Corrigendum: Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease

[This corrects the article DOI: 10.3389/fphar.2023.1243505.].

Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease

Background: We hypothesize that the poor survival outcomes of end-stage kidney disease (ESKD) patients undergoing hemodialysis are associated with a low filtering efficiency and selectivity. The current gold standard criteria using single or several markers show an inability to predict or disclose the treatment effect and disease progression accurately. Methods: We performed an integrated mass spectrometry-based metabolomic and proteomic workflow capable of detecting and quantifying circulating small molecules and proteins in the serum of ESKD patients. Markers linked to cardiovascular disease (CVD) were validated on human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. Results: We identified dozens of elevated molecules in the serum of patients compared with healthy controls. Surprisingly, many metabolites, including lipids, remained at an elevated blood concentration despite dialysis. These molecules and their associated physical interaction networks are correlated with clinical complications in chronic kidney disease. This study confirmed two uremic toxins associated with CVD, a major risk for patients with ESKD. Conclusion: The retained molecules and metabolite-protein interaction network address a knowledge gap of candidate uremic toxins associated with clinical complications in patients undergoing dialysis, providing mechanistic insights and potential drug discovery strategies for ESKD.

Human wild-type and D76N β2-microglobulin variants are significant proteotoxic and metabolic stressors for transgenic C. elegans

β2-microglobulin (β2-m) is a plasma protein derived from physiological shedding of the class I major histocompatibility complex (MHCI), causing human systemic amyloidosis either due to persistently high concentrations of the wild-type (WT) protein in hemodialyzed patients, or in presence of mutations, such as D76N β2-m, which favor protein deposition in the adulthood, despite normal plasma levels. Here we describe a new transgenic Caenorhabditis elegans (C. elegans) strain expressing human WT β2-m at high concentrations, mimicking the condition that underlies dialysis-related amyloidosis (DRA) and we compare it to a previously established strain expressing the highly amyloidogenic D76N β2-m at lower concentrations. Both strains exhibit behavioral defects, the severity of which correlates with β2-m levels rather than with the presence of mutations, being more pronounced in WT β2-m worms. β2-m expression also has a deep impact on the nematodes' proteomic and metabolic profiles. Most significantly affected processes include protein degradation and stress response, amino acids metabolism, and bioenergetics. Molecular alterations are more pronounced in worms expressing WT β2-m at high concentration compared to D76N β2-m worms. Altogether, these data show that β2-m is a proteotoxic protein in vivo also in its wild-type form, and that concentration plays a key role in modulating pathogenicity. Our transgenic nematodes recapitulate the distinctive features subtending DRA compared to hereditary β2-m amyloidosis (high levels of non-mutated β2-m vs. normal levels of variant β2-m) and provide important clues on the molecular bases of these human diseases.

Transcriptional and translational dynamics underlying heat shock response in the thermophilic crenarchaeon Sulfolobus acidocaldarius

IMPORTANCE

Heat shock response is the ability to respond adequately to sudden temperature increases that could be harmful for cellular survival and fitness. It is crucial for microorganisms living in volcanic hot springs that are characterized by high temperatures and large temperature fluctuations. In this study, we investigated how S. acidocaldarius, which grows optimally at 75°C, responds to heat shock by altering its gene expression and protein production processes. We shed light on which cellular processes are affected by heat shock and propose a hypothesis on underlying regulatory mechanisms. This work is not only relevant for the organism's lifestyle, but also with regard to its evolutionary status. Indeed, S. acidocaldarius belongs to the archaea, an ancient group of microbes that is more closely related to eukaryotes than to bacteria. Our study thus also contributes to a better understanding of the early evolution of heat shock response.

Human Bone Marrow-Derived Mesenchymal Stem Cell Applications in Neurodegenerative Disease Treatment and Integrated Omics Analysis for Successful Stem Cell Therapy

