Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications.

BACKGROUND: Intratumoral heterogeneity (ITH) and tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) play important roles in tumor evolution and patient outcomes. However, the precise characterization of diverse cell populations and their crosstalk associated with PDAC progression and metastasis is still challenging. METHODS: We performed single-cell RNA sequencing (scRNA-seq) of treatment-naïve primary PDAC samples with and without paired liver metastasis samples to understand the interplay between ITH and TME in the PDAC evolution and its clinical associations. RESULTS: scRNA-seq analysis revealed that even a small proportion (22%) of basal-like malignant ductal cells could lead to poor chemotherapy response and patient survival and that epithelial-mesenchymal transition programs were largely subtype-specific. The clonal homogeneity significantly increased with more prevalent and pronounced copy number gains of oncogenes, such as KRAS and ETV1, and losses of tumor suppressor genes, such as SMAD2 and MAP2K4, along PDAC progression and metastasis. Moreover, diverse immune cell populations, including naïve SELLhi regulatory T cells (Tregs) and activated TIGIThi Tregs, contributed to shaping immunosuppressive TMEs of PDAC through cellular interactions with malignant ductal cells in PDAC evolution. Importantly, the proportion of basal-like ductal cells negatively correlated with that of immunoreactive cell populations, such as cytotoxic T cells, but positively correlated with that of immunosuppressive cell populations, such as Tregs. CONCLUSION: We uncover that the proportion of basal-like subtype is a key determinant for chemotherapy response and patient outcome, and that PDAC clonally evolves with subtype-specific dosage changes of cancer-associated genes by forming immunosuppressive microenvironments in its progression and metastasis.

Establishment of a patient-specific avatar organoid model derived from endoscopic ultrasonography-guided fine needle biopsy for timely clinical application in pancreatic ductal adenocarcinoma.

BACKGROUND AND AIMS: Pancreatic ductal adenocarcinoma (PDAC) has the worst survival rate among tumors. At the time of diagnosis, more than 80% of PDACs are considered to be surgically unresectable, and there is an unmet need for treatment options in these inoperable PDACs. This study aimed to establish a patient-derived organoid (PDO) platform from endoscopic ultrasound-guided fine needle biopsy (EUS-FNB) collected at diagnosis and to determine its clinical applicability for the timely treatment of unresectable PDAC. METHODS: Patients with suspected PDAC were prospectively enrolled at the Samsung Medical Center from 2015 to 2019. PDAC tissues were acquired by means of EUS-FNB to establish PDAC PDOs, which were comprehensively analyzed for histology, genomic sequencing, and high-throughput screening (HTS) drug sensitivity test. RESULTS: PDAC PDOs were established with a success rate of 83.2% (94/113). It took approximately 3 weeks from acquiring minimal EUS-FNB specimens to generating sufficient PDAC PDOs for the simultaneous HTS drug sensitivity test and genomic sequencing. The high concordance between PDAC tissues and matched PDOs was confirmed, and whole-exome sequencing revealed the increased detection of genetic alterations in PDOs compared with EUS-FNB tissues. The HTS drug sensitivity test showed clinical correlation between the ex vivo PDO response and the actual chemotherapeutic response of the study patients in the real world (13 out of 15 cases). In addition, whole-transcriptome sequencing identified candidate genes associated with nab-paclitaxel resistance, such as ITGB7, ANPEP, and ST3GAL1. CONCLUSIONS: This PDAC PDO platform allows several therapeutic drugs to be tested within a short time window and opens the possibility for timely personalized medicine as a "patient avatar model" in clinical practice.

Single-cell RNA Sequencing Reveals Novel Cellular Factors for Response to Immunosuppressive Therapy in Aplastic Anemia.

Aplastic anemia (AA) is a lethal hematological disorder; however, its pathogenesis is not fully understood. Although immunosuppressive therapy (IST) is a major treatment option for AA, one-third of patients do not respond to IST and its resistance mechanism remains elusive. To understand AA pathogenesis and IST resistance, we performed single-cell RNA sequencing (scRNA-seq) of bone marrow (BM) from healthy controls and patients with AA at diagnosis. We found that CD34+ early-stage erythroid precursor cells and PROM1+ hematopoietic stem cells were significantly depleted in AA, which suggests that the depletion of CD34+ early-stage erythroid precursor cells and PROM1+ hematopoietic stem cells might be one of the major mechanisms for AA pathogenesis related with BM-cell hypoplasia. More importantly, we observed the significant enrichment of CD8+ T cells and T cell-activating intercellular interactions in IST responders, indicating the association between the expansion and activation of T cells and the positive response of IST in AA. Taken together, our findings represent a valuable resource offering novel insights into the cellular heterogeneity in the BM of AA and reveal potential biomarkers for IST, building the foundation for future precision therapies in AA.

