Lipidomics Data Research Articles

Multiomic comparison of GLP1R analogues interaction networks

Abstract Background GLP1R analogs are drugs increasingly used in clinical practice in patients with type 2 diabetes, and obesity. Subsequent clinical trials indicate their beneficial effects, including cardioprotection, but the exact mechanisms of those actions are still not known. Aim of the study: This study aims to understand how GLP1R analogs exert cardioprotective and therapeutic effects in patients with type 2 diabetes and obesity. Through analyzing GLP1R interaction networks and associated metabolomic and lipidomic data, we seek to uncover the molecular mechanisms underlying these effects and their potential therapeutic implications. Methods We performed an integrative analysis of four GLP1Ri interaction networks and associated metabolomic and lipidomic datasets. Interactomics revealed 130 common genes among semaglutide, exenatide, tirzapeptide, and retatrutide, primarily associated with diabetes-related processes, including obesity and hyperglycemia. Noteworthy findings encompassed ontological terms related to cardiovascular diseases (CVDs), such as hypertension, calcium regulation in cardiac cells, and amino acid accumulation-induced mTOR activation. Additionally, enrichment in gene sets linked to longevity, less recognized terms like fatty liver disease, chemokine signaling, and sirtuin interactome, were observed. Results Specific processes for tirzepatide included behavior-related terms and hydrochloric acid secretion, while retatrutide exhibited associations with fibrosarcoma, thinking and speaking disturbances, and adipogenesis signaling. Shared processes like primary ciliary dyskinesia differed in implicated genes. Metabolomics identified shared metabolites associated with diverse pathways, including galactose metabolism and nitric oxide-related pathways. Lipidomics highlighted enriched phenotypes and pathways, such as arachidonic acid metabolism and cholesterol biosynthesis. Conclusion Integration of interaction networks with metabolomic and lipidomic data revealed top interactors and impacted metabolites, shedding light on the intricate molecular landscape of GLP1Ri. These findings contribute valuable insights into the potential therapeutic implications and broader health considerations of GLP1R drugsfig1fig2

Mobile Phase Contaminants Affect Neutral Lipid Analysis in LC-MS-Based Lipidomics Studies.

Lipidomics is a well-established field, enabled by modern liquid chromatography mass spectrometry (LC-MS) technology, rapidly generating large amounts of data. Lipid extracts derived from biological samples are complex, and most spectral features in LC-MS lipidomics data sets remain unidentified. In-depth analyses of commercial triacylglycerol, diacylglycerol, and cholesterol ester standards revealed the expected ammoniated and sodiated ions as well as five additional unidentified higher mass peaks with relatively high intensities. The identities and origin of these unknown peaks were investigated by modifying the chromatographic mobile-phase components and LC-MS source parameters. Tandem MS (MS/MS) of each unknown adduct peak yielded no lipid structural information, producing only an intense ion of the adducted species. The unknown adducts were identified as low-mass contaminants originating from methanol and isopropanol in the mobile phase. Each contaminant was determined to be an alkylated amine species using their monoisotopic masses to calculate molecular formulas. Analysis of bovine liver extract identified 33 neutral lipids with an additional 73 alkyl amine adducts. Analysis of LC-MS-grade methanol and isopropanol from different vendors revealed substantial alkylated amine contamination in one out of three different brands that were tested. Substituting solvents for ones with lower levels of alkyl amine contamination increased lipid annotations by 36.5% or 27.4%, depending on the vendor, and resulted in >2.5-fold increases in peak area for neutral lipid species without affecting polar lipid analysis. These findings demonstrate the importance of solvent selection and disclosure for lipidomics protocols and highlight some of the major challenges when comparing data between experiments.

