Transcriptomic and metabolomic data integration

Integrated multi-omics analysis of metabolome and transcriptome profiles during bovine adipocyte differentiation reveals functional divergence of FADS2 isoforms in lipid metabolism regulation

BACKGROUND

Fat metabolism plays an important role in animal health and economic benefits. However, the changes in gene expression and metabolites during fat metabolism have not been systematically studied in bovine.

RESULTS

This study integrates transcriptomic and metabolomic strategies to delineate the metabolic and gene expression profiles during the adipogenesis of bovine preadipocytes in four different stages. Totally, we identified 328 differentially expressed metabolites (DEMs) and 5257 differentially expressed genes (DEGs) during adipogenesis. Functional enrichment of both DEMs and DEGs highlighted the important roles of fatty acid metabolic pathways. By integrating transcriptomic and metabolomic data, we identified key genes potentially regulating fatty acid metabolism, including FADS2, ACOT7 and ACOT2. We further applied comparison for the functional differences between two FADS2 isoforms (FADS2-T0 and FADS2-T2). The results proved that the lipid metabolism regulated by FADS2-2 has changed due to the loss of 46 amino acids with a transmembrane domain, which finally altering its promoting effect on bovine fat deposition.

CONCLUSIONS

In summary, our research provides important resources and key candidate genes for a systematic understanding of the changes in gene expression and lipid metabolism during the process of fat deposition.

Exploring associations between metabolites and gene transcripts of common bean (Phaseolus vulgaris L.) in response to rust (Uromyces appendiculatus) infection

Common bean (Phaseolus vulgaris L.) faces escalating challenges resulting from the increasing prevalence of fungal pathogens such as rust caused by Uromyces appendiculatus, threatening yields and quality of the crop. Understanding P. vulgaris' disease response mechanisms is pivotal for the crop's resilience and food security. Current scientific understanding of underlying molecular mechanisms of P. vulgaris to U. appendiculatus is limited, particularly with respect to specialised molecular data, including metabolite profiles and gene expression. There is a significant knowledge gap in explicating precise metabolomic and transcriptional changes that occur in P. vulgaris upon interaction with U. appendiculatus, which limits strategies aimed at enhancing pathogen resistance. In this study, biological stress response strategies of common bean to the rust pathogen were elucidated through a combined metabolomic and transcriptomic profiling approach. Our findings revealed that U. appendiculatus triggered diverse levels of 30 known metabolites, primarily flavonoids, lipids, nucleosides, and phenylpropanoids among others. Transcriptome sequencing detected over 3000 differentially expressed genes, including multiple transcription factor families such as heat shock proteins (HSPs), cytochrome P450 monooxygenases (CYP), terpene synthases and WRKY transcription factors (TFs) among others. Integrative metabolome and transcriptome analysis showed that rust infection enriched metabolomic pathways, biosynthesis of secondary metabolites, protein processing in the endoplasmic reticulum, and purine metabolism among others. The metabolome and transcriptome integration approach employed in this study provides insights on molecular mechanisms underlying U. appendiculatus response in P. vulgaris' key developmental stages.

Integrated metabolomics and proteomics analysis in children with cerebral palsy exposed to botulinum toxin-A

BACKGROUND

We previously examined plasma metabolic changes before and after botulinum toxin-A injections of cerebral palsy (CP) children and showed that the glycine, serine and threonine metabolism may play a key role in neuritogenesis. This study analysed untargeted metabolomics combined with proteomics of plasma to discussed which substances are meaningfully changed, to what extent they affect the effects of action.

METHODS

Blood samples were collected from 91 children with spastic CP at 4 time points: pre-injection (T1), 1 month post-injection (T2), 3 months post-injection (T3) and 6 months post-injection (T4). Differentially changed metabolites and proteins were selected, and co-expression pathways were constructed to explore the key molecular processes.

RESULTS

A total of 674 proteins and 354 metabolites were identified. The differential metabolites were mainly involved in the linoleic acid metabolism, beta-Alanine metabolism, citrate cycle, pyruvate metabolism and glycolysis or gluconeogenesis. Differential proteins were primarily associated with glucose metabolism, lipid metabolism, immune and inflammation responses. Co-expression pathways showed that ECM-receptor interaction, complement and coagulation cascades, glycolysis or gluconeogenesis, pyruvate metabolism, and linoleic acid metabolism were the main pathways.

CONSLUSIONS

Our results indicated the botulinum toxin-A predominantly activated the glucose metabolism, lipid metabolism, and immune and inflammation responses, and energy metabolism changed significantly in this process.

TRIAL REGISTRATION DETAILS

ChiCTR2000033800, Research on the mechanism of botulinum toxin relieving spasticity in children with cerebral palsy. Approval No. 202023041. Registered 13 June 2020, http://www.chictr.org.cn/showproj.html?proj=52267 .

IMPACT STATEMENT

This is the first study that combined dynamic metabolomics and proteomics analysis to investigate the molecular changes in children with spastic cerebral palsy after botulinum toxin-A injections, which might provide a theoretical reference for the further subsequent study for targets to increase the efficacy and prolong the duration of botulinum toxin-A, and would be a valuable resource for the metabolomics and proteomics field in this group.

Cathepsin D inhibits AGEs-induced phenotypic transformation in vascular smooth muscle cells

This study investigates the role of Cathepsin D (CTSD) in diabetic vascular complications, particularly its impact on the phenotypic transformation of vascular smooth muscle cells (VSMCs) induced by advanced glycation end-products (AGEs), and explores its potential molecular mechanisms. CTSD was overexpressed in VSMCs using lentiviral vectors. Various methods, including CCK-8, immunofluorescence, SA-β-Gal staining, EdU assay, scratch assay, cell cycle analysis, and Western blotting, were employed to assess VSMC viability, proliferation, migration, senescence, and apoptosis. Additionally, transcriptomic and metabolomic analyses were conducted to investigate the molecular mechanisms underlying CTSD overexpression in VSMCs. AGEs treatment significantly inhibited CTSD expression in VSMCs, leading to reduced cell viability, enhanced proliferation and migration, increased senescence, and apoptosis. In contrast, overexpression of CTSD effectively inhibited AGEs-induced VSMCs proliferation, migration, senescence, and apoptosis. Combined transcriptomic and metabolomic analyses suggested that CTSD may affect VSMCs phenotypic transformation by inhibiting the glycolysis pathway. This study highlights the critical role of CTSD in the phenotypic transformation of VSMCs induced by AGEs and provides a new perspective for cardiovascular and cerebrovascular disease treatment. CTSD may emerge as a novel therapeutic target, though its specific molecular mechanisms and clinical application prospects in VSMCs phenotypic transformation require further investigation.

Galactinol synthase 4 influences plant height by affecting phenylpropanoid metabolism and the balance of soluble carbohydrates in tomato

Plant height is a key trait that significantly influences plant architecture, disease resistance, adaptability to mechanical cultivation, and overall economic yield. Galactinol synthase (GolS) is a crucial enzyme involved in the biosynthesis of raffinose family oligosaccharides (RFOs). It plays a significant role in carbohydrate transport and storage, combating abiotic and biotic stresses, and regulating plant growth and development. The present study employed CRISPR/Cas9 gene-editing technology to create the gols4 mutant in tomato (Solanum lycopersicum), which exhibits a semi-dwarf phenotype. Results showed that glucose, sucrose, myo-inositol, galactinol, and raffinose levels were significantly reduced in the slgols4 mutant, impairing material transport and affecting the balance of soluble carbohydrates. Integration of transcriptomics and metabolomics data indicated not only a decrease in the expression of synthesis genes related to phenylpropanoid biosynthesis but also a significant reduction in the content of lignin and flavonoids, which are byproducts of phenylpropanoid metabolism. This may be a key factor contributing to dwarfism. Overall, these findings provide evidence for the role of SlGolS4 in regulating sugar metabolism and phenylpropanoid metabolism, offering new insights into tomato dwarfing cultivation and germplasm resources.

