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Omics-based measurements enable the study of biomolecules in a high-throughput fashion, leading to the characterization and quantification of biological systems. Multi-omics methods aim to incorporate several omics measurements for a more holistic approach, which is crucial for advancing our understanding of the diversity and redundancy of biological systems. Current multi-omics sample preparation methods have achieved proteomics, lipidomics, and metabolomics from individual samples; however, the bioinformatic tools currently available for interpreting data generated from these omics are limited. Alternately, transcriptomics has a wide arsenal of available bioinformatic tools offering intensive sample characterization but has yet to be incorporated into a unified, multi-omics sample preparation technique. Herein we describe the modified bead-enabled accelerated monophasic multi-omics (mBAMM) method, which incorporates RNA extraction for transcriptomics analysis. mBAMM was shown to enable RNA-seq without compromising the isolation of biomolecules for proteomics, lipidomics, and metabolomics. This methodology greatly improves sample characterization and represents a major innovation toward cohesive insights into biological systems.

Background: Pumpkin (Cucurbita moschata) is an important vegetable crop that often suffers from low-temperature stress during growth. However, the molecular mechanism involved in its response to chilling stress remains unknown. In this study, we comprehensively investigated the effect of chilling stress in pumpkin seedlings by conducting physiological, transcriptomic, and metabolomic analyses., Results: Under chilling stress, there was an overall increase in relative electrical conductivity, along with malondialdehyde, soluble sugar, and soluble protein contents, but decreased superoxide dismutase and peroxidase activities and chlorophyll contents in seedling leaves compared with controls. Overall, 5,780 differentially expressed genes (DEGs) and 178 differentially expressed metabolites (DEMs) were identified under chilling stress. Most DEGs were involved in plant hormone signal transduction and the phenylpropanoid biosynthesis pathway, and ERF, bHLH, WRKY, MYB, and HSF transcription factors were induced. Metabolomic analysis revealed that the contents of salicylic acid (SA), phenylalanine, and tyrosine increased in response to chilling stress. The findings indicated that the SA signaling and phenylpropanoid biosynthesis pathways are key to regulating the responses to chilling stress in pumpkins., Conclusion: Overall, our study provides valuable insights into the comprehensive response of C. moschata to chilling stress, enriching the theoretical basis of this mechanism and facilitating the development of molecular breeding strategies for pumpkin tolerance to chilling stress., (© 2024. The Author(s).)

Purpose: The purpose of this study was to investigate biologically meaningful endotypes of open-angle glaucoma (OAG) by applying unsupervised machine learning to plasma metabolites., Methods: This retrospective longitudinal cohort study enrolled consecutive patients aged ≥20 years with OAG at Tohoku University Hospital from January 2017 to January 2020. OAG was confirmed based on comprehensive ophthalmic examinations. Among the 523 patients with OAG with available clinical metabolomic data, 173 patients were longitudinally followed up for ≥2 years, with available data from ≥5 reliable visual field (VF) tests without glaucoma surgery. We collected fasting blood samples and clinical data at enrollment and nuclear magnetic resonance spectroscopy to profile 45 plasma metabolites in a targeted approach. After computing a distance matrix of preprocessed metabolites with Pearson distance, gap statistics determined the optimal number of OAG endotypes. Its risk factors, clinical presentations, metabolomic profiles, and progression rate of sector-based VF loss were compared across endotypes., Results: Five distinct OAG endotypes were identified. The highest-risk endotype (endotype B) showed a significant faster progression of central VF loss (P = 0.007). Compared with patients with other endotypes, those with endotype B were more likely to have a high prevalence of dyslipidemia, cold extremities, oxidative stress, and low OAG genetic risk scores. Pathway analysis of metabolomic profiles implicated altered fatty acid and ketone body metabolism in this endotype, with 34 differentially enriched pathways (false discovery rate [FDR] < 0.05)., Conclusions: Integrated metabolomic profiles identified five distinct etiologic endotypes of OAG, suggesting pathological mechanisms related with a high-risk group of central vision loss progression in the Japanese population.

Diabetic nephropathy (DN), a leading cause of chronic kidney disease and end-stage kidney disease (ESKD), poses global health challenges given its increasing prevalence. DN increases the risk of mortality and cardiovascular events. Early identification and appropriate DN management are crucial. However, current diagnostic methods rely on general traditional markers, highlighting the need for DN-specific diagnostics. Metabolomics, the study of small molecules produced by metabolic activity, promises to identify specific biomarkers that distinguish DN from other kidney diseases, decode the underlying disease mechanisms, and predict the disease course. Profound changes in metabolic pathways are apparent in individuals with DN, alterations in the tricarboxylic acid cycle and amino acid and lipid metabolism, suggestive of mitochondrial dysfunction. Metabolomics aids prediction of chronic kidney disease progression; several metabolites serve as indicators of renal functional decline and the risk of ESKD. Integration of such information with other omics data will further enhance our understanding of DN, paving the way to personalized treatment. In summary, metabolomics and multi-omics offer valuable insights into DN and are promising diagnostic and prognostic tools.

Metabolic dysfunction in the liver represents a predominant feature in the early stages of alcohol-associated liver disease (ALD). However, the mechanisms underlying this are only partially understood. To investigate the metabolic characteristics of the liver in ALD, we did a relative quantification of polar metabolites and lipids in the liver of mice with experimental ALD using untargeted metabolomics and untargeted lipidomics. A total of 99 polar metabolites had significant abundance alterations in the livers of alcohol-fed mice. Pathway analysis revealed that amino acid metabolism was the most affected by alcohol in the mouse liver. Metabolites involved in glycolysis and the TCA cycle were decreased, while glycerol 3-phosphate (G3P) and long-chain fatty acids were increased. Relative quantification of lipids unveiled an upregulation of multiple lipid classes, suggesting that alcohol consumption drives metabolism toward lipid synthesis. Results from enzyme expression and activity detection indicated that the decreased activity of mitochondrial glycerol 3-phosphate dehydrogenase contributed to the disordered metabolism.

Salmonella infection, also known as Salmonellosis , is one of the most common food-borne illnesses. Salmonella infection can trigger host defensive functions, including an inflammatory response. The provoked-host inflammatory response has a significant impact on the bacterial population in the gut. In addition, Salmonella competes with other gut microorganisms for survival and growth within the host. Compositional and functional alterations in gut bacteria occur because of the host immunological response and competition between Salmonella and the gut microbiome. Host variation and the inherent complexity of the gut microbial community make understanding commensal and pathogen interactions particularly difficult during a Salmonella infection. Here, we present metabolomics and lipidomics analyses along with the 16S rRNA sequence analysis, revealing a comprehensive view of the metabolic interactions between the host and gut microbiota during Salmonella infection in a CBA/J mouse model. We found that different metabolic pathways were altered over the four investigated time points of Salmonella infection (days -2, +2, +6, and +13). Furthermore, metatranscriptomics analysis integrated with metabolomics and lipidomics analysis facilitated an understanding of the heterogeneous response of mice, depending on the degree of dysbiosis.