Highly reliable LC-MS lipidomics database for efficient human plasma profiling based on NIST SRM 1950

Liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS)-based methods have become the gold standard methodology for the comprehensive profiling of the human plasma lipidome. However, both the complexity of lipid chemistry and LC-HRMS-associated data pose challenges to the characterization of this biological matrix. In accordance with the current consensus of quality requirements for LC-HRMS lipidomics data, we aimed to characterize the NIST® Standard Reference Material for Human Plasma (SRM 1950) using an LC-ESI(+/–)-MS method compatible with high-throughput lipidome profiling. We generated a highly curated lipid database with increased coverage, quality, and consistency, including additional quality assurance procedures involving adduct formation, within-method m/z evaluation, retention behavior of species within lipid chain isomers, and expert-driven resolution of isomeric and isobaric interferences. As a proof-of-concept, we showed the utility of our in-house LC-MS lipidomic database –consisting of 592 lipid entries– for the fast, comprehensive, and reliable lipidomic profiling of the human plasma from healthy human volunteers. We are confident that the implementation of this robust resource and methodology will have a significant impact by reducing data redundancy and the current delays and bottlenecks in untargeted plasma lipidomic studies.

Integrative transcriptomics and lipidomics unravels the amelioration effects of Radix Bupleuri on non-alcoholic fatty liver disease

Ethnopharmacological relevanceRadix Bupleuri (Bupleurum chinense DC.) is the most commonly used traditional Chinese medicine (TCM) for the treatment of liver diseases. While the effects of Radix Bupleuri (BR) on lipid-lowering and liver protection have been established, its role in the development of non-alcoholic fatty liver disease (NAFLD) induced by a high-fat diet remains unclear. Aim of the studyThe objective of this study was to evaluate the alleviation effects of the active fraction of BR on NAFLD in vivo and to explore the underlying mechanisms through an analysis of liver transcriptome and lipidomics. Materials and methodsThe NAFLD model was established in SD rats by administering a high-fat diet (HFD) for 8 weeks. Subsequently, the NAFLD model rats were continuously gavaged with different polarity fractions of BR (25 g/kg/d) and melatonin (MT) (30 mg/kg/d) for an additional 6 weeks to assess therapeutic effects. The potential mechanism of the low polarity fraction of BR (LBR) in treating NAFLD was investigated through hepatic transcriptome analysis, non-targeted lipidomics, RT-qPCR, protein-protein interaction (PPI) network construction, molecular docking, and Western blotting, aiming to elucidate the underlying mechanisms by which LBR may ameliorate NAFLD. ResultsThese results demonstrated that LBR significantly alleviated the effects of HFD-induced NAFLD, as evidenced by reductions in body weight (BW), liver weight (LW), and epididymal fat weight (EFW) compared to model rats and other polarity fractions of BR. Furthermore, LBR notably down-regulated serum and liver levels of total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C), while up-regulating high-density lipoprotein cholesterol (HDL-C) in serum. Mechanistically, liver transcriptome analysis indicated that fatty acid metabolism may be a crucial pathway for the improvement of NAFLD following LBR treatment. Lipidomics data suggested that LBR can modulate the metabolic profile in NAFLD rats. Enrichment analysis revealed that glycerophospholipid and glycerolipid metabolism might be key pathways involved in the development of NAFLD. RT-qPCR analysis demonstrated that LBR could regulate the expression of lipid-related genes in these critical pathways. Additionally, Spearman correlation analysis showed a strong relationship between lipid metabolic biomarkers, pathological indices, and lipid-related genes. Moreover, protein-protein interaction (PPI) network and molecular docking analyses identified seven key targets with six ingredients of LBR exhibiting good binding capacity (< -5.0 kcal/mol). Finally, Western blotting analysis indicated that LBR up-regulates the expression levels of PPARα, CPT1, and FABP1 while down-regulating the expression levels of SREBF1 and SCD1, thereby improving metabolism and exerting a lipid-lowering effect. ConclusionIn conclusion, the present research elucidated the lipid-lowering mechanisms of the active fractions of BR. Both BR and LBR presented themselves as promising candidates for the development of novel pharmacological agents targeting NAFLD. LBR effectively ameliorated lipid disturbances associated with HFD-induced NAFLD by modulating the metabolism of fatty acids, cholesterol, glycerolipid, and glycerophospholipids. Consequently, LBR held significant potential for development as an effective lipid-lowering therapeutic.

