A GAPDH serotonylation system couples CD8+ T cell glycolytic metabolism to antitumor immunity.

Apart from the canonical serotonin (5-hydroxytryptamine [5-HT])-receptor signaling transduction pattern, 5-HT-involved post-translational serotonylation has recently been noted. Here, we report a glyceraldehyde-3-phosphate dehydrogenase (GAPDH) serotonylation system that promotes the glycolytic metabolism and antitumor immune activity of CD8+ T cells. Tissue transglutaminase 2 (TGM2) transfers 5-HT to GAPDH glutamine 262 and catalyzes the serotonylation reaction. Serotonylation supports the cytoplasmic localization of GAPDH, which induces a glycolytic metabolic shift in CD8+ T cells and contributes to antitumor immunity. CD8+ T cells accumulate intracellular 5-HT for serotonylation through both synthesis by tryptophan hydroxylase 1 (TPH1) and uptake from the extracellular compartment via serotonin transporter (SERT). Monoamine oxidase A (MAOA) degrades 5-HT and acts as an intrinsic negative regulator of CD8+ T cells. The adoptive transfer of 5-HT-producing TPH1-overexpressing chimeric antigen receptor T (CAR-T) cells induced a robust antitumor response. Our findings expand the known range of neuroimmune interaction patterns by providing evidence of receptor-independent serotonylation post-translational modification.

Water- and heat-activated dynamic passivation for perovskite photovoltaics.

Further improvements in perovskite solar cells require better control of ionic defects in the perovskite photoactive layer during the manufacturing stage and their usage1-5. Here we report a living passivation strategy using a hindered urea/thiocarbamate bond6-8 Lewis acid-base material (HUBLA), where dynamic covalent bonds with water and heat-activated characteristics can dynamically heal the perovskite to ensure device performance and stability. Upon exposure to moisture or heat, HUBLA generates new agents and further passivates defects in the perovskite. This passivation strategy achieved high-performance devices with a power conversion efficiency (PCE) of 25.1 per cent. HUBLA devices retained 94 per cent of their initial PCE for approximately 1,500 hours of ageing at 85 degrees Celsius in nitrogen and maintained 88 per cent of their initial PCE after 1,000 hours of ageing at 85 degrees Celsius and 30 per cent relative humidity in air.

Inhibition of IL-11 signalling extends mammalian healthspan and lifespan.

For healthspan and lifespan, ERK, AMPK and mTORC1 represent critical pathways and inflammation is a centrally important hallmark1-7. Here we examined whether IL-11, a pro-inflammatory cytokine of the IL-6 family, has a negative effect on age-associated disease and lifespan. As mice age, IL-11 is upregulated across cell types and tissues to regulate an ERK-AMPK-mTORC1 axis to modulate cellular, tissue- and organismal-level ageing pathologies. Deletion of Il11 or Il11ra1 protects against metabolic decline, multi-morbidity and frailty in old age. Administration of anti-IL-11 to 75-week-old mice for 25 weeks improves metabolism and muscle function, and reduces ageing biomarkers and frailty across sexes. In lifespan studies, genetic deletion of Il11 extended the lives of mice of both sexes, by 24.9% on average. Treatment with anti-IL-11 from 75 weeks of age until death extends the median lifespan of male mice by 22.5% and of female mice by 25%. Together, these results demonstrate a role for the pro-inflammatory factor IL-11 in mammalian healthspan and lifespan. We suggest that anti-IL-11 therapy, which is currently in early-stage clinical trials for fibrotic lung disease, may provide a translational opportunity to determine the effects of IL-11 inhibition on ageing pathologies in older people.

Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer.