Biobanking of tissue from clinically obtained kidney biopsies for later analysis with multiomic approaches, such as single-cell technologies, proteomics, metabolomics, and the different types of imaging, is an inevitable step to overcome the need of disease model systems and toward translational medicine. Hence, collection protocols that ensure integration into daily clinical routines by the usage of preservation media that do not require liquid nitrogen but instantly preserve kidney tissue for both clinical and scientific analyses are necessary. Thus, we modified a robust single-nucleus dissociation protocol for kidney tissue stored snap-frozen or in the preservation media RNAlater and CellCover. Using at first porcine kidney tissue as a surrogate for human kidney tissue, we conducted single-nucleus RNA sequencing with the widely recognized Chromium 10X Genomics platform. The resulting datasets from each storage condition were analyzed to identify any potential variations in transcriptomic profiles. Furthermore, we assessed the suitability of the preservation media for additional analysis techniques such as proteomics, metabolomics, and the preservation of tissue architecture for histopathological examination including immunofluorescence staining. In this study, we show that in daily clinical routines, the preservation medium RNAlater facilitates the collection of highly preserved human kidney biopsies and enables further analysis with cutting-edge techniques like single-nucleus RNA sequencing, proteomics, and histopathological evaluation. Only metabolome analysis is currently restricted to snap-frozen tissue. This work will contribute to build tissue biobanks with well-defined cohorts of the respective kidney disease that can be deeply molecularly characterized, opening up new horizons for the identification of unique cells, pathways and biomarkers for the prevention, early identification, and targeted therapy of kidney diseases. NEW & NOTEWORTHY In this study, we addressed challenges in integrating clinically obtained kidney biopsies into everyday clinical routines. Using porcine kidneys, we evaluated preservation media (RNAlater and CellCover) versus snap freezing for multi-omics processing. Our analyses highlighted RNAlater's suitability for single-nucleus RNA sequencing, proteome analysis and histopathological evaluation. Only metabolomics are currently restricted to snap-frozen biopsies. Our research established a cryopreservation protocol that facilitates tissue biobanking for advancing precision medicine in nephrology.

Ethnopharmacological Relevance: Simiao Pills, a classical traditional Chinese medicine prescription recorded in Cheng Fang Bian Du, has been traditionally used to treat hyperuricemia due to its heat-clearing and diuretic properties. Studies have shown that Simiao Pills effectively reduce uric acid levels. However, further research is needed to elucidate the precise composition of Simiao Pills for treating hyperuricemia and their potential pharmacological mechanism., Aim of the Study: This study aimed to investigate the therapeutic effects of Simiao Pills on hyperuricemia, with a particular focus on evaluating their protective role against hyperuricemia-induced renal injury and elucidating the underlying mechanism of action., Materials and Methods: UPLC-MS/MS was used to identify the components of Simiao Pills. The hyperuricemia model mice were established by intraperitoneal injecting potassium oxonate (PO) and oral administrating hypoxanthine (HX). Network pharmacology, transcriptome, and metabolomics analyses were integrated to explore the mechanism of Simiao Pills in reducing uric acid and protecting the kidney. Mechanistic and functional studies were conducted to validate the potential mechanisms., Results: Simiao Pills were found to contain 12 characteristic components. Treatment with Simiao Pills significantly reduced serum uric acid levels and ameliorated hyperuricemia-induced renal injury. Simiao Pills inhibited the enzymatic activities of XOD and XDH, and regulated the uric acid transporters in the kidney and ileum. Transcriptome and network pharmacology analyses highlighted quercetin, berberine, kaempferol, and baicalein as the principal active components of Simiao Pills acting on the kidney during hyperuricemia treatment, primarily impacting fibrosis, apoptosis, and inflammation-related signaling pathways. Metabolomic analysis unveiled 21 differential metabolites and 5 metabolic pathways associated with Simiao Pills against renal injury associated with hyperuricemia. Further experimental results validated that Simiao Pills reduced renal fibrosis, apoptotic renal cells, serum inflammation levels, and inhibited the NF-κB/NLRP3/IL-1β signaling pathway., Conclusion: This study demonstrated that Simiao Pills significantly reduced serum uric acid levels and improved renal injury by regulating inflammation, apoptosis, and renal fibrosis. These findings have provided a robust scientific pharmacological basis for the use of Simiao Pills in treating hyperuricemia patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The aim of this study is to perform proteomic and metabolomic analyses in bilateral renal pelvis urine of patients with unilateral uric acid kidney stones to identify the specific urinary environment associated with uric acid stone formation. Using cystoscopy-guided insertion of ureteral catheters, bilateral renal pelvis urine samples are collected. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is employed to identify differentially expressed proteins and metabolites in the urine environment. Differentially expressed proteins and metabolites are further analyzed for their biological functions and potential metabolic pathways through Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. In the urine from the stone-affected side, eight differential proteins were significantly upregulated, and six metabolites were dysregulated. The uric acid stone urinary environment showed an excess of α-ketoisovaleric acid and 3-methyl-2-oxovaleric acid, which may contribute to the acidification of the urine. Functional and pathway analyses indicate that the dysregulated metabolites are mainly associated with insulin resistance and branched chain amino acid metabolism., (© 2024. The Author(s).)

Monitoring tumor evolution and predicting survival using non-invasive liquid biopsy is an unmet need for glioblastoma patients. The era of proteomics and metabolomics blood analyzes, may help in this context. A case-control study was conducted. Patients were included in the GLIOPLAK trial (ClinicalTrials.gov Identifier: NCT02617745), a prospective bicentric study conducted between November 2015 and December 2022. Patients underwent biopsy alone and received radiotherapy and temozolomide. Blood samples were collected at three different time points: before and after concomitant radiochemotherapy, and at the time of tumor progression. Plasma samples from patients and controls were analyzed using metabolomics and proteomics, generating 371 omics features. Descriptive, differential, and predictive analyses were performed to assess the relationship between plasma omics feature levels and patient outcome. Diagnostic performance and longitudinal variations were also analyzed. The study included 67 subjects (34 patients and 33 controls). A significant differential expression of metabolites and proteins between patients and controls was observed. Predictive models using omics features showed high accuracy in distinguishing patients from controls. Longitudinal analysis revealed temporal variations in a few omics features including CD22, CXCL13, EGF, IL6, GZMH, KLK4, and TNFRSP6B. Survival analysis identified 77 omics features significantly associated with OS, with ERBB2 and ITGAV consistently linked to OS at all timepoints. Pathway analysis revealed dynamic oncogenic pathways involved in glioblastoma progression. This study provides insights into the potential of plasma omics features as biomarkers for glioblastoma diagnosis, progression and overall survival. Clinical implication should now be explored in dedicated prospective trials., (© 2024. The Author(s).)

Background: Polycystic ovary syndrome (PCOS) is a common heterogeneous disorder linked with endocrine and metabolic disturbances. The underlying mechanism of PCOS, especially its effect on oocyte competence, remains unclear. The study aimed to identify abnormal follicular metabolic changes using a multi-omics approach in follicular fluid from PCOS patients and to determine their effects on oocyte competence., Methods: A total of 36 women with PCOS and 35 women without PCOS who underwent in vitro fertilization and embryo transfer were included in the study. Cumulus cells and follicular fluid samples were collected. Follicular fluid samples underwent metabolomic analysis, while cumulus cell clusters from the same patients were assessed using transcriptomic analysis. Clinical information of patients and assisted reproductive technology (ART) results were recorded. Transcriptomics and metabolomics were integrated to identify disrupted pathways, and receiver operation characteristics (ROC) analysis was conducted to identify potential diagnostic biomarkers for PCOS. Pearson correlation analysis was conducted to assess the relationship between metabolites in follicular fluid and oocyte competence (fertilization and early embryo development potential)., Results: Through multi-omics analysis, we identified aberrantly expressed pathways at both transcriptional and metabolic levels, such as the citrate cycle (TCA cycle), oxidative phosphorylation, the cAMP signaling pathway, the mTOR signaling pathway, and steroid hormone biosynthesis. Ten candidate metabolites were identified based on metabolic profiling data from these altered pathways. Phytic acid, succinic acid, 2'-deoxyinosine triphosphate, and 4-trimethylammoniobutanoic acid in the follicular fluid exhibited high specificity and sensitivity in distinguishing PCOS. Among these metabolites, L-arginine showed a negative correlation with the 2PN fertilization rate and cleavage rate, while estrone sulfate showed a negative correlation with the high-quality embryo rate in the in-vitro fertilization (IVF) cycle., Conclusions: We have conducted a preliminary study of a novel metabolic signature in women with PCOS using a multi-omics approach. The alterations in key metabolic pathways may enhance our understanding of the pathogenesis of PCOS., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Chen, Xie, Wu, Deng, Tang, Guan, Ye, He and Li.)