Investigation of oral toxicity of WS2 nanosheets to mouse intestine: Pathological injury, trace element balance, lipid profile changes, and autophagy.

The success of graphene oxides has gained extensive research interests in developing novel 2D nanomaterials (NMs). WS2 nanosheets (NSs) are novel transition metal-based 2D NMs, but their toxicity is unclear. In this study, we investigated the oral toxicity of WS2 NSs to mouse intestines. Male mice were administrated with vehicles, 1, 10, or 100 mg/kg NSs via intragastric route, once a day, for 5days. The results indicate that the NSs did not induce pathological or ultrastructural changes in intestines. There were minimal changes of trace elements that the exposure did not induce W accumulation, and only Co levels were dose-dependently increased. Lipid droplets were observed in all groups of mice, but lipidomics data indicate that WS2 NSs only significantly decreased four lipid species, all belonging to phosphatidylcholine (PC). The levels of proteins regulating autophagic lipolysis, namely, LC3, lysosomal associated membrane protein 2 (LAMP2) and perilipin 2 (PLIN2), were increased, but it was only statistically significantly different for LC3. The results of this study suggest that repeated intragastric exposure to WS2 NSs only induced minimal influences on pathological injury, trace element balance, autophagy, and lipid profiles in mouse intestines, indicating relatively high biocompatibility of WS2 NSs to mouse intestine via oral route.

Heat shock factor ZmHsf17 positively regulates phosphatidic acid phosphohydrolase ZmPAH1 and enhances maize thermotolerance.

Heat stress (HS) adversely impacts plant growth, development and grain yield. Heat shock factors (Hsf), especially HsfA2 subclass, play a pivotal role in the transcriptional regulation of genes in response to HS. In this study, the coding sequence of maize ZmHsf17 was cloned. ZmHsf17 contains conserved domains: DNA binding, oligomerization and transcriptional activation. The protein was nuclear localized and had transcription activation activity. Yeast two hybrid and split luciferase complementary assays confirmed the interaction of ZmHsf17 with members of the maize HsfA2 subclass. Overexpression of ZmHsf17 in maize significantly increased chlorophyll content and net photosynthesis rate of maize leaves, and enhanced the stability of cellular membranes. Through integrative analysis of ChIP-seq and RNA-seq datasets, ZmPAH1, encoding phosphatidic acid phosphohydrolase of lipid metabolic pathways, was identified as a target gene of ZmHsf17. The promoter fragment of ZmPAH1 was bound by ZmHsf17 in protein-DNA interaction experiments in vivo and in vitro. Lipidomic data also indicates that the overexpression of ZmHsf17 increased levels of some critical membrane lipid components of maize leaves under HS. This research provides new insights into the role of the ZmHsf17-ZmPAH1 module in regulating thermotolerance in maize.

Streamlining Phenotype Classification and Highlighting Feature Candidates: A Screening Method for Non-Targeted Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS) Data.

Nontargeted analysis (NTA) is increasingly utilized for its ability to identify key molecular features beyond known targets in complex samples. NTA is particularly advantageous in exploratory studies aimed at identifying phenotype-associated features or molecules able to classify various sample types. However, implementing NTA involves extensive data analyses and labor-intensive annotations. To address these limitations, we developed a rapid data screening capability compatible with NTA data collected on a liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) platform that allows for sample classification while highlighting potential features of interest. Specifically, this method aggregates the thousands of IMS-MS spectra collected across the LC space for each sample and collapses the LC dimension, resulting in a single summed IMS-MS spectrum for screening. The summed IMS-MS spectra are then analyzed with a bootstrapped Lasso technique to identify key regions or coordinates for phenotype classification via support vector machines. Molecular annotations are then performed by examining the features present in the selected coordinates, highlighting potential molecular candidates. To demonstrate this summed IMS-MS screening approach, we applied it to clinical plasma lipidomic NTA data and exposomic NTA data from water sites with varying contaminant levels. Distinguishing coordinates were observed in both studies, enabling the evaluation of phenotypic molecular annotations and resulting in screening models capable of classifying samples with up to a 25% increase in accuracy compared to models using annotated data.