Neurodegenerative diseases (NDDs), which are chronic and progressive diseases, are a growing health concern. Among the therapeutic methods, stem-cell-based therapy is an attractive approach to NDD treatment owing to stem cells' characteristics such as their angiogenic ability, anti-inflammatory, paracrine, and anti-apoptotic effects, and homing ability to the damaged brain region. Human bone-marrow-derived mesenchymal stem cells (hBM-MSCs) are attractive NDD therapeutic agents owing to their widespread availability, easy attainability and in vitro manipulation and the lack of ethical issues. Ex vivo hBM-MSC expansion before transplantation is essential because of the low cell numbers in bone marrow aspirates. However, hBM-MSC quality decreases over time after detachment from culture dishes, and the ability of hBM-MSCs to differentiate after detachment from culture dishes remains poorly understood. Conventional analysis of hBM-MSCs characteristics before transplantation into the brain has several limitations. However, omics analyses provide more comprehensive molecular profiling of multifactorial biological systems. Omics and machine learning approaches can handle big data and provide more detailed characterization of hBM-MSCs. Here, we provide a brief review on the application of hBM-MSCs in the treatment of NDDs and an overview of integrated omics analysis of the quality and differentiation ability of hBM-MSCs detached from culture dishes for successful stem cell therapy.

Transcriptomics and metabolomics reveal the role of CYP1A2 in psoralen/isopsoralen-induced metabolic activation and hepatotoxicity

Psoralen and isopsoralen are the pharmacologically important but hepatotoxic components in Psoraleae Fructus. The purpose of this study was to reveal the underlying mechanism of psoralen/isopsoralen-induced hepatotoxicity. Initially, we applied integrated analyses of transcriptomic and metabolomic profiles in mice treated with psoralen and isopsoralen, highlighting the xenobiotic metabolism by cytochromes P450 as a potential pathway. Then, with verifications of expression levels by qRT-PCR and western blot, affinities by molecular docking, and metabolic contributions by recombinant human CYP450 and mouse liver microsomes, CYP1A2 was screened out as the key metabolic enzyme. Afterwards, CYP1A2 induction and inhibition models in HepG2 cells and mice were established to verify the role of CYP1A2, demonstrating that induction of CYP1A2 aggravated the hepatotoxicity, and conversely inhibition alleviated the hepatotoxic effects. Additionally, we detected glutathione adducts with reactive intermediates of psoralen and isopsoralen generated by CYP1A2 metabolism in biosystems of recombinant human CYP1A2 and mouse liver microsomes, CYP1A2-overexpressed HepG2 cells, mice livers and the chemical reaction system using UPLC-Q-TOF-MS/MS. Ultimately, the high-content screening presented the cellular oxidative stress and relevant hepatotoxicity due to glutathione depletion by reactive intermediates. In brief, our findings illustrated that CYP1A2-mediated metabolic activation is responsible for the psoralen/isopsoralen-induced hepatotoxicity.

Prior metabolite extraction fully preserves RNAseq quality and enables integrative multi-'omics analysis of the liver metabolic response to viral infection

Here, we provide an in-depth analysis of the usefulness of single-sample metabolite/RNA extraction for multi-'omics readout. Using pulverized frozen livers of mice injected with lymphocytic choriomeningitis virus (LCMV) or vehicle (Veh), we isolated RNA prior (RNA) or following metabolite extraction (MetRNA). RNA sequencing (RNAseq) data were evaluated for differential expression analysis and dispersion, and differential metabolite abundance was determined. Both RNA and MetRNA clustered together by principal component analysis, indicating that inter-individual differences were the largest source of variance. Over 85% of LCMV versus Veh differentially expressed genes were shared between extraction methods, with the remaining 15% evenly and randomly divided between groups. Differentially expressed genes unique to the extraction method were attributed to randomness around the 0.05 FDR cut-off and stochastic changes in variance and mean expression. In addition, analysis using the mean absolute difference showed no difference in the dispersion of transcripts between extraction methods. Altogether, our data show that prior metabolite extraction preserves RNAseq data quality, which enables us to confidently perform integrated pathway enrichment analysis on metabolomics and RNAseq data from a single sample. This analysis revealed pyrimidine metabolism as the most LCMV-impacted pathway. Combined analysis of genes and metabolites in the pathway exposed a pattern in the degradation of pyrimidine nucleotides leading to uracil generation. In support of this, uracil was among the most differentially abundant metabolites in serum upon LCMV infection. Our data suggest that hepatic uracil export is a novel phenotypic feature of acute infection and highlight the usefulness of our integrated single-sample multi-'omics approach.