RAMP: response-aware multi-task learning with contrastive regularization for cancer drug response prediction.

The accurate prediction of cancer drug sensitivity according to the multiomics profiles of individual patients is crucial for precision cancer medicine. However, the development of prediction models has been challenged by the complex crosstalk of input features and the resistance-dominant drug response information contained in public databases. In this study, we propose a novel multidrug response prediction framework, response-aware multitask prediction (RAMP), via a Bayesian neural network and restrict it by soft-supervised contrastive regularization. To utilize network embedding vectors as representation learning features for heterogeneous networks, we harness response-aware negative sampling, which applies cell line-drug response information to the training of network embeddings. RAMP overcomes the prediction accuracy limitation induced by the imbalance of trained response data based on the comprehensive selection and utilization of drug response features. When trained on the Genomics of Drug Sensitivity in Cancer dataset, RAMP achieved an area under the receiver operating characteristic curve > 89%, an area under the precision-recall curve > 59% and an $\textrm{F}_1$ score > 52% and outperformed previously developed methods on both balanced and imbalanced datasets. Furthermore, RAMP predicted many missing drug responses that were not included in the public databases. Our results showed that RAMP will be suitable for the high-throughput prediction of cancer drug sensitivity and will be useful for guiding cancer drug selection processes. The Python implementation for RAMP is available at https://github.com/hvcl/RAMP.

Cellular plasticity and immune microenvironment of malignant pleural effusion are associated with EGFR-TKI resistance in non-small-cell lung carcinoma.

Malignant pleural effusion (MPE) is a complication of lung cancer that can be used as an alternative method for tissue sampling because it is generally simple and minimally invasive. Our study evaluated the diagnostic potential of non-small-cell lung carcinoma (NSCLC)-associated MPE in terms of understanding tumor heterogeneity and identifying response factors for EGFR tyrosine kinase inhibitor (TKI) therapy. We performed a single-cell RNA sequencing analysis of 31,743 cells isolated from the MPEs of 9 patients with NSCLC (5 resistant and 4 sensitive to EGFR TKI) with EGFR mutations. Interestingly, lung epithelial precursor-like cells with upregulated GNB2L1 and CAV1 expression were enriched in the EGFR TKI-resistant group. Moreover, GZMK upregulated transitional effector T cells, and plasmacytoid dendritic cells were significantly enriched in the EGFR TKI-resistant patients. Our results suggest that cellular plasticity and immunosuppressive microenvironment in MPEs are potentially associated with the TKI response of patients with EGFR-mutated NSCLC.

Circulating Tumor Cell Clusters Are Cloaked with Platelets and Correlate with Poor Prognosis in Unresectable Pancreatic Cancer.

Circulating tumor cells (CTCs) are known to be heterogeneous and clustered with tumor-associated cells, such as macrophages, neutrophils, fibroblasts, and platelets. However, their molecular profile and clinical significance remain largely unknown. Thus, we aimed to perform a comprehensive gene expression analysis of single CTCs and CTC clusters in patients with pancreatic cancer and to identify their potential clinical relevance to provide personalized medicine. Epitope-independent, rapid (>3 mL of whole blood/min) isolation of single CTCs and CTC clusters was achieved from a prospective cohort of 16 patients with unresectable pancreatic cancer using a centrifugal microfluidic device. Forty-eight mRNA expressions of individual CTCs and CTC clusters were analyzed to identify pancreatic CTC phenotype. CTC clusters had a larger proportion of mesenchymal expression than single CTCs (p = 0.0004). The presence of CTC clusters positively correlated with poor prognosis (progression-free survival, p = 0.0159; overall survival, p = 0.0186). Furthermore, we found that most CTCs in these patients (90.7%) were cloaked with platelets and found the presence of a positive correlation between the increase in CTC clusters and rapid disease progression during follow-ups. Efficient CTC cluster isolation and analysis techniques will enhance the understanding of complex tumor metastasis processes and can facilitate personalized disease management.

Microbiome of Saliva and Plaque in Children According to Age and Dental Caries Experience.

Dental caries are one of the chronic diseases caused by organic acids made from oral microbes. However, there was a lack of knowledge about the oral microbiome of Korean children. The aim of this study was to analyze the metagenome data of the oral microbiome obtained from Korean children and to discover bacteria highly related to dental caries with machine learning models. Saliva and plaque samples from 120 Korean children aged below 12 years were collected. Bacterial composition was identified using Illumina HiSeq sequencing based on the V3-V4 hypervariable region of the 16S rRNA gene. Ten major genera accounted for approximately 70% of the samples on average, including Streptococcus, Neisseria, Corynebacterium, and Fusobacterium. Differential abundant analyses revealed that Scardovia wiggsiae and Leptotrichia wadei were enriched in the caries samples, while Neisseria oralis was abundant in the non-caries samples of children aged below 6 years. The caries and non-caries samples of children aged 6-12 years were enriched in Streptococcus mutans and Corynebacterium durum, respectively. The machine learning models based on these differentially enriched taxa showed accuracies of up to 83%. These results confirmed significant alterations in the oral microbiome according to dental caries and age, and these differences can be used as diagnostic biomarkers.

Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number.

Periodontitis is a widespread chronic inflammatory disease caused by interactions between periodontal bacteria and homeostasis in the host. We aimed to investigate the performance and reliability of machine learning models in predicting the severity of chronic periodontitis. Mouthwash samples from 692 subjects (144 healthy controls and 548 generalized chronic periodontitis patients) were collected, the genomic DNA was isolated, and the copy numbers of nine pathogens were measured using multiplex qPCR. The nine pathogens are as follows: Porphyromonas gingivalis (Pg), Tannerella forsythia (Tf), Treponema denticola (Td), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Campylobacter rectus (Cr), Aggregatibacter actinomycetemcomitans (Aa), Peptostreptococcus anaerobius (Pa), and Eikenella corrodens (Ec). By adding the species one by one in order of high accuracy to find the optimal combination of input features, we developed an algorithm that predicts the severity of periodontitis using four machine learning techniques. The accuracy was the highest when the models classified "healthy" and "moderate or severe" periodontitis (H vs. M-S, average accuracy of four models: 0.93, AUC = 0.96, sensitivity of 0.96, specificity of 0.81, and diagnostic odds ratio = 112.75). One or two red complex pathogens were used in three models to distinguish slight chronic periodontitis patients from healthy controls (average accuracy of 0.78, AUC = 0.82, sensitivity of 0.71, and specificity of 0.84, diagnostic odds ratio = 12.85). Although the overall accuracy was slightly reduced, the models showed reliability in predicting the severity of chronic periodontitis from 45 newly obtained samples. Our results suggest that a well-designed combination of salivary bacteria can be used as a biomarker for classifying between a periodontally healthy group and a chronic periodontitis group.

Treatment strategy for papillary renal cell carcinoma type 2: a case series of seven patients treated based on next generation sequencing data.

BACKGROUND: Papillary renal cell carcinoma type 2 (PRCC2) is refractory to systemic treatment and has a dismal prognosis. Previous studies showed that genetic alterations in PRCC2 were heterogeneous regardless of germline or somatic mutations. In this study, we aimed to perform precision treatment of PRCC2 based on genetic information. METHODS: We performed exome and genome sequencing of tumor tissues and matched normal samples. Based on sequencing data, we treated patients with metastatic PRCC2 using precision oncology. RESULTS: Four patients underwent curative surgery of PRCC2 and three patients had metastatic PRCC2. All PRCC2 heterogeneously harbored own driver mutations. Two out of the three patients with metastatic disease had fumarate hydratase (FH) germline mutations. One patient with a germline FH mutation was diagnosed with hereditary leiomyomatosis RCC. He was treated with bevacizumab and erlotinib combination and showed a durable response. The other metastatic PRCC2 patient harboring a germline FH mutation had an additional somatic FH mutation and was durably controlled with pazopanib. Other metastatic PRCC2 patient with somatic PBRM1 and SETD2 mutations had over 5 years of overall survival with axitinib treatment. CONCLUSIONS: We performed precision systemic treatment based on genetic information. Genome sequencing could help identify candidates for targeted therapy in PRCC2, a genetically heterogeneous disease.

A lab-on-a-disc platform enables serial monitoring of individual CTCs associated with tumor progression during EGFR-targeted therapy for patients with NSCLC.

Rationale: Unlike traditional biopsy, liquid biopsy, which is a largely non-invasive diagnostic and monitoring tool, can be performed more frequently to better track tumors and mutations over time and to validate the efficiency of a cancer treatment. Circulating tumor cells (CTCs) are considered promising liquid biopsy biomarkers; however, their use in clinical settings is limited by high costs and a low throughput of standard platforms for CTC enumeration and analysis. In this study, we used a label-free, high-throughput method for CTC isolation directly from whole blood of patients using a standalone, clinical setting-friendly platform. Methods: A CTC-based liquid biopsy approach was used to examine the efficacy of therapy and emergent drug resistance via longitudinal monitoring of CTC counts, DNA mutations, and single-cell-level gene expression in a prospective cohort of 40 patients with epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer. Results: The change ratio of the CTC counts was associated with tumor response, detected by CT scan, while the baseline CTC counts did not show association with progression-free survival or overall survival. We achieved a 100% concordance rate for the detection of EGFR mutation, including emergence of T790M, between tumor tissue and CTCs. More importantly, our data revealed the importance of the analysis of the epithelial/mesenchymal signature of individual pretreatment CTCs to predict drug responsiveness in patients. Conclusion: The fluid-assisted separation technology disc platform enables serial monitoring of CTC counts, DNA mutations, as well as unbiased molecular characterization of individual CTCs associated with tumor progression during targeted therapy.

Novel Role of Lck in Leptin-Induced Inflammation and Implications for Renal Aging.

Aging is associated with increased fat mass and elevated serum leptin levels (hyperleptinemia), causing proinflammation in the kidneys where it plays a primary role in the removal of endogenous leptin from the circulation. Lymphocyte-specific kinase (Lck) is a positive regulator of inflammatory signaling and a potential treatment target for age-related diseases, but its role in leptin signaling is unknown. Here, we investigated how Lck influences hyperleptinemia-induced inflammation in kidney tissues from 6- and 21-month-old rats. Results indicate that Lck expression and activation increased significantly in aged rat kidneys, especially at renal tubules. Furthermore, we identified interactions between Lck and short leptin-receptor isoforms, suggesting that Lck is a protein tyrosine kinase regulating leptin signaling. We further investigated whether increased Lck expression in renal tubular epithelial cells and macrophage infiltration are associated with leptin-induced inflammation. We then demonstrated that leptin activates Lck and proinflammatory transcription factors (STAT3 and NF-κB), while Lck knockdown modulates the expression of both transcription factors. Collectively, these data implicate that Lck leads to development of leptin-induced renal inflammation during aging. Inhibition of this protein tyrosine kinase may therefore be an appropriate therapeutic option for protection against age-related hyperleptinemia.

CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network.

With recent advances in DNA sequencing technologies, fast acquisition of large-scale genomic data has become commonplace. For cancer studies, in particular, there is an increasing need for the classification of cancer type based on somatic alterations detected from sequencing analyses. However, the ever-increasing size and complexity of the data make the classification task extremely challenging. In this study, we evaluate the contributions of various input features, such as mutation profiles, mutation rates, mutation spectra and signatures, and somatic copy number alterations that can be derived from genomic data, and further utilize them for accurate cancer type classification. We introduce a novel ensemble of machine learning classifiers, called CPEM (Cancer Predictor using an Ensemble Model), which is tested on 7,002 samples representing over 31 different cancer types collected from The Cancer Genome Atlas (TCGA) database. We first systematically examined the impact of the input features. Features known to be associated with specific cancers had relatively high importance in our initial prediction model. We further investigated various machine learning classifiers and feature selection methods to derive the ensemble-based cancer type prediction model achieving up to 84% classification accuracy in the nested 10-fold cross-validation. Finally, we narrowed down the target cancers to the six most common types and achieved up to 94% accuracy.

Depression and suicide risk prediction models using blood-derived multi-omics data.

More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R2 values of 0.961 and 0.943 for Hamilton Rating Scale for Depression-17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment.

Mechanism of Action of Magnesium Lithospermate B against Aging and Obesity-Induced ER Stress, Insulin Resistance, and Inflammsome Formation in the Liver.

Magnesium lithospermate B (MLB) is the biologically active compound of the water-soluble fraction of Salvia miltiorrhiza. Magnesium lithospermate B exhibits various biological functions, including antidiabetic, neuroprotective, and antioxidant effects. However, its beneficial effects on insulin sensitivity and related signaling pathways in the liver need to be elucidated. Our previous study reported that MLB is a PPARß/δ agonist in fibroblasts. Because insulin-sensitizing and anti-inflammatory effects of PPARß/δ has been reported in the liver, we investigated whether MLB has a beneficial effect on insulin-, ER stress- and inflammasome-related signaling in the livers of aging and obese animal models. Western blotting and protein-ligand docking simulation showed that MLB activated PPARß/δ and improved glucose tolerance in the livers of aging and obese animal models. MLB supplementation ameliorated aging or obesity-induced disruption of insulin signaling in the liver. Consistently, aging and obesity-induced increase in the protein levels of a gluconeogenic phosphoenolpyruvate carboxykinase was decreased by MLB. When molecular signaling pathways related to insulin signaling were examined in the liver, MLB supplementation suppressed ER stress- and inflammasome-related signaling molecules induced by aging and obesity. These results suggest that MLB may improve insulin resistance in the liver at least partially by suppressing ER stress and inflammasome formation in aging and obese animal models.

The involvement of serum exosomal miR-500-3p and miR-770-3p in aging: modulation by calorie restriction.

Recent studies have shown a role for miRNAs in aging and age-related diseases, and the modulation of miRNA expression by diet attracts attention as a new therapeutic strategy. Here, we focused on identifying specific exosomal miRNAs derived from serum of aged rats and the effect of short-term calorie restriction (CR) on their expression. Exosomes from serum of young (7-month), old (22-month), and old-CR Sprague Dawley rats were isolated and characterized by transmission electron microscopy analyses, dynamic light scattering measurements, and Western blotting. A total of 12 significantly expressed miRNAs in serum exosomes of young and old rats were identified by next generation sequencing. After analysis of qRT-PCR, we found that miR-500-3p and miR-770-3p expression was significantly upregulated by aging and downregulated by CR. Furthermore, receiver operating characteristic (ROC) curve revealed that the selected miRNAs represented high accuracy in discriminating old rats from young rats. Finally, PANTHER analysis predicted selected miRNAs targets genes involved in Wnt/chemokines and cytokines -related inflammatory signaling pathway and function as transcription factor. In conclusion, our results suggest that the expression of serum exosomal miR-500-3p and miR-770-3p was significantly increased with aging, whereas these were decreased by CR, and age-/CR-modulated exosomal miR-500-3p and miR-770-3p could potentially be used as informative biomarkers candidates for aging.

Novel SIRT1 activator MHY2233 improves glucose tolerance and reduces hepatic lipid accumulation in db/db mice.

The NAD+-dependent deacetylase SIRT1, which is associated with the improvement of metabolic syndromes, such as type 2 diabetes, is a well-known longevity-related gene. Several in vitro and in vivo studies have shown the known protective effects of SIRT1 activators, such as resveratrol and SRT1720, on diabetes- or obesity-induced fatty liver and insulin resistance. Here, we newly synthesized 18 benzoxazole hydrochloride derivatives based on the structure of resveratrol and SRT1720. We performed an in vitro SIRT1 activity assay to identify the strongest SIRT1 activator. The assay confirmed MHY2233 to be the strongest SIRT1 activator (1.5-fold more potent than resveratrol), and docking simulation showed that the binding affinity of MHY2233 was higher than that of resveratrol and SRT1720. To investigate its beneficial effects, db/db mice were orally administered MHY2233 for 1 month, and various metabolic parameters were assessed in the serum and liver tissues. MHY2233 markedly ameliorated insulin signaling without affecting body weight in db/db mice. In particular, the mRNA expression of lipogenic genes, such as acetyl CoA carboxylase, fatty acid synthase, and sterol regulatory element-binding protein, which increased in db/db mice, decreased following oral treatment with MHY2233. In conclusion, the novel SIRT1 activator MHY2233 reduced lipid accumulation and improved insulin resistance. This finding may contribute toward therapeutic approaches for fatty liver disease and glucose tolerance.

2-(3, 4-dihydroxybenzylidene)malononitrile as a novel anti-melanogenic compound.

Tyrosinase is a key player in ultraviolet-induced melanogenesis. Because excessive melanin accumulation in the skin can induce hyperpigmentation, the development of tyrosinase inhibitors has attracted attention in cosmetic-related fields. However, side effects including toxicity and low selectivity have limited the use of many tyrosinase inhibitors in cosmetics. We synthesized 12 novel 2-(substituted benzylidene)malononitrile derivatives and investigated their anti-melanogenic activities. Of these 12 compounds, 2-(3, 4-dihydroxy benzylidene)malononitrile (BMN11) exhibited the strongest inhibitory activity against tyrosinase (IC50 = 17.05 µM). In parallel with this, BMN11 treatment notably decreased alpha-melanocyte-stimulating hormone-induced melanin accumulation in B16F10, cells without toxicity and also decreased melanin accumulation in a human skin model. As a mechanism underlying the BMN11-mediated anti-melanogenic effect, docking simulation showed that BMN11 can directly bind to tyrosinase by forming two hydrogen bonds with GLY281 and ASN260 residues, and via three hydrophobic interactions with VAL283, PHE264, and ALA286 residues in the tyrosinase binding pocket, and this likely contributes to its inhibitory effect on tyrosinase. Consistently, Lineweaver-Burk and Cornish-Bowden plots showed that BMN11 is a competitive inhibitor of tyrosinase. We concluded that BMN11 may be a novel tyrosinase inhibitor that could be used in cosmetics.

miR-10a and miR-204 as a Potential Prognostic Indicator in Low-Grade Gliomas.

This study aimed to identify and characterize microRNAs (miRNAs) that are related to radiosensitivity in low-grade gliomas (LGGs). The miRNA expression levels in radiosensitive and radioresistant LGGs were compared using The Cancer Genome Atlas database, and differentially expressed miRNAs were identified using the EBSeq package. The miRNA target genes were predicted using Web databases. Fifteen miRNAs were differentially expressed between the groups, with miR-10a and miR-204 being related to overall survival (OS) of patients with LGG. Patients with upregulated miR-10a expression had a higher mortality rate and shorter OS time, whereas patients with downregulated miR-204 expression had a lower mortality rate and longer OS time. Two genes, HSP90AA1 and CREB5, were targets for both miRNAs. Thus, this study suggests that expression of miR-10a and miR-204 is significantly related to both radiosensitivity and the survival of patients with LGG. These miRNAs could therefore act as clinical biomarkers for LGG prognosis and diagnosis.

Physiological characterization of a novel PPAR pan agonist, 2-(4-(5,6-methylenedioxybenzo[d]thiazol-2-yl)-2-methylphenoxy)-2-methylpropanoic acid (MHY2013).

Recently, agonists targeting multiple peroxisome proliferator-activated receptors (PPARs) have been developed to improve metabolic disorders and minimize the side effects of selective PPAR agonists such as weight gain and dyslipidemia. We newly synthesized six 2-methyl-2-(o-tolyloxy)propanoic acid derivatives based on the structure of a well-known PPAR pan agonist, bezafibrate. Of six compounds, MHY2013 was screened as the strongest activator of three PPAR subtypes based on protein docking simulation and luciferase assays. When treated orally in db/db mice, MHY2013 ameliorated obesity-induced insulin resistance, dyslipidemia, and hepatic steatosis without changes of the body weight and levels of liver and kidney injury markers. MHY2013 decreased the serum triglyceride and fatty acid levels, which is associated with an increase in fatty acid oxidation signaling in the liver and thermogenic signaling on white adipose tissue, respectively. Furthermore, MHY2013 markedly increased serum levels of insulin-sensitizing hormones including fibroblast growth factor 21 (FGF21) and adiponectin. In conclusion, this study suggests that, MHY2013 is a novel PPAR pan agonist that improves obesity-induced insulin resistance, dyslipidemia and hepatic steatosis and elevates insulin-sensitizing hormones in the blood.