Identification Of Drug Targets Research Articles

Structural basis for antibiotic transport and inhibition in PepT2

The uptake and elimination of beta-lactam antibiotics in the human body are facilitated by the proton-coupled peptide transporters PepT1 (SLC15A1) and PepT2 (SLC15A2). The mechanism by which SLC15 family transporters recognize and discriminate between different drug classes and dietary peptides remains unclear, hampering efforts to improve antibiotic pharmacokinetics through targeted drug design and delivery. Here, we present cryo-EM structures of the proton-coupled peptide transporter, PepT2 from Rattus norvegicus, in complex with the widely used beta-lactam antibiotics cefadroxil, amoxicillin and cloxacillin. Our structures, combined with pharmacophore mapping, molecular dynamics simulations and biochemical assays, establish the mechanism of beta-lactam antibiotic recognition and the important role of protonation in drug binding and transport.

The function of serine/threonine-specific protein kinases in B cells

The serine/threonine-specific protein kinases (STKs) are important for cell survival, proliferation, differentiation, and apoptosis. In B cells, these kinases play indispensable roles in regulating important cellular functions. Multiple studies on human and other animal cells have shown that multiple STKs are involved in different stages of B cell development and antibody production. However, how STKs affect B cell development and function is still not completely understood. Considering that B cells are clinically important in immunity and diseases, our understanding of STKs’ roles in B cells is in great need of investigation with current technologies. Investigating serine/threonine kinases will not only deepen our insight into B cell-related disorders but also facilitate the identification of more effective drug targets for conditions like lymphoma and systemic lupus erythematosus.

Disease coverage of human genome-wide association studies and pharmaceutical research and development

BackgroundDespite the growing interest in the use of human genomic data for drug target identification and validation, the extent to which the spectrum of human disease has been addressed by genome-wide association studies (GWAS), or by drug development, and the degree to which these efforts overlap remain unclear.MethodsIn this study we harmonize and integrate different data sources to create a sample space of all the human drug targets and diseases and identify points of convergence or divergence of GWAS and drug development efforts.ResultsWe show that only 612 of 11,158 diseases listed in Human Disease Ontology have an approved drug treatment in at least one region of the world. Of the 1414 diseases that are the subject of preclinical or clinical phase drug development, only 666 have been investigated in GWAS. Conversely, of the 1914 human diseases that have been the subject of GWAS, 1121 have yet to be investigated in drug development.ConclusionsWe produce target-disease indication lists to help the pharmaceutical industry to prioritize future drug development efforts based on genetic evidence, academia to prioritize future GWAS for diseases without effective treatments, and both sectors to harness genetic evidence to expand the indications for licensed drugs or to identify repurposing opportunities for clinical candidates that failed in their originally intended indication.

An engineered model of metastatic colonization of human bone marrow reveals breast cancer cell remodeling of the hematopoietic niche

Incomplete understanding of metastatic disease mechanisms continues to hinder effective treatment of cancer. Despite remarkable advancements toward the identification of druggable targets, treatment options for patients in remission following primary tumor resection remain limited. Bioengineered human tissue models of metastatic sites capable of recreating the physiologically relevant milieu of metastatic colonization may strengthen our grasp of cancer progression and contribute to the development of effective therapeutic strategies. We report the use of an engineered tissue model of human bone marrow (eBM) to identify microenvironmental cues regulating cancer cell proliferation and to investigate how triple-negative breast cancer (TNBC) cell lines influence hematopoiesis. Notably, individual stromal components of the bone marrow niche (osteoblasts, endothelial cells, and mesenchymal stem/stromal cells) were each critical for regulating tumor cell quiescence and proliferation in the three-dimensional eBM niche. We found that hematopoietic stem and progenitor cells (HSPCs) impacted TNBC cell growth and responded to cancer cell presence with a shift of HSPCs (CD34+CD38-) to downstream myeloid lineages (CD11b+CD14+). To account for tumor heterogeneity and show proof-of-concept ability for patient-specific studies, we demonstrate that patient-derived tumor organoids survive and proliferate in the eBM, resulting in distinct shifts in myelopoiesis that are similar to those observed for aggressively metastatic cell lines. We envision that this human tissue model will facilitate studies of niche-specific metastatic progression and individualized responses to treatment.

Genome-wide association testing beyond SNPs.

Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.

Identifying prothrombin and bone sialoprotein as potential drug targets for idiopathic pulmonary fibrosis

BackgroundIdiopathic Pulmonary Fibrosis (IPF) is a fatal disease with scarce therapeutic alternatives, which imposes a significant economic burden on society. The identification of novel drug targets is thus critically essential. Plasma proteins with discernible causal evidence hold promise as viable drug targets for this condition.MethodsWe performed a proteome-wide Mendelian randomization (MR) analysis to assess the causal effects of 4,907 circulating proteins from the deCODE study on the risk of IPF from the Finngen Database (2,018 cases vs. 373,064 controls). We further replicated the MR analysis in 1426 proteins from the ARIC study and IPF from the UK Biobank (1,369 cases vs. 435,866 controls). Then a series of analyses including Bayesian colocalization, Steiger filtering, and phenotype scanning were conducted to validate the credibility of the MR results. Subsequently, protein-protein interaction (PPI) analysis, pathway enrichment analysis, and druggability assessment were executed to elucidate the underlying mechanisms. Finally, the findings were corroborated using a bleomycin-induced pulmonary fibrosis mouse model.ResultsThe MR analysis bolstered by robust evidence of colocalization, indicated a significant positive association between Prothrombin and increased IPF risk (OR = 3.26,95%CI 1.75–6.07). Conversely, Bone Sialoprotein (IBSP) demonstrated an inverse association with IPF susceptibility (OR = 0.27,95%CI 0.14–0.55).ConclusionsThe integrative analysis suggests that Prothrombin and IBSP are promising candidates as potential drug targets for IPF. Additional clinical investigations are warranted to substantiate these findings.

Plasma Proteomic Insights for Identification of Novel Predictors and Potential Drug Targets in Atrial Fibrillation: A Prospective Cohort Study and Mendelian Randomization Analysis.

Currently, there are no reliable methods for predicting and preventing atrial fibrillation (AF) in its early stages. This study aimed to identify plasma proteins associated with AF to discover biomarkers and potential drug targets. The UK Biobank Pharma Proteomics Project examined 2923 circulating proteins using the Olink platform, forming the basis of this prospective cohort study. The UK Biobank Pharma Proteomics Project included a randomly selected discovery cohort and the consortium-selected replication cohort. The study's end point was incident AF, identified using International Classification of Diseases, Tenth Revision codes. The association between plasma proteins and incident AF was evaluated using Cox proportional hazard models in both cohorts. Proteins present in both cohorts underwent Mendelian randomization analysis to delineate causal connections, utilizing cis-protein quantitative trait loci as genetic tools. The predictive efficacy of the identified proteins for AF was assessed using the area under the receiver operating characteristic curve, and their druggability was explored. Data from 53 032 participants were included in this study. Incident AF cases were identified in the discovery cohort (1894; 5.5%) within a median follow-up of 14.5 years and in the replication cohort (451; 10.6%) within a median follow-up of 14.4 years. Twenty-one proteins linked to AF were identified in both cohorts. Specifically, COL4A1 (collagen IV α-1; odds ratio, 1.11 [95% CI, 1.04-1.19]; false discovery rate, 0.016) and RET (proto-oncogene tyrosine-protein kinase receptor Ret; odds ratio, 0.96 [95% CI, 0.94-0.98]; false discovery rate, 0.013) demonstrated a causal link with AF, and RET is druggable. COL4A1 improved the short- and long-term predictive performance of established AF models, as evidenced by significant enhancements in the area under the receiver operating characteristic, integrated discrimination improvement, and net reclassification index, all with P values below 0.05. COL4A1 and RET are associated with the development of AF. RET is identified as a potential drug target for AF prevention, while COL4A1 serves as a biomarker for AF prediction. Future studies are needed to evaluate the effectiveness of targeting these proteins to reduce AF risk.

Codes between Poles: Linking Transcriptomic Insights into the Neurobiology of Bipolar Disorder

