-C, glucose, and high risk of T2DM, and low risk of having high visceral abdominal fat. On the other hand, in individuals with HC five, polymorphisms were associated with high levels of C-reactive protein. The IL-37 rs2708961, rs2723187, rs2708947 polymorphisms were associated with low risk of HC, and some IL-37 polymorphisms were associated with cardiometabolic factors in both individuals with and without HC. Full article

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21 pages, 1774 KiB

Open AccessArticle

Quantitative Lipidomic Analysis of Osteosarcoma Cell-Derived Products by UHPLC-MS/MS

by Sara Casati, Chiara Giannasi, Mauro Minoli, Stefania Niada, Alessandro Ravelli, Ilaria Angeli, Veronica Mergenthaler, Roberta Ottria, Pierangela Ciuffreda, Marica Orioli and Anna T. Brini

Biomolecules 2020, 10(9), 1302; https://doi.org/10.3390/biom10091302 - 9 Sep 2020

Cited by 14 | Viewed by 3506

Abstract

Changes in lipid metabolism are involved in several pathological conditions, such as cancer. Among lipids, eicosanoids are potent inflammatory mediators, synthesized from polyunsaturated fatty acids (PUFAs), which coexist with other lipid-derived ones, including endocannabinoids (ECs) and N-acylethanolamides (NAEs). In this work, a [...] Read more.

Changes in lipid metabolism are involved in several pathological conditions, such as cancer. Among lipids, eicosanoids are potent inflammatory mediators, synthesized from polyunsaturated fatty acids (PUFAs), which coexist with other lipid-derived ones, including endocannabinoids (ECs) and N-acylethanolamides (NAEs). In this work, a bioanalytical assay for 12 PUFAs/eicosanoids and 20 ECs/NAEs in cell culture medium and human biofluids was validated over a linear range of 0.1–2.5 ng/mL. A fast pretreatment method consisting of protein precipitation with acetonitrile followed by a double step liquid–liquid extraction was developed. The final extracts were injected onto a Kinetex ultra-high-performance liquid chromatography (UHPLC) XB-C18 column with a gradient elution of 0.1% formic acid in water and methanol/acetonitrile (5:1; v/v) mobile phase. Chromatographic separation was followed by detection with a triple-quadrupole mass spectrometer operating both in positive and negative ion-mode. A full validation was carried out in a small amount of cell culture medium and then applied to osteosarcoma cell-derived products. To the best of our knowledge, this is the first lipid profiling of bone tumor cell lines (SaOS-2 and MG-63) and their secretome. Our method was also partially validated in other biological matrices, such as serum and urine, ensuring its broad applicability as a powerful tool for lipidomic translational research. Full article

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19 pages, 3763 KiB

Open AccessArticle

Untargeted Profiling of Bile Acids and Lysophospholipids Identifies the Lipid Signature Associated with Glycemic Outcome in an Obese Non-Diabetic Clinical Cohort

by Nicolas Christinat, Armand Valsesia and Mojgan Masoodi

Biomolecules 2020, 10(7), 1049; https://doi.org/10.3390/biom10071049 - 15 Jul 2020

Cited by 10 | Viewed by 3319

Abstract

The development of high throughput assays for assessing lipid metabolism in metabolic disorders, especially in diabetes research, nonalcoholic fatty liver disease (NAFLD), and nonalcoholic steatohepatitis (NASH), provides a reliable tool for identifying and characterizing potential biomarkers in human plasma for early diagnosis or [...] Read more.

The development of high throughput assays for assessing lipid metabolism in metabolic disorders, especially in diabetes research, nonalcoholic fatty liver disease (NAFLD), and nonalcoholic steatohepatitis (NASH), provides a reliable tool for identifying and characterizing potential biomarkers in human plasma for early diagnosis or prognosis of the disease and/or responses to a specific treatment. Predicting the outcome of weight loss or weight management programs is a challenging yet important aspect of such a program’s success. The characterization of potential biomarkers of metabolic disorders, such as lysophospholipids and bile acids, in large human clinical cohorts could provide a useful tool for successful predictions. In this study, we validated an LC-MS method combining the targeted and untargeted detection of these lipid species. Its potential for biomarker discovery was demonstrated in a well-characterized overweight/obese cohort subjected to a low-caloric diet intervention, followed by a weight maintenance phase. Relevant markers predicting successful responses to the low-caloric diet intervention for both weight loss and glycemic control improvements were identified. The response to a controlled weight loss intervention could be best predicted using the baseline concentration of three lysophospholipids (PC(22:4/0:0), PE(17:1/0:0), and PC(22:5/0:0)). Insulin resistance on the other hand could be best predicted using clinical parameters and levels of circulating lysophospholipids and bile acids. Our approach provides a robust tool not only for research purposes, but also for clinical practice, as well as designing new clinical interventions or assessing responses to specific treatment. Considering this, it presents a step toward personalized medicine. Full article