Disruption of HSD17B12 in mouse hepatocytes leads to reduced body weight and defect in the lipid droplet expansion associated with microvesicular steatosis.

The function of hydroxysteroid dehydrogenase 12 (HSD17B12) in lipid metabolism is poorly understood. To study this further, we created mice with hepatocyte-specific knockout of HSD17B12 (LiB12cKO). From 2 months on, these mice showed significant fat accumulation in their liver. As they aged, they also had a reduced whole-body fat percentage. Interestingly, the liver fat accumulation did not result in the typical formation of large lipid droplets (LD); instead, small droplets were more prevalent. Thus, LiB12KO liver did not show increased macrovesicular steatosis with the increasing fat content, while microvesicular steatosis was the predominant feature in the liver. This indicates a failure in the LD expansion. This was associated with liver damage, presumably due to lipotoxicity. Notably, the lipidomics data did not support an essential role of HSD17B12 in fatty acid (FA) elongation. However, we did observe a decrease in the quantity of specific lipid species that contain FAs with carbon chain lengths of 18 and 20 atoms, including oleic acid. Of these, phosphatidylcholine and phosphatidylethanolamine have been shown to play a key role in LD formation, and a limited amount of these lipids could be part of the mechanism leading to the dysfunction in LD expansion. The increase in the Cidec expression further supported the deficiency in LD expansion in the LiB12cKO liver. This protein is crucial for the fusion and growth of LDs, along with the downregulation of several members of the major urinary protein family of proteins, which have recently been shown to be altered during endoplasmic reticulum stress.

Adipose Tissue Oxylipin Profile Changes with Subclinical Ketosis and Depot in Postpartum Dairy Cows

Lipolysis of adipose tissue is a natural occurrence during the periparturient period in dairy cows. However, when lipolysis rates exceed the capacity of other tissues to utilize nonesterified fatty acids (NEFA), it may lead to the development of ketosis and other diseases. Additionally, polyunsaturated NEFA (PUFA) can become oxidized into oxylipins, which modulate inflammation and metabolism. The objective of this work was to identify depot-specific differences on adipose tissue oxylipin profile in dairy cows with and without subclinical ketosis and assess the effects of oxylipins on adipocyte function in vitro. Subcutaneous adipose tissue from the flank (SAT) and visceral adipose tissue from the omentum (VAT) were collected through laparotomy from multiparous dairy cows (5-14 d in milk) and grouped according to blood β-hydroxybutyrate (BHB) into non-ketotic (NK; n = 5; BHB ≤ 0.8 mmol/L) and subclinical ketotic (SCK; n = 5; BHB 1.4 and ≤ 2.6 mmol/L). A targeted lipidome capable of detecting a 154 oxylipins was performed in paired SAT and VAT samples from all animals. Data were analyzed using the PROC GLIMMIX procedure in SAS (v9.4, SAS Institute Inc., Cary, NC, USA) for the effect of depot (SAT, VAT), ketosis status (SCK, NK), and their interaction (depot × ketosis status) on oxylipin abundance. The oxylipins thromboxane-B2 (TXB2), prostaglandin-A2 (PGA2), and 5-hydroxeicostretanoic acid (5-HETE) were selected from lipidomic data based on effects of ketosis status and depot-specificity to further investigate their effects on SAT and VAT adipocyte function. Lipidomic data revealed 50 oxylipins across both adipose tissue depots. SCK was associated with a decreased abundance of TXB2 and tended to associate with an increase in prostaglandinA2 (PGA2) and prostaglandinE1 (PGE1). Additionally, PGE1, 15-keto-prostaglandin-E2 (15-Keto-PGE2), 13,14-dihydro-15-keto-prostaglandin-E2 (13-14dh-15k-PGE2), 5-HETE, and 15-HETE were increased in SAT. While VAT had a greater abundance of 9,10-dihydroxy-12Z-octadecenoic acid (9,10-DiHOME), 12,13-dihydroxy-9Z-octadecenoic acid (12,13-DiHOME), 9-oxo-10E,12Z,15Z-octadecatrienoic acid (9-OxoOTrE) and 13S-hydroxy-9Z,11E,15Z-octadecatrienoic acid (13(s)-HOTrE). In vitro, AVG dose of 5-HETE on VAT cells tended to increase proliferation at d 7 compared with the control, HIGH dose of TXB2 tended to decrease lipid accumulation in SAT compared with control, and AVG dose of PGA2 on VAT cells tended to lower ROS compared with the control. Overall, postpartum dairy cows have depot-specific adipose tissue lipidomic profiles which are associated with changes in ketosis status.