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder with a global impact, yet its pathogenesis remains poorly understood. While age, metabolic abnormalities, and accumulation of neurotoxic substances are potential risk factors for AD, their effects are confounded by other factors. To address this challenge, we first utilized multi-omics data from 87 well phenotyped AD patients and generated plasma proteomics and metabolomics data, as well as gut and saliva metagenomics data to investigate the molecular-level alterations accounting the host-microbiome interactions. Second, we analyzed individual omics data and identified the key parameters involved in the severity of the dementia in AD patients. Next, we employed Artificial Intelligence (AI) based models to predict AD severity based on the significantly altered features identified in each omics analysis. Based on our integrative analysis, we found the clinical relevance of plasma proteins, including SKAP1 and NEFL, plasma metabolites including homovanillate and glutamate, and Paraprevotella clara in gut microbiome in predicting the AD severity. Finally, we validated the predictive power of our AI based models by generating additional multi-omics data from the same group of AD patients by following up for 3 months. Hence, we observed that these results may have important implications for the development of potential diagnostic and therapeutic approaches for AD patients., (© 2024. Crown.)

The integration of multi-omics data offers a robust approach to understanding the complexity of diseases by combining information from various biological levels, such as genomics, transcriptomics, proteomics, and metabolomics. This integrated approach is essential for a comprehensive understanding of disease mechanisms and for developing more effective diagnostic and therapeutic strategies. Nevertheless, most current methodologies fail to effectively extract both shared and specific representations from omics data. This study introduces MOSDNET, a multi-omics classification framework that effectively extracts shared and specific representations. This framework leverages Simplified Multi-view Deep Discriminant Representation Learning (S-MDDR) and Dynamic Edge GCN (DEGCN) to enhance the accuracy and efficiency of disease classification. Initially, MOSDNET utilizes S-MDDR to establish similarity and orthogonal constraints for extracting these representations, which are then concatenated to integrate the multi-omics data. Subsequently, MOSDNET constructs a comprehensive view of the sample data by employing patient similarity networks. By incorporating similarity networks into DEGCN, MOSDNET learns intricate network structures and node representations, which enables superior classification outcomes. MOSDNET is trained through a multitask learning approach, effectively leveraging the complementary knowledge from both the data integration and classification components. After conducting extensive comparative experiments, we have conclusively demonstrated that MOSDNET outperforms leading state-of-the-art multi-omics classification models in terms of classification accuracy. Simultaneously, we employ MOSDNET to identify pivotal biomarkers within the multi-omics data, providing insights into disease etiology and progression., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

To understand the effects and related potential mechanism of H 2 O 2 on pigment metabolism in postharvest broccoli, an integrated analysis of transcriptome and metabolome was performed. Results suggested that 65 differentially expressed genes and 26 differentially accumulated metabolites involved in chlorophyll, carotenoid, and flavonoid metabolism were identified. H 2 O 2 treatment delayed the decrease of chlorophyll content by upregulating the expressions of chlorophyll synthetic genes, thylakoid synthetic genes, and 15 light-harvesting complex genes compared with the control and diphenylene iodonium treatments. H 2 O 2 treatment decreased the accumulation of 11 flavonoids and 5 flavonols by downregulating the flavonoid synthetic genes. In addition, H 2 O 2 treatment promoted carotenoid biosynthesis to eliminate reactive oxygen species in thylakoids, thereby protecting chlorophyll molecules from degradation. The inhibition of flavonoids and flavonols accumulation and chlorophyll decrease was the crucial reason for the delayed yellowing in H 2 O 2 treatment. This study provides a new method and theoretical support for delaying the yellowing process in postharvest broccoli., (© 2024 Institute of Food Technologists.)

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

The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers., Competing Interests: The authors declare there are no competing interests., (©2024 Liang et al.)

Multi-omics analysis offers a promising avenue to a better understanding of complex biological phenomena. In particular, untangling the pathophysiology of multifactorial health conditions such as the inflammatory bowel disease (IBD) could benefit from simultaneous consideration of several omics levels. However, taking full advantage of multi-omics data requires the adoption of suitable new tools. Multi-view learning, a machine learning technique that natively joins together heterogeneous data, is a natural source for such methods. Here we present a new approach to variable selection in unsupervised multi-view learning by applying stability selection to canonical correlation analysis (CCA). We apply our method, StabilityCCA, to simulated and real multi-omics data, and demonstrate its ability to find relevant variables and improve the stability of variable selection. In a case study on an IBD microbiome data set, we link together metagenomics and metabolomics, revealing a connection between their joint structure and the disease, and identifying potential biomarkers. Our results showcase the usefulness of multi-view learning in multi-omics analysis and demonstrate StabilityCCA as a powerful tool for biomarker discovery., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Pusa, Rousu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Microbial anaerobic metabolism is a key driver of biogeochemical cycles, influencing ecosystem function and health of both natural and engineered environments. However, the temporal dynamics of the intricate interactions between microorganisms and the organic metabolites are still poorly understood. Leveraging metagenomic and metabolomic approaches, we unveiled the principles governing microbial metabolism during a 96-day anaerobic bioreactor experiment., Results: During the turnover and assembly of metabolites, homogeneous selection was predominant, peaking at 84.05% on day 12. Consistent dynamic coordination between microbes and metabolites was observed regarding their composition and assembly processes. Our findings suggested that microbes drove deterministic metabolite turnover, leading to consistent molecular conversions across parallel reactors. Moreover, due to the more favorable thermodynamics of N-containing organic biotransformations, microbes preferentially carried out sequential degradations from N-containing to S-containing compounds. Similarly, the metabolic strategy of C18 lipid-like molecules could switch from synthesis to degradation due to nutrient exhaustion and thermodynamical disadvantage. This indicated that community biotransformation thermodynamics emerged as a key regulator of both catabolic and synthetic metabolisms, shaping metabolic strategy shifts at the community level. Furthermore, the co-occurrence network of microbes-metabolites was structured around microbial metabolic functions centered on methanogenesis, with CH 4 as a network hub, connecting with 62.15% of total nodes as 1st and 2nd neighbors. Microbes aggregate molecules with different molecular traits and are modularized depending on their metabolic abilities. They established increasingly positive relationships with high-molecular-weight molecules, facilitating resource acquisition and energy utilization. This metabolic complementarity and substance exchange further underscored the cooperative nature of microbial interactions., Conclusions: All results revealed three key rules governing microbial anaerobic degradation. These rules indicate that microbes adapt to environmental conditions according to their community-level metabolic trade-offs and synergistic metabolic functions, further driving the deterministic dynamics of molecular composition. This research offers valuable insights for enhancing the prediction and regulation of microbial activities and carbon flow in anaerobic environments. Video Abstract., (© 2024. The Author(s).)

