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Metabolomics as a Tool for Functional Genomics

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A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cellular Metabolism".

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 35120

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Dr. Sara Rinalducci

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Guest Editor

Department of Ecological and Biological Sciences (DEB), University of Tuscia, 01100 Viterbo, Italy
Interests: proteomics/metabolomics/lipidomics; systems biology; post-translational modifications; plant responses to biotic and abiotic stresses; omics for health and disease
Special Issues, Collections and Topics in MDPI journals

Dr. María Peña-Chilet

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Guest Editor

Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Seville, Spain
Rare Diseases Networking Biomedical Research Centre (CIBERER), 41013 Sevilla, Spain
Interests: systems biology; personalized medicine; genomics; transcriptomics; cancer; rare diseases; biomedicine; mechanistic disease modeling

Dr. Camilla Hill

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Guest Editor

Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA 6150, Australia
Interests: plant science; genetics; genomics; metabolomics; analytical biochemistry; plant stress physiology

Special Issue Information

Dear Colleagues,

A key objective in biology is understanding how genes, proteins, and metabolites interact in a network to control a variety of cellular processes. Transcriptomics, proteomics, and metabolomics, together with integrated bioinformatics, are functional genomics technologies that allow the large-scale interrogation of mRNA, proteins, and metabolites, respectively. Compared to either the transcriptome or proteome of an organism, the metabolome is the most direct readout of the current status of an organism, and the fastest to register a measured response. Moreover, metabolomics provides a new and interesting layer of knowledge when modeling cellular functions from a mechanistic point of view; however, more studies are needed in order to achieve a level of knowledge that will allow us to accurately predict complex cellular responses. Metabolomics is defined as the total complement of metabolite changes according to the developmental, physiological, or pathological state of an organism, specific tissue, or cell. As a multidisciplinary science, metabolomics comprises areas of chemistry, bioinformatics, statistics, genetics, ecology, biotechnology, nutrition, and cytology. Targeted and untargeted mass spectrometry-based approaches for metabolite profiling have been applied to diverse scientific domains and opened up new ways to assess animal and human physiology in health and disease, including in clinical settings (“from bench to bedside”), environmental stress responses in plants, or pathogenicity and drug resistance in microbial strains.

This Special Issue welcomes but is not limited to contributions dealing with:

Animal and human metabolomics, including metabolic studies of disease, aging or nutrition;
Plant metabolomics for crop improvement, genotype classification, characterization of the response to environmental perturbations, and development of plant-derived functional foods and nutraceuticals;
Microbial metabolomics to analyze the effect of genetic variation on strain-specific adaptive capacity and vulnerability, as well as the function and performance of transgenic and fermentation microorganisms;
Yeast metabolomics to analyze genetic determinants relevant to bioengineering strategies;
Integrative modeling of metabolomics and other omics, such as transcriptomics, proteomics, or genomics.

Prof. Dr. Sara Rinalducci
Dr. María Peña-Chilet
Dr. Camilla Hill
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cells is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

transcriptomics
next-generation sequencing
single-nucleotide polymorphisms
plant breading
equivalence of genetically modified and conventional crops
medicinal plants
metabolomics-assisted quantitative trait locus (QTL) mapping
genome-wide association studies (GWASs)
biotechnology
metabolic engineering
genome editing
industrial microbes
metabolic disorders
systems biology
mechanistic analysis
biomarkers
complex human diseases
cellular response modeling

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Published Papers (9 papers)

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Research

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20 pages, 3983 KiB

Open AccessArticle

Intracellular Metabolomics Identifies Efflux Transporter Inhibitors in a Routine Caco-2 Cell Permeability Assay—Biological Implications

by Afia Naseem, Akos Pal, Sharon Gowan, Yasmin Asad, Adam Donovan, Csilla Temesszentandrási-Ambrus, Emese Kis, Zsuzsanna Gaborik, Gurdip Bhalay and Florence Raynaud