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21 pages, 427 KiB

Open AccessArticle

Untargeted LC-MS Metabolomics Differentiates Between Virulent and Avirulent Clinical Strains of Pseudomonas aeruginosa

by Tobias Depke, Janne Gesine Thöming, Adrian Kordes, Susanne Häussler and Mark Brönstrup

Biomolecules 2020, 10(7), 1041; https://doi.org/10.3390/biom10071041 - 13 Jul 2020

Cited by 23 | Viewed by 4643

Abstract

Pseudomonas aeruginosa is a facultative pathogen that can cause, inter alia, acute or chronic pneumonia in predisposed individuals. The gram-negative bacterium displays considerable genomic and phenotypic diversity that is also shaped by small molecule secondary metabolites. The discrimination of virulence phenotypes is highly [...] Read more.

Pseudomonas aeruginosa is a facultative pathogen that can cause, inter alia, acute or chronic pneumonia in predisposed individuals. The gram-negative bacterium displays considerable genomic and phenotypic diversity that is also shaped by small molecule secondary metabolites. The discrimination of virulence phenotypes is highly relevant to the diagnosis and prognosis of P. aeruginosa infections. In order to discover small molecule metabolites that distinguish different virulence phenotypes of P. aeruginosa, 35 clinical strains were cultivated under standard conditions, characterized in terms of virulence and biofilm phenotype, and their metabolomes were investigated by untargeted liquid chromatography—mass spectrometry. The data was both mined for individual candidate markers as well as used to construct statistical models to infer the virulence phenotype from metabolomics data. We found that clinical strains that differed in their virulence and biofilm phenotype also had pronounced divergence in their metabolomes, as underlined by 332 features that were significantly differentially abundant with fold changes greater than 1.5 in both directions. Important virulence-associated secondary metabolites like rhamnolipids, alkyl quinolones or phenazines were found to be strongly upregulated in virulent strains. In contrast, we observed little change in primary metabolism. A hitherto novel cationic metabolite with a sum formula of C₁₂H₁₅N₂ could be identified as a candidate biomarker. A random forest model was able to classify strains according to their virulence and biofilm phenotype with an area under the Receiver Operation Characteristics curve of 0.84. These findings demonstrate that untargeted metabolomics is a valuable tool to characterize P. aeruginosa virulence, and to explore interrelations between clinically important phenotypic traits and the bacterial metabolome. Full article

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12 pages, 1018 KiB

Open AccessArticle

Implication of Opioid Receptors in the Antihypertensive Effect of a Novel Chicken Foot-Derived Peptide

by Anna Mas-Capdevila, Lisard Iglesias-Carres, Anna Arola-Arnal, Gerard Aragonès, Begoña Muguerza and Francisca Isabel Bravo

Biomolecules 2020, 10(7), 992; https://doi.org/10.3390/biom10070992 - 2 Jul 2020

Cited by 9 | Viewed by 2705

Abstract

The peptide AVFQHNCQE demonstrated to produce nitric oxide-mediated antihypertensive effect. This study investigates the bioavailability and the opioid-like activity of this peptide after its oral administration. For this purpose, in silico and in vitro approaches were used to study the peptide susceptibility to [...] Read more.

The peptide AVFQHNCQE demonstrated to produce nitric oxide-mediated antihypertensive effect. This study investigates the bioavailability and the opioid-like activity of this peptide after its oral administration. For this purpose, in silico and in vitro approaches were used to study the peptide susceptibility to GI digestion. In addition, AVFQHNCQE absorption was studied both in vitro by using Caco-2 cell monolayers and in vivo evaluating peptide presence in plasma from Wistar rats by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and by ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). Both in vivo and in vitro experiments demonstrated that peptide AVFQHNCQE was not absorbed. Thus, the potential involvement of opioid receptors in the BP-lowering effect of AVFQHNCQE was studied in the presence of opioid receptors-antagonist Naloxone. No changes in blood pressure were recorded in rats administered Naloxone, demonstrating that AVFQHNCQE antihypertensive effect is mediated through its interaction with opioid receptors. AVFQHNCQE opioid-like activity would clarify the antihypertensive properties of AVFQHNCQE despite its lack of absorption. Full article

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