Bipolar disorder (BPD) is a serious psychiatric condition that is characterized by the frequent shifting of mood patterns, ranging from manic to depressive episodes. Although there are already treatment strategies that aim at regulating the manifestations of this disorder, its etiology remains unclear and continues to be a question of interest within the scientific community. The development of RNA sequencing techniques has provided newer and better approaches to studying disorders at the transcriptomic level. Hence, using RNA-seq data, we employed intramodular connectivity analysis and network pharmacology assessment of disease-associated variants to elucidate the biological pathways underlying the complex nature of BPD. This study was intended to characterize the expression profiles obtained from three regions in the brain, which are the nucleus accumbens (nAcc), the anterior cingulate cortex (AnCg), and the dorsolateral prefrontal cortex (DLPFC), provide insights into the specific roles of these regions in the onset of the disorder, and present potential targets for drug design and development. The nAcc was found to be highly associated with genes responsible for the deregulated transcription of neurotransmitters, while the DLPFC was greatly correlated with genes involved in the impairment of components crucial in neurotransmission. The AnCg did show association with some of the expressions, but the relationship was not as strong as the other two regions. Furthermore, disease-associated variants or single nucleotide polymorphisms (SNPs) were identified among the significant genes in BPD, which suggests the genetic interrelatedness of such a disorder and other mental illnesses. DRD2, GFRA2, and DCBLD1 were the genes with disease-associated variants expressed in the nAcc; ST8SIA2 and ADAMTS16 were the genes with disease-associated variants expressed in the AnCg; and FOXO3, ITGA9, CUBN, PLCB4, and RORB were the genes with disease-associated variants expressed in the DLPFC. Aside from unraveling the molecular and cellular mechanisms behind the expression of BPD, this investigation was envisioned to propose a new research pipeline in studying the transcriptome of psychiatric disorders to support and improve existing studies.

Antifungal Drug Resistance in Candida albicans: Identifying Novel Targets for the Development of Effective Antifungal Agents.

Candidiasis, a fungal infection initiated primarily through Candida albicans, affects various portions of the human body and is particularly prevalent in immunocompromised individuals. The pathogenicity of C. albicans is facilitated by numerous virulence causes, including adhesins, morphogenesis, and phenotypic switching. The organism’s capability in the direction of switching between yeast and hyphal forms contributes to the severity of infections. The appearance of resistant strains has rendered current treatments less effective, necessitating the exploration of new drug targets and the progress of novel antifungal agents. Antifungal drug resistance is a multifaceted phenomenon involving genetic mutations, overexpression of efflux pumps, epigenetic changes, and biofilm formation, all regulated by complex genetic and transcriptional networks. These resistance mechanisms can cause treatment failures, highlighting the need for new antifungal agents and improved diagnostic tools. The identification of potential drug targets in C. albicans is critical due to increasing resistance to existing antifungal agents. Recent studies have identified promising targets, for example the riboflavin metabolic pathway and unique protein kinases involved in regulating virulence and pathogenicity. Developing new antifungals is difficult due to C. albicans’ eukaryotic nature and resistance. Ongoing research is essential to find novel targets and strategies, especially with the limited antifungal drug classes available.

Computer-aided pattern scoring (C@PS): a novel cheminformatic workflow to predict ligands with rare modes-of-action

The identification, establishment, and exploration of potential pharmacological drug targets are major steps of the drug development pipeline. Target validation requires diverse chemical tools that come with a spectrum of functionality, e.g., inhibitors, activators, and other modulators. Particularly tools with rare modes-of-action allow for a proper kinetic and functional characterization of the targets-of-interest (e.g., channels, enzymes, receptors, or transporters). Despite, functional innovation is a prime criterion for patentability and commercial exploitation, which may lead to therapeutic benefit. Unfortunately, data on new, and thus, undruggable or barely druggable targets are scarce and mostly available for mainstream modes-of-action only (e.g., inhibition). Here we present a novel cheminformatic workflow—computer-aided pattern scoring (C@PS)—which was specifically designed to project its prediction capabilities into an uncharted domain of applicability.

In Silico Identification of Novel Drug Targets in the Honeybee Pathogen Paenibacillus larvae and Molecular Docking Studies

In Silico Identification of Novel Drug Targets in the Honeybee Pathogen <i>Paenibacillus larvae</i> and Molecular Docking Studies

Oxadiazolines as Photoreleasable Labels for Drug Target Identification.

Photoaffinity labeling is a widely used technique for studying ligand-protein and protein-protein interactions. Traditional photoaffinity labels utilize nonspecific C-H bond insertion reactions mediated by a highly reactive intermediate. Despite being the most widely used photoaffinity labels, diazirines exhibit limited compatibility with downstream organic reactions and suffer from storage stability concerns. This study introduces oxadiazolines as innovative and complementary photoactivatable labels for addition to the toolbox and demonstrates their application in vitro and through in cellulo labeling experiments. Oxadiazolines can be easily synthesized from ketone moieties and cleaved with 302-330 nm light to cleanly liberate a diazo reactive moiety that can covalently modify nucleophilic amino acid residues. Notably, oxadiazolines are compatible with various organic reaction conditions and functional groups, allowing for the exploration of a large chemical space. Several known inhibitors featuring the oxadiazoline functionality were prepared without affecting their binding affinity. Furthermore, we confirmed the ability of oxadiazolines to form covalent bonds with proteins upon UV-irradiation, both in vitro and in cellulo, yielding comparable results to those of the matched diazirine compounds.

