LP340, a novel histone deacetylase inhibitor, decreases liver injury and fibrosis in mice: role of oxidative stress and microRNA-23a.

Effective therapy for liver fibrosis is lacking. Here, we examined whether LP340, the lead candidate of a new-generation of hydrazide-based HDAC1,2,3 inhibitors (HDACi), decreases liver fibrosis. Liver fibrosis was induced by CCl4 treatment and bile duct ligation (BDL) in mice. At 6 weeks after CCl4, serum alanine aminotransferase increased, and necrotic cell death and leukocyte infiltration occurred in the liver. Tumor necrosis factor-α and myeloperoxidase markedly increased, indicating inflammation. After 6 weeks, α-smooth muscle actin (αSMA) and collagen-1 expression increased by 80% and 575%, respectively, indicating hepatic stellate cell (HSC) activation and fibrogenesis. Fibrosis detected by trichrome and Sirius-red staining occurred primarily in pericentral regions with some bridging fibrosis in liver sections. 4-Hydroxynonenal adducts (indicator of oxidative stress), profibrotic cytokine transforming growth factor-ß (TGFß), and TGFß downstream signaling molecules phospho-Smad2/3 also markedly increased. LP340 attenuated indices of liver injury, inflammation, and fibrosis markedly. Moreover, Ski-related novel protein-N (SnoN), an endogenous inhibitor of TGFß signaling, decreased, whereas SnoN expression suppressor microRNA-23a (miR23a) increased markedly. LP340 (0.05 mg/kg, ig., daily during the last 2 weeks of CCl4 treatment) decreased 4-hydroxynonenal adducts and miR23a production, blunted SnoN decreases, and inhibited the TGFß/Smad signaling. By contrast, LP340 had no effect on matrix metalloproteinase-9 expression. LP340 increased histone-3 acetylation but not tubulin acetylation, indicating that LP340 inhibited Class-I but not Class-II HDAC in vivo. After BDL, focal necrosis, inflammation, ductular reactions, and portal and bridging fibrosis occurred at 2 weeks, and αSMA and collagen-1 expression increased by 256% and 560%, respectively. LP340 attenuated liver injury, ductular reactions, inflammation, and liver fibrosis. LP340 also decreased 4-hydroxynonenal adducts and miR23a production, prevented SnoN decreases, and inhibited the TGFß/Smad signaling after BDL. In vitro, LP340 inhibited immortal human hepatic stellate cells (hTERT-HSC) activation in culture (αSMA and collagen-1 expression) as well as miR23a production, demonstrating its direct inhibitory effects on HSC. In conclusions, LP340 is a promising therapy for both portal and pericentral liver fibrosis, and it works by inhibiting oxidative stress and decreasing miR23a.

Stable quantum-correlated many-body states through engineered dissipation.

Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.

Global metabolomics revealed deviations from the metabolic aging clock in colorectal cancer patients.

Background: Markers of aging hold promise in the context of colorectal cancer (CRC) care. Utilizing high-resolution metabolomic profiling, we can unveil distinctive age-related patterns that have the potential to predict early CRC development. Our study aims to unearth a panel of aging markers and delve into the metabolomic alterations associated with aging and CRC. Methods: We assembled a serum cohort comprising 5,649 individuals, consisting of 3,002 healthy volunteers, 715 patients diagnosed with colorectal advanced precancerous lesions (APL), and 1,932 CRC patients, to perform a comprehensive metabolomic analysis. Results: We successfully identified unique age-associated patterns across 42 metabolic pathways. Moreover, we established a metabolic aging clock, comprising 9 key metabolites, using an elastic net regularized regression model that accurately estimates chronological age. Notably, we observed significant chronological disparities among the healthy population, APL patients, and CRC patients. By combining the analysis of circulative carcinoembryonic antigen levels with the categorization of individuals into the "hypo" metabolic aging subgroup, our blood test demonstrates the ability to detect APL and CRC with positive predictive values of 68.4% (64.3%, 72.2%) and 21.4% (17.8%, 25.9%), respectively. Conclusions: This innovative approach utilizing our metabolic aging clock holds significant promise for accurately assessing biological age and enhancing our capacity to detect APL and CRC.

HDAC/NAMPT dual inhibitors overcome initial drug-resistance in p53-null leukemia cells.

The occurrence of cancer is closely related to metabolism and epigenetics. Histone deacetylases (HDACs) play a crucial role in the regulation of gene expression as epigenetic regulators, while nicotinamide phosphoribosyltransferase (NAMPT) is significantly involved in maintaining cellular metabolism. In this study, we rationally designed a series of novel HDAC/NAMPT dual inhibitors based on the structural similarity between HDAC and NAMPT inhibitors. The representative compounds 39a and 39h exhibit significant selective inhibitory activity on HDAC1-3 with IC50 values of 0.71-25.1 nM, while displaying modest activity against NAMPT. Compound 39h did not exhibit inhibitory activity against 370 kinases, demonstrating its target specificity. These two compounds exhibit potent anti-proliferative activity in multiple leukemia cell lines with low nanomolar IC50s. It is worth noticing that the dual inhibitors 39a and 39h overcome the primary resistance of HDAC or NAMPT single target inhibitor in p53-null AML cell lines, with the induction of apoptosis-related cell death. NMN recovers the cell death induced by HDAC/NAMPT dual inhibitors, which indicates the lethal effects are caused by the inhibition of NAD biosynthesis pathway as well as HDAC. This research provides an effective strategy to overcome the limitations of HDAC inhibitors in treating p53-null leukemia.

Evaluation of architectural distortion with contrast-enhanced mammography.

Architectural distortion (AD) is the third most common abnormality detected on mammograms. In the absence of an accurate non-invasive tool to evaluate ADs, clinical management often requires surgical excision for histological diagnosis. This problem is expected to worsen with the growing use of digital breast tomosynthesis (DBT) and the resultant increasing detection of ADs. There is therefore a great clinical need for a diagnostic imaging tool to complement non-enhanced mammography for the evaluation of AD. Contrast-enhanced mammography (CEM) is an emerging breast imaging method that uses contrast media and the principle of dual-energy subtraction to evaluate vascularity of suspicious breast lesions. CEM, a cost-effective alternative to breast magnetic resonance imaging (MRI), can be used to evaluate AD by juxtaposing CEM images with non-enhanced mammograms for comparison. In this review, the authors aim to provide readers with an overview of the interpretation of AD on CEM using imaging examples. Relevant imaging features of CEM and their respective significance will be matched with information from a literature review. Finally, the authors would like to highlight the added value of CEM in relevant clinical applications in the assessment of AD.

A machine learning model to predict therapeutic inertia in type 2 diabetes using electronic health record data.

OBJECTIVE: To estimate the therapeutic inertia prevalence for patients with type 2 diabetes, develop and validate a machine learning model predicting therapeutic inertia, and determine the added predictive value of area-level social determinants of health (SDOH). METHODS: This prognostic study with a retrospective cohort design used OneFlorida data (linked electronic health records (EHRs) from 1240 practices/clinics in Florida). The study cohort included adults (aged ≥ 18) with type 2 diabetes, HbA1C ≥ 7% (53 mmol/mol), ≥one ambulatory visit, and ≥one antihyperglycemic medication prescribed (excluded patients prescribed insulin before HbA1C). The outcome was therapeutic inertia, defined as absence of treatment intensification within six months after HbA1C ≥ 7% (53 mmol/mol). The predictors were patient, provider, and healthcare system factors. Machine learning methods included gradient boosting machines (GBM), random forests (RF), elastic net (EN), and least absolute shrinkage and selection operator (LASSO). The DeLong test compared the discriminative ability (represented by C-statistics) between models. RESULTS: The cohort included 31,087 patients with type 2 diabetes (mean age = 58.89 (SD = 13.27) years, 50.50% male, 58.89% White). The therapeutic inertia prevalence was 39.80% among the 68,445 records. GBM outperformed (C-statistic from testing sample = 0.84, 95% CI = 0.83-0.84) RF (C-statistic = 0.80, 95% CI = 0.79-0.80), EN (C-statistic = 0.80, 95% CI = 0.80-0.81), and LASSO (C-statistic = 0.80, 95% CI = 0.80-0.81), p < 0.05. Area-level SDOH significantly increased the discriminative ability versus models without SDOH (C-statistic for GBM = 0.84, 95% CI = 0.84-0.85 vs. 0.84, 95% CI = 0.83-0.84), p < 0.05. CONCLUSIONS: Using EHRs of patients with type 2 diabetes from a large state, machine learning predicted therapeutic inertia (prevalence = 40%). The model's ability to predict patients at high risk of therapeutic inertia is clinically applicable to diabetes care.

High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers.

Background: Due to the poor prognosis and rising occurrence, there is a crucial need to improve the diagnosis of Primary Central Nervous System Lymphoma (PCNSL), which is a rare type of non-Hodgkin's lymphoma. This study utilized targeted metabolomics of cerebrospinal fluid (CSF) to identify biomarker panels for the improved diagnosis or differential diagnosis of primary central nervous system lymphoma (PCNSL). Methods: In this study, a cohort of 68 individuals, including patients with primary central nervous system lymphoma (PCNSL), non-malignant disease controls, and patients with other brain tumors, was recruited. Their cerebrospinal fluid samples were analyzed using the Ultra-high performance liquid chromatography - tandem mass spectrometer (UHPLC-MS/MS) technique for targeted metabolomics analysis. Multivariate statistical analysis and logistic regression modeling were employed to identify biomarkers for both diagnosis (Dx) and differential diagnosis (Diff) purposes. The Dx and Diff models were further validated using a separate cohort of 34 subjects through logistic regression modeling. Results: A targeted analysis of 45 metabolites was conducted using UHPLC-MS/MS on cerebrospinal fluid (CSF) samples from a cohort of 68 individuals, including PCNSL patients, non-malignant disease controls, and patients with other brain tumors. Five metabolic features were identified as biomarkers for PCNSL diagnosis, while nine metabolic features were found to be biomarkers for differential diagnosis. Logistic regression modeling was employed to validate the Dx and Diff models using an independent cohort of 34 subjects. The logistic model demonstrated excellent performance, with an AUC of 0.83 for PCNSL vs. non-malignant disease controls and 0.86 for PCNSL vs. other brain tumor patients. Conclusion: Our study has successfully developed two logistic regression models utilizing metabolic markers in cerebrospinal fluid (CSF) for the diagnosis and differential diagnosis of PCNSL. These models provide valuable insights and hold promise for the future development of a non-invasive and reliable diagnostic tool for PCNSL.

Exploring the feasibility of using long-term stored newborn dried blood spots to identify metabolic features for congenital heart disease screening.

Congenital heart disease (CHD) represents a significant contributor to both morbidity and mortality in neonates and children. There's currently no analogous dried blood spot (DBS) screening for CHD immediately after birth. This study was set to assess the feasibility of using DBS to identify reliable metabolite biomarkers with clinical relevance, with the aim to screen and classify CHD utilizing the DBS. We assembled a cohort of DBS datasets from the California Department of Public Health (CDPH) Biobank, encompassing both normal controls and three pre-defined CHD categories. A DBS-based quantitative metabolomics method was developed using liquid chromatography with tandem mass spectrometry (LC-MS/MS). We conducted a correlation analysis comparing the absolute quantitated metabolite concentration in DBS against the CDPH NBS records to verify the reliability of metabolic profiling. For hydrophilic and hydrophobic metabolites, we executed significant pathway and metabolite analyses respectively. Logistic and LightGBM models were established to aid in CHD discrimination and classification. Consistent and reliable quantification of metabolites were demonstrated in DBS samples stored for up to 15 years. We discerned dysregulated metabolic pathways in CHD patients, including deviations in lipid and energy metabolism, as well as oxidative stress pathways. Furthermore, we identified three metabolites and twelve metabolites as potential biomarkers for CHD assessment and subtypes classifying. This study is the first to confirm the feasibility of validating metabolite profiling results using long-term stored DBS samples. Our findings highlight the potential clinical applications of our DBS-based methods for CHD screening and subtype classification.

Altered expression of the L-arginine/nitric oxide pathway in ovarian cancer: metabolic biomarkers and biological implications.

MOTIVATION: Ovarian cancer (OC) is a highly lethal gynecological malignancy. Extensive research has shown that OC cells undergo significant metabolic alterations during tumorigenesis. In this study, we aim to leverage these metabolic changes as potential biomarkers for assessing ovarian cancer. METHODS: A functional module-based approach was utilized to identify key gene expression pathways that distinguish different stages of ovarian cancer (OC) within a tissue biopsy cohort. This cohort consisted of control samples (n = 79), stage I/II samples (n = 280), and stage III/IV samples (n = 1016). To further explore these altered molecular pathways, minimal spanning tree (MST) analysis was applied, leading to the formulation of metabolic biomarker hypotheses for OC liquid biopsy. To validate, a multiple reaction monitoring (MRM) based quantitative LCMS/MS method was developed. This method allowed for the precise quantification of targeted metabolite biomarkers using an OC blood cohort comprising control samples (n = 464), benign samples (n = 3), and OC samples (n = 13). RESULTS: Eleven functional modules were identified as significant differentiators (false discovery rate, FDR < 0.05) between normal and early-stage, or early-stage and late-stage ovarian cancer (OC) tumor tissues. MST analysis revealed that the metabolic L-arginine/nitric oxide (L-ARG/NO) pathway was reprogrammed, and the modules related to "DNA replication" and "DNA repair and recombination" served as anchor modules connecting the other nine modules. Based on this analysis, symmetric dimethylarginine (SDMA) and arginine were proposed as potential liquid biopsy biomarkers for OC assessment. Our quantitative LCMS/MS analysis on our OC blood cohort provided direct evidence supporting the use of the SDMA-to-arginine ratio as a liquid biopsy panel to distinguish between normal and OC samples, with an area under the ROC curve (AUC) of 98.3%. CONCLUSION: Our comprehensive analysis of tissue genomics and blood quantitative LC/MSMS metabolic data shed light on the metabolic reprogramming underlying OC pathophysiology. These findings offer new insights into the potential diagnostic utility of the SDMA-to-arginine ratio for OC assessment. Further validation studies using adequately powered OC cohorts are warranted to fully establish the clinical effectiveness of this diagnostic test.

Erratum:

This corrects the article DOI: 10.1103/PhysRevLett.123.033201.

Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy.

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

LP342, a novel histone deacetylase inhibitor, decreases nitro-oxidative stress, mitochondrial dysfunction and hepatic ischaemia/reperfusion injury in mice.

Objectives: Some histone deacetylase (HDAC) isoforms contribute to ischaemia/reperfusion (IR) injury (IRI). Here, we examined whether LP342, the lead candidate of a new generation of hydrazide-based HDAC inhibitors (HDACi), decreases hepatic IRI. Methods: IR was induced by clamping blood vessels to ~70% of the livers of mice for 1 h. Key findings: At 6 h after reperfusion, ALT markedly increased, and wide-spread necrosis, leukocyte infiltration, and apoptosis occurred. LP342 treatment (1 mg/kg, ip) at 20 h or 1 h before ischaemia markedly decreased IRI whereas LP342 treatment upon reperfusion was marginally protective. Nitro-oxidative stress, c-Jun-N-terminal kinase (JNK) activation, and mitochondrial dysfunction contribute to IRI. 4-Hydroxynonenal, 3-nitrotyrosine, inducible nitric oxide synthase (iNOS), JNK activation and Sab binding increased markedly after IR, which LP342 blunted. LP342 also induced thioredoxin-1 expression before and after IR. LP342 also decreased mitochondrial depolarisation as detected by intravital microscopy at 2 h after IR. Lastly, LP342 increased acetylation of both histone-3 (class I HDAC substrate) and NFκB p65 but not tubulin (class II HDAC substrate) before and after IR. Conclusions: This novel HDACi protects against IRI most likely by epigenetic upregulation of antioxidant proteins and post-translational modifications of NFκB thus inhibiting iNOS expression and inflammatory responses.

Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan.