Multi-omics intervention in Setaria to dissect climate-resilient traits: Progress and prospects

Millets constitute a significant proportion of underutilized grasses and are well known for their climate resilience as well as excellent nutritional profiles. Among millets, foxtail millet (Setaria italica) and its wild relative green foxtail (S. viridis) are collectively regarded as models for studying broad-spectrum traits, including abiotic stress tolerance, C₄ photosynthesis, biofuel, and nutritional traits. Since the genome sequence release, the crop has seen an exponential increase in omics studies to dissect agronomic, nutritional, biofuel, and climate-resilience traits. These studies have provided first-hand information on the structure, organization, evolution, and expression of several genes; however, knowledge of the precise roles of such genes and their products remains elusive. Several open-access databases have also been instituted to enable advanced scientific research on these important crops. In this context, the current review enumerates the contemporary trend of research on understanding the climate resilience and other essential traits in Setaria, the knowledge gap, and how the information could be translated for the crop improvement of related millets, biofuel crops, and cereals. Also, the review provides a roadmap for studying other underutilized crop species using Setaria as a model.

Cerebrospinal Fluid Metabolome in Parkinson's Disease and Multiple System Atrophy

Parkinson’s disease (PD) and multiple system atrophy (MSA) belong to the neurodegenerative group of synucleinopathies; differential diagnosis between PD and MSA is difficult, especially at early stages, owing to their clinical and biological similarities. Thus, there is a pressing need to identify metabolic biomarkers for these diseases. The metabolic profile of the cerebrospinal fluid (CSF) is reported to be altered in PD and MSA; however, the altered metabolites remain unclear. We created a single network with altered metabolites in PD and MSA based on the literature and assessed biological functions, including metabolic disorders of the nervous system, inflammation, concentration of ATP, and neurological disorder, through bioinformatics methods. Our in-silico prediction-based metabolic networks are consistent with Parkinsonism events. Although metabolomics approaches provide a more quantitative understanding of biochemical events underlying the symptoms of PD and MSA, limitations persist in covering molecules related to neurodegenerative disease pathways. Thus, omics data, such as proteomics and microRNA, help understand the altered metabolomes mechanism. In particular, integrated omics and machine learning approaches will be helpful to elucidate the pathological mechanisms of PD and MSA. This review discusses the altered metabolites between PD and MSA in the CSF and omics approaches to discover diagnostic biomarkers.

Diesel-derived PM2.5 induces impairment of cardiac movement followed by mitochondria dysfunction in cardiomyocytes

Particulate matter (PM) in polluted air can be exposed to the human body through inhalation, ingestion, and skin contact, accumulating in various organs throughout the body. Organ accumulation of PM is a growing health concern, particularly in the cardiovascular system. PM emissions are formed in the air by solid particles, liquid droplets, and fuel - particularly diesel - combustion. PM_2.5 (size < 2.5 μm particle) is a major risk factor for approximately 200,000 premature deaths annually caused by air pollution. This study assessed the deleterious effects of diesel-derived PM_2.5 exposure in HL-1 mouse cardiomyocyte cell lines. The PM_2.5-induced biological changes, including ultrastructure, intracellular reactive oxygen species (ROS) generation, viability, and intracellular ATP levels, were analyzed. Moreover, we analyzed changes in transcriptomics using RNA sequencing and metabolomics using gas chromatography-tandem mass spectrometry (GC-MS/MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) in PM_2.5-treated HL-1 cells. Ultrastructural analysis using transmission electron microscopy revealed disruption of mitochondrial cristae structures in a PM_2.5 dose-dependent manner. The elevation of ROS levels and reduction in cell viability and ATP levels were similarly observed in a PM_2.5 dose-dependently. In addition, 6,005 genes were differentially expressed (fold change cut-off ± 4) from a total of 45,777 identified genes, and 20 amino acids (AAs) were differentially expressed (fold change cut-off ± 1.2) from a total of 28 identified AAs profiles. Using bioinformatic analysis with ingenuity pathway analysis (IPA) software, we found that the changes in the transcriptome and metabolome are highly related to changes in biological functions, including homeostasis of Ca²⁺, depolarization of mitochondria, the function of mitochondria, synthesis of ATP, and cardiomyopathy. Moreover, an integrated single omics network was constructed by combining the transcriptome and the metabolome. In silico prediction analysis with IPA predicted that upregulation of mitochondria depolarization, ROS generation, cardiomyopathy, suppression of Ca²⁺ homeostasis, mitochondrial function, and ATP synthesis occurred in PM_2.5-treated HL-1 cells. In particular, the cardiac movement of HL-1 was significantly reduced after PM_2.5 treatment. In conclusion, our results assessed the harmful effects of PM_2.5 on mitochondrial function and analyzed the biological changes related to cardiac movement, which is potentially associated with cardiovascular diseases.