Cells 2022, 11(20), 3286; https://doi.org/10.3390/cells11203286 - 19 Oct 2022

Cited by 4 | Viewed by 2497

Abstract

Caco-2 screens are routinely used in laboratories to measure the permeability of compounds and can identify substrates of efflux transporters. In this study, we hypothesized that efflux transporter inhibition of a compound can be predicted by an intracellular metabolic signature in Caco-2 cells in the assay used to test intestinal permeability. Using selective inhibitors and transporter knock-out (KO) cells and a targeted Liquid Chromatography tandem Mass Spectrometry (LC-MS) method, we identified 11 metabolites increased in cells with depleted P-glycoprotein (Pgp) activity. Four metabolites were altered with Breast Cancer Resistance (BCRP) inhibition and nine metabolites were identified in the Multidrug Drug Resistance Protein 2 (MRP2) signature. A scoring system was created that could discriminate among the three transporters and validated with additional inhibitors. Pgp and MRP2 substrates did not score as inhibitors. In contrast, BCRP substrates and inhibitors showed a similar intracellular metabolomic signature. Network analysis of signature metabolites led us to investigate changes of enzymes in one-carbon metabolism (folate and methionine cycles). Our data shows that methylenetetrahydrofolate reductase (MTHFR) protein levels increased with Pgp inhibition and Thymidylate synthase (TS) protein levels were reduced with Pgp and MRP2 inhibition. In addition, the methionine cycle is also affected by both Pgp and MRP2 inhibition. In summary, we demonstrated that the routine Caco-2 assay has the potential to identify efflux transporter inhibitors in parallel with substrates in the assays currently used in many DMPK laboratories and that inhibition of efflux transporters has biological consequences. Full article

(This article belongs to the Special Issue Metabolomics as a Tool for Functional Genomics)

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16 pages, 2011 KiB

Open AccessCommunication

Untargeted Multimodal Metabolomics Investigation of the Haemonchus contortus Exsheathment Secretome

by Nikola Palevich, Paul H. Maclean, Paul M. Candy, Wendy Taylor, Ivona Mladineo and Mingshu Cao

Cells 2022, 11(16), 2525; https://doi.org/10.3390/cells11162525 - 15 Aug 2022

Cited by 6 | Viewed by 3527

Abstract

In nematodes that invade the gastro-intestinal tract of the ruminant, the process of larval exsheathment marks the transition from the free-living to the parasitic stages of these parasites. To investigate the secretome associated with larval exsheathment, a closed in vitro system that effectively reproduces the two basic components of an anaerobic rumen environment (CO₂ and 39 °C) was developed to trigger exsheathment in one of the most pathogenic and model gastrointestinal parasitic nematodes, Haemonchus contortus (barber‘s pole worm). This study reports the use of multimodal untargeted metabolomics and lipidomics methodologies to identify the metabolic signatures and compounds secreted during in vitro larval exsheathment in the H. contortus infective third-stage larva (iL3). A combination of statistical and chemoinformatic analyses using three analytical platforms revealed a panel of metabolites detected post exsheathment and associated with amino acids, purines, as well as select organic compounds. The major lipid classes identified by the non-targeted lipidomics method applied were lysophosphatidylglycerols, diglycerides, fatty acyls, glycerophospholipids, and a triglyceride. The identified metabolites may serve as metabolic signatures to improve tractability of parasitic nematodes for characterizing small molecule host–parasite interactions related to pathogenesis, vaccine and drug design, as well as the discovery of metabolic biomarkers. Full article

(This article belongs to the Special Issue Metabolomics as a Tool for Functional Genomics)

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19 pages, 5837 KiB

Open AccessArticle

Transcriptomic, Hormonomic and Metabolomic Analyses Highlighted the Common Modules Related to Photosynthesis, Sugar Metabolism and Cell Division in Parthenocarpic Tomato Fruits during Early Fruit Set

by Miyako Kusano, Kanjana Worarad, Atsushi Fukushima, Ken Kamiya, Yuka Mitani, Yozo Okazaki, Yasuhiro Higashi, Ryo Nakabayashi, Makoto Kobayashi, Tetsuya Mori, Tomoko Nishizawa, Yumiko Takebayashi, Mikiko Kojima, Hitoshi Sakakibara, Kazuki Saito, Shuhei Hao, Yoshihito Shinozaki, Yoshihiro Okabe, Junji Kimbara, Tohru Ariizumi and Hiroshi Ezura Show full author list Hide full author list