Utilize proteomic analysis to identify potential therapeutic targets for combating sepsis and sepsis-related death.

Sepsis is an inflammatory disease that leads to severe mortality, highlighting the urgent need to identify new therapeutic strategies for sepsis. Proteomic research serves as a primary source for drug target identification. We employed proteome-wide Mendelian randomization (MR), genetic correlation analysis, and colocalization analysis to identify potential targets for sepsis and sepsis-related death. Genetic data for plasma proteomics were obtained from 35,559 Icelandic individuals and an initial MR analysis was conducted using 13,531 sepsis cases from the FinnGen R10 cohort to identify associations between plasma proteins and sepsis. Subsequently, significant proteins underwent genetic correlation analysis, followed by replication in 54,306 participants from the UK Biobank Pharma Proteomics Project and validation in 11,643 sepsis cases from the UK Biobank. The identified proteins were then subjected to colocalization analysis, enrichment analysis, and protein-protein interaction network analysis. Additionally, we also investigated a MR analysis using plasma proteins on 1,896 sepsis cases with 28-day mortality from the UK Biobank. After FDR correction, MR analysis results showed a significant causal relationship between 113 plasma proteins and sepsis. Genetic correlation analysis revealed that only 8 proteins had genetic correlations with sepsis. In the UKB-PPP replication analysis, only 4 proteins were found to be closely associated with sepsis, while validation in the UK Biobank sepsis cases found overlaps for 21 proteins. In total, 30 proteins were identified in the aforementioned analyses, and colocalization analysis revealed that only 2 of these proteins were closely associated with sepsis. Additionally, in the 28-day mortality MR analysis of sepsis, we also found that only 2 proteins were significant. The identified plasma proteins and their associated metabolic pathways have enhanced our understanding of the complex relationship between proteins and sepsis. This provides new avenues for the development of drug targets and paves the way for further research in this field.

Identification of novel drug targets for osteoarthritis by integrating genetics and proteomes from blood

BackgroundOsteoarthritis (OA) is a degenerative osteoarticular disease, involving genetic predisposition. How the risk variants confer the risk of OA through their effects on proteins remains largely unknown. Therefore, we aimed to discover new and effective drug targets for OA and its subtypes.MethodsA proteome-wide association study (PWAS) was performed based on OA and its subtypes genome-wide association studies (GWAS) summary datasets and the protein quantitative trait loci (pQTL) data. Subsequently, Mendelian randomization (MR) and colocalization analysis was conducted to estimate the associations between protein and OA risk. The replication analysis was performed in an independent dataset of human plasma pQTL data.ResultsThe abundance of seven proteins was causally related to OA, two proteins to knee OA and six proteins to hip OA, respectively. We replicated 2 of these proteins using an independent pQTL dataset. With the further support of colocalization, and higher ECM1 level was causally associated with a higher risk of OA and hip OA. Higher PCSK1 level was causally associated with a lower risk of OA. And higher levels of ITIH1, EFEMP1, and ERLEC1 were associated with decreased risk of hip OA.ConclusionOur study provides new insights into the genetic component of protein abundance in OA and a promising therapeutic target for future drug development.

Structural and Functional Annotations of Hypothetical Proteins of Candida auris for Novel Drug Target Identification: An insilico Approach

Candida auris is an emerging fungal pathogen that causes severe invasive infections in healthcare facilities which are difficult to control and treat due to its resistance to major antifungal drugs. A large fraction of C. auris proteins are uncharacterized and hypothetical, and structural and functional characterization of these proteins can aid in the selection of novel drug targets. The present study involves a computational approach for the structural and functional characterization of hypothetical proteins from Candida auris. After the sequenceretrieval, hypothetical proteins were predicted for physiochemical properties and subcellular localization, structurally modeled using I-TASSER, quality assessed through Verify3D, ERRAT, and PROCHECK, and functionally annotated to reveal conserved domains and roles in pathogen pathways. Finally, the immunogenic assessments, non-human homologous analysis, and druggability analysis reveal three hypothetical proteins of C. auris (KND95408.2, KND95415.2, and KND95429.2) as novel drug targets. Furthermore, the stable conformations of theseselected drug targets with the minimum root mean square deviations (RMSD) and fluctuation (RMSF) were analyzed by MD simulations. Conclusively, the functional annotations of these hypothetical proteins can help in understanding the disease mechanisms at the molecular level, as well as provide new targets for drug development against Candida auris.

Bioinformatics Analysis Screening and Identification of Key Biomarkers and Drug Targets in Human Glioblastoma.

Glioblastoma is the most common type of brain cancer, with a prognosis that is unfortunately poor. Despite considerable progress in the field, the intricate molecular basis of this cancer remains elusive. The aim of this study was to identify genetic indicators of glioblastoma and reveal the processes behind its development. The advent and integration of supercomputing technology have led to a significant advancement in gene expression analysis platforms. Microarray analysis has gained recognition for its pivotal role in oncology, crucial for the molecular categorization of tumors, diagnosis, prognosis, stratification of patients, forecasting tumor responses, and pinpointing new targets for drug discovery. Numerous databases dedicated to cancer research, including the Gene Expression Omnibus (GEO) database, have been established. Identifying differentially expressed genes (DEGs) and key genes deepens our understanding of the initiation of glioblastoma, potentially unveiling novel markers for diagnosis and prognosis, as well as targets for the treatment of glioblastoma. This research sought to discover genes implicated in the development and progression of glioblastoma by analyzing microarray datasets GSE13276, GSE14805, and GSE109857 from the GEO database. DEGs were identified, and a function enrichment analysis was performed. Additionally, a protein-protein interaction network (PPI) was constructed, followed by module analysis using the tools STRING and Cytoscape. The analysis yielded 88 DEGs, consisting of 66 upregulated and 22 downregulated genes. These genes' functions and pathways primarily involved microtubule activity, mitotic cytokinesis, cerebral cortex development, localization of proteins to the kinetochore, and the condensation of chromosomes during mitosis. A group of 27 pivotal genes was pinpointed, with biological process analysis indicating significant enrichment in activities, such as division of the nucleus during mitosis, cell division, maintaining cohesion between sister chromatids, segregation of sister chromatids during mitosis, and cytokinesis. The survival analysis indicated that certain genes, including PCNA clamp-associated factor (PCLAF), ribonucleoside- diphosphate reductase subunit M2 (RRM2), nucleolar and spindle-associated protein 1 (NUSAP1), and kinesin family member 23 (KIF23), could be instrumental in the development, invasion, or recurrence of glioblastoma. The identification of DEGs and key genes in this study advances our comprehension of the molecular pathways that contribute to the oncogenesis and progression of glioblastoma. This research provides valuable insights into potential diagnostic and therapeutic targets for glioblastoma.

Discovery of the selective and nanomolar inhibitor of DPP-4 more potent than sitagliptin by structure-guided rational design

Various therapeutic targets and approaches are commonly employed in the management of Type 2 Diabetes. These encompass diverse groups of drugs that target different mechanisms involved in glucose regulation. Inhibition of the DPP-4 enzyme has been proven an excellent target for antidiabetic drug design. Our previous work on discovering multitarget antidiabetic drugs led to the identification of a gallic acid-thiazolidinedione hybrid as a potent DPP4 inhibitor (IC50 = 36 nM). In current research, our efforts resulted in a new dihydropyrimidine-based scaffold with enhanced DPP4 inhibition potential. After virtual evaluation, the designed molecules with excellent interaction patterns and binding energy values were synthesized in the wet laboratory. The inhibition potential of synthesized compounds was assessed against the DPP-4 enzyme. Compound 46 with single digit IC50 value 2 nM exhibited 4-fold and 18-fold higher activity than Sitagliptin and our previously reported hybrid respectively. Moreover, compounds 46, 47 and 50 have shown manyfold selectivity against DPP8 and DPP9. Further pretreatment with compounds 43, 45–47 and 50 (at doses of 10 and 20 mg/kg) in OGTT conducted on rats resulted in a significant decrease in the serum glucose levels compared to the control group. In the long-term STZ-induced diabetic rats, tested compound 50 performed similarly to the reference drug. Molecular dynamics simulations and in-silico molecular docking studies were employed to elucidate the time-dependent interactions of inhibitors within the active sites of DPP4. The compounds examined in this work might serve as a possible lead in the development of effective diabetic mellitus treatments.

Discovery of Aldehyde Dehydrogenase as a Potential Fungicide Target and Screening of its Natural Inhibitors against Fusarium verticillioides.

Fusarium verticillioides is the primary pathogen causing ear rot and stalk rot in corn (Zea mays). It not only affects yields but also produces mycotoxins endangering both human and animal health. Aldehyde dehydrogenase (ALDH) is essential for the oxidation of aldehydes in living organisms, making it a potential target for human drug design. However, there are limited reports on its function in plant pathogenic fungus. In this study, we analyzed the expression levels and gene knockout mutants, revealing that ALDH genes FvALDH-43 and FvALDH-96 in F. verticillioides played significant roles in pathogenicity and resistance to low-temperature stress by affecting antioxidant capacity. Virtual screening for natural product inhibitors and molecular docking were performed targeting FvALDH-43 and FvALDH-96. Following the biological activity analysis, three natural flavonoid compounds featuring a 2-hydroxyphenol chromene were identified. Among these, Taxifolin exhibited the highest biological activity and low toxicity. Both in vitro and in vivo biological evaluations confirmed that Taxifolin targeted ALDH and inhibited its activity. These findings indicate that aldehyde dehydrogenase may serve as a promising target for the design of novel fungicides.

Bioinformatics and Chemoinformatics Analysis Explored the Role of Linum usitatissimum in Diabetic Heart Conditions: Experimental Analysis in H9c2 Rat Embryonic Cardiomyocytes Cell Lines.

Cytokine storms and inflammation lead to heart failure (HF). Bioactive compounds, as complementary medicine, can be the primary source of compounds with anti-inflammatory properties. Linum usitatissimum (LiU) has antioxidant capacity and anti-inflammatory activity. Here, candidate hugeness was selected based on the in silico studies, bio-cheminformatics, and bioinformatic analysis for excremental validation. We selected the vital genes with differential expression from the GSE26887 dataset. Based on the bioinformatics analysis, several parameters are determined to choose switchable genes involved in diabetic HF (DHF). We designed the protein-protein interactions network to consider the nodes' degree, modularity, and betweenness centrality. Hence, we selected the interleukin (IL)-6 protein as a target for drug design and discovery to reduce diabetes complications in the heart. Here, H9c2 cell lines of rat embryonic cardiomyocytes induce HF using hyperglycemic and hyperlipidemic conditions. Real-time polymerase chain reaction evaluated the relative expression of SMAD7/NRF-2/STAT3. Furthermore, we assessed the concentration of IL-6 using the enzyme-linked immunosorbent assay technique. Based on the bioinformatic analysis, we found that IL-6 with the highest network parameters score might be presented as a druggable protein in the DHF. Bioactive compounds and phytochemicals have potential strategies to manage DHF. LiUs decreased the expression level of the SMAD7 (P <0.0001) and STAT3 (P < 0.0001), and increased the expression level of the NRF2 (P < 0.0001). In addition, LiUs significantly reduced the concentration of IL-6 (P < 0.0001). Our data proposed that LiUs regulated inflammation and triggered the antioxidant defense in HF. Moreover, LiUs could have potential approaches to managing and preventing DHF.

Sex- and age-associated factors drive the pathophysiology of MASLD.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is strongly associated with obesity. Sex and age affect MASLD prevalence and pathophysiology. The use of animal models fed Western-style diets is vital for investigating the molecular mechanisms contributing to metabolic dysregulation and for facilitating novel drug target identification. However, the sex-associated and age-associated mechanisms underlying the pathophysiology remain poorly understood. This knowledge gap limits the development of personalized sex-specific and age-specific drug treatments. Young (7wk) and aged (52wk) male and female mice were fed a high-fat diet (HFD) or low-fat diet. Liver metabolome (>600 molecules) and transcriptome profiles were analyzed. Male and female mice fed an HFD developed obesity, glucose intolerance, and hepatic steatosis. However, fasting blood glucose, insulin, and serum alanine aminotransferase levels were higher in males fed an HFD, indicating a more severe metabolic disease. In addition, males showed significant increases in liver diacylglycerides and glycosylceramides (known mediators of insulin resistance and fibrosis), and more changes in the transcriptome: extracellular matrix organization and proinflammatory genes were elevated only in males. In contrast, no major increase in damaging lipid classes was observed in females fed an HFD. However, aging affected the liver to a greater extent in females. Acylcarnitine levels were significantly reduced, suggestive of changes in fatty acid oxidation, and broad changes in the transcriptome were observed, including reduced oxidative stress response gene expression and alterations in lipid partitioning genes. Here, we show distinct responses to an HFD between males and females. Our study underscores the need for using both sexes in drug target identification studies, and characterizing the molecular mechanisms contributing to the MASLD pathophysiology in aging animals.