Integrated metabolomic and transcriptomic analysis of triterpenoid accumulation in the roots of Codonopsis pilosula var. modesta (Nannf.) L.T.Shen at different altitudes

INTRODUCTION

Codonopsis Radix is a beneficial traditional Chinese medicine, and triterpenoid are the major bioactive constituents. Codonopsis pilosula var. modesta (Nannf.) L.T.Shen (CPM) is a precious variety of Codonopsis Radix, which is distributed at high mountain areas. The environment plays an important role in the synthesis and metabolism of active ingredients in medicinal plants, but there is no report elaborating on the effect of altitude on terpenoid metabolites accumulation in CPM.

OBJECTIVES

This study aims to analyse the effects of altitude on triterpenoid biosynthetic pathways and secondary metabolite accumulation in CPM.

MATERIAL AND METHODS

The untargeted metabolomics based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) and 10 triterpenoids based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) method were analysed at the low-altitude (1480 m) and high-altitude (2300 m) CPM fresh roots. The transcriptome based on high-throughput sequencing technology were combined to analyse the different altitude CPM triterpenoid biosynthetic pathways.

RESULTS

A total of 17,351 differentially expressed genes (DEGs) and 55 differentially accumulated metabolites (DAMs) were detected from the different altitude CPM, and there are significant differences in the content of the 10 triterpenoids. The results of transcriptome study showed that CPM could significantly up-regulate the gene expression levels of seven key enzymes in the triterpenoid biosynthetic pathway.

CONCLUSIONS

The CPM at high altitude is more likely to accumulate triterpenes than those at low altitude, which was related to the up-regulation of the gene expression levels of seven key enzymes. These results expand our understanding of how altitude affects plant metabolite biosynthesis.

Metabolomic and proteomic stratification of equine osteoarthritis

BACKGROUND

Equine osteoarthritis (OA) is predominantly diagnosed through clinical examination and radiography, leading to detection only after significant joint pathology. The pathogenesis of OA remains unclear and while many medications modify the disease's inflammatory components, no curative or reversal treatments exist. Identifying differentially abundant metabolites and proteins correlated with osteoarthritis severity could improve early diagnosis, track disease progression, and evaluate responses to interventions.

OBJECTIVES

To identify molecular markers of osteoarthritis severity based on histological and macroscopic grading.

STUDY DESIGN

Cross-sectional study.

METHODS

Post-mortem synovial fluid was collected from 58 Thoroughbred racehorse joints and 83 joints from mixed breeds. Joints were histologically and macroscopically scored and categorised by OA and synovitis grade. Synovial fluid nuclear magnetic resonance metabolomic and mass spectrometry proteomic analyses were performed, individually and combined.

RESULTS

In Thoroughbreds, synovial fluid concentrations of metabolites 2-aminobutyrate, alanine and creatine were elevated for higher OA grades, while glutamate was reduced for both Thoroughbreds and mixed breeds. In mixed breeds, concentrations of three uncharacterised proteins, lipopolysaccharide binding protein and immunoglobulin kappa constant were lower for higher OA grades; concentrations of an uncharacterised protein were higher for OA grade 1 only, and apolipoprotein A1 concentrations were higher for OA grades 1 and 2 compared with lower grades. For Thoroughbreds, gelsolin concentrations were lower for higher OA grades, and afamin was lower at a higher synovitis grade. Correlation analyses of combined metabolomics and proteomics datasets revealed 58 and 32 significant variables for Thoroughbreds and mixed breeds, respectively, with correlations from -0.48 to 0.42 and -0.44 to 0.49.

MAIN LIMITATIONS

The study's reliance on post-mortem assessments limits correlation with clinical osteoarthritis severity.

CONCLUSIONS

Following stratification of equine OA severity through histological and macroscopic grading, synovial fluid metabolomic and proteomic profiling identified markers that may support earlier diagnosis and progression tracking. Further research is needed to correlate these markers with clinical osteoarthritis severity.

Transcriptomic and metabolomic study of the biosynthetic pathways of bioactive components in Amomum tsaoko fruits

Amomum tsaoko is a significant medicinal and edible plant with documented efficacy in the treatment of various diseases. Additionally, it is a crucial food additive and spice. 1,8-cineole and curcumin are the main bioactive compounds of A. tsaoko, and research on these compounds has mainly focused on their chemical composition and pharmacological activity, with relatively less exploration of synthetic pathways and identification of key genes. This study employed transcriptome sequencing and metabolomic analysis of A. tsaoko at five different developmental stages (May fruit - September fruit) to assess the accumulation patterns of terpenoid and curcuminoid compounds and to explore the key genes and transcription factors (TFs) involved in their synthesis pathways. The results showed that three genes encoding 1-deoxy-D-xylulose-5-phosphate synthase (DXS), hydroxymethylglutaryl-CoA synthase (HMGCS) and phosphomevalonate kinase (mvaK2) and TFs such as AP2-ERF, bHLH, WRKY were screened for involvement in terpenoid biosynthesis. In addition, three genes encoding trans-cinnamate 4-monooxygenase (C4H), curcumin synthase (CURS) and TFs such as MYB, bHLH, bZIP were screened for involvement in curcuminoid biosynthesis. This study provides a theoretical foundation for further research into the biosynthesis of active components in A. tsaoko, establishing a basis for in-depth investigations into the mechanisms underlying its medicinal quality formation. Additionally, it offers guidance for the utilisation of its aromatic components and natural pigments.

Paromomycin targets HDAC1-mediated SUMOylation and IGF1R translocation in glioblastoma

Objective

This study investigates the effects of Paromomycin on SUMOylation-related pathways in glioblastoma (GBM), specifically targeting HDAC1 inhibition.

Methods

Using TCGA and GTEx datasets, we identified SUMOylation-related genes associated with GBM prognosis. Molecular docking analysis suggested Paromomycin as a potential HDAC1 inhibitor. In vitro assays on U-251MG GBM cells were performed to assess Paromomycin's effects on cell viability, SUMOylation gene expression, and IGF1R translocation using CCK8 assays, qRT-PCR, and immunofluorescence.

Results

Paromomycin treatment led to a dose-dependent reduction in GBM cell viability, colony formation, and migration. It modulated SUMO1 expression and decreased IGF1R nuclear translocation, an effect reversible by the HDAC1 inhibitor Trochostatin A (TSA), suggesting Paromomycin's involvement in SUMO1-regulated pathways.

Conclusion

This study highlights Paromomycin's potential as a therapeutic agent for GBM by targeting HDAC1-mediated SUMOylation pathways and influencing IGF1R translocation, warranting further investigation for its clinical application.

Integration of metabolomics and transcriptomics to reveal metabolic characteristics and key targets associated with lncRNA Vof-16 in H19-7 cells

Cognitive disorders represent one of the most common chronic complications of diabetes. Our previous study has demonstrated that long non-coding RNA (lncRNA) Vof-16 is upregulated in the hippocampal tissue of streptozotocin (STZ)-induced diabetic rats. Despite this finding, the specific roles and underlying mechanisms of lncRNA Vof-16 in diabetes-related cognitive dysfunction remain largely unexplored. To elucidate the mechanism involved, lncRNA Vof-16 was overexpressed in rat hippocampal cell line H19-7 through lentivirus transfection. We integrated metabolomics and transcriptomics approaches to identify potential targets and metabolic pathways influenced by lncRNA Vof-16. Key proteins and pathways were subsequently validated using western blotting and immunofluorescence staining. Transcriptomics indicated that lncRNA Vof-16 overexpression may modulate autophagic activity in H19-7 cells. Metabolomic profiling revealed that the primary differential metabolic pathways included trehalose degradation, tryptophan metabolism, vitamin B6 metabolism, glycolysis, pterine biosynthesis, and the pentose phosphate pathway. Ingenuity Pathway Analysis (IPA) of gene-metabolite networks demonstrated that the high lncRNA Vof-16 expression group exhibited a significantly higher association with autophagy compared to the low lncRNA Vof-16 expression group. Western blot results confirmed that lncRNA Vof-16 overexpression led to decreased protein expression levels of ATG3 and ATG12. Specifically, lncRNA Vof-16 reduces autophagy in hippocampal neurons by targeting the elevated levels of phospho-p70S6K, a downstream effector of mTORC1, potentially contributing to the pathogenesis of diabetic cognitive impairment. The construction of gene-metabolite networks may offer promising new strategies for addressing the growing issue of diabetic cognitive impairment.