Mitochondrial Effects in the Liver of C57BL/6 Mice by Low Dose, High Energy, High Charge Irradiation

Galactic cosmic rays are primarily composed of protons (85%), helium (14%), and high charge/high energy ions (HZEs) such as ⁵⁶Fe, ²⁸Si, and ¹⁶O. HZE exposure is a major risk factor for astronauts during deep-space travel due to the possibility of HZE-induced cancer. A systems biology integrated omics approach encompassing transcriptomics, proteomics, lipidomics, and functional biochemical assays was used to identify microenvironmental changes induced by HZE exposure. C57BL/6 mice were placed into six treatment groups and received the following irradiation treatments: 600 MeV/n ⁵⁶Fe (0.2 Gy), 1 GeV/n ¹⁶O (0.2 Gy), 350 MeV/n ²⁸Si (0.2 Gy), ¹³⁷Cs (1.0 Gy) gamma rays, ¹³⁷Cs (3.0 Gy) gamma rays, and sham irradiation. Left liver lobes were collected at 30, 60, 120, 270, and 360 days post-irradiation. Analysis of transcriptomic and proteomic data utilizing ingenuity pathway analysis identified multiple pathways involved in mitochondrial function that were altered after HZE irradiation. Lipids also exhibited changes that were linked to mitochondrial function. Molecular assays for mitochondrial Complex I activity showed significant decreases in activity after HZE exposure. HZE-induced mitochondrial dysfunction suggests an increased risk for deep space travel. Microenvironmental and pathway analysis as performed in this research identified possible targets for countermeasures to mitigate risk.

Top Trends in Multiomics Research: Evaluation of 52 Published Studies and New Ways of Thinking Terminology and Visual Displays

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.

Distinct Clinical Impact and Biological Function of Angiopoietin and Angiopoietin-like Proteins in Human Breast Cancer

Secreted angiopoietin/angiopoietin-like (ANGPT/ANGPTL) proteins are involved in many biological processes. However, the role of these proteins in human breast cancers (BCs) remains largely unclear. Here, we conducted integrated omics analyses to evaluate the clinical impact of ANGPT/ANGPTL proteins and to elucidate their biological functions. In BCs, we identified rare mutations in ANGPT/ANGPTL genes, frequent gains of ANGPT1, ANGPT4, and ANGPTL1, and frequent losses of ANGPT2, ANGPTL5, and ANGPTL7, but observed that ANGPTL1, 2, and 4 were robustly downregulated in multiple datasets. The expression levels of ANGPTL1, 5, and 8 were positively correlated with overall survival (OS), while the expression levels of ANGPTL4 were negatively correlated with OS. Additionally, the expression levels of ANGPTL1 and 7 were positively correlated with distant metastasis-free survival (DMFS), while the expression levels of ANGPT2 and ANGPTL4 were negatively correlated with DMFS. The prognostic impacts of ANGPT/ANGPTL genes depended on the molecular subtypes and on clinical factors. We discovered that various ANGPT/ANGPTL genes were co-expressed with various genes involved in different pathways. Finally, with the exception of ANGPTL3, the remaining genes showed significant correlations with cancer-associated fibroblasts, endothelial cells, and microenvironment score, whereas only ANGPTL6 was significantly correlated with immune score. Our findings provide strong evidence for the distinct clinical impact and biological function of ANGPT/ANGPTL proteins, but the question of whether some of them could be potential therapeutic targets still needs further investigation in BCs.

Analysis of Nanotoxicity with Integrated Omics and Mechanobiology

Nanoparticles (NPs) in biomedical applications have benefits owing to their small size. However, their intricate and sensitive nature makes an evaluation of the adverse effects of NPs on health necessary and challenging. Since there are limitations to conventional toxicological methods and omics analyses provide a more comprehensive molecular profiling of multifactorial biological systems, omics approaches are necessary to evaluate nanotoxicity. Compared to a single omics layer, integrated omics across multiple omics layers provides more sensitive and comprehensive details on NP-induced toxicity based on network integration analysis. As multi-omics data are heterogeneous and massive, computational methods such as machine learning (ML) have been applied for investigating correlation among each omics. This integration of omics and ML approaches will be helpful for analyzing nanotoxicity. To that end, mechanobiology has been applied for evaluating the biophysical changes in NPs by measuring the traction force and rigidity sensing in NP-treated cells using a sub-elastomeric pillar. Therefore, integrated omics approaches are suitable for elucidating mechanobiological effects exerted by NPs. These technologies will be valuable for expanding the safety evaluations of NPs. Here, we review the integration of omics, ML, and mechanobiology for evaluating nanotoxicity.