Ethnopharmacological Relevance: The Roucongrong Pill (RCRP), originating from the historical General Medical Collection of Royal Benevolence, is frequently used to treat postmenopausal osteoporosis (PMOP). Despite its prevalent application, the specific anti-osteoporotic mechanisms of RCRP remain to be elucidated., Aim of the Study: This study aims to elucidate the therapeutic mechanism of RCRP in the context of ovariectomy (OVX)-induced PMOP in rats. By employing an integrative approach, the research combines medicinal chemistry, gut microbiota (GM) profiling, metabolomics, MetOrigin traceability, network pharmacology, molecular docking, and molecular dynamics simulations to deliver a comprehensive analysis., Materials and Methods: Sprague-Dawley (SD) rats underwent bilateral OVX to establish a PMOP model. The therapeutic efficacy of RCRP was evaluated through bone metrics (BMD, bone strength, BV/TV, Tb.Sp), hematoxylin and eosin (H&E) histological assessment, and bone metabolism markers (OPG, BALP, TRACP-5b, β-CTX, RANKL). Fecal metabolomics and 16S rDNA sequencing were employed to assess the influence of RCRP on GM and metabolite profiles. Furthermore, MetOrigin facilitated the traceability analysis of relevant metabolites. Molecular docking identified potential RCRP compounds with anti-PMOP activity, while their stability and protein interactions were assessed through molecular dynamics simulations. Network pharmacology further confirms the targets of action., Results: RCRP alleviated PMOP in rats, enhancing bone strength, cortical and trabecular BMD, BV/TV, and serum OPG levels, while reducing Tb.Sp, serum BALP, TRACP-5b, β-CTX, and RANKL concentrations. A total of twenty-six distinct metabolites were identified, of which ten-tribufos, sulfoacetic acid, betamethasone dipropionate, 9-oxooctadeca-10,12,15-trienoic acid, menatetrenone, piperlongumine, maltopentaose, enol-phenylpyruvate, catechol, pentaacetate, and (+)-2-methylpropanoic acid-exhibited correlations with six GM species: Turicibacter, Roseburia, Colidextribacter, Helicobacter, Odoribacter, and Lachnoclostridium, as determined by Spearman's correlation analysis. Notably, MetOrigin revealed the microbial metabolism of taurine and hypotaurine, along with host-specific steroid hormone synthesis. Computational docking studies demonstrated robust interactions between five RCRP-derived steroids (hydroxyecdysone, corticosterone, trilostane, 5α-androstan-3,6,17-trione, and cortisol) and key enzymes (estradiol 17α-dehydrogenase and UDP-glucuronosyltransferase), suggesting a potential enhancement of therapeutic efficacy against PMOP. Furthermore, molecular dynamics simulations indicated stable interactions between hydroxyecdysone and two proteins, with binding free energies of -67.427 kJ/mol and -156.948 kJ/mol, respectively. Through network pharmacology and molecular docking approaches, potential targets of these metabolites were identified, including estrogen receptors ESR1 and ESR2, dual specificity phosphatase 6 (DUSP6), sex hormone-binding globulin (SHBG), prostaglandin E receptor 4 (PTGER4), cannabinoid receptor 2 (CNR2), cathepsin K (CTSK), and androgen receptor (AR)., Conclusions: RCRP effectively mitigates OVX-induced bone loss in PMOP rats by modulating GM and associated metabolites, along with their potential targets and key metabolic pathways, including taurine and hypotaurine metabolism, as well as steroid hormone biosynthesis. These findings offer new insights into the therapeutic mechanisms by which RCRP may alleviate PMOP., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Ethnopharmacological Relevance: The Yishenjiangya formula (YSJ) is a traditional Chinese medicine (TCM) primarily composed of qi-tonifying components. This classic formula is commonly utilized to treat kidney qi deficiency in elderly patients with hypertension. According to TCM, maintaining a balance between qi and blood is crucial for stable blood pressure. Kidney qi deficiency can disrupt this balance, altering fluid shear force and, ultimately, leading to hypertension, particularly in elderly populations. Despite YSJ's efficacy in treating hypertension, its specific anti-hypertensive mechanisms remain unclear., Aim of the Study: YSJ is commonly prescribed for elderly patients with hypertension. Earlier metabolomics studies demonstrated that YSJ exerts antihypertensive effects by influencing four key pathways: linoleic acid metabolism, glycerol phospholipid metabolism, arginine and proline metabolism, and steroid hormone biosynthesis. This study aims to combine metabolomic and proteomic analyses to thoroughly understand the molecular biological mechanisms responsible for YSJ's anti-hypertensive properties., Methods: Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) metabolomics, combined with Label-Free Quantitation (LFQ) proteomics, was employed to analyze serum samples from elderly individuals with and without hypertension pre- and post-YSJ intervention. Serum levels of candidate proteins were assessed using enzyme-linked immunosorbent assay, and receiver operating characteristic curves were used to evaluate the diagnostic performance of the target proteins., Results: Eight differentially expressed metabolites and three differentially expressed proteins were identified as potential therapeutic targets of YSJ. These substances are primarily involved in unsaturated fatty acid metabolism, fluid shear stress and atherosclerosis pathway, primary bile acid biosynthesis, proline metabolism, apoptosis, and endoplasmic reticulum stress. YSJ exerts its therapeutic effects on hypertension in the elderly by modulating these pathways., Conclusions: YSJ effectively treats senile hypertension. By analyzing the correlation between therapeutic targets and pathways, YSJ's anti-hypertensive effect was achieved by inhibiting lipid peroxidation and matrix degeneration. Combining metabolomics and proteomics provides an effective method for uncovering YSJ's anti-hypertensive mechanisms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Hemoglobin H (HbH) disease, a form of alpha-thalassemia, poses significant clinical challenges due to its complex molecular underpinnings. It is characterized by reduced synthesis of the alpha-globin chain. The integration of multi-omics and precision medicine holds immense potential to comprehensively understand and capture interactions at the molecular and genetic levels. This review integrates current multi-omics approaches and advanced technologies in HbH research. Furthermore, it delves into detailed pathophysiology and possible therapeutics in the upcoming future. We explore the role of genomics, transcriptomics, proteomics, and metabolomics studies, alongside bioinformatics tools and gene-editing technologies like CRISPR/Cas9, to identify genetic modifiers, decipher molecular pathways, and discover therapeutic targets. Recent advancements are unveiling novel genetic and epigenetic modifiers impacting HbH disease severity, paving the way for personalized precision medicine interventions. The significance of multi-omics research in unraveling the complexities of rare diseases like HbH is underscored, highlighting its potential to revolutionize clinical practice through precision medicine approaches. This paradigm shift can pave the way for a deeper understanding of HbH complexities and improved disease management., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

The economic significance of ruminants in agriculture underscores the need for advanced research methodologies to enhance their traits. This review aims to elucidate the transformative role of pan-omics technologies in ruminant research, focusing on their application in uncovering the genetic mechanisms underlying complex traits such as growth, reproduction, production performance, and rumen function. Pan-omics analysis not only helps in identifying key genes and their regulatory networks associated with important economic traits but also reveals the impact of environmental factors on trait expression. By integrating genomics, epigenomics, transcriptomics, metabolomics, and microbiomics, pan-omics enables a comprehensive analysis of the interplay between genetics and environmental factors, offering a holistic understanding of trait expression. We explore specific examples of economic traits where these technologies have been pivotal, highlighting key genes and regulatory networks identified through pan-omics approaches. Additionally, we trace the historical evolution of each omics field, detailing their progression from foundational discoveries to high-throughput platforms. This review provides a critical synthesis of recent advancements, offering new insights and practical recommendations for the application of pan-omics in the ruminant industry. The broader implications for modern animal husbandry are discussed, emphasizing the potential for these technologies to drive sustainable improvements in ruminant production systems.