Gastric cancer (GC) is the fifth most common cancer worldwide and is a heterogeneous disease. Among GC subtypes, the mesenchymal phenotype (Mes-like) is more invasive than the epithelial phenotype (Epi-like). Although gene expression of the epithelial-to-mesenchymal transition (EMT) has been studied, the regulatory landscape shaping this process is not fully understood. Here we use ATAC-seq and RNA-seq data from a compendium of GC cell lines and primary tumors to detect drivers of regulatory state changes and their transcriptional responses. Using the ATAC-seq data, we developed a machine learning approach to determine the transcription factors (TFs) regulating the subtypes of GC. We identified TFs driving the mesenchymal (RUNX2, ZEB1, SNAI2, AP-1 dimer) and the epithelial (GATA4, GATA6, KLF5, HNF4A, FOXA2, GRHL2) states in GC. We identified DNA copy number alterations associated with dysregulation of these TFs, specifically deletion of GATA4 and amplification of MAPK9 Comparisons with bulk and single-cell RNA-seq data sets identified activation toward fibroblast-like epigenomic and expression signatures in Mes-like GC. The activation of this mesenchymal fibrotic program is associated with differentially accessible DNA cis-regulatory elements flanking upregulated mesenchymal genes. These findings establish a map of TF activity in GC and highlight the role of copy number driven alterations in shaping epigenomic regulatory programs as potential drivers of GC heterogeneity and progression.

Graphormer supervised de novo protein design method and function validation.

Protein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological functions, such as improving the catalytic efficiency of enzymes. One key facet of protein design, fixed-backbone protein sequence design, seeks to design new sequences that will conform to a prescribed protein backbone structure. Nonetheless, existing sequence design methods present limitations, such as low sequence diversity and shortcomings in experimental validation of the designed functional proteins. These inadequacies obstruct the goal of functional protein design. To improve these limitations, we initially developed the Graphormer-based Protein Design (GPD) model. This model utilizes the Transformer on a graph-based representation of three-dimensional protein structures and incorporates Gaussian noise and a sequence random masks to node features, thereby enhancing sequence recovery and diversity. The performance of the GPD model was significantly better than that of the state-of-the-art ProteinMPNN model on multiple independent tests, especially for sequence diversity. We employed GPD to design CalB hydrolase and generated nine artificially designed CalB proteins. The results show a 1.7-fold increase in catalytic activity compared to that of the wild-type CalB and strong substrate selectivity on p-nitrophenyl acetate with different carbon chain lengths (C2-C16). Thus, the GPD method could be used for the de novo design of industrial enzymes and protein drugs. The code was released at https://github.com/decodermu/GPD.

CPADS: a web tool for comprehensive pancancer analysis of drug sensitivity.

Drug therapy is vital in cancer treatment. Accurate analysis of drug sensitivity for specific cancers can guide healthcare professionals in prescribing drugs, leading to improved patient survival and quality of life. However, there is a lack of web-based tools that offer comprehensive visualization and analysis of pancancer drug sensitivity. We gathered cancer drug sensitivity data from publicly available databases (GEO, TCGA and GDSC) and developed a web tool called Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS) using Shiny. CPADS currently includes transcriptomic data from over 29 000 samples, encompassing 44 types of cancer, 288 drugs and more than 9000 gene perturbations. It allows easy execution of various analyses related to cancer drug sensitivity. With its large sample size and diverse drug range, CPADS offers a range of analysis methods, such as differential gene expression, gene correlation, pathway analysis, drug analysis and gene perturbation analysis. Additionally, it provides several visualization approaches. CPADS significantly aids physicians and researchers in exploring primary and secondary drug resistance at both gene and pathway levels. The integration of drug resistance and gene perturbation data also presents novel perspectives for identifying pivotal genes influencing drug resistance. Access CPADS at https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS.

Multi-indicator comparative evaluation for deep learning-based protein sequence design methods.