The Evolution of Lipidomics during Oil Accumulation of Plukenetia volubilis Seeds.

Sacha inchi (Plukenetia volubilis) is a valuable oilseed crop with a high content of polyunsaturated fatty acids (PUFAs). However, there is a lack of in-depth understanding of the lipidomics in Sacha inchi seeds (SIDs). Saturated fatty acids occupied more than half of the proportion (59.31%) in early development, while PUFAs accounted for 78.92% at maturation. The main triacylglycerols were TAG(18:3/18:3/18:3), TAG(18:2/18:2/18:3), and TAG(16:0/18:2/18:2). The corresponding species (18:3/18:3, 18:2/18:2, and 16:0/18:2) were also the main ingredients in diacylglycerol and phosphatidic acid, indicating high PUFA composition in the sn-1 and sn-2 positions of TAG. Only LPC(18:3), LPC(18:2), and LPC(16:0) were identified in SIDs, implying that those PUFAs on the sn-2 positions of the PC(18:3/-), PC(18:2/-), and PC(16:0/-) categories were released into the acyl-CoA pool for the Kennedy pathway. Conversely, the PC(18:1/-) and PC(18:0/-) categories might be responsible for the generation of PC-derived DAG and TAG. The lipidomics data will contribute to understanding the TAG assembly in developing SIDs, especially for PUFAs.

Liver Tissue Proteins Improve the Accuracy of Plasma Proteins as Biomarkers in Diagnosing Metabolic Dysfunction-Associated Steatohepatitis.

Biomarkers for metabolic dysfunction-associated steatohepatitis (MASH) have been considered based on proteomic and lipidomic data from plasma and liver tissue without clinical benefits. This study evaluated proteomics-based plasma and liver tissue biomarkers collected simultaneously from patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Liver tissue and plasma samples were collected during liver biopsy to diagnose MASLD. Untargeted proteomics was performed on 64 patients. Twenty plasma proteins were up- or downregulated in patients with MASH compared with those without MASH. The potential biomarkers utilizing the best combinations of these plasma proteins had an area under the receiver operating curve (AUROC) of 0.671 for detecting those with MASH compared with those without it. However, none of the 20 plasma proteins were represented among the significantly regulated liver tissue proteins in patients with MASH. Ten of them displayed a trend and relevance in liver tissue with MASLD progression. These 10 plasma proteins had an AUROC of 0.793 for MASH identification and higher positive and negative predictive values. The plasma and liver protein expressions of patients with MASH were not directly comparable. Plasma protein biomarkers that are also expressed in liver tissue can help improve MASH detection.

Longitudinal integrative cell-free DNA analysis in gestational diabetes mellitus

Gestational diabetes mellitus (GDM) presents varied manifestations throughout pregnancy and poses a complex clinical challenge. High-depth cell-free DNA (cfDNA) sequencing analysis holds promise in advancing our understanding of GDM pathogenesis and prediction. In 299 women with GDM and 299 matched healthy pregnant women, distinct cfDNA fragment characteristics associated with GDM are identified throughout pregnancy. Integrating cfDNA profiles with lipidomic and single-cell transcriptomic data elucidates functional changes linked to altered lipid metabolism processes in GDM. Transcription start site (TSS) scores in 50 feature genes are used as the cfDNA signature to distinguish GDM cases from controls effectively. Notably, differential coverage of the islet acinar marker gene PRSS1 emerges as a valuable biomarker for GDM. A specialized neural network model is developed, predicting GDM occurrence and validated across two independent cohorts. This research underscores the high-depth cfDNA early prediction and characterization of GDM, offering insights into its molecular underpinnings and potential clinical applications.