In this study, metabolomics and proteomics were performed to investigate the fluctuations of non-volatile compounds and proteins in tea leaves from three tea cultivars with varying colours during withering. A total of 2798 compounds were detected, exhibiting considerable variations in amino acids, phenylpropanoids, and flavonoids. The ZH1 cultivar displayed increased levels of amino acids but decreased levels of polyphenols, which might be associated with the up-regulation of enzymes responsible for protein degradation and subsequent amino acid production, as well as the down-regulation of enzymes involved in phenylpropanoid and flavonoid biosynthesis. The FUD and ZH1 cultivars had elevated levels of flavanols and flavanol-O-glycosides, which were regulated by the upregulation of FLS. The ZJ and ZH1 cultivars displayed elevated levels of theaflavin and peroxidase. This work presents a novel investigation into the alterations of metabolites and proteins between tea cultivars during withering, and helps with the tea cultivar selection and manufacturing development., Competing Interests: Declaration of competing interest The authors have stated that they have no conflicts of interest with the research, authorship, and/or publishing of this paper. The following are the supplementary data related to this article. Supplementary data to this article can be found online at https://doi.org/10.1016/j.foodchem.2024.139281., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Osteoporosis (OP) is a metabolic bone disease that can lead to major health challenges. The theory of Traditional Chinese medicine believes that kidney-Yin deficiency (KYD) is the main cause of postmenopausal osteoporosis. This study was aimed to investigate the effect of EZW on anti-osteoporosis with KYD, and explore potential mechanisms from the perspective of the kidney, bone and bone marrow through analysis of metabolomics and proteomics. The model of OP with KYD was established by rats treated with bilateral ovariectomy (OVX), and then given intragastric administration of thyroid and reserpine to induce. Micro-CT was applied to determine the microstructures of bone. Serum levels associated with bone turnover markers and kidney-Yin deficiency were detected by enzyme-linked immunosorbent (ELISA) assay. The differential metabolites in the kidney, bone and bone marrow were analyzed by metabolomics. The differentially expressed proteins in these three tissues were detected via proteomics. The findings suggested that EZW could alleviate a variety of metabolites and proteins among the kidney, bone and bone marrow, primarily in amino acid metabolism, carbohydrate metabolism, nucleotide metabolism and lipid metabolism, thus leading to improvements of OP with KYD, which provided theoretical basis for clinical treatment of EZW on OP with KYD., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

A multimetabo-lipid-prote-omics workflow was developed to characterize the molecular interplay within proximal (PC) and distal (DC) colonic epithelium of healthy mice. This multiomics data set lays the foundation to better understand the two tissue types and can be used to study, for example, colon-related diseases like colorectal cancer or inflammatory bowel disease. First, the methyl tert -butyl ether extraction method was optimized, so that from a single tissue biopsy >350 reference-matched metabolites, >1850 reference-matched lipids, and >4500 proteins were detected by using targeted and untargeted metabolomics, untargeted lipidomics, and proteomics. Next, each omics-data set was analyzed individually and then merged with the additional omics disciplines to generate a deep understanding of the underlying complex regulatory network within the colon. Our data demonstrates, for example, differences in mucin formation, detected on substrate level as well as on enzyme level, and altered lipid metabolism by the detection of phospholipases hydrolyzing sphingomyelins to ceramides. In conclusion, the combination of the three mass spectrometry-based omics techniques can better entangle the functional and regional differences between PC and DC tissue compared to each single omics technique.

Preoperative risk biomarkers for delirium may aid in identifying high-risk patients and developing intervention therapies, which would minimize the health and economic burden of postoperative delirium. Previous studies have typically used single omics approaches to identify such biomarkers. Preoperative cerebrospinal fluid (CSF) from the Healthier Postoperative Recovery study of adults ≥ 63 years old undergoing elective major orthopedic surgery was used in a matched pair delirium case-no delirium control design. We performed metabolomics and lipidomics, which were combined with our previously reported proteomics results on the same samples. Differential expression, clustering, classification, and systems biology analyses were applied to individual and combined omics datasets. Probabilistic graph models were used to identify an integrated multi-omics interaction network, which included clusters of heterogeneous omics interactions among lipids, metabolites, and proteins. The combined multi-omics signature of 25 molecules attained an AUC of 0.96 [95% CI: 0.85-1.00], showing improvement over individual omics-based classification. We conclude that multi-omics integration of preoperative CSF identifies potential risk markers for delirium and generates new insights into the complex pathways associated with delirium. With future validation, this hypotheses-generating study may serve to build robust biomarkers for delirium and improve our understanding of its pathophysiology.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with a complex pathological mechanism involving autoimmune response, local inflammation and bone destruction. Metabolic pathways play an important role in immune-related diseases and their immune responses. The pathogenesis of rheumatoid arthritis may be related to its metabolic dysregulation. Moreover, histological techniques, including genomics, transcriptomics, proteomics and metabolomics, provide powerful tools for comprehensive analysis of molecular changes in biological systems. The present study explores the molecular and metabolic mechanisms of RA, emphasizing the central role of metabolic dysregulation in the RA disease process and highlighting the complexity of metabolic pathways, particularly metabolic remodeling in synovial tissues and its association with cytokine-mediated inflammation. This paper reveals the potential of histological techniques in identifying metabolically relevant therapeutic targets in RA; specifically, we summarize the genetic basis of RA and the dysregulated metabolic pathways, and explore their functional significance in the context of immune cell activation and differentiation. This study demonstrates the critical role of histological techniques in decoding the complex metabolic network of RA and discusses the integration of histological data with other types of biological data., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Gong, Su, Huang, Liu, Wang, Luo, Yang and Chi.)

An effective tool to assess embryo quality in the assisted reproduction clinical practice will enhance successful implantation rates and mitigate high risks of multiple pregnancies. Potential biomarkers secreted into culture medium (CM) during embryo development enable rapid and noninvasive methods of assessing embryo quality. However, small volumes, low biomolecule concentrations, and impurity interference collectively preclude the identification of quality-related biomarkers in single blastocyst CM. Here, we developed a noninvasive trace multiomics approach to screen for potential markers in individual human blastocyst CM. We collected 84 CM samples and divided them into high-quality (HQ) and low-quality (LQ) groups. We evaluated the differentially expressed proteins (DEPs) and metabolites (DEMs) in HQ and LQ CM. A total of 504 proteins and 189 metabolites were detected in individual blastocyst CM. Moreover, 9 DEPs and 32 DEMs were identified in different quality embryo CM. We also categorized HQ embryos into positive implantation (PI) and negative implantation (NI) groups based on ultrasound findings on day 28. We identified 41 DEPs and 4 DEMs associated with clinical implantation outcomes in morphologically HQ embryos using a multiomics analysis approach. This study provides a noninvasive multiomics analysis technique and identifies potential biomarkers for clinical embryo developmental quality assessment.