Integrated mass spectrometry-based multi-omics for elucidating mechanisms of bacterial virulence

Despite being considered the simplest form of life, bacteria remain enigmatic, particularly in light of pathogenesis and evolving antimicrobial resistance. After three decades of genomics, we remain some way from understanding these organisms, and a substantial proportion of genes remain functionally unknown. Methodological advances, principally mass spectrometry (MS), are paving the way for parallel analysis of the proteome, metabolome and lipidome. Each provides a global, complementary assay, in addition to genomics, and the ability to better comprehend how pathogens respond to changes in their internal (e.g. mutation) and external environments consistent with infection-like conditions. Such responses include accessing necessary nutrients for survival in a hostile environment where co-colonizing bacteria and normal flora are acclimated to the prevailing conditions. Multi-omics can be harnessed across temporal and spatial (sub-cellular) dimensions to understand adaptation at the molecular level. Gene deletion libraries, in conjunction with large-scale approaches and evolving bioinformatics integration, will greatly facilitate next-generation vaccines and antimicrobial interventions by highlighting novel targets and pathogen-specific pathways. MS is also central in phenotypic characterization of surface biomolecules such as lipid A, as well as aiding in the determination of protein interactions and complexes. There is increasing evidence that bacteria are capable of widespread post-translational modification, including phosphorylation, glycosylation and acetylation; with each contributing to virulence. This review focuses on the bacterial genotype to phenotype transition and surveys the recent literature showing how the genome can be validated at the proteome, metabolome and lipidome levels to provide an integrated view of organism response to host conditions.

Myb10-D confers PHS-3D resistance to pre-harvest sprouting by regulating NCED in ABA biosynthesis pathway of wheat

Pre-harvest sprouting (PHS), the germination of grain before harvest, is a serious problem resulting in wheat yield and quality losses. Here, we mapped the PHS resistance gene PHS-3D from synthetic hexaploid wheat to a 2.4 Mb presence-absence variation (PAV) region and found that its resistance effect was attributed to the pleiotropic Myb10-D by integrated omics and functional analyses. Three haplotypes were detected in this PAV region among 262 worldwide wheat lines and 16 Aegilops tauschii, and the germination percentages of wheat lines containing Myb10-D was approximately 40% lower than that of the other lines. Transcriptome and metabolome profiling indicated that Myb10-D affected the transcription of genes in both the flavonoid and abscisic acid (ABA) biosynthesis pathways, which resulted in increases in flavonoids and ABA in transgenic wheat lines. Myb10-D activates 9-cis-epoxycarotenoid dioxygenase (NCED) by biding the secondary wall MYB-responsive element (SMRE) to promote ABA biosynthesis in early wheat seed development stages. We revealed that the newly discovered function of Myb10-D confers PHS resistance by enhancing ABA biosynthesis to delay germination in wheat. The PAV harboring Myb10-D associated with grain color and PHS will be useful for understanding and selecting white grained PHS resistant wheat cultivars.

Integrated Metabolomics and Transcriptomics Using an Optimised Dual Extraction Process to Study Human Brain Cancer Cells and Tissues

The integration of untargeted metabolomics and transcriptomics from the same population of cells or tissue enhances the confidence in the identified metabolic pathways and understanding of the enzyme–metabolite relationship. Here, we optimised a simultaneous extraction method of metabolites/lipids and RNA from ependymoma cells (BXD-1425). Relative to established RNA (mirVana kit) or metabolite (sequential solvent addition and shaking) single extraction methods, four dual-extraction techniques were evaluated and compared (methanol:water:chloroform ratios): cryomill/mirVana (1:1:2); cryomill-wash/Econospin (5:1:2); rotation/phenol-chloroform (9:10:1); Sequential/mirVana (1:1:3). All methods extracted the same metabolites, yet rotation/phenol-chloroform did not extract lipids. Cryomill/mirVana and sequential/mirVana recovered the highest amounts of RNA, at 70 and 68% of that recovered with mirVana kit alone. sequential/mirVana, involving RNA extraction from the interphase of our established sequential solvent addition and shaking metabolomics-lipidomics extraction method, was the most efficient approach overall. Sequential/mirVana was applied to study a) the biological effect caused by acute serum starvation in BXD-1425 cells and b) primary ependymoma tumour tissue. We found (a) 64 differentially abundant metabolites and 28 differentially expressed metabolic genes, discovering four gene-metabolite interactions, and (b) all metabolites and 62% lipids were above the limit of detection, and RNA yield was sufficient for transcriptomics, in just 10 mg of tissue.