Metabolomics analysis of human spermatozoa reveals impaired metabolic pathways in asthenozoospermia.

Infertility is a major health issue, affecting 15% of reproductive-age couples with male factors contributing to 50% of cases. Asthenozoospermia (AS), or low sperm motility, is a common cause of male infertility with complex aetiology, involving genetic and metabolic alterations, inflammation and oxidative stress. However, the molecular mechanisms behind low motility are unclear. In this study, we used a metabolomics approach to identify metabolic biomarkers and pathways involved in sperm motility. We compared the metabolome and lipidome of spermatozoa of men with normozoospermia (n = 44) and AS (n = 22) using untargeted LC-MS and the metabolome of seminal fluid using 1H-NMR. Additionally, we evaluated the seminal fluid redox status to assess the oxidative stress in the ejaculate. We identified 112 metabolites and 209 lipids in spermatozoa and 27 metabolites in the seminal fluid of normozoospermic and asthenozoospermic men. PCA analysis of the spermatozoa's metabolomics and lipidomics data showed a clear separation between groups. Spermatozoa of asthenozoospermic men presented lower levels of several amino acids, and increased levels of energetic substrates and lysophospholipids. However, the metabolome and redox status of the seminal fluid was not altered inAS. Our results indicate impaired metabolic pathways associated with redox homeostasis and amino acid, energy and lipid metabolism in AS. Taken together, these findings suggest that the metabolome and lipidome of human spermatozoa are key factors influencing their motility and that oxidative stress exposure during spermatogenesis or sperm maturation may be in the aetiology of decreased motility in AS.

Lipidome changes due to improved dietary fat quality inform cardiometabolic risk reduction and precision nutrition

Current cardiometabolic disease prevention guidelines recommend increasing dietary unsaturated fat intake while reducing saturated fats. Here we use lipidomics data from a randomized controlled dietary intervention trial to construct a multilipid score (MLS), summarizing the effects of replacing saturated fat with unsaturated fat on 45 lipid metabolite concentrations. In the EPIC-Potsdam cohort, a difference in the MLS, reflecting better dietary fat quality, was associated with a significant reduction in the incidence of cardiovascular disease (−32%; 95% confidence interval (95% CI): −21% to −42%) and type 2 diabetes (−26%; 95% CI: −15% to −35%). We built a closely correlated simplified score, reduced MLS (rMLS), and observed that beneficial rMLS changes, suggesting improved dietary fat quality over 10 years, were associated with lower diabetes risk (odds ratio per standard deviation of 0.76; 95% CI: 0.59 to 0.98) in the Nurses’ Health Study. Furthermore, in the PREDIMED trial, an olive oil-rich Mediterranean diet intervention primarily reduced diabetes incidence among participants with unfavorable preintervention rMLS levels, suggestive of disturbed lipid metabolism before intervention. Our findings indicate that the effects of dietary fat quality on the lipidome can contribute to a more precise understanding and possible prediction of the health outcomes of specific dietary fat modifications.

Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases.

COVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the biosignatures. To identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data analysis. We leveraged a multi-omics network-based approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization Ordinal Scale WHO Ordinal Scale was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state specific omics-graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment analysis. Network analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed distinct biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. Mild COVID-19 cases were characterized by a unique biosignature comprising C-C Motif Chemokine Ligand 4 (CCL4), and Interferon Regulatory Factor 1 (IRF1). Hepatocyte Growth Factor (HGF), Matrix Metallopeptidase 12 (MMP12), Interleukin 10 (IL10), Nuclear Factor Kappa B Subunit 1 (NFKB1), and suberoylcarnitine form hubs in the omics network that characterizes the moderate disease state. The severe cases were marked by biosignatures such as Signal Transducer and Activator of Transcription 1 (STAT1), Superoxide Dismutase 2 (SOD2), HGF, taurine, lysophosphatidylcholine, diacylglycerol, triglycerides, and sphingomyelin that characterize the disease state. This study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions (such as protein-protein, protein-transcript, protein-metabolite, transcript-metabolite, and lipid-lipid interactions) that are unique to mild, moderate, and severe COVID-19 disease states. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. The approach implemented here can be used to find associations between transcripts, proteins, lipids, and metabolites in other diseases.

LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis.

In the field of lipidomics, where the complexity of lipid structures and functions presents significant analytical challenges, LipidSig stands out as the first web-based platform providing integrated, comprehensive analysis for efficient data mining of lipidomic datasets. The upgraded LipidSig 2.0 (https://lipidsig.bioinfomics.org/) simplifies the process and empowers researchers to decipher the complex nature of lipids and link lipidomic data to specific characteristics and biological contexts. This tool markedly enhances the efficiency and depth of lipidomic research by autonomously identifying lipid species and assigning 29 comprehensive characteristics upon data entry. LipidSig 2.0 accommodates 24 data processing methods, streamlining diverse lipidomic datasets. The tool's expertise in automating intricate analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis, allows researchers to profoundly investigate lipid properties and their biological implications. Additional innovative features, such as the 'Network' function, offer a system biology perspective on lipid interactions, and the 'Multiple Group' analysis aids in examining complex experimental designs. With its comprehensive suite of features for analyzing and visualizing lipid properties, LipidSig 2.0 positions itself as an indispensable tool for advanced lipidomics research, paving the way for new insights into the role of lipids in cellular processes and disease development.

Transgenerational hormesis in healthy aging and antiaging medicine from bench to clinics: Role of food components

Neurodegenerative diseases have multifactorial pathogenesis, mainly involving neuroinflammatory processes. Finding drugs able to treat these diseases, expecially because for most of these diseases there are no effective drugs, and the current drugs cause undesired side effects, represent a crucial point. Most in vivo and in vitro studies have been concentrated on various aspects related to neurons (e.g. neuroprotection), however, there has not been focus on the prevention of early stages involving glial cell activation and neuroinflammation. Recently, it has been demonstrated that nutritional phytochemicals including polyphenols, the main active constituents of the Mediterranean diet, maintain redox balance and neuroprotection through the activation of hormetic vitagene pathway. Recent lipidomics data from our laboratory indicate mushrooms as strong nutritional neuronutrients with strongly activity against neuroinflammation in Meniere’ diseaseas, a model of cochleovestibular neural degeneration, as well as in animal model of traumatic brain injury, or rotenone induced parkinson’s disease. Moreover, Hidrox®, an aqueous extract of olive containing hydroxytyrosol, and Boswellia, acting as Nrf2 activators, promote resilience by enhancing the redox potential, and thus, regulate through hormetic mechanisms, cellular stress response mechanisms., Thus, modulation of cellular stress pathways, in particular vitagenes system, may be an innovative approach for therapeutic intervention in neurodegenerative disorders.

The Neurolipid Atlas: a lipidomics resource for neurodegenerative diseases uncovers cholesterol as a regulator of astrocyte reactivity impaired by ApoE4.