Oil palm ( Elaeis guineensis Jacq.) is a typical tropical oil crop with a temperature of 26-28 °C, providing approximately 35% of the total world's vegetable oil. Growth and productivity are significantly affected by low-temperature stress, resulting in inhibited growth and substantial yield losses. To comprehend the intricate molecular mechanisms underlying the response and acclimation of oil palm under low-temperature stress, multi-omics approaches, including metabolomics, proteomics, and transcriptomics, have emerged as powerful tools. This comprehensive review aims to provide an in-depth analysis of recent advancements in multi-omics studies on oil palm under low-temperature stress, including the key findings from omics-based research, highlighting changes in metabolite profiles, protein expression, and gene transcription, as well as including the potential of integrating multi-omics data to reveal novel insights into the molecular networks and regulatory pathways involved in the response to low-temperature stress. This review also emphasizes the challenges and prospects of multi-omics approaches in oil palm research, providing a roadmap for future investigations. Overall, a better understanding of the molecular basis of the response of oil palm to low-temperature stress will facilitate the development of effective breeding and biotechnological strategies to improve the crop's resilience and productivity in changing climate scenarios.

Enrichment analysis, crucial for interpreting genomic, transcriptomic, and proteomic data, is expanding into metabolomics. Furthermore, there is a rising demand for integrated enrichment analysis that combines data from different studies and omics platforms, as seen in meta-analysis and multi-omics research. To address these growing needs, we have updated WebGestalt to include enrichment analysis capabilities for both metabolites and multiple input lists of analytes. We have also significantly increased analysis speed, revamped the user interface, and introduced new pathway visualizations to accommodate these updates. Notably, the adoption of a Rust backend reduced gene set enrichment analysis time by 95% from 270.64 to 12.41 s and network topology-based analysis by 89% from 159.59 to 17.31 s in our evaluation. This performance improvement is also accessible in both the R package and a newly introduced Python package. Additionally, we have updated the data in the WebGestalt database to reflect the current status of each source and have expanded our collection of pathways, networks, and gene signatures. The 2024 WebGestalt update represents a significant leap forward, offering new support for metabolomics, streamlined multi-omics analysis capabilities, and remarkable performance enhancements. Discover these updates and more at https://www.webgestalt.org., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Objective: To link changes in the B-cell transcriptome from systemic lupus erythematosus (SLE) patients with those in their macroenvironment, including cellular and fluidic components., Methods: Analysis was performed on 363 patients and 508 controls, encompassing transcriptomics, metabolomics, and clinical data. B-cell and whole-blood transcriptomes were analysed using DESeq and GSEA. Plasma and urine metabolomics peak changes were quantified and annotated using Ceu Mass Mediator database. Common sources of variation were identified using MOFA integration analysis., Results: Cellular macroenvironment was enriched in cytokines, stress responses, lipidic synthesis/mobility pathways and nucleotide degradation. B cells shared these pathways, except nucleotide degradation diverted to nucleotide salvage pathway, and distinct glycosylation, LPA receptors and Schlafen proteins., Conclusions: B cells showed metabolic changes shared with their macroenvironment and unique changes directly or indirectly induced by IFN-α signalling. This study underscores the importance of understanding the interplay between B cells and their macroenvironment in SLE pathology., Competing Interests: Declaration of competing interest The authors have declared that no conflict of interest exists., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Mapping gut microecological features to serum metabolites (SMs) will help identify functional links between gut microbiome and cardiometabolic health., Methods: This study encompassed 836-1021 adults over 9.7 year in a cohort, assessing metabolic syndrome (MS), carotid atherosclerotic plaque (CAP), and other metadata triennially. We analyzed mid-term microbial metagenomics, targeted fecal and serum metabolomics, host genetics, and serum proteomics., Findings: Gut microbiota and metabolites (GMM) accounted for 15.1% overall variance in 168 SMs, with individual GMM factors explaining 5.65%-10.1%, host genetics 3.23%, and sociodemographic factors 5.95%. Specifically, GMM elucidated 5.5%-49.6% variance in the top 32 GMM-explained SMs. Each 20% increase in the 32 metabolite score (derived from the 32 SMs) correlated with 73% (95% confidence interval [CI]: 53%-95%) and 19% (95% CI: 11%-27%) increases in MS and CAP incidences, respectively. Among the 32 GMM-explained SMs, sebacic acid, indoleacetic acid, and eicosapentaenoic acid were linked to MS or CAP incidence. Serum proteomics revealed certain proteins, particularly the apolipoprotein family, mediated the relationship between GMM-SMs and cardiometabolic risks., Interpretation: This study reveals the significant influence of GMM on SM profiles and illustrates the intricate connections between GMM-explained SMs, serum proteins, and the incidence of MS and CAP, providing insights into the roles of gut dysbiosis in cardiometabolic health via regulating blood metabolites., Funding: This study was jointly supported by the National Natural Science Foundation of China, Key Research and Development Program of Guangzhou, 5010 Program for Clinical Research of Sun Yat-sen University, and the 'Pioneer' and 'Leading goose' R&D Program of Zhejiang., Competing Interests: Declaration of interests The authors declare that they have no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

After long-term clinical application, traditional Chinese medicine (TCM) has accumulated rich experience in the stroke treatment. Huang-Qi-Long-Dan Granule (HQLDG) is a TCM formula that has been used in clinical for the treatment of acute ischemic stroke. However, its mechanism against ischemic stroke is still unknown. This study aimed to identify HQLDG's effect against ischemic stroke and explore its underlying mechanism. 16s rRNA sequencing, metabolomics/tryptophan (Trp)-targeted metabolomics analysis and transcriptomic analysis were used to investigate HQLDG underlying therapeutic mechanism. Our results revealed that HQLDG significantly decreased the infarct volume, improved mouse behavior and brain slices pathological staining. In addition, it could ameliorate intestinal barrier damage and regulate tight junction gene expression. 16s rRNA, metabolomics and transcriptomics analysis revealed that HQLDG treatment significantly improved the composition of gut microbiota and Trp metabolism pathway, and further downregulated Th17/IL-17 signaling pathway. HQLDG treatment could significantly decrease serum inflammatory cytokines, IL-17A and IL-22; down-regulate Trp metabolism receptor gene (Ahr), inflammatory cytokines genes (IL-17a, IL-22), and an important coding gene for maintaining the mature Th17 (rorc) in both brain and intestinal tissues. In the contrary, after gut microbiota removal, this effect of HQLDG was impaired. HQLDG treated mouse fecal microbiota transplantation also had positive effect against tMCAO injury. Moreover, AhR inhibitor could decrease IL-17A immunofluorescence. These results suggested that the gut microbiota regulation might be an important intermediate in HQLDG against tMCAO injury. HQLDG might exert anti-ischemic stroke effects through the gut microbiota-Trp metabolism-Th17/IL-17 signaling, which provides new insights into HQLDG-mediated prevention in ischemic stroke., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials., (© 2023. The Author(s).)

A very low calorie ketogenic diet (VLCKD) impacts host metabolism in people marked by an excess of visceral adiposity, and it affects the microbiota composition in terms of taxa presence and relative abundances. As a matter of fact, there is little available literature dealing with microbiota differences in obese patients marked by altered intestinal permeability. With the aim of inspecting consortium members and their related metabolic pathways, we inspected the microbial community profile, together with the set of volatile organic compounds (VOCs) from untargeted fecal and urine metabolomics, in a cohort made of obese patients, stratified based on both normal and altered intestinal permeability, before and after VLCKD administration. Based on the taxa relative abundances, we predicted microbiota-derived metabolic pathways whose variations were explained in light of our cohort symptom picture. A totally different number of statistically significant pathways marked samples with altered permeability, reflecting an important shift in microbiota taxa. A combined analysis of taxa, metabolic pathways, and metabolomic compounds delineates a set of markers that is useful in describing obesity dysfunctions and comorbidities.