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

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

Plasma Metabolomic Signatures of Chronic Obstructive Pulmonary Disease and the Impact of Genetic Variants on Phenotype-Driven Modules

Background: Small studies have recently suggested that there are specific plasma metabolic signatures in chronic obstructive pulmonary disease (COPD), but there have been no large comprehensive study of metabolomic signatures in COPD that also integrate genetic variants. Materials and Methods: Fresh frozen plasma from 957 non-Hispanic white subjects in COPDGene was used to quantify 995 metabolites with Metabolon's global metabolomics platform. Metabolite associations with five COPD phenotypes (chronic bronchitis, exacerbation frequency, percent emphysema, post-bronchodilator forced expiratory volume at one second [FEV1]/forced vital capacity [FVC], and FEV1 percent predicted) were assessed. A metabolome-wide association study was performed to find genetic associations with metabolite levels. Significantly associated single-nucleotide polymorphisms were tested for replication with independent metabolomic platforms and independent cohorts. COPD phenotype-driven modules were identified in network analysis integrated with genetic associations to assess gene-metabolite-phenotype interactions. Results: Of metabolites tested, 147 (14.8%) were significantly associated with at least 1 COPD phenotype. Associations with airflow obstruction were enriched for diacylglycerols and branched chain amino acids. Genetic associations were observed with 109 (11%) metabolites, 72 (66%) of which replicated in an independent cohort. For 20 metabolites, more than 20% of variance was explained by genetics. A sparse network of COPD phenotype-driven modules was identified, often containing metabolites missed in previous testing. Of the 26 COPD phenotype-driven modules, 6 contained metabolites with significant met-QTLs, although little module variance was explained by genetics. Conclusion: A dysregulation of systemic metabolism was predominantly found in COPD phenotypes characterized by airflow obstruction, where we identified robust heritable effects on individual metabolite abundances. However, network analysis, which increased the statistical power to detect associations missed previously in classic regression analyses, revealed that the genetic influence on COPD phenotype-driven metabolomic modules was modest when compared with clinical and environmental factors.

Multiple Small RNAs Interact to Co-regulate Ethanol Tolerance in Zymomonas mobilis

sRNAs represent a powerful class of regulators that influences multiple mRNA targets in response to environmental changes. However, very few direct sRNA-sRNA interactions have been deeply studied in any organism. Zymomonas mobilis is a bacterium with unique ethanol-producing metabolic pathways in which multiple small RNAs (sRNAs) have recently been identified, some of which show differential expression in ethanol stress. In this study, we show that two sRNAs (Zms4 and Zms6) are upregulated under ethanol stress and have significant impacts on ethanol tolerance and production in Z. mobilis. We conducted multi-omics analysis (combining transcriptomics and sRNA-immunoprecipitation) to map gene networks under the influence of their regulation. We confirmed that Zms4 and Zms6 bind multiple RNA targets and regulate their expressions, influencing many downstream pathways important to ethanol tolerance and production. In particular, Zms4 and Zms6 interact with each other as well as many other sRNAs, forming a novel sRNA-sRNA direct interaction network. This study thus uncovers a sRNA network that co-orchestrates multiple ethanol related pathways through a diverse set of mRNA targets and a large number of sRNAs. To our knowledge, this study represents one of the largest sRNA-sRNA direct interactions uncovered so far.

Integrated Metabolome and Transcriptome Analyses Reveal Etiolation-Induced Metabolic Changes Leading to High Amino Acid Contents in a Light-Sensitive Japanese Albino Tea Cultivar