Lipid changes in the brain have been implicated in many neurodegenerative diseases including Alzheimer's Disease (AD), Parkinson's disease and Amyotrophic Lateral Sclerosis. To facilitate comparative lipidomic research across brain-diseases we established a data commons named the Neurolipid Atlas, that we have pre-populated with novel human, mouse and isogenic induced pluripotent stem cell (iPSC)-derived lipidomics data for different brain diseases. We show that iPSC-derived neurons, microglia and astrocytes display distinct lipid profiles that recapitulate in vivo lipotypes. Leveraging multiple datasets, we show that the AD risk gene ApoE4 drives cholesterol ester (CE) accumulation in human astrocytes recapitulating CE accumulation measured in the human AD brain. Multi-omic interrogation of iPSC-derived astrocytes revealed that cholesterol plays a major role in astrocyte interferon-dependent pathways such as the immunoproteasome and major histocompatibility complex (MHC) class I antigen presentation. We show that through enhanced cholesterol esterification ApoE4 suppresses immune activation of astrocytes. Our novel data commons, available at neurolipidatlas.com, provides a user-friendly tool and knowledge base for a better understanding of lipid dyshomeostasis in neurodegenerative diseases.

Profiling lipid changes in Panax notoginseng upon Alternaria panax infection

The growth of Panax notoginseng is stressed by various biotic and abiotic factors. Alternaria panax Whetzel is a destructive pathogen causing black spot disease of P. notoginseng, thereby severely reducing the yield and quality of P. notoginseng. However, it is still unknown in lipids change of P. notoginseng after A. panax infection. In this study, we compared the lipidomics data of normal and infected P. notoginseng to evaluate the effects of A. panax stress on lipids, and a total of 1202 lipid molecules were identified. All lipid molecules involved 34 subclasses, among which 12 subclasses were significantly up-regulated after pathogen stress. WGCNA co-expression network analysis revealed four significantly related lipid modules, including 459 lipid molecules. Differential expression analysis of all lipid molecules showed that 196 lipid molecules were significantly up-regulated and 78 lipid molecules were significantly down-regulated. The up-regulated lipid molecules were mainly Cer subclasses, while the down-regulated lipid subclasses were mainly DG, MG, PA, SQDG and SQMG. Autocorrelation analysis of differentially expressed lipid molecules showed that lipid networks in response to pathogen stress were mainly composed of lipid molecules in TG, Cer, SQDG and DGDG. Meanwhile, the lipid synthesis pathway found that 17 lipid subclasses had mutual regulatory relationships, 10 subclasses were significantly up-regulated after pathogen stress. All the results showed that these significantly different lipid molecules were involved in pathogen stress response. This study provides new reference for the search of antifungal candidate biomarkers and defense response metabolites based on lipids.

From transcriptomics to digital twins of organ function.

Cell level functions underlie tissue and organ physiology. Gene expression patterns offer extensive views of the pathways and processes within and between cells. Single cell transcriptomics provides detailed information on gene expression within cells, cell types, subtypes and their relative proportions in organs. Functional pathways can be scalably connected to physiological functions at the cell and organ levels. Integrating experimentally obtained gene expression patterns with prior knowledge of pathway interactions enables identification of networks underlying whole cell functions such as growth, contractility, and secretion. These pathways can be computationally modeled using differential equations to simulate cell and organ physiological dynamics regulated by gene expression changes. Such computational systems can be thought of as parts of digital twins of organs. Digital twins, at the core, need computational models that represent in detail and simulate how dynamics of pathways and networks give rise to whole cell level physiological functions. Integration of transcriptomic responses and numerical simulations could simulate and predict whole cell functional outputs from transcriptomic data. We developed a computational pipeline that integrates gene expression timelines and systems of coupled differential equations to generate cell-type selective dynamical models. We tested our integrative algorithm on the eicosanoid biosynthesis network in macrophages. Converting transcriptomic changes to a dynamical model allowed us to predict dynamics of prostaglandin and thromboxane synthesis and secretion by macrophages that matched published lipidomics data obtained in the same experiments. Integration of cell-level system biology simulations with genomic and clinical data using a knowledge graph framework will allow us to create explicit predictive models that mechanistically link genomic determinants to organ function. Such integration requires a multi-domain ontological framework to connect genomic determinants to gene expression and cell pathways and functions to organ level phenotypes in healthy and diseased states. These integrated scalable models of tissues and organs as accurate digital twins predict health and disease states for precision medicine.