MOTIVATION: Proteins found in nature represent only a fraction of the vast space of possible proteins. Protein design presents an opportunity to explore and expand this protein landscape. Within protein design, protein sequence design plays a crucial role, and numerous successful methods have been developed. Notably, deep learning-based protein sequence design methods have experienced significant advancements in recent years. However, a comprehensive and systematic comparison and evaluation of these methods have been lacking, with indicators provided by different methods often inconsistent or lacking effectiveness. RESULTS: To address this gap, we have designed a diverse set of indicators that cover several important aspects, including sequence recovery, diversity, root-mean-square deviation of protein structure, secondary structure, and the distribution of polar and nonpolar amino acids. In our evaluation, we have employed an improved weighted inferiority-superiority distance method to comprehensively assess the performance of eight widely used deep learning-based protein sequence design methods. Our evaluation not only provides rankings of these methods but also offers optimization suggestions by analyzing the strengths and weaknesses of each method. Furthermore, we have developed a method to select the best temperature parameter and proposed solutions for the common issue of designing sequences with consecutive repetitive amino acids, which is often encountered in protein design methods. These findings can greatly assist users in selecting suitable protein sequence design methods. Overall, our work contributes to the field of protein sequence design by providing a comprehensive evaluation system and optimization suggestions for different methods.

Ultralow Auger-Assisted Interlayer Exciton Annihilation in WS2/WSe2 Moiré Heterobilayers.

Transition metal dichalcogenide (TMD) heterobilayers have emerged as a promising platform for exploring solid-state quantum simulators and many-body quantum phenomena. Their type II band alignment, combined with the moiré superlattice, inevitably leads to nontrivial exciton interactions and dynamics. Here, we unveil the distinct Auger annihilation processes for delocalized interlayer excitons in WS2/WSe2 moiré heterobilayers. By fitting the characteristic efficiency droop and bimolecular recombination rate, we quantitatively determine an ultralow Auger coefficient of 1.3 × 10-5 cm2 s-1, which is >100-fold smaller than that of excitons in TMD monolayers. In addition, we reveal selective exciton upconversion into the WSe2 layer, which highlights the significance of intralayer electron Coulomb interactions in dictating the microscopic scattering pathways. The distinct Auger processes arising from spatial electron-hole separation have important implications for TMD heterobilayers while endowing interlayer excitons and their strongly correlated states with unique layer degrees of freedom.

Dual-Modal Cellular Nanoparticles for Continuous Neurotoxin Detoxification.

Neurotoxins are known for their extreme lethality. However, due to their enormous diversity, effective and broad-spectrum countermeasures are lacking. This study presents a dual-modal cellular nanoparticle (CNP) formulation engineered for continuous neurotoxin neutralization. The formulation involves encapsulating the metabolic enzyme N-sulfotransferase (SxtN) into metal-organic framework (MOF) nanoparticle cores and coating them with a natural neuronal membrane, termed "Neuron-MOF/SxtN-NPs". The resulting nanoparticles combine membrane-enabled broad-spectrum neurotoxin neutralization with enzyme payload-enabled continuous neurotoxin neutralization. The studies confirm the protection of the enzyme payload by the MOF core and validate the continuous neutralization of saxitoxin (STX). In vivo studies conducted using a mouse model of STX intoxication reveal markedly improved survival rates compared with control groups. Furthermore, acute toxicity assessments show no adverse effects associated with the administration of Neuron-MOF/SxtN-NPs in healthy mice. Overall, Neuron-MOF/SxtN-NPs represent a unique biomimetic nanomedicine platform poised to effectively neutralize neurotoxins, marking an important advancement in the field of countermeasure nanomedicine.

Mapping the Morphology of DNA on Carbon Nanotubes in Solution Using X-ray Scattering Interferometry.