Plant albinism causes the etiolation of leaves because of factors such as deficiency of chloroplasts or chlorophylls. In general, albino tea leaves accumulate higher free amino acid (FAA) contents than do conventional green tea leaves. To explore the metabolic changes of etiolated leaves (EL) in the light-sensitive Japanese albino tea cultivar "Koganemidori," we performed integrated metabolome and transcriptome analyses by comparing EL with green leaves induced by bud-sport mutation (BM) or shading treatments (S-EL). Comparative omics analyses indicated that etiolation-induced molecular responses were independent of the light environment and were largely influenced by the etiolation itself. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and pathway analyses revealed the downregulation of genes involved in chloroplast development and chlorophyll biosynthesis and upregulation of protein degradation-related pathways, such as the ubiquitin-proteasome system and autophagy in EL. Metabolome analysis showed that most quantified FAAs in EL were highly accumulated compared with those in BM and S-EL. Genes involved in the tricarboxylic acid (TCA) cycle, nitrogen assimilation, and the urea cycle, including the drastically downregulated Arginase-1 homolog, which functions in nitrogen excretion for recycling, showed lower expression levels in EL. The high FAA contents in EL might result from the increased FAA pool and nitrogen source contributed by protein degradation, low N consumption, and stagnation of the urea cycle rather than through enhanced amino acid biosynthesis.

Protein and Proteome Atlas for Plants under Stresses: New Highlights and Ways for Integrated Omics in Post-Genomics Era

In the post-genomics era, integrative omics studies for biochemical, physiological, and molecular changes of plants in response to stress conditions play more crucial roles. Among them, atlas analysis of plants under different abiotic stresses, including salinity, drought, and toxic conditions, has become more important for uncovering the potential key genes and proteins in different plant tissues. High-quality genomic data and integrated analyses of transcriptomic, proteomic, metabolomics, and phenomic patterns provide a deeper understanding of how plants grow and survive under environmental stresses. This editorial mini-review aims to synthesize the 27 papers including two timely reviews that have contributed to this Special Issue, which focuses on concluding the recent progress in the Protein and Proteome Atlas in plants under different stresses. It covers various aspects of plant proteins ranging from agricultural proteomics, structure and function of proteins, novel techniques and approaches for gene and protein identification, protein quantification, proteomics for post-translational modifications (PTMs), and new insights into proteomics. The proteomics-based results in this issue will help the readers to gain novel insights for the understanding of complicated physiological processes in crops and other important plants in response to stressed conditions. Furthermore, these target genes and proteins that are important candidates for further functional validation in economic plants and crops can be studied.

Integration of transcriptomic and proteomic data identifies biological functions in cell populations from human infant lung

Systems biology uses computational approaches to integrate diverse data types to understand cell and organ behavior. Data derived from complementary technologies, for example transcriptomic and proteomic analyses, are providing new insights into development and disease. We compared mRNA and protein profiles from purified endothelial, epithelial, immune, and mesenchymal cells from normal human infant lung tissue. Signatures for each cell type were identified and compared at both mRNA and protein levels. Cell-specific biological processes and pathways were predicted by analysis of concordant and discordant RNA-protein pairs. Cell clustering and gene set enrichment comparisons identified shared versus unique processes associated with transcriptomic and/or proteomic data. Clear cell-cell correlations between mRNA and protein data were obtained from each cell type. Approximately 40% of RNA-protein pairs were coherently expressed. While the correlation between RNA and their protein products was relatively low (Spearman rank coefficient r_s ~0.4), cell-specific signature genes involved in functional processes characteristic of each cell type were more highly correlated with their protein products. Consistency of cell-specific RNA-protein signatures indicated an essential framework for the function of each cell type. Visualization and reutilization of the protein and RNA profiles are supported by a new web application, "LungProteomics," which is freely accessible to the public.

Can Omics Approaches Improve Microalgal Biofuels under Abiotic Stress?

Microalgae hold the promise of an inexpensive and sustainable source of biofuels. The existing microalgal cultivation technologies need significant improvement to outcompete other biofuel sources such as terrestrial plants. Application of 'algomics' approaches under different abiotic stress conditions could be an effective strategy for optimization of microalgal growth and production of high-quality biofuels. In this review, we discuss the roles of omics in understanding genome structure and biocomponents metabolism in various microalgal species to optimize sustainable biofuel production. Application of individual and integrated omics revealed that genes and metabolic pathways of microalgae have been altered under multiple stress conditions, resulting in an increase in biocomponents, providing a research platform for expansion of genetic engineering studies in microalgal strains.