Cells 2022, 11(9), 1420; https://doi.org/10.3390/cells11091420 - 22 Apr 2022

Cited by 3 | Viewed by 3520

Abstract

Parthenocarpy, the pollination-independent fruit set, can raise the productivity of the fruit set even under adverse factors during the reproductive phase. The application of plant hormones stimulates parthenocarpy, but artificial hormones incur extra financial and labour costs to farmers and can induce the formation of deformed fruit. This study examines the performance of parthenocarpic mutants having no transcription factors of SlIAA9 and SlTAP3 and sldella that do not have the protein-coding gene, SlDELLA, in tomato (cv. Micro-Tom). At 0 day after the flowering (DAF) stage and DAFs after pollination, the sliaa9 mutant demonstrated increased pistil development compared to the other two mutants and wild type (WT). In contrast to WT and the other mutants, the sliaa9 mutant with pollination efficiently stimulated the build-up of auxin and GAs after flowering. Alterations in both transcript and metabolite profiles existed for WT with and without pollination, while the three mutants without pollination demonstrated the comparable metabolomic status of pollinated WT. Network analysis showed key modules linked to photosynthesis, sugar metabolism and cell proliferation. Equivalent modules were noticed in the famous parthenocarpic cultivars ‘Severianin’, particularly for emasculated samples. Our discovery indicates that controlling the genes and metabolites proffers future breeding policies for tomatoes. Full article

(This article belongs to the Special Issue Metabolomics as a Tool for Functional Genomics)

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16 pages, 2490 KiB

Open AccessEditor’s ChoiceArticle

Alterations in Protein Translation and Carboxylic Acid Catabolic Processes in Diabetic Kidney Disease

by Kimberly S. Collins, Michael T. Eadon, Ying-Hua Cheng, Daria Barwinska, Ricardo Melo Ferreira, Thomas W. McCarthy, Danielle Janosevic, Farooq Syed, Bernhard Maier, Tarek M. El-Achkar, Katherine J. Kelly, Carrie L. Phillips, Takashi Hato, Timothy A. Sutton and Pierre C. Dagher

Cells 2022, 11(7), 1166; https://doi.org/10.3390/cells11071166 - 30 Mar 2022

Cited by 6 | Viewed by 2788

Abstract

Diabetic kidney disease (DKD) remains the leading cause of end-stage kidney disease despite decades of study. Alterations in the glomerulus and kidney tubules both contribute to the pathogenesis of DKD although the majority of investigative efforts have focused on the glomerulus. We sought to examine the differential expression signature of human DKD in the glomerulus and proximal tubule and corroborate our findings in the db/db mouse model of diabetes. A transcriptogram network analysis of RNAseq data from laser microdissected (LMD) human glomerulus and proximal tubule of DKD and reference nephrectomy samples revealed enriched pathways including rhodopsin-like receptors, olfactory signaling, and ribosome (protein translation) in the proximal tubule of human DKD biopsy samples. The translation pathway was also enriched in the glomerulus. Increased translation in diabetic kidneys was validated using polyribosomal profiling in the db/db mouse model of diabetes. Using single nuclear RNA sequencing (snRNAseq) of kidneys from db/db mice, we prioritized additional pathways identified in human DKD. The top overlapping pathway identified in the murine snRNAseq proximal tubule clusters and the human LMD proximal tubule compartment was carboxylic acid catabolism. Using ultra-performance liquid chromatography–mass spectrometry, the fatty acid catabolism pathway was also found to be dysregulated in the db/db mouse model. The Acetyl-CoA metabolite was down-regulated in db/db mice, aligning with the human differential expression of the genes ACOX1 and ACACB. In summary, our findings demonstrate that proximal tubular alterations in protein translation and carboxylic acid catabolism are key features in both human and murine DKD. Full article

(This article belongs to the Special Issue Metabolomics as a Tool for Functional Genomics)

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15 pages, 3031 KiB

Open AccessArticle

Metabolic Reprogramming and Its Relationship to Survival in Hepatocellular Carcinoma

by Qingqing Wang, Yexiong Tan, Tianyi Jiang, Xiaolin Wang, Qi Li, Yanli Li, Liwei Dong, Xinyu Liu and Guowang Xu