Single-walled carbon nanotubes (SWCNTs) with adsorbed single-stranded DNA (ssDNA) are applied as sensors to investigate biological systems, with potential applications ranging from clinical diagnostics to agricultural biotechnology. Unique ssDNA sequences render SWCNTs selectively responsive to target analytes such as (GT)n-SWCNTs recognizing the neuromodulator, dopamine. It remains unclear how the ssDNA conformation on the SWCNT surface contributes to functionality, as observations have been limited to computational models or experiments under dehydrated conditions that differ substantially from the aqueous biological environments in which the nanosensors are applied. We demonstrate a direct mode of measuring in-solution ssDNA geometries on SWCNTs via X-ray scattering interferometry (XSI), which leverages the interference pattern produced by AuNP tags conjugated to ssDNA on the SWCNT surface. We employ XSI to quantify distinct surface-adsorbed morphologies for two (GT)n ssDNA oligomer lengths (n = 6, 15) that are used on SWCNTs in the context of dopamine sensing and measure the ssDNA conformational changes as a function of ionic strength and during dopamine interaction. We show that the shorter oligomer, (GT)6, adopts a more periodically ordered ring structure along the SWCNT axis (inter-ssDNA distance of 8.6 ± 0.3 nm), compared to the longer (GT)15 oligomer (most probable 5'-to-5' distance of 14.3 ± 1.1 nm). During molecular recognition, XSI reveals that dopamine elicits simultaneous axial elongation and radial constriction of adsorbed ssDNA on the SWCNT surface. Our approach using XSI to probe solution-phase morphologies of polymer-functionalized SWCNTs can be applied to yield insights into sensing mechanisms and inform future design strategies for nanoparticle-based sensors.

Single-cell landscape of functionally cured chronic hepatitis B patients reveals activation of innate and altered CD4-CTL-driven adaptive immunity.

BACKGROUND & AIMS: Hepatitis B surface antigen (HBsAg) loss or functional cure (FC) is considered the optimal therapeutic outcome for patients with chronic hepatitis B (CHB). However, the immune-pathological biomarkers and underlying mechanisms of FC remain unclear. In this study we comprehensively interrogate disease-associated cell states identified within intrahepatic tissue and matched PBMCs (peripheral blood mononuclear cells) from patients with CHB or after FC, at the resolution of single cells, to provide novel insights into putative mechanisms underlying FC. METHODS: We combined single-cell transcriptomics (single-cell RNA sequencing) with multiparametric flow cytometry-based immune phenotyping, and multiplexed immunofluorescence to elucidate the immunopathological cell states associated with CHB vs. FC. RESULTS: We found that the intrahepatic environment in CHB and FC displays specific cell identities and molecular signatures that are distinct from those found in matched PBMCs. FC is associated with the emergence of an altered adaptive immune response marked by CD4 cytotoxic T lymphocytes, and an activated innate response represented by liver-resident natural killer cells, specific Kupffer cell subtypes and marginated neutrophils. Surprisingly, we found MHC class II-expressing hepatocytes in patients achieving FC, as well as low but persistent levels of covalently closed circular DNA and pregenomic RNA, which may play an important role in FC. CONCLUSIONS: Our study provides conceptually novel insights into the immuno-pathological control of HBV cure, and opens exciting new avenues for clinical management, biomarker discovery and therapeutic development. We believe that the discoveries from this study, as it relates to the activation of an innate and altered immune response that may facilitate sustained, low-grade inflammation, may have broader implications in the resolution of chronic viral hepatitis. IMPACT AND IMPLICATIONS: This study dissects the immuno-pathological cell states associated with functionally cured chronic hepatitis B (defined by the loss of HBV surface antigen or HBsAg). We identified the sustained presence of very low viral load, accessory antigen-presenting hepatocytes, adaptive-memory-like natural killer cells, and the emergence of helper CD4 T cells with cytotoxic or effector-like signatures associated with functional cure, suggesting previously unsuspected alterations in the adaptive immune response, as well as a key role for the innate immune response in achieving or maintaining functional cure. Overall, the insights generated from this study may provide new avenues for the development of alternative therapies as well as patient surveillance for better clinical management of chronic hepatitis B.

Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.