Pairing omics to decode the diversity of plant specialized metabolism

Plants have evolved complex bouquets of specialized natural products that are utilized in medicine, agriculture, and industry. Untargeted natural product discovery has benefitted from growing plant omics data resources. Yet, plant genome complexity limits the identification and curation of biosynthetic pathways via single omics. Pairing multi-omics types within experiments provides multiple layers of evidence for biosynthetic pathway mining. The extraction of paired biological information facilitates connecting genes to transcripts and metabolites, especially when captured across time points, conditions and chemotypes. Experimental design requires specific adaptations to enable effective paired-omics analysis. Ultimately, metadata standards are required to support the integration of paired and unpaired public datasets and to accelerate collaborative efforts for natural product discovery in the plant research community.

Metabolic pathways from the gut metatranscriptome are associated with COPD and respiratory function in lung cancer patients

Introduction

Changes in the human gut microbiome have been linked to various chronic diseases, including chronic obstructive pulmonary disease (COPD). While substantial knowledge is available on the genomic features of fecal communities, little is known about the microbiome's transcriptional activity. Here, we analyzed the metatranscriptomic (MTR) abundance of MetaCyc pathways, SuperPathways, and protein domain families (PFAM) represented by the gut microbiome in a cohort of non-small cell lung cancer (NSCLC) patients with- or without COPD comorbidity.

Methods

Fecal samples of 40 NSCLC patients with- or without COPD comorbidity were collected at the time of diagnosis. Data was preprocessed using the Metaphlan3/Humann3 pipeline and BioCyc© to identify metabolic SuperPathways. LEfSe analysis was conducted on Pathway- and PFAM abundance data to determine COPD- and non-COPD-related clusters.

Results

Key genera Streptococcus, Escherichia, Gemella, and Lactobacillus were significantly more active transcriptionally compared to their metagenomic presence. LEfSe analysis identified 11 MetaCyc pathways that were significantly overrepresented in patients with- and without COPD comorbidity. According to Spearman's rank correlation, Smoking PY showed a significant negative correlation with Glycolysis IV, Purine Ribonucleoside Degradation and Glycogen Biosynthesis I, and a significant positive correlation with Superpathway of Ac-CoA Biosynthesis and Glyoxylate cycle, whereas forced expiratory volume in the first second (FEV1) showed a significant negative correlation with Glycolysis IV and a significant positive correlation with Glycogen Biosynthesis I. Furthermore, COPD patients showed a significantly increased MTR abundance in ~60% of SuperPathways, indicating a universally increased MTR activity in this condition. FEV1 showed a significant correlation with SuperPathways Carbohydrate degradation, Glycan biosynthesis, and Glycolysis. Taxonomic analysis suggested a more prominent MTR activity from multiple Streptococcus species, Enterococcus (E.) faecalis, E. faecium and Escherichia (E.) coli than expected from their metagenomic abundance. Multiple protein domain families (PFAMs) were identified as more associated with COPD, E. faecium, E.coli, and Streptococcus salivarius, contributing the most to these PFAMs.

Conclusion

Metatranscriptome analysis identified COPD-related subsets of lung cancer with potential therapeutic relevance.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Statistical approaches applicable in managing OMICS data: Urinary proteomics as exemplary case

With urinary proteomics profiling (UPP) as exemplary omics technology, this review describes a workflow for the analysis of omics data in large study populations. The proposed workflow includes: (i) planning omics studies and sample size considerations; (ii) preparing the data for analysis; (iii) preprocessing the UPP data; (iv) the basic statistical steps required for data curation; (v) the selection of covariables; (vi) relating continuously distributed or categorical outcomes to a series of single markers (e.g., sequenced urinary peptide fragments identifying the parental proteins); (vii) showing the added diagnostic or prognostic value of the UPP markers over and beyond classical risk factors, and (viii) pathway analysis to identify targets for personalized intervention in disease prevention or treatment. Additionally, two short sections respectively address multiomics studies and machine learning. In conclusion, the analysis of adverse health outcomes in relation to omics biomarkers rests on the same statistical principle as any other data collected in large population or patient cohorts. The large number of biomarkers, which have to be considered simultaneously requires planning ahead how the study database will be structured and curated, imported in statistical software packages, analysis results will be triaged for clinical relevance, and presented.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite-gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

Propylthiouracil Induced Rat Model Reflects Heterogeneity Observed in Clinically Non-Obese Subjects with Nonalcoholic Fatty Liver Disease

The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing, affecting up to 30% of the population, with approximately 20% of cases occurring in non-obese individuals. The recent shift to the term metabolic dysfunction-associated steatosis liver disease (MASLD) highlights the disease's heterogeneity. However, there are no well-established animal models replicating non-obese NAFLD (NO-NAFLD). This study aimed to evaluate the relevance of the high-fat diet (HFD) combined with the propylthiouracil (PTU)-induced rat model in mimicking the histopathology and pathophysiology of NO-NAFLD. We first analyzed metabolic and clinical parameters between NO-NAFLD patients (Average BMI = 21.96 kg/m2) and obese NAFLD patients (Average BMI = 29.7 kg/m2). NO-NAFLD patients exhibited significantly higher levels of carnitines, phospholipids, and triglycerides. In the animal model, we examined serum lipid profiles, liver inflammation, histology, and transcriptomics. Hepatic steatosis in the HFD+PTU model at week 4 was comparable to that of the HFD model at week 8. The HFD+PTU model showed higher levels of carnitines, phospholipids, and triglycerides, supporting its relevance for NO-NAFLD. Additionally, the downregulation of lipid synthesis-related genes indicated differences in lipid accumulation between the two models. Overall, the HFD+PTU-induced rat model is a promising tool for studying the molecular mechanisms of NO-NAFLD.

The chemical composition of secondary organic aerosols regulates transcriptomic and metabolomic signaling in an epithelial-endothelial in vitro coculture

BACKGROUND

The formation of secondary organic aerosols (SOA) by atmospheric oxidation reactions substantially contributes to the burden of fine particulate matter (PM2.5), which has been associated with adverse health effects (e.g., cardiovascular diseases). However, the molecular and cellular effects of atmospheric aging on aerosol toxicity have not been fully elucidated, especially in model systems that enable cell-to-cell signaling.

METHODS

In this study, we aimed to elucidate the complexity of atmospheric aerosol toxicology by exposing a coculture model system consisting of an alveolar (A549) and an endothelial (EA.hy926) cell line seeded in a 3D orientation at the air‒liquid interface for 4 h to model aerosols. Simulation of atmospheric aging was performed on volatile biogenic (β-pinene) or anthropogenic (naphthalene) precursors of SOA condensing on soot particles. The similar physical properties for both SOA, but distinct differences in chemical composition (e.g., aromatic compounds, oxidation state, unsaturated carbonyls) enabled to determine specifically induced toxic effects of SOA.

RESULTS

In A549 cells, exposure to naphthalene-derived SOA induced stress-related airway remodeling and an early type I immune response to a greater extent. Transcriptomic analysis of EA.hy926 cells not directly exposed to aerosol and integration with metabolome data indicated generalized systemic effects resulting from the activation of early response genes and the involvement of cardiovascular disease (CVD) -related pathways, such as the intracellular signal transduction pathway (PI3K/AKT) and pathways associated with endothelial dysfunction (iNOS; PDGF). Greater induction following anthropogenic SOA exposure might be causative for the observed secondary genotoxicity.

CONCLUSION

Our findings revealed that the specific effects of SOA on directly exposed epithelial cells are highly dependent on the chemical identity, whereas non directly exposed endothelial cells exhibit more generalized systemic effects with the activation of early stress response genes and the involvement of CVD-related pathways. However, a greater correlation was made between the exposure to the anthropogenic SOA compared to the biogenic SOA. In summary, our study highlights the importance of chemical aerosol composition and the use of cell systems with cell-to-cell interplay on toxicological outcomes.

Metabolomic and transcriptomic basis of photoperiodic response regulation in broomcorn millet (Panicum miliaceum L.)

To elucidate the mechanisms underlying photoperiodic responses, we investigated the genomic and metabolomic responses of two broomcorn millet (Panicum miliaceum L.) genotypes. For this purpose, light-insensitive (D32) and light-sensitive (M51) genotypes were exposed to a 16 h photoperiod (long-day (LD) conditions) and an 8 h photoperiod (short-day (SD) conditions), and various transcriptomic and metabolomic changes were investigated. A total of 1664, 2564, 13,017, and 15548 DEGs were identified in the SD-D, LD-D, LD-M, and SD-M groups, respectively. Furthermore, 112 common DEGs were identified as well. Interestingly, most DEGs in the different groups were associated with photosynthesis and phenylpropanoid and carotenoid biosynthesis. In addition, 822 metabolites were identified under different treatments. The main metabolites, including L-malic and fumaric acids, were identified in the negative mode, whereas brucine and loperamide were identified in the positive mode. KEGG analysis revealed that the metabolites in the different groups were enriched in the same metabolic pathway of the TCA cycle. Furthermore, in negative mode, the metabolites of M51 were mainly D-glucose, whereas those of D32 were mainly L-malic and fumaric acids. One photoperiod candidate gene (C2845_PM11G01290), annotated as ATP6B, significantly increased the levels of L-malic and fumaric acids. In conclusion, our study provides a theoretical basis for understanding the molecular mechanisms of photoperiodic response regulation and can be used as a reference for marker development and resource identification in Panicum miliaceum L..

Comparison of transcriptome and metabolome analysis revealed cold-resistant metabolic pathways in cucumber roots under low-temperature stress in root zone

Introduction

Low ground temperature is a major factor limiting overwintering in cucumber cultivation facilities in northern alpine regions. Lower temperatures in the root zone directly affect the physiological function of the root system, which in turn affects the normal physiological activity of plants. However, the importance of the ground temperature in facilities has not attracted sufficient attention.

Methods

Therefore, this study tested the cucumber variety Jinyou 35 under three root zone temperatures (room temperature, 20-22°C; suboptimal temperature, 13- 15°C; and low temperature, 8-10°C) to investigated possible cold resistance mechanisms in the root of cucumber seedlings through hormone, metabolomics, and transcriptomics analyses.

Results and discussion

The results showed that cucumber roots were subjected to chilling stress at different temperatures. Hormone analysis indicated that auxin content was highest in the roots. Jasmonic acid and strigolactone participated in the low-temperature stress response. Auxin and jasmonate are key hormones that regulate the response of cucumber roots to low temperatures. Phenolic acid was the most abundant metabolite in cucumber roots under chilling stress. Additionally, triterpenes may play an important role in chilling resistance. Differentially expressed genes and metabolites were significantly enriched in benzoxazinoid biosynthesis in the room temperature vs. suboptimal temperature groups and the room temperature vs. low temperature groups. Most differentially expressed transcription factor genes in AP2/ERF were strongly induced in cucumber roots by both suboptimal and low-temperature stress conditions. These results provide guidance for the cultivation of cucumber in facilities.

Joint metabolomics and transcriptomics analysis systematically reveal the impact of MYCN in neuroblastoma

The limited understanding of the molecular mechanism underlying MYCN-amplified (MNA) neuroblastoma (NB) has hindered the identification of effective therapeutic targets for MNA NB, contributing to its higher mortality rate compared to MYCN non-amplified (non-MNA) NB. Therefore, a comprehensive analysis integrating metabolomics and transcriptomics was conducted to systematically investigate the MNA NB. Metabolomics analysis utilized plasma samples from 28 MNA NB patients and 68 non-MNA NB patients, while transcriptomics analysis employed tissue samples from 15 MNA NB patients and 37 non-MNA NB patients. Notably, joint metabolomics and transcriptomics analysis was performed. A total of 46 metabolites exhibited alterations, with 21 displaying elevated levels and 25 demonstrating reduced levels in MNA NB. In addition, 884 mRNAs in MNA NB showed significant changes, among which 766 mRNAs were higher and 118 mRNAs were lower. Joint-pathway analysis revealed three aberrant pathways involving glycerolipid metabolism, purine metabolism, and lysine degradation. This study highlights the substantial differences in metabolomics and transcriptomics between MNA NB and non-MNA NB, identifying three abnormal metabolic pathways that may serve as potential targets for understanding the molecular mechanisms underlying MNA NB.

Multimodal functional deep learning for multiomics data

With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data.

Biofilm marker discovery with cloud-based dockerized metagenomics analysis of microbial communities

In an environment, microbes often work in communities to achieve most of their essential functions, including the production of essential nutrients. Microbial biofilms are communities of microbes that attach to a nonliving or living surface by embedding themselves into a self-secreted matrix of extracellular polymeric substances. These communities work together to enhance their colonization of surfaces, produce essential nutrients, and achieve their essential functions for growth and survival. They often consist of diverse microbes including bacteria, viruses, and fungi. Biofilms play a critical role in influencing plant phenotypes and human microbial infections. Understanding how these biofilms impact plant health, human health, and the environment is important for analyzing genotype-phenotype-driven rule-of-life functions. Such fundamental knowledge can be used to precisely control the growth of biofilms on a given surface. Metagenomics is a powerful tool for analyzing biofilm genomes through function-based gene and protein sequence identification (functional metagenomics) and sequence-based function identification (sequence metagenomics). Metagenomic sequencing enables a comprehensive sampling of all genes in all organisms present within a biofilm sample. However, the complexity of biofilm metagenomic study warrants the increasing need to follow the Findability, Accessibility, Interoperability, and Reusable (FAIR) Guiding Principles for scientific data management. This will ensure that scientific findings can be more easily validated by the research community. This study proposes a dockerized, self-learning bioinformatics workflow to increase the community adoption of metagenomics toolkits in a metagenomics and meta-transcriptomics investigation. Our biofilm metagenomics workflow self-learning module includes integrated learning resources with an interactive dockerized workflow. This module will allow learners to analyze resources that are beneficial for aggregating knowledge about biofilm marker genes, proteins, and metabolic pathways as they define the composition of specific microbial communities. Cloud and dockerized technology can allow novice learners-even those with minimal knowledge in computer science-to use complicated bioinformatics tools. Our cloud-based, dockerized workflow splits biofilm microbiome metagenomics analyses into four easy-to-follow submodules. A variety of tools are built into each submodule. As students navigate these submodules, they learn about each tool used to accomplish the task. The downstream analysis is conducted using processed data obtained from online resources or raw data processed via Nextflow pipelines. This analysis takes place within Vertex AI's Jupyter notebook instance with R and Python kernels. Subsequently, results are stored and visualized in Google Cloud storage buckets, alleviating the computational burden on local resources. The result is a comprehensive tutorial that guides bioinformaticians of any skill level through the entire workflow. It enables them to comprehend and implement the necessary processes involved in this integrated workflow from start to finish. This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach

Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.

Integration of Proteomic and Metabolomic Data Reveals the Lipid Metabolism Disorder in the Liver of Rats Exposed to Simulated Microgravity

Long-term exposure to microgravity is considered to cause liver lipid accumulation, thereby increasing the risk of non-alcoholic fatty liver disease (NAFLD) among astronauts. However, the reasons for this persistence of symptoms remain insufficiently investigated. In this study, we used tandem mass tag (TMT)-based quantitative proteomics techniques, as well as non-targeted metabolomics techniques based on liquid chromatography-tandem mass spectrometry (LC-MS/MS), to comprehensively analyse the relative expression levels of proteins and the abundance of metabolites associated with lipid accumulation in rat liver tissues under simulated microgravity conditions. The differential analysis revealed 63 proteins and 150 metabolites between the simulated microgravity group and the control group. By integrating differentially expressed proteins and metabolites and performing pathway enrichment analysis, we revealed the dysregulation of major metabolic pathways under simulated microgravity conditions, including the biosynthesis of unsaturated fatty acids, linoleic acid metabolism, steroid hormone biosynthesis and butanoate metabolism, indicating disrupted liver metabolism in rats due to weightlessness. Finally, we examined differentially expressed proteins associated with lipid metabolism in the liver of rats exposed to stimulated microgravity. These findings contribute to identifying the key molecules affected by microgravity and could guide the design of rational nutritional or pharmacological countermeasures for astronauts.