Background: Kawasaki disease (KD) is the leading cause of acquired heart disease in children. The major challenge in KD diagnosis is that it shares clinical signs with other childhood febrile control (FC) subjects. We sought to determine if our algorithmic approach applied to a Taiwan cohort. Methods: A single center (Chang Gung Memorial Hospital in Taiwan) cohort of patients suspected with acute KD were prospectively enrolled by local KD specialists for KD analysis. Our previously single-center developed computer-based two-step algorithm was further tested by a five-center validation in US. This first blinded multi-center trial validated our approach, with sufficient sensitivity and positive predictive value, to identify most patients with KD diagnosed at centers across the US. This study involved 418 KDs and 259 FCs from the Chang Gung Memorial Hospital in Taiwan. Findings: Our diagnostic algorithm retained sensitivity (379 of 418; 90.7%), specificity (223 of 259; 86.1%), PPV (379 of 409; 92.7%), and NPV (223 of 247; 90.3%) comparable to previous US 2016 single center and US 2020 fiver center results. Only 4.7% (15 of 418) of KD and 2.3% (6 of 259) of FC patients were identified as indeterminate. The algorithm identified 18 of 50 (36%) KD patients who presented 2 or 3 principal criteria. Of 418 KD patients, 157 were infants younger than one year and 89.2% (140 of 157) were classified correctly. Of the 44 patients with KD who had coronary artery abnormalities, our diagnostic algorithm correctly identified 43 (97.7%) including all patients with dilated coronary artery but one who found to resolve in 8 weeks. Interpretation: This work demonstrates the applicability of our algorithmic approach and diagnostic portability in Taiwan.

First-in-Class Hydrazide-Based HDAC6 Selective Inhibitor with Potent Oral Anti-Inflammatory Activity by Attenuating NLRP3 Inflammasome Activation.

In this study, we report the first highly selective HDAC6 inhibitor with hydrazide as the zinc-binding group (ZBG), which displays superior pharmacokinetic properties to the current hydroxamic acid inhibitors. Structure-activity relationship study reveals that ethyl group substituent hydrazide-based ZBG and cap group with more substantial rigidity and larger volume increase the HDAC6 selectivity of designed compounds. Representative inhibitor 35m exhibits potent HDAC6 inhibitory activity with an IC50 value of 0.019 µM. To our surprise, 35m establishes significant improvement in the pharmacokinetic property with much higher AUC0-inf (10292 ng·h/mL) and oral bioavailability (93.4%) than hydroximic acid-based HDAC6 inhibitors Tubastatin A and ACY-1215. Low-dose 35m remarkably decreases LPS-induced IL-1ß release both in vitro and in vivo by blocking the activation of NLRP3, indicating that 35m can be a potential orally active therapeutic agent for the treatment of NLRP3-related diseases.

Histone Deacetylase Inhibition by Gut Microbe-Generated Short-Chain Fatty Acids Entrains Intestinal Epithelial Circadian Rhythms.

BACKGROUND & AIMS: The circadian clock orchestrates ∼24-hour oscillations of gastrointestinal epithelial structure and function that drive diurnal rhythms in gut microbiota. Here, we use experimental and computational approaches in intestinal organoids to reveal reciprocal effects of gut microbial metabolites on epithelial timekeeping by an epigenetic mechanism. METHODS: We cultured enteroids in media supplemented with sterile supernatants from the altered Schaedler Flora (ASF), a defined murine microbiota. Circadian oscillations of bioluminescent PER2 and Bmal1 were measured in the presence or absence of individual ASF supernatants. Separately, we applied machine learning to ASF metabolomics to identify phase-shifting metabolites. RESULTS: Sterile filtrates from 3 of 7 ASF species (ASF360 Lactobacillus intestinalis, ASF361 Ligilactobacillus murinus, and ASF502 Clostridium species) induced minimal alterations in circadian rhythms, whereas filtrates from 4 ASF species (ASF356 Clostridium species, ASF492 Eubacterium plexicaudatum, ASF500 Pseudoflavonifactor species, and ASF519 Parabacteroides goldsteinii) induced profound, concentration-dependent phase shifts. Random forest classification identified short-chain fatty acid (SCFA) (butyrate, propionate, acetate, and isovalerate) production as a discriminating feature of ASF "shifters." Experiments with SCFAs confirmed machine learning predictions, with a median phase shift of 6.2 hours in murine enteroids. Pharmacologic or botanical histone deacetylase (HDAC) inhibitors yielded similar findings. Further, mithramycin A, an inhibitor of HDAC inhibition, reduced SCFA-induced phase shifts by 20% (P < .05) and conditional knockout of HDAC3 in enteroids abrogated butyrate effects on Per2 expression. Key findings were reproducible in human Bmal1-luciferase enteroids, colonoids, and Per2-luciferase Caco-2 cells. CONCLUSIONS: Gut microbe-generated SCFAs entrain intestinal epithelial circadian rhythms by an HDACi-dependent mechanism, with critical implications for understanding microbial and circadian network regulation of intestinal epithelial homeostasis.

Immunogenicity of the mRNA-1273 and ChAdOx1 nCoV-19 vaccines in Asian patients with autoimmune rheumatic diseases under biologic and/or conventional immunosuppressant treatments.

OBJECTIVE: Nucleic acid-based vaccines against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection are effective in the general population. However, it is unknown whether this is true in Asian patients with autoimmune rheumatic diseases (ARDs) who have received various combinations of disease-modifying anti-rheumatic drugs (DMARDs). METHOD: We designed a large prospective observational study recruiting 228 patients with ARDs in a tertiary rheumatology centre in Taiwan. Altogether, 142 received biological or targeted synthetic DMARDs and 86 received only conventional synthetic (cs) DMARDs. Serum levels of immunoglobulin G antibody against SARS-CoV-2 spike proteins were measured 2-6 weeks after COVID-19 vaccination with mRNA-1273 (Moderna®) or ChAdOx1 nCoV-19 (Oxford/AstraZeneca®). The immunomodulatory therapies were not modified before or after vaccination. RESULTS: Overall, 194 patients (85.09%) exhibited antibodies (758.33 ± 808.43 ng/mL) but 34 patients did not (103.24 ± 41.08 ng/mL). Patients with systemic lupus erythematosus or rheumatoid arthritis had significantly lower humoral responses to COVID-19 vaccination than those with other ARDs (p < 0.05). There was no significant difference in immunogenicity among patients on different csDMARD treatments. Compared to patients treated with only csDMARDs, those on rituximab or abatacept therapy had significantly lower immune response to the vaccination (p = 0.008 and p = 0.035, respectively). Patients who were treated with anti-tumour necrosis factor-α or interleukin-6 inhibitor exhibited higher titres of vaccination antibodies than those treated with direct lymphocyte inhibitors. CONCLUSIONS: mRNA-1273 and ChAdOx1 nCoV-19 vaccines were immunogenic in the majority of ARD patients. Rituximab and abatacept were associated with significantly diminished COVID-19 vaccination immunogenicity.

Comparison of three zinc binding groups for HDAC inhibitors - A potency, selectivity and enzymatic kinetics study.

Hydroxamic acid and benzamide are the most commonly used zinc binding group (ZBG) for HDAC inhibitors both in clinic and pre-clinic. Recently, we discovered several analogs of new type HDAC inhibitors with hydrazide as ZBG. Representative compounds displayed high potency, class I HDAC selectivity and excellent pharmacokinetics profile. In this research, we synthesize tool compounds 4 and 6 by modifying the hydroxamic acid of SAHA with benzamide and hydrazide, respectively, and compare the potency, isoform selectivity, binding profile and enzymatic kinetics for the hydroxamate, benzamide and hydrazide-based inhibitors. It is well known that SAHA with hydroxamic acid is a pan-HDAC inhibitor with competitive binding and fast-on/fast-off profile. Compound 6 is a slow-binding class I selective inhibitor with mixed (competitive and non-competitive) binding mode, which is the same as the hydrazide inhibitors in our previous study. Compound 4 is a class I selective, fast-on/fast-off inhibitor with competitive binding mode to HDAC1/2/3, which is different with published benzamide MS275 and 106. Therefore, the kinetics profile of benzamide is not only due to the ZBG, but also rely on the cap and linker groups. To the best of our knowledge, this is the first report to compare the enzymatic profile of three promising ZBGs of HDAC inhibitors.