Integration of "omics" techniques: Dronedarone affects cardiac remodeling in the infarction border zone

Dronedarone improves microvascular flow during atrial fibrillation and reduces the infarct size in acute models of myocardial infarction. However, dronedarone might be harmful in patients with recent decompensated heart failure and increases mortality in patients with permanent atrial fibrillation. A pathophysiological explanation for these discrepant data is lacking. This study investigated the effects of dronedarone on gene and protein expression in the infarcted area and border zone in pigs subjected to anterior ischemia/reperfusion myocardial infarction. The ischemia/reperfusion myocardial infarction was induced in 16 pigs. Eight pigs were treated with dronedarone for 28 days after myocardial infarction, the remaining pigs served as control. Microarray-based transcriptome profiling and 2D-DIGE-based proteome analysis were used to assess the effects of dronedarone on left ventricular gene expression in healthy (LV), infarcted (MI), and border zone tissue. Selected targets were validated by RT-qPCR or immunoblot analyses, with special emphasize given to the transcriptome/proteome overlap. Combined "omics" analysis was performed to identify most significant disease and function charts affected by dronedarone and to establish an integrated network. The levels of 879 (BZ) or 7 (MI) transcripts and 51 (LV) or 15 (BZ) proteins were significantly altered by dronedarone, pointing to a substantial efficacy of dronedarone in the border zone. Transcriptome and proteome data indicate that dronedarone influences post-infarction remodeling processes and identify matricellular proteins as major targets of dronedarone in this setting. This finding is fully supported by the disease and function charts as well as by the integrated network established by combined "omics". Dronedarone therapy alters myocardial gene expression after acute myocardial infarction with pronounced effects in the border zone. Dronedarone promotes infarct healing via regulation of periostin and might contribute to the limitation of its expansion as well as cardiac rupture. Thus, there are no experimental hints that dronedarone per se has direct harmful effects after MI in ventricular tissue. Impact statement Dronedarone reduced the infarct size in models of acute myocardial infarction (MI). Here, we show that dronedarone attenuates many of the substantial changes in gene expression that are provoked by acute myocardial infarction (AMI) in pigs. Dronedarone modifies the expression of gene panels related to post-infarction cardiac healing and remodeling processes and, most remarkably, this occurs predominantly in the infarction border-zone and much less so in the vital or infarcted myocardium. Combined "omics" identified matricellular proteins and ECM as major dronedarone-regulated targets and emphasizes their relevance for Disease Charts and Tox Function Charts associated with tissue remodeling and cellular movement. The results demonstrate dronedarone's capability of regulating cardiac repair and remodeling processes specifically in the infarction border zone and identify underlying mechanisms and pathways that might be employed in future therapeutic strategies to improve long-term cardiac tissue function and stability.

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Integration of metabolomics and transcriptomics in nanotoxicity studies

Biomedical research involving nanoparticles has produced useful products with medical applications. However, the potential toxicity of nanoparticles in biofluids, cells, tissues, and organisms is a major challenge. The '-omics' analyses provide molecular profiles of multifactorial biological systems instead of focusing on a single molecule. The 'omics' approaches are necessary to evaluate nanotoxicity because classical methods for the detection of nanotoxicity have limited ability in detecting miniscule variations within a cell and do not accurately reflect the actual levels of nanotoxicity. In addition, the 'omics' approaches allow analyses of in-depth changes and compensate for the differences associated with high-throughput technologies between actual nanotoxicity and results from traditional cytotoxic evaluations. However, compared with a single omics approach, integrated omics provides precise and sensitive information by integrating complex biological conditions. Thus, these technologies contribute to extended safety evaluations of nanotoxicity and allow the accurate diagnoses of diseases far earlier than was once possible in the nanotechnology era. Here, we review a novel approach for evaluating nanotoxicity by integrating metabolomics with metabolomic profiling and transcriptomics, which is termed "metabotranscriptomics". [BMB Reports 2018; 51(1): 14-20].

A hundred years of activated sludge: time for a rethink

Biological wastewater treatment plants (BWWTPs) based on the activated sludge (AS) process have dramatically improved worldwide water sanitation despite increased urbanization and industrialization. However, current AS-based operations are considered economically and environmentally unsustainable. In this Perspective, we discuss our current understanding of microbial populations and their metabolic transformations in AS-based BWWTPs in view of developing more sustainable processes in the future. In particular, much has been learned over the course of the past 25 years about specialized microorganisms, which could be more comprehensively leveraged to recover energy and/or nutrients from wastewater streams. To achieve this, we propose a bottom-up design approach, focused around the concept of a "wastewater biorefinery column", which would rely on the engineering of distinct ecological niches into a BWWTP in order to guarantee the targeted enrichment of specific organismal groups which in turn will allow the harvest of high-value resources from wastewater. This concept could be seen as a possible grand challenge to microbial ecologists and engineers alike at the centenary of the discovery of the AS process.