Multi-omics analysis reveals a crosstalk between ferroptosis and peroxisomes on steatotic graft failure after liver transplantation

To identify the mechanism underlying macrosteatosis (MaS)-related graft failure (GF) in liver transplantation (LT) by multi-omics network analysis. The transcriptome and metabolome were assayed in graft and recipient plasma in discovery (n = 68) and validation (n = 89) cohorts. Differentially expressed molecules were identified by MaS and GF status. Transcriptional regulatory networks were generated to explore the mechanism for MaS-related inferior post-transplant prognosis. The differentially expressed molecules associated with MaS and GF were enriched in ferroptosis and peroxisome-related pathways. Core features of MaS-related GF were presented on decreased transferrin and impaired anti-oxidative capacity dependent upon dysregulation of transcription factors hepatocyte nuclear factor 4A (HNF4A) and hypoxia-inducible factor 1A (HIF1A). Furthermore, miR-362-3p and miR-299-5p inhibited transferrin and HIF1A expression, respectively. Lower M2 macrophages but higher memory CD4 T cells were observed in MaS-related GF cases. These results were validated in clinical specimens and cellular models. Systemic analysis of multi-omics data depicted a panorama of biological pathways deregulated in MaS-related GF. Transcriptional regulatory networks centered on transferrin and anti-oxidant responses were associated with poor MaS graft quality, qualifying as potential targets to improve prognosis of patients after LT.

A pipeline for the development and analysis of extracellular vesicle-based transcriptomic biomarkers in molecular diagnostics

Extracellular vesicles are shed by every cell type and can be found in any biofluid. They contain different molecules that can be utilized as biomarkers, including several RNA species which they protect from degradation. Here, we present a pipeline for the development and analysis of extracellular vesicle-associated transcriptomic biomarkers that our group has successfully applied multiple times. We highlight the key steps of the pipeline and give particular emphasis to the necessary quality control checkpoints, which are linked to numerous available guidelines that should be considered along the workflow. Our pipeline starts with patient recruitment and continues with blood sampling and processing. The purification and characterization of extracellular vesicles is explained in detail, as well as the isolation and quality control of extracellular vesicle-associated RNA. We point out the possible pitfalls during library preparation and RNA sequencing and present multiple bioinformatic tools to pinpoint biomarker signature candidates from the sequencing data. Finally, considerations and pitfalls during the validation of the biomarker signature using RT-qPCR will be elaborated.

A Review of Transcriptomics and Metabolomics in Plant Quality and Environmental Response: From Bibliometric Analysis to Science Mapping and Future Trends

Transcriptomics and metabolomics offer distinct advantages in investigating the differentially expressed genes and cellular entities that have the greatest influence on end-phenotype, making them crucial techniques for studying plant quality and environmental responses. While numerous relevant articles have been published, a comprehensive summary is currently lacking. This review aimed to understand the global and longitudinal research trends of transcriptomics and metabolomics in plant quality and environmental response (TMPQE). Utilizing bibliometric methods, we presented a comprehensive science mapping of the social structure, conceptual framework, and intellectual foundation of TMPQE. We uncovered that TMPQE research has been categorized into three distinct stages since 2020. A citation analysis of the 29 most cited articles, coupled with a content analysis of recent works (2020-2023), highlight five potential research streams in plant quality and environmental responses: (1) biosynthetic pathways, (2) abiotic stress, (3) biotic stress, (4) development and ripening, and (5) methodologies and tools. Current trends and future directions are shaped by technological advancements, species diversity, evolving research themes, and an environmental ecology focus. Overall, this review provides a novel and comprehensive perspective to understand the longitudinal trend on TMPQE.

Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont

Since their radiation in the Middle Triassic period ∼240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.

Combined sensory, volatilome and transcriptome analyses identify a limonene terpene synthase as a major contributor to the characteristic aroma of a Coffea arabica L. specialty coffee

BACKGROUND

The fruity aromatic bouquet of coffee has attracted recent interest to differentiate high value market produce as specialty coffee. Although the volatile compounds present in green and roasted coffee beans have been extensively described, no study has yet linked varietal molecular differences to the greater abundance of specific substances and support the aroma specificity of specialty coffees.

RESULTS

This study compared four Arabica genotypes including one, Geisha Especial, suggested to generate specialty coffee. Formal sensory evaluations of coffee beverages stressed the importance of coffee genotype in aroma perception and that Geisha Especial-made coffee stood out by having fine fruity, and floral, aromas and a more balanced acidity. Comparative SPME-GC-MS analyses of green and roasted bean volatile compounds indicated that those of Geisha Especial differed by having greater amounts of limonene and 3-methylbutanoic acid in agreement with the coffee cup aroma perception. A search for gene ontology differences of ripening beans transcriptomes of the four varieties revealed that they differed by metabolic processes linked to terpene biosynthesis due to the greater gene expression of prenyl-pyrophosphate biosynthetic genes and terpene synthases. Only one terpene synthase (CaTPS10-like) had an expression pattern that paralleled limonene loss during the final stage of berry ripening and limonene content in the studied four varieties beans. Its functional expression in tobacco leaves confirmed its functioning as a limonene synthase.

CONCLUSIONS

Taken together, these data indicate that coffee variety genotypic specificities may influence ripe berry chemotype and final coffee aroma unicity. For the specialty coffee variety Geisha Especial, greater expression of terpene biosynthetic genes including CaTPS10-like, a limonene synthase, resulted in the greater abundance of limonene in green beans, roasted beans and a unique citrus note of the coffee drink.

Investigating the Role of β-Disodium Glycerophosphate and Urea in Promoting Growth of Streptococcus thermophilus from Omics-Integrated Genome-Scale Models

This study investigates the impact of urea and β-GP on the growth of Streptococcus thermophilus S-3, a bacterium commonly used in industrial fermentation processes. Through a series of growth experiments, transcriptome, metabolome, and omics-based analyses, the research demonstrates that both urea and β-GP can enhance the biomass of S. thermophilus, with urea showing a more significant effect. The optimal urea concentration for growth was determined to be 3 g/L in M17 medium. The study also highlights the metabolic pathways influenced by urea and β-GP, particularly the galactose metabolism pathway, which is crucial for cell growth when lactose is the substrate. The integration of omics data into the genome-scale metabolic model of S. thermophilus, iCH502, allowed for a more accurate prediction of metabolic fluxes and growth rates. The study concludes that urea can serve as a viable substitute for β-GP in the cultivation of S. thermophilus, offering potential cost and efficiency benefits in industrial fermentation processes. The findings are supported by validation experiments with 11 additional strains of S. thermophilus, which showed increased biomass in UM17 medium.

Bis(2-ethylhexyl)-2,3,4,5-tetrabromophthalate Enhances foxo1-Mediated Lipophagy to Remodel Lipid Metabolism in Zebrafish Liver

An emerging environmental contaminant, bis(2-ethylhexyl)-2,3,4,5-tetrabromophthalate (TBPH), can bioaccumulate in the liver and affect hepatic lipid metabolism. However, the in-depth mechanism has yet to be comprehensively explored. In this study, we utilized transgenic zebrafish Tg (Apo14: GFP) to image the interference of TBPH on zebrafish liver development and lipid metabolism at the early development stage. Using integrated lipidomic and transcriptomic analyses to profile the lipid remodeling effect, we uncovered the potential effects of TBPH on lipophagy-related signaling pathways in zebrafish larvae. Decreased lipid contents accompanied by enhanced lipophagy were confirmed by the measurements of Oil Red O staining and transmission electron microscopy in liver tissues. Particularly, the regulatory role of the foxo1 factor was validated via its transcriptional inhibitor. Double immunofluorescence staining integrated with biochemical analysis indicated that the enhanced lipophagy and mitochondrial fatty acid oxidation induced by TBPH were reversed by the foxo1 inhibitor. To summarize, our study reveals, for the first time, the essential role of foxo1-mediated lipophagy in TBPH-induced lipid metabolic disorders and hepatoxicity, providing new insights for metabolic disease studies and ecological health risk assessment of TBPH.

Stress adaptation in Tibetan cashmere goats is governed by inherent metabolic differences and manifested through variable cashmere phenotypes

Tibetan cashmere goats are not only served as a valuable model for studying adaptation to hypoxia and high-altitude conditions but also playing a pivotal role in bolstering local economies through the provision of premium quality cashmere yarn. In this study, we performed an integration and network analysis of metabolomic, transcriptomic and proteomic to elucidate the role of differentially expressed genes, important metabolites, and relevant cellular and metabolic pathways between the fine (average 12.04 ± 0.03 μm of mean fiber diameter) and coarse cashmere (average 14.88 ± 0.05 μm of mean fber diameter) producing by Tibetan cashmere goats. We identified a distinction of 56 and 71 differential metabolites (DMs) between the F and C cashmere groups under positive and negative ion modes, respectively. The KEGG pathway enrichment analysis of these DMs highlighted numerous pathways predominantly involved in amino acid and protein metabolism, as indicated by the finding that the most impactful pathway was the mammalian target of rapamycin (mTOR) signalling pathway. In the F group, we identified a distinctive metabolic profile where amino acid metabolites including serine, histidine, asparagine, glutamic acid, arginine, valine, aspartic acid, tyrosine, and methionine were upregulated, while lysine, isoleucine, glutamine, tryptophan, and threonine were downregulated. The regulatory network and gene co-expression network revealed crucial genes, metabolites, and metabolic pathways. The integrative omics analysis revealed a high enrichment of several pathways, notably encompassing protein digestion and absorption, sphingolipid signalling, and the synaptic vesicle cycle. Within the sphere of our integrative analysis, DNMT3B was identified as a paramount gene, intricately associated with significant proteins such as HMCN1, CPB2, GNG12, and LRP1. Our present study delineated the molecular underpinnings governing the variations in cashmere characteristics by conducting comprehensive analyses across metabolomic, transcriptomic, and proteomic dimensions. This research provided newly insights into the mechanisms regulating cashmere traits and facilitated the advancement of selective breeding programs aimed at cultivating high-quality superfine Tibetan cashmere goats.

Quantitative transcriptomic and epigenomic data analysis: a primer

The advent of microarray and second generation sequencing technology has revolutionized the field of molecular biology, allowing researchers to quantitatively assess transcriptomic and epigenomic features in a comprehensive and cost-efficient manner. Moreover, technical advancements have pushed the resolution of these sequencing techniques to the single cell level. As a result, the bottleneck of molecular biology research has shifted from the bench to the subsequent omics data analysis. Even though most methodologies share the same general strategy, state-of-the-art literature typically focuses on data type specific approaches and already assumes expert knowledge. Here, however, we aim at providing conceptual insight in the principles of genome-wide quantitative transcriptomic and epigenomic (including open chromatin assay) data analysis by describing a generic workflow. By starting from a general framework and its assumptions, the need for alternative or additional data-analytical solutions when working with specific data types becomes clear, and are hence introduced. Thus, we aim to enable readers with basic omics expertise to deepen their conceptual and statistical understanding of general strategies and pitfalls in omics data analysis and to facilitate subsequent progression to more specialized literature.

Integration of proteomics and metabolomics reveals energy and metabolic alterations induced by glucokinase (GCK) partial inactivation in hepatocytes

AIMS

Glucokinase (GCK) acts as the glucose sensor in maintaining glucose homeostasis. The inactivating mutation of the GCK gene leads to glucokinase-maturity onset diabetes of the young (GCK-MODY). This study aims to gain further insights into the molecular alterations triggered by GCK partial inactivation in hepatocytes, potentially underlying the favorable prognosis of GCK-MODY.

MAIN METHODS

A GCK knockdown HepG2 cell model was established, and the integration of proteomics and metabolomics was used to gain a comprehensive understanding of the molecular pathway changes caused by GCK inactivation in the liver.

KEY FINDINGS

Proteomic analysis identified 257 differential proteins. KEGG pathway enrichment analysis showed that protein expression changes in the GCK knockdown group were significantly enriched in central carbon metabolism, the TCA cycle, amino acid metabolism and the oxidative phosphorylation pathway. Among them, enzymes in the TCA cycle (PC, IDH2, SDH) were significantly downregulated in GCK-knockdown group. Targeted metabolomics revealed that in the GCK knockdown hepatocytes, TCA cycle intermediates were significantly decreased, including pyruvate, oxaloacetate, citrate and succinic acid, and three metabolites increased including glycine, betaine and homocysteine. These metabolic alterations in turn reduced the accumulation of reactive oxygen species in GCK knockdown hepatocytes. Correlation analysis indicated that TCA cycle metabolites were positively correlated with proteins involved in the TCA cycle, carbon metabolism, glycolysis, Ras signaling, fibrosis and inflammation.

SIGNIFICANCE

In conclusion, GCK knockdown reduced TCA cycle flux and oxidative stress in hepatocytes by influencing the levels of key transcription factors and enzymes, providing a comprehensive understanding of the effects of GCK partial inactivation on liver metabolism and molecular mechanisms.

PathBank 2.0-the pathway database for model organism metabolomics

PathBank (https://pathbank.org) and its predecessor database, the Small Molecule Pathway Database (SMPDB), have been providing comprehensive metabolite pathway information for the metabolomics community since 2010. Over the past 14 years, these pathway databases have grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in computing technology. This year's update, PathBank 2.0, brings a number of important improvements and upgrades that should make the database more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of primary or canonical pathways (from 1720 to 6951); (ii) a massive increase in the total number of pathways (from 110 234 to 605 359); (iii) significant improvements to the quality of pathway diagrams and pathway descriptions; (iv) a strong emphasis on drug metabolism and drug mechanism pathways; (v) making most pathway images more slide-compatible and manuscript-compatible; (vi) adding tools to support better pathway filtering and selecting through a more complete pathway taxonomy; (vii) adding pathway analysis tools for visualizing and calculating pathway enrichment. Many other minor improvements and updates to the content, the interface and general performance of the PathBank website have also been made. Overall, we believe these upgrades and updates should greatly enhance PathBank's ease of use and its potential applications for interpreting metabolomics data.

Metabolomics-transcriptomics joint analysis: unveiling the dysregulated cell death network and developing a diagnostic model for high-grade neuroblastoma

High-grade neuroblastoma (HG-NB) exhibits a significantly diminished survival rate in comparison to low-grade neuroblastoma (LG-NB), primarily attributed to the mechanism of HG-NB is unclear and the lacking effective therapeutic targets and diagnostic model. Therefore, the current investigation aims to study the dysregulated network between HG-NB and LG-NB based on transcriptomics and metabolomics joint analysis. Meanwhile, a risk diagnostic model to distinguish HG-NB and LG-NB was also developed. Metabolomics analysis was conducted using plasma samples obtained from 48 HG-NB patients and 36 LG-NB patients. A total of 39 metabolites exhibited alterations, with 20 showing an increase and 19 displaying a decrease in HG-NB. Additionally, transcriptomics analysis was performed on NB tissue samples collected from 31 HG-NB patients and 20 LG-NB patients. Results showed that a significant alteration was observed in a total of 1,199 mRNAs in HG-NB, among which 893 were upregulated while the remaining 306 were downregulated. In particular, the joint analysis of both omics data revealed three aberrant pathways, namely the cAMP signaling pathway, PI3K-Akt signaling pathway, and TNF signaling pathway, which were found to be associated with cell death. Notably, a diagnostic model for HG-NB risk classification was developed based on the genes MGST1, SERPINE1, and ERBB3 with an area under the receiver operating characteristic curve of 0.915. In the validation set, the sensitivity and specificity were determined to be 75.0% and 80.0%, respectively.

CAT Bridge: an efficient toolkit for gene-metabolite association mining from multiomics data

BACKGROUND

With advancements in sequencing and mass spectrometry technologies, multiomics data can now be easily acquired for understanding complex biological systems. Nevertheless, substantial challenges remain in determining the association between gene-metabolite pairs due to the nonlinear and multifactorial interactions within cellular networks. The complexity arises from the interplay of multiple genes and metabolites, often involving feedback loops and time-dependent regulatory mechanisms that are not easily captured by traditional analysis methods.

FINDINGS

Here, we introduce Compounds And Transcripts Bridge (abbreviated as CAT Bridge, available at https://catbridge.work), a free user-friendly platform for longitudinal multiomics analysis to efficiently identify transcripts associated with metabolites using time-series omics data. To evaluate the association of gene-metabolite pairs, CAT Bridge is a pioneering work benchmarking a set of statistical methods spanning causality estimation and correlation coefficient calculation for multiomics analysis. Additionally, CAT Bridge features an artificial intelligence agent to assist users interpreting the association results.

CONCLUSIONS

We applied CAT Bridge to experimentally obtained Capsicum chinense (chili pepper) and public human and Escherichia coli time-series transcriptome and metabolome datasets. CAT Bridge successfully identified genes involved in the biosynthesis of capsaicin in C. chinense. Furthermore, case study results showed that the convergent cross-mapping method outperforms traditional approaches in longitudinal multiomics analyses. CAT Bridge simplifies access to various established methods for longitudinal multiomics analysis and enables researchers to swiftly identify associated gene-metabolite pairs for further validation.

Integrated Transcriptomic and Metabolomic Analyses Identify Critical Genes and Metabolites Associated with Seed Vigor of Common Wheat

Seed aging is a common physiological phenomenon during storage which has a great impact on seed quality. An in-depth analysis of the physiological and molecular mechanisms of wheat seed aging is of great significance for cultivating high-vigor wheat varieties. This study reveals the physiological mechanisms of wheat seed aging in two cultivars differing in seed vigor, combining metabolome and transcriptome analyses. Differences between cultivars were examined based on metabolomic differential analysis. Artificial aging had a significant impact on the metabolism of wheat seeds. A total of 7470 (3641 upregulated and 3829 downregulated) DEGs were detected between non-aging HT and LT seeds; however, 10,648 (4506 up and 6142 down) were detected between the two cultivars after aging treatment. Eleven, eight, and four key metabolic-related gene families were identified in the glycolysis/gluconeogenesis and TCA cycle pathways, starch and sucrose metabolism pathways, and galactose metabolism pathways, respectively. In addition, 111 up-regulated transcription factor genes and 85 down-regulated transcription factor genes were identified in the LT 48h group. A total of 548 metabolites were detected across all samples. Cultivar comparisons between the non-aged groups and aged groups revealed 46 (30 upregulated and 16 downregulated) and 62 (38 upregulated and 24 downregulated) DIMs, respectively. Network analysis of the metabolites indicated that glucarate O-phosphoric acid, L-methionine sulfoxide, isocitric acid, and Gln-Gly might be the most crucial DIMs between HT and LT. The main related metabolites were enriched in pathways such as glyoxylate and dicarboxylate metabolism, biosynthesis of secondary metabolites, fatty acid degradation, etc. However, metabolites that exhibited differences between cultivars were mainly enriched in carbon metabolism, the TCA cycle, etc. Through combined metabolome and transcriptome analyses, it was found that artificial aging significantly affected glycolysis/gluconeogenesis, pyruvate metabolism, and glyoxylate and dicarboxylate metabolism, which involved key genes such as ACS, F16P2, and PPDK1. We thus speculate that these genes may be crucial in regulating physiological changes in seeds during artificial aging. In addition, an analysis of cultivar differences identified pathways related to amino acid and polypeptide metabolism, such as cysteine and methionine metabolism, glutathione metabolism, and amino sugar and nucleotide sugar metabolism, involving key genes such as BCAT3, CHI1, GAUT1, and GAUT4, which may play pivotal roles in vigor differences between cultivars.

Novel Time-dependent Multi-omics Integration in Sepsis-associated Liver Dysfunction

The recently developed technologies that allow the analysis of each single omics have provided an unbiased insight into ongoing disease processes. However, it remains challenging to specify the study design for the subsequent integration strategies that can associate sepsis pathophysiology and clinical outcomes. Here, we conducted a time-dependent multi-omics integration (TDMI) in a sepsis-associated liver dysfunction (SALD) model. We successfully deduced the relation of the Toll-like receptor 4 (TLR4) pathway with SALD. Although TLR4 is a critical factor in sepsis progression, it is not specified in single-omics analyses but only in the TDMI analysis. This finding indicates that the TDMI-based approach is more advantageous than single-omics analyses in terms of exploring the underlying pathophysiological mechanism of SALD. Furthermore, TDMI-based approach can be an ideal paradigm for insightful biological interpretations of multi-omics datasets that will potentially reveal novel insights into basic biology, health, and diseases, thus allowing the identification of promising candidates for therapeutic strategies.

Non-Targeted Spectranomics for the Early Detection of Xylella fastidiosa Infection in Asymptomatic Olive Trees, cv. Cellina di Nardò

Olive quick decline syndrome (OQDS) is a disease that has been seriously affecting olive trees in southern Italy since around 2009. During the disease, caused by Xylella fastidiosa subsp. pauca sequence type ST53 (Xf), the flow of water and nutrients within the trees is significantly compromised. Initially, infected trees may not show any symptoms, making early detection challenging. In this study, young artificially infected plants of the susceptible cultivar Cellina di Nardò were grown in a controlled environment and co-inoculated with additional xylem-inhabiting fungi. Asymptomatic leaves of olive plants at an early stage of infection were collected and analyzed using nuclear magnetic resonance (NMR), hyperspectral reflectance (HSR), and chemometrics. The application of a spectranomic approach contributed to shedding light on the relationship between the presence of specific hydrosoluble metabolites and the optical properties of both asymptomatic Xf-infected and non-infected olive leaves. Significant correlations between wavebands located in the range of 530-560 nm and 1380-1470 nm, and the following metabolites were found to be indicative of Xf infection: malic acid, fructose, sucrose, oleuropein derivatives, and formic acid. This information is the key to the development of HSR-based sensors capable of early detection of Xf infections in olive trees.

Transcriptional reprogramming of nucleotide metabolism in response to altered pyrimidine availability in Arabidopsis seedlings

In Arabidopsis seedlings, inhibition of aspartate transcarbamoylase (ATC) and de novo pyrimidine synthesis resulted in pyrimidine starvation and developmental arrest a few days after germination. Synthesis of pyrimidine nucleotides by salvaging of exogenous uridine (Urd) restored normal seedling growth and development. We used this experimental system and transcriptional profiling to investigate genome-wide responses to changes in pyrimidine availability. Gene expression changes at different times after Urd supplementation of pyrimidine-starved seedlings were mapped to major pathways of nucleotide metabolism, in order to better understand potential coordination of pathway activities, at the level of transcription. Repression of de novo synthesis genes and induction of intracellular and extracellular salvaging genes were early and sustained responses to pyrimidine limitation. Since de novo synthesis is energetically more costly than salvaging, this may reflect a reduced energy status of the seedlings, as has been shown in recent studies for seedlings growing under pyrimidine limitation. The unexpected induction of pyrimidine catabolism genes under pyrimidine starvation may result from induction of nucleoside hydrolase NSH1 and repression of genes in the plastid salvaging pathway, diverting uracil (Ura) to catabolism. Identification of pyrimidine-responsive transcription factors with enriched binding sites in highly coexpressed genes of nucleotide metabolism and modeling of potential transcription regulatory networks provided new insights into possible transcriptional control of key enzymes and transporters that regulate nucleotide homeostasis in plants.

Multi-omics network analysis on samples from sequential biopsies reveals vital role of proliferation arrest for Macrosteatosis related graft failure in rats after liver transplantation

To investigate the molecular impact of graft MaS on post-transplant prognosis, based on multi-omics integrative analysis. Rats were fed by methionine-choline deficient diet (MCD) for MaS grafts. Samples were collected from grafts by sequential biopsies. Transcriptomic and metabolomic profilings were assayed. Post-transplant MaS status showed a close association with graft failure. Differentially expressed genes (DEGs) for in-vivo MaS were mainly enriched on pathways of cell cycle and DNA replication. Post-transplant MaS caused arrests of graft regeneration via inhibiting the E2F1 centered network, which was confirmed by an in vitro experiment. Data from metabolomics assays found insufficient serine/creatine which is located on one‑carbon metabolism was responsible for MaS-related GF. Pre-transplant MaS caused severe fibrosis in long-term survivors. DEGs for grafts from long-term survivors with pre-transplant MaS were mainly enriched in pathways of ECM-receptor interaction and focal adhesion. Transcriptional regulatory network analysis confirmed SOX9 as a key transcription factor (TF) for MaS-related fibrosis. Metabolomic assays found elevation of aromatic amino acid (AAA) was a major feature of fibrosis in long-term survivors. Graft MaS in vivo increased post-transplant GF via negative regulations on graft regeneration. Pre-transplant MaS induced severe fibrosis in long-term survivors via activations on ECM-receptor interaction and AAA metabolism.

Molecular systems biology approaches to investigate mechanisms of gut-brain communication in neurological diseases

BACKGROUND

Whilst the incidence of neurological diseases is increasing worldwide, treatment remains mostly limited to symptom management. The gut-brain axis, which encompasses the communication routes between microbiota, gut and brain, has emerged as a crucial area of investigation for identifying new preventive and therapeutic targets in neurological disease.

METHODS

Due to the inter-organ, systemic nature of the gut-brain axis, together with the multitude of biomolecules and microbial species involved, molecular systems biology approaches are required to accurately investigate the mechanisms of gut-brain communication. High-throughput omics profiling, together with computational methodologies such as dimensionality reduction or clustering, machine learning, network inference and genome-scale metabolic models, allows novel biomarkers to be discovered and elucidates mechanistic insights.

RESULTS

In this review, the general concepts of experimental and computational methodologies for gut-brain axis research are introduced and their applications are discussed, mainly in human cohorts. Important aspects are further highlighted concerning rational study design, sampling procedures and data modalities relevant for gut-brain communication, strengths and limitations of methodological approaches and some future perspectives.

CONCLUSION

Multi-omics analyses, together with advanced data mining, are essential to functionally characterize the gut-brain axis and put forward novel preventive or therapeutic strategies in neurological disease.

Small networks of expressed genes in the whole blood and relationships to profiles in circulating metabolites provide insights in inter-individual variability of feed efficiency in growing pigs

BACKGROUND

Feed efficiency is a research priority to support a sustainable meat production. It is recognized as a complex trait that integrates multiple biological pathways orchestrated in and by various tissues. This study aims to determine networks between biological entities to explain inter-individual variation of feed efficiency in growing pigs.

RESULTS

The feed conversion ratio (FCR), a measure of feed efficiency, and its two component traits, average daily gain and average daily feed intake, were obtained from 47 growing pigs from a divergent selection for residual feed intake and fed high-starch or high-fat high-fiber diets during 58 days. Datasets of transcriptomics (60 k porcine microarray) in the whole blood and metabolomics (1H-NMR analysis and target gas chromatography) in plasma were available for all pigs at the end of the trial. A weighted gene co-expression network was built from the transcriptomics dataset, resulting in 33 modules of co-expressed molecular probes. The eigengenes of eight of these modules were significantly ([Formula: see text]) or tended to be ([Formula: see text]) correlated to FCR. Great homogeneity in the enriched biological pathways was observed in these modules, suggesting co-expressed and co-regulated constitutive genes. They were mainly enriched in genes participating to immune and defense-related processes, and to a lesser extent, to translation, cell development or learning. They were also generally associated with growth rate and percentage of lean mass. In the whole network, only one module composed of genes participating to the response to substances, was significantly associated with daily feed intake and body adiposity. The plasma profiles in circulating metabolites and in fatty acids were summarized by weighted linear combinations using a dimensionality reduction method. Close association was thus found between a module composed of co-expressed genes participating to T cell receptor signaling and cell development process in the whole blood and related to FCR, and the circulating concentrations of polyunsaturated fatty acids in plasma.

CONCLUSION

These systemic approaches have highlighted networks of entities driving key biological processes involved in the phenotypic difference in feed efficiency between animals. Connecting transcriptomics and metabolic levels together had some additional benefits.

Multiomics characterization of cell type repertoires for urine liquid biopsies

Urine is assayed alongside blood in medicine, yet current clinical diagnostic tests utilize only a small fraction of its total biomolecular repertoire, potentially foregoing high-resolution insights into human health and disease. In this work, we characterized the joint landscapes of transcriptomic and metabolomic signals in human urine. We also compared the urine transcriptome to plasma cell-free RNA, identifying a distinct cell type repertoire and enrichment for metabolic signal. Untargeted metabolomic measurements identified a complementary set of pathways to the transcriptomic analysis. Our findings suggest that urine is a promising biofluid yielding prognostic and detailed insights for hard-to-biopsy tissues with low representation in the blood, offering promise for a new generation of liquid biopsies.

A primer on correlation-based dimension reduction methods for multi-omics analysis

The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Multiomic Investigations into Lung Health and Disease

Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.

Clonostachys rosea 'omics profiling: identification of putative metabolite-gene associations mediating its in vitro antagonism against Fusarium graminearum

BACKGROUND

Clonostachys rosea is an established biocontrol agent. Selected strains have either mycoparasitic activity against known pathogens (e.g. Fusarium species) and/or plant growth promoting activity on various crops. Here we report outcomes from a comparative 'omics analysis leveraging a temporal variation in the in vitro antagonistic activities of C. rosea strains ACM941 and 88-710, toward understanding the molecular mechanisms underpinning mycoparasitism.

RESULTS

Transcriptomic data highlighted specialized metabolism and membrane transport related genes as being significantly upregulated in ACM941 compared to 88-710 at a time point when the ACM941 strain had higher in vitro antagonistic activity than 88-710. In addition, high molecular weight specialized metabolites were differentially secreted by ACM941, with accumulation patterns of some metabolites matching the growth inhibition differences displayed by the exometabolites of the two strains. In an attempt to identify statistically relevant relationships between upregulated genes and differentially secreted metabolites, transcript and metabolomic abundance data were associated using IntLIM (Integration through Linear Modeling). Of several testable candidate associations, a putative C. rosea epidithiodiketopiperazine (ETP) gene cluster was identified as a prime candidate based on both co-regulation analysis and transcriptomic-metabolomic data association.

CONCLUSIONS

Although remaining to be validated functionally, these results suggest that a data integration approach may be useful for identification of potential biomarkers underlying functional divergence in C. rosea strains.

Integrated transcriptomics and lipidomics investigation of the mechanism underlying the gastrointestinal mucosa damage of Loropetalum chinense (R.Br.) and its representative component

BACKGROUND

Loropetalum chinensis (R.Br) Oliv (Bhjm), a Chinese folk herbal medicine, was traditionally used in the treatment of wound bleeding and skin ulcers. A new drug named JIMUSAN granules used for gastrosia was developed by our group, and clinical trials have been approved. However, as the principal herb, the material basis and underlying mechanisms of Bhjm in attenuating gastrointestinal mucosa damage (GMD) remain to be systemically illuminated.

PURPOSE

An integrated strategy was used to explore the therapeutic effects and mechanisms of Bhjm and ellagic acid (EA) on GMD zebrafish, using network pharmacology, transcriptomics, lipidomics, and real-time quantitative PCR (RT-qPCR) verification.

METHODS

First, network pharmacological analysis was used to infer the major effective constituents and targets of Bhjm. Ultra high performance liquid chromatography-linear ion trap/orbitrap high resolution mass spectrometry (UHPLC-LTQ-Orbitrap HRMS) and ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) were employed to identify the chemical constituents and quantify the different types of constituents. Second, zebrafish model of GMD was established by using 2,4,6-trinitrobenzenesulfonic acid (TNBS) to evaluate the efficacy of Bhjm and EA. The potential mechanism was examined by integrated transcriptomics and lipidomics analysis. Finally, validation tests were implemented using RT-qPCR.

RESULTS

In this study, targets indentified by network pharmacology were related to inflammation and mucosal damage. Ten representative components that interacted with these targets were simultaneously determined by UHPLC-MS/MS. Sixty four compounds were identified or tentatively characterized, most of which were flavonoids and polyphenols. Bhjm and EA alleviated mucosal damage and reduced inflammation in a TNBS-induced zebrafish GMD model, indicating that EA was the main active compounds. Eight common differentially expressed genes were downregulated by Bhjm and EA, as determined by transcriptomics analysis. Lipidomics analysis confirmed 12 differential lipids, including phosphatidylcholine (PC) and triglyceride (TG). Further network enrichment analysis demonstrated that differential lipid metabolism was regulated by klf4 and hist1h2ba, and were validated by RT-qPCR.

CONCLUSION

In our study, the chemical profile of Bhjm was clarified. Moreover, the GMD repair effect and the mechanism of Bhjm and EA was comprehensively analyzed for the first time, involving inflammation and lipid metabolism. Collectively, these findings will be significantly helpful for deeply exploring the clinical application value of Bhjm.