Serological Phenotyping Analysis Uncovers a Unique Metabolomic Pattern Associated With Early Onset of Type 2 Diabetes Mellitus.

Background: Type 2 diabetes mellitus (T2DM) is a multifaceted disorder affecting epidemic proportion at global scope. Defective insulin secretion by pancreatic ß-cells and the inability of insulin-sensitive tissues to respond effectively to insulin are the underlying biology of T2DM. However, circulating biomarkers indicative of early diabetic onset at the asymptomatic stage have not been well described. We hypothesized that global and targeted mass spectrometry (MS) based metabolomic discovery can identify novel serological metabolic biomarkers specifically associated with T2DM. We further hypothesized that these markers can have a unique pattern associated with latent or early asymptomatic stage, promising an effective liquid biopsy approach for population T2DM risk stratification and screening. Methods: Four independent cohorts were assembled for the study. The T2DM cohort included sera from 25 patients with T2DM and 25 healthy individuals for the biomarker discovery and sera from 15 patients with T2DM and 15 healthy controls for the testing. The Pre-T2DM cohort included sera from 76 with prediabetes and 62 healthy controls for the model training and sera from 35 patients with prediabetes and 27 healthy controls for the model testing. Both global and targeted (amino acid, acylcarnitine, and fatty acid) approaches were used to deep phenotype the serological metabolome by high performance liquid chromatography-high resolution mass spectrometry. Different machine learning approaches (Random Forest, XGBoost, and ElasticNet) were applied to model the unique T2DM/Pre-T2DM metabolic patterns and contrasted with their effectiness to differentiate T2DM/Pre-T2DM from controls. Results: The univariate analysis identified unique panel of metabolites (n = 22) significantly associated with T2DM. Global metabolomics and subsequent structure determination led to the identification of 8 T2DM biomarkers while targeted LCMS profiling discovered 14 T2DM biomarkers. Our panel can effectively differentiate T2DM (ROC AUC = 1.00) or Pre-T2DM (ROC AUC = 0.84) from the controls in the respective testing cohort. Conclusion: Our serological metabolite panel can be utilized to identifiy asymptomatic population at risk of T2DM, which may provide utility in identifying population at risk at an early stage of diabetic development to allow for clinical intervention. This early detection would guide ehanced levels of care and accelerate development of clinical strategies to prevent T2DM.

Single cell transcriptomics in human osteoarthritis synovium and in silico deconvoluted bulk RNA sequencing.

OBJECTIVES: To reveal the heterogeneity of different cell types of osteoarthritis (OA) synovial tissues at a single-cell resolution, and determine by novel methodology whether bulk-RNA-seq data could be deconvoluted to create in silico scRNA-seq data for synovial tissue analyses. METHODS: OA scRNA-seq data (102,077 synoviocytes) were provided by 17 patients undergoing total knee arthroplasty; 9 tissues with matched scRNA-seq and bulk RNA-seq data were used to evaluate six in silico gene deconvolution tools. Predicted and observed cell types and proportions were compared to identify the best deconvolution tool for synovium. RESULTS: We identified seven distinct cell types in OA synovial tissues. Gene deconvolution identified three (of six) platforms as suitable for extrapolating cellular gene expression from bulk RNA-seq data. Using paired scRNA-seq and bulk RNA-seq data, an "arthritis" specific signature matrix was created and validated to have a significantly better predictive performance for synoviocytes than a default signature matrix. Use of the machine learning tool, Cell-type Identification By Estimating Relative Subsets of RNA Transcripts x (CIBERSORTx), to analyze rheumatoid arthritis (RA) and OA bulk RNA-seq data yielded proportions of T cells and fibroblasts that were similar to the gold standard observations from RA and OA scRNA-seq data, respectively. CONCLUSION: This novel study revealed heterogeneity of synovial cell types in OA and the feasibility of gene deconvolution for synovial tissue.