Cells 2022, 11(7), 1066; https://doi.org/10.3390/cells11071066 - 22 Mar 2022

Cited by 14 | Viewed by 3383

Abstract

Hepatocarcinogenesis is frequently accompanied by substantial metabolic reprogramming to maximize the growth and proliferation of cancer cells. In this study, we carried out a comprehensive study of metabolomics and lipidomics profiles combined with gene expression analysis to characterize the metabolic reprogramming in hepatocellular carcinoma (HCC). Compared with adjacent noncancerous liver tissue, the enhanced aerobic glycolysis and de novo lipogenesis (DNL) and the repressed urea cycle were underscored in HCC tissue. Furthermore, multiscale embedded correlation analysis was performed to construct differential correlation networks and reveal pathologically relevant molecule modules. The obtained hub nodes were further screened according to the maximum biochemical diversity and the least intraclass correlation. Finally, a panel of ornithine, FFA 18:1, PC O-32:1 and TG (18:1_17:1_18:2) was generated to achieve the prognostic risk stratification of HCC patients (p < 0.001 by log-rank test). Altogether, our findings suggest that the metabolic dysfunctions of HCC detected via metabolomics and lipidomics would contribute to a better understanding of clinical relevance of hepatic metabolic reprogramming and provide potential sources for the identification of therapeutic targets and the discovery of biomarkers. Full article

(This article belongs to the Special Issue Metabolomics as a Tool for Functional Genomics)

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16 pages, 1892 KiB

Open AccessArticle

Distinct Metabolomic Signatures in Preclinical and Obstructive Hypertrophic Cardiomyopathy

by Maike Schuldt, Beau van Driel, Sila Algül, Rahana Y. Parbhudayal, Daniela Q. C. M. Barge-Schaapveld, Ahmet Güçlü, Mark Jansen, Michelle Michels, Annette F. Baas, Mark A. van de Wiel, Max Nieuwdorp, Evgeni Levin, Tjeerd Germans, Judith J. M. Jans and Jolanda van der Velden

Cells 2021, 10(11), 2950; https://doi.org/10.3390/cells10112950 - 29 Oct 2021

Cited by 8 | Viewed by 2980

Abstract

Hypertrophic Cardiomyopathy (HCM) is a common inherited heart disease with poor risk prediction due to incomplete penetrance and a lack of clear genotype–phenotype correlations. Advanced imaging techniques have shown altered myocardial energetics already in preclinical gene variant carriers. To determine whether disturbed myocardial energetics with the potential to serve as biomarkers are also reflected in the serum metabolome, we analyzed the serum metabolome of asymptomatic carriers in comparison to healthy controls and obstructive HCM patients (HOCM). We performed non-quantitative direct-infusion high-resolution mass spectrometry-based untargeted metabolomics on serum from fasted asymptomatic gene variant carriers, symptomatic HOCM patients and healthy controls (n = 31, 14 and 9, respectively). Biomarker panels that discriminated the groups were identified by performing multivariate modeling with gradient-boosting classifiers. For all three group-wise comparisons we identified a panel of 30 serum metabolites that best discriminated the groups. These metabolite panels performed equally well as advanced cardiac imaging modalities in distinguishing the groups. Seven metabolites were found to be predictive in two different comparisons and may play an important role in defining the disease stage. This study reveals unique metabolic signatures in serum of preclinical carriers and HOCM patients that may potentially be used for HCM risk stratification and precision therapeutics. Full article

(This article belongs to the Special Issue Metabolomics as a Tool for Functional Genomics)

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23 pages, 5354 KiB

Open AccessArticle

Biological and Clinical Factors Contributing to the Metabolic Heterogeneity of Hospitalized Patients with and without COVID-19

by Angelo D’Alessandro, Tiffany Thomas, Imo J. Akpan, Julie A. Reisz, Francesca I. Cendali, Fabia Gamboni, Travis Nemkov, Kiruphagaran Thangaraju, Upendra Katneni, Kenichi Tanaka, Stacie Kahn, Alexander Z. Wei, Jacob E. Valk, Krystalyn E. Hudson, David Roh, Chiara Moriconi, James C. Zimring, Eldad A. Hod, Steven L. Spitalnik, Paul W. Buehler and Richard O. Francis Show full author list Hide full author list

Cells 2021, 10(9), 2293; https://doi.org/10.3390/cells10092293 - 2 Sep 2021

Cited by 40 | Viewed by 5619

Abstract

The Corona Virus Disease 2019 (COVID-19) pandemic represents an ongoing worldwide challenge. The present large study sought to understand independent and overlapping metabolic features of samples from acutely ill patients (n = 831) that tested positive (n = 543) or negative (n = 288) for COVID-19. High-throughput metabolomics analyses were complemented with antigen and enzymatic activity assays on plasma from acutely ill patients collected while in the emergency department, at admission, or during hospitalization. Lipidomics analyses were also performed on COVID-19-positive or -negative subjects with the lowest and highest body mass index (n = 60/group). Significant changes in amino acid and fatty acid/acylcarnitine metabolism emerged as highly relevant markers of disease severity, progression, and prognosis as a function of biological and clinical variables in these patients. Further, machine learning models were trained by entering all metabolomics and clinical data from half of the COVID-19 patient cohort and then tested on the other half, yielding ~78% prediction accuracy. Finally, the extensive amount of information accumulated in this large, prospective, observational study provides a foundation for mechanistic follow-up studies and data sharing opportunities, which will advance our understanding of the characteristics of the plasma metabolism in COVID-19 and other acute critical illnesses. Full article

(This article belongs to the Special Issue Metabolomics as a Tool for Functional Genomics)

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Review

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20 pages, 1187 KiB

Open AccessReview

Tapping into Plant–Microbiome Interactions through the Lens of Multi-Omics Techniques

by Ajay Kumar Mishra, Naganeeswaran Sudalaimuthuasari, Khaled M. Hazzouri, Esam Eldin Saeed, Iltaf Shah and Khaled M. A. Amiri

Cells 2022, 11(20), 3254; https://doi.org/10.3390/cells11203254 - 17 Oct 2022

Cited by 21 | Viewed by 5402

Abstract

This review highlights the pivotal role of root exudates in the rhizosphere, especially the interactions between plants and microbes and between plants and plants. Root exudates determine soil nutrient mobilization, plant nutritional status, and the communication of plant roots with microbes. Root exudates contain diverse specialized signaling metabolites (primary and secondary). The spatial behavior of these metabolites around the root zone strongly influences rhizosphere microorganisms through an intimate compatible interaction, thereby regulating complex biological and ecological mechanisms. In this context, we reviewed the current understanding of the biological phenomenon of allelopathy, which is mediated by phytotoxic compounds (called allelochemicals) released by plants into the soil that affect the growth, survival, development, ecological infestation, and intensification of other plant species and microbes in natural communities or agricultural systems. Advances in next-generation sequencing (NGS), such as metagenomics and metatranscriptomics, have opened the possibility of better understanding the effects of secreted metabolites on the composition and activity of root-associated microbial communities. Nevertheless, understanding the role of secretory metabolites in microbiome manipulation can assist in designing next-generation microbial inoculants for targeted disease mitigation and improved plant growth using the synthetic microbial communities (SynComs) tool. Besides a discussion on different approaches, we highlighted the advantages of conjugation of metabolomic approaches with genetic design (metabolite-based genome-wide association studies) in dissecting metabolome diversity and understanding the genetic components of metabolite accumulation. Recent advances in the field of metabolomics have expedited comprehensive and rapid profiling and discovery of novel bioactive compounds in root exudates. In this context, we discussed the expanding array of metabolomics platforms for metabolome profiling and their integration with multivariate data analysis, which is crucial to explore the biosynthesis pathway, as well as the regulation of associated pathways at the gene, transcript, and protein levels, and finally their role in determining and shaping the rhizomicrobiome. Full article

(This article belongs to the Special Issue Metabolomics as a Tool for Functional Genomics)

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26 pages, 1903 KiB

Open AccessReview

Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases

by Ali Kishk, Maria Pires Pacheco, Tony Heurtaux, Lasse Sinkkonen, Jun Pang, Sabrina Fritah, Simone P. Niclou and Thomas Sauter

Cells 2022, 11(16), 2486; https://doi.org/10.3390/cells11162486 - 10 Aug 2022

Cited by 4 | Viewed by 3677

Abstract

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition. Full article

(This article belongs to the Special Issue Metabolomics as a Tool for Functional Genomics)

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