Gut microbiota-derived microbial compounds may link to the pathogenesis of colorectal cancer (CRC). However, the role of the host-microbiome in the incidence and progression of CRC remains elusive. We performed 16S rRNA sequencing, metabolomics, and proteomic studies on samples from 85 CRC patients who underwent colonoscopy examination and found two distinct changed patterns of microbiome in CRC patients. The relative abundances of Catabacter and Mogibacterium continuously increased from intramucosal carcinoma to advanced stages, whereas Clostridium , Anaerostipes , Vibrio , Flavonifractor , Holdemanella , and Hungatella were significantly altered only in intermediate lesions. Fecal metabolomics analysis exhibited consistent increases in bile acids, indoles, and urobilin as well as a decrease in heme. Serum metabolomics uncovered the highest levels of bilin, glycerides, and nucleosides together with the lowest levels of bile acids and amino acids in the stage of intermediate lesions. Three fecal and one serum dipeptides were elevated in the intermediate lesions. Proteomics analysis of colorectal tissues showed that oxidation and autophagy through the PI3K/Akt-mTOR signaling pathway contribute to the development of CRC. Diagnostic analysis showed multiomics features have good predictive capability, with AUC greater than 0.85. Our overall findings revealed new candidate biomarkers for CRC, with potentially significant diagnostic and prognostic capabilities.

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations., (© 2024 The Authors. PROTEOMICS published by Wiley‐VCH GmbH.)

Actinomycetes are a phylogenetically diverse bacterial group which are widely distributed across terrestrial and aquatic ecosystems. Within this order, the genus Pseudonocardia and their specialised metabolites have been the focus of previous ecological studies due to their antagonistic interactions with other microorganisms and their mutualistic interactions with insects. However, the chemical ecology of free-living Pseudonocardia remains understudied. This study applies a multi-omics approach to investigate the chemical ecology of free-living actinomycetes from the genus Pseudonocardia. In a comparative genomics analysis, it was observed that the biosynthetic gene cluster family distribution was influenced mainly by phylogenetic distance rather than the geographic or ecological origin of strains. This finding was also observed in the mass spectrometry-based metabolomic profiles of nine Pseudonocardia species isolated from marine sediments and two terrestrial species. Antagonist interactions between these 11 species were examined, and matrix-assisted laser desorption/ionisation-mass spectrometry imaging was used to examine in situ chemical interactions between the Southern Ocean strains and their phylogenetically close relatives. Overall, it was demonstrated that phylogeny was the main predictor of antagonistic interactions among free-living Pseudonocardia. Moreover, two features at m/z 441.15 and m/z 332.20 were identified as metabolites related to these interspecies interactions., (© 2024 The Author(s). Environmental Microbiology published by John Wiley & Sons Ltd.)

Introduction: Cannabidiol (CBD) has important pharmacological activity, which includes antispasmodic, antioxidant, antithrombotic, and antianxiety properties. CBD has been applied as a health supplement to atherosclerosis. However, CBDs effect on gut microbiota and metabolic phenotype is unclear. Materials and Methods: We constructed a high production of cardiovascular risk factors, such as trimethylamine-N-oxide (TMAO) and phenylacetylglutamine (PAGln), in a mouse model using Clostridium sporogenes colonization. We used 16S ribosomal RNA (rRNA) gene sequencing and ultra-high performance liquid chromatography-quadrupole time-of flight mass spectrometry-based metabolomics to evaluate the effect of CBD on gut microbiota and plasma metabolites. Results: CBD decreased the levels of creatine kinase (CK), alanine transaminase (ALT), and low-density lipoprotein cholesterol and markedly increased high-density lipoprotein cholesterol. Furthermore, CBD treatment increased the abundance of beneficial bacteria, which include Lachnospiraceae&#95;NK4A136 and Blautia in the gut, but it decreased the levels of TMAO and PAGln in the plasma. Conclusion: CBD might have beneficial effects for cardiovascular protection.

Omics refers to the measurement and analysis of the totality of molecules or biological processes involved within an organism. Examples of omics data include genomics, transcriptomics, epigenomics, proteomics, metabolomics, and more. In this review, we present the available literature reporting omics data on heart failure that can inform the development of novel treatments or innovative treatment strategies for this disease. This includes polygenic risk scores to improve prediction of genomic data and the potential of multiomics to more efficiently identify potential treatment targets for further study. We also discuss the limitations of omic analyses and the barriers that must be overcome to maximize the utility of these types of studies. Finally, we address the current state of the field and future opportunities for using multiomics to better personalize heart failure treatment strategies., Competing Interests: Disclosures None.

Background: Understanding the metabolic changes in colorectal cancer (CRC) and exploring potential diagnostic biomarkers is crucial for elucidating its pathogenesis and reducing mortality. Cancer cells are typically derived from cancer tissues and can be easily obtained and cultured. Systematic studies on CRC cells at different stages are still lacking. Additionally, there is a need to validate our previous findings from human serum., Methods: Ultrahigh-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics and lipidomics were employed to comprehensively measure metabolites and lipids in CRC cells at four different stages and serum samples from normal control (NR) and CRC subjects. Univariate and multivariate statistical analyses were applied to select the differential metabolites and lipids between groups. Biomarkers with good diagnostic efficacy for CRC that existed in both cells and serum were screened by the receiver operating characteristic curve (ROC) analysis. Furthermore, potential biomarkers were validated using metabolite standards., Results: Metabolite and lipid profiles differed significantly among CRC cells at stages A, B, C, and D. Dysregulation of glycerophospholipid (GPL), fatty acid (FA), and amino acid (AA) metabolism played a crucial role in the CRC progression, particularly GPL metabolism dominated by phosphatidylcholine (PC). A total of 46 differential metabolites and 29 differential lipids common to the four stages of CRC cells were discovered. Eight metabolites showed the same trends in CRC cells and serum from CRC patients compared to the control groups. Among them, palmitoylcarnitine and sphingosine could serve as potential biomarkers with the values of area under the curve (AUC) more than 0.80 in the serum and cells. Their panel exhibited excellent performance in discriminating CRC cells at different stages from normal cells (AUC = 1.00)., Conclusions: To our knowledge, this is the first research to attempt to validate the results of metabolism studies of serum from CRC patients using cell models. The metabolic disorders of PC, FA, and AA were closely related to the tumorigenesis of CRC, with PC being the more critical factor. The panel composed of palmitoylcarnitine and sphingosine may act as a potential biomarker for the diagnosis of CRC, aiding in its prevention., (© 2024. The Author(s).)

We aim to systemically review the genomics, transcriptomics, epigenetics, proteomics, metabonomics and microbiota of psoriatic arthritis and psoriasis, illustrating the differences of these two diseases, broadening our understanding of the pathogenesis of them and providing important clues for valuable biomarkers of earlier diagnosis and treatments. To our knowledge, this is the first study that combine all omics studies from genomics to microbiota and may serve as a reference for future studies to identify the key underlying pathways in psoriatic arthritis., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment., (© The Author(s) 2024. Published by Oxford University Press.)

Purpose: Injury prevention is a crucial aspect of sports, particularly in high-performance settings such as elite female football. This study aimed to develop an injury prediction model that incorporates clinical, Global-Positioning-System (GPS), and multiomics (genomics and metabolomics) data to better understand the factors associated with injury in elite female football players., Methods: We designed a prospective cohort study over 2 seasons (2019-20 and 2021-22) of noncontact injuries in 24 elite female players in the Spanish Premiership competition. We used GPS data to determine external workload, genomic data to capture genetic susceptibility, and metabolomic data to measure internal workload., Results: Forty noncontact injuries were recorded, the most frequent of which were muscle (63%) and ligament (20%) injuries. The baseline risk model included fat mass and the random effect of the player. Six genetic polymorphisms located at the DCN, ADAMTS5, ESRRB, VEGFA, and MMP1 genes were associated with injuries after adjusting for player load (P < .05). The genetic score created with these 6 variants determined groups of players with different profile risks (P = 3.1 × 10-4). Three metabolites (alanine, serotonin, and 5-hydroxy-tryptophan) correlated with injuries. The model comprising baseline variables, genetic score, and player load showed the best prediction capacity (C-index: .74)., Conclusions: Our model could allow efficient, personalized interventions based on an athlete's vulnerability. However, we emphasize the necessity for further research in female athletes with an emphasis on validation studies involving other teams and individuals. By expanding the scope of our research and incorporating diverse populations, we can bolster the generalizability and robustness of our proposed model.

Plant biostimulants are widely applied in agriculture for their ability to improve plant fitness. In the present work, the impact of Graminaceae-derived protein hydrolysate (P) and its lighter molecular fraction F3 (< 1 kDa) on lettuce plants, subjected to either no salt or high salt conditions, was investigated through the combination of metabolomics and transcriptomics. The results showed that both treatments significantly modulated the transcriptome and metabolome of plants under salinity stress, highlighting an induction of the hormonal response. Nevertheless, P and F3 also displayed several peculiarities. F3 specifically modulated the response to ethylene and MAPK signaling pathway, whereas P treatment induced a down-accumulation of secondary metabolites, albeit genes controlling the biosynthesis of osmoprotectants and antioxidants were up-regulated. Moreover, according with the auxin response modulation, P promoted cell wall biogenesis and plasticity in salt-stressed plants. Notably, our data also outlined an epigenetic control of gene expression induced by P treatment. Contrarily, experimental data are just partially in agreement when not stressed plants, treated with P or F3, were considered. Indeed, the reduced accumulation of secondary metabolites and the analyses of hormone pathways modulation would suggest a preferential allocation of resources towards growth, that is not coherent with the down-regulation of the photosynthetic machinery, the CO 2 assimilation rate and leaves biomass. In conclusion, our data demonstrate that, although they might activate different mechanisms, both the P and F3 can result in similar benefits, as far as the accumulation of protective osmolytes and the enhanced tolerance to oxidative stress are concerned. Notably, the F3 fraction exhibits slightly greater growth promotion effects under high salt conditions. Most importantly, this research further corroborates that biostimulants' mode of action is dependent on plants' physiological status and their composition, underscoring the importance of investigating the bioactivity of the different molecular components to design tailored applications for the agricultural practice., (© 2024. The Author(s).)

Background: Ulcerative colitis (UC) is a multifactorial chronic inflammatory bowel disease (IBD) that affects the large intestine with superficial mucosal inflammation. A dysbiotic gut microbial profile has been associated with UC. Our study aimed to characterize the UC gut bacterial, fungal, and metabolic fingerprints by omic approaches., Methods: The 16S rRNA- and ITS2-based metataxonomics and gas chromatography-mass spectrometry/solid phase microextraction (GC-MS/SPME) metabolomic analysis were performed on stool samples of 53 UC patients and 37 healthy subjects (CTRL). Univariate and multivariate approaches were applied to separated and integrated omic data, to define microbiota, mycobiota, and metabolic signatures in UC. The interaction between gut bacteria and fungi was investigated by network analysis., Results: In the UC cohort, we reported the increase of Streptococcus , Bifidobacterium , Enterobacteriaceae, TM7-3, Granulicatella , Peptostreptococcus , Lactobacillus , Veillonella , Enterococcus , Peptoniphilus , Gemellaceae, and phenylethyl alcohol; and we also reported the decrease of Akkermansia ; Ruminococcaceae; Ruminococcus ; Gemmiger ; Methanobrevibacter ; Oscillospira ; Coprococus ; Christensenellaceae ; Clavispora ; Vishniacozyma ; Quambalaria ; hexadecane; cyclopentadecane; 5-hepten-2-ol, 6 methyl; 3-carene; caryophyllene; p-Cresol; 2-butenal; indole, 3-methyl-; 6-methyl-3,5-heptadiene-2-one; 5-octadecene; and 5-hepten-2-one, 6 methyl. The integration of the multi-omic data confirmed the presence of a distinctive bacterial, fungal, and metabolic fingerprint in UC gut microbiota. Moreover, the network analysis highlighted bacterial and fungal synergistic and/or divergent interkingdom interactions., Conclusion: In this study, we identified intestinal bacterial, fungal, and metabolic UC-associated biomarkers. Furthermore, evidence on the relationships between bacterial and fungal ecosystems provides a comprehensive perspective on intestinal dysbiosis and ecological interactions between microorganisms in the framework of UC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Scanu, Toto, Petito, Masi, Fidaleo, Puca, Baldelli, Reddel, Vernocchi, Pani, Putignani, Scaldaferri and Del Chierico.)

A large-scale multimodal atlas that includes major kidney regions is lacking. Here, we employed simultaneous high-throughput single-cell ATAC/RNA sequencing (SHARE-seq) and spatially resolved metabolomics to profile 54 human samples from distinct kidney anatomical regions. We generated transcriptomes of 446,267 cells and chromatin accessibility profiles of 401,875 cells and developed a package to analyze 408,218 spatially resolved metabolomes. We find that the same cell type, including thin limb, thick ascending limb loop of Henle and principal cells, display distinct transcriptomic, chromatin accessibility, and metabolomic signatures, depending on anatomic location. Surveying metabolism-associated gene profiles revealed non-overlapping metabolic signatures between nephron segments and dysregulated lipid metabolism in diseased proximal tubule (PT) cells. Integrating multimodal omics with clinical data identified PLEKHA1 as a disease marker, and its in vitro knockdown increased gene expression in PT differentiation, suggesting possible pathogenic roles. This study highlights previously underrepresented cellular heterogeneity underlying the human kidney anatomy., Competing Interests: Declaration of interests B.D.H. is a consultant for Janssen Research & Development, LLC, Pfizer, and Chinook Therapeutics, held equity in Chinook Therapeutics and grant funding from Chinook Therapeutics, Pfizer and Janssen Research & Development, LLC; all interests are unrelated to the current work., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The growing number of multi-omics studies demands clear conceptual workflows coupled with easy-to-use software tools to facilitate data analysis and interpretation. This protocol covers three key components involved in multi-omics analysis, including single-omics data analysis, knowledge-driven integration using biological networks and data-driven integration through joint dimensionality reduction. Using the dataset from a recent multi-omics study of human pancreatic islet tissue and plasma samples, the first section introduces how to perform transcriptomics/proteomics data analysis using ExpressAnalyst and lipidomics data analysis using MetaboAnalyst. On the basis of significant features detected in these workflows, the second section demonstrates how to perform knowledge-driven integration using OmicsNet. The last section illustrates how to perform data-driven integration from the normalized omics data and metadata using OmicsAnalyst. The complete protocol can be executed in ~2 h. Compared with other available options for multi-omics integration, the Analyst software suite described in this protocol enables researchers to perform a wide range of omics data analysis tasks via a user-friendly web interface., (© 2024. Springer Nature Limited.)

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., (© The Author(s) 2024. Published by Oxford University Press GigaScience.)