BACKGROUND: Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI to AD is desired but, to date, remains challenging. Here, we developed an interpretable deep learning model featuring a novel design that incorporates interaction effects and multimodality to improve the prediction accuracy and horizon for MCI-to-AD progression. METHODS: This multi-center, multi-cohort retrospective study collected structural magnetic resonance imaging (sMRI), clinical assessments, and genetic polymorphism data of 252 patients with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our deep learning model was cross-validated on the ADNI-1 and ADNI-2/GO cohorts and further generalized in the ongoing ADNI-3 cohort. We evaluated the model performance using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score. RESULTS: On the cross-validation set, our model achieved superior results for predicting MCI conversion within 4 years (AUC, 0.962; accuracy, 92.92%; sensitivity, 88.89%; specificity, 95.33%) compared to all existing studies. In the independent test, our model exhibited consistent performance with an AUC of 0.939 and an accuracy of 92.86%. Integrating interaction effects and multimodal data into the model significantly increased prediction accuracy by 4.76% (P = 0.01) and 4.29% (P = 0.03), respectively. Furthermore, our model demonstrated robustness to inter-center and inter-scanner variability, while generating interpretable predictions by quantifying the contribution of multimodal biomarkers. CONCLUSIONS: The proposed deep learning model presents a novel perspective by combining interaction effects and multimodality, leading to more accurate and longer-term predictions of AD progression, which promises to improve pre-dementia patient care.

Potential anti-tumor effects of regulatory T cells in the tumor microenvironment: a review.

Regulatory T cells (Tregs) expressing the transcription factor FoxP3 are essential for maintaining immunological balance and are a significant component of the immunosuppressive tumor microenvironment (TME). Single-cell RNA sequencing (ScRNA-seq) technology has shown that Tregs exhibit significant plasticity and functional diversity in various tumors within the TME. This results in Tregs playing a dual role in the TME, which is not always centered around supporting tumor progression as typically believed. Abundant data confirms the anti-tumor activities of Tregs and their correlation with enhanced patient prognosis in specific types of malignancies. In this review, we summarize the potential anti-tumor actions of Tregs, including suppressing tumor-promoting inflammatory responses and boosting anti-tumor immunity. In addition, this study outlines the spatial and temporal variations in Tregs function to emphasize that their predictive significance in malignancies may change. It is essential to comprehend the functional diversity and potential anti-tumor effects of Tregs to improve tumor therapy strategies.

The response of mesophyll conductance to short-term CO2 variation is related to stomatal conductance.

The response of mesophyll conductance (gm) to CO2 plays a key role in photosynthesis and ecosystem carbon cycles under climate change. Despite numerous studies, there is still debate about how gm responds to short-term CO2 variations. Here we used multiple methods and looked at the relationship between stomatal conductance to CO2 (gsc) and gm to address this aspect. We measured chlorophyll fluorescence parameters and online carbon isotope discrimination (Δ) at different CO2 mole fractions in sunflower (Helianthus annuus L.), cowpea (Vigna unguiculata L.), and wheat (Triticum aestivum L.) leaves. The variable J and Δ based methods showed that gm decreased with an increase in CO2 mole fraction, and so did stomatal conductance. There were linear relationships between gm and gsc across CO2 mole fractions. gm obtained from A-Ci curve fitting method was higher than that from the variable J method and was not representative of gm under the growth CO2 concentration. gm could be estimated by empirical models analogous to the Ball-Berry model and the USO model for stomatal conductance. Our results suggest that gm and gsc respond in a coordinated manner to short-term variations in CO2, providing new insight into the role of gm in photosynthesis modelling.

Impact of Postarrest Vasoactive-Inotropic Score on Acute Kidney Injury in Cardiac Arrest Survivors: A Retrospective Cohort Study.

Background: Postarrest acute kidney injury (AKI) is a major health burden because it is associated with prolonged hospitalization, increased dialysis requirement, high mortality, and unfavorable neurological outcomes. Managing hemodynamic instability during the early postarrest period is critical; however, the role of quantified vasopressor dependence in AKI development in relation to illness severity remains unclear. Methods: A retrospective, observational cohort study that enrolled 411 non-traumatic adult cardiac arrest survivors without pre-arrest end-stage kidney disease between January 2017 and December 2019, grouped according to their baseline kidney function. The criteria for kidney injury were based on the Kidney Disease: Improving Global Outcomes definition and AKI staging system. The degree of vasopressor dependence within the first 24 h following return of spontaneous circulation (ROSC) was presented using the maximum vasoactive-inotropic score ( VIS max ). Results: Of the 411 patients, 181 (44%) had early AKI after ROSC. Patients with AKI showed an increased risk of in-hospital mortality (adjusted OR [aOR] 5.40, 95% CI 3.36-8.69, p < 0.001) and unfavorable neurological outcome (aOR 5.70, 95% CI 3.45-9.43, p < 0.001) compared to patients without AKI. The risk of adverse outcomes increased with illness severity. Patients with vasopressor support had an increased risk of early AKI. A low VIS max was associated with AKI stage 1-2 (aOR 2.51, 95% CI 1.20-5.24), whereas a high VIS max was associated with an increased risk for AKI stage 3 (aOR 2.46, 95% CI 1.28-4.75). Conclusions: Early AKI is associated with an increased risk of in-hospital mortality and unfavorable neurologic recovery in cardiac arrest survivors. Postarrest VIS max is an independent predictor of the development and severity of AKI following ROSC, regardless of baseline kidney function.

Dominant dystrophic epidermolysis bullosa is associated with glycolytically active GATA3+ T helper 2 cells which may contribute to pruritus in lesional skin.

BACKGROUND: Dominant dystrophic epidermolysis bullosa (DDEB) is characterized by trauma-induced blisters and, in some individuals, intense pruritus. Precisely what causes itch in DDEB and optimal ways to reduce it have not been fully determined. OBJECTIVES: To characterize DDEB skin transcriptomes to identify therapeutic targets to reduce pruritus in patients. METHODS: Using bulk RNA sequencing, we evaluated affected and unaffected skin biopsy samples from six patients with DDEB (all with the very itchy pruriginosa subtype) and four healthy individuals. Single-cell transcriptomes of affected (n = 2) and unaffected (n = 1) DDEB skin and healthy skin (n = 2) were obtained. Dupilumab treatment was provided for three patients. RESULTS: The skin bulk transcriptome showed significant enrichment of T helper (Th)1/2 and Th17 pathways in affected DDEB skin compared with nonlesional DDEB skin and healthy skin. Single-cell transcriptomics showed an association of glycolytically active GATA3+ Th2 cells in affected DDEB skin. Treatment with dupilumab in three people with DDEB led to significantly reduced visual analogue scale (VAS) itch scores after 12 weeks (mean VAS 3.83) compared with pretreatment (mean VAS 7.83). Bulk RNAseq and quantitative polymerase chain reaction showed that healthy skin and dupilumab-treated epidermolysis bullosa (EB) pruriginosa skin have similar transcriptomic profiles and reduced Th1/Th2 and Th17 pathway enrichment. CONCLUSIONS: Single-cell RNAseq helps define an enhanced DDEB-associated Th2 profile and rationalizes drug repurposing of anti-Th2 drugs in treating DDEB pruritus.

Dominant dystrophic epidermolysis bullosa (DDEB) is a rare inherited skin disease that causes fragile skin that blisters easily, often triggered by minor injuries. These blisters are accompanied by intense itching, which can be distressing. The underlying cause of DDEB lies in genetic mutations in a gene called COL7A1. This gene encodes 'type VII collagen', a protein crucial for attaching the outer skin layer (epidermis) to the layer beneath (dermis). Although the genetic basis of DDEB is understood, the causes of itch are not known. As well as this, effective treatments for DDEB are lacking, which has driven scientists to explore innovative approaches like repurposing existing drugs. Drug repurposing involves using medications that have already been approved for other health conditions. One such drug is dupilumab, which is used for severe atopic dermatitis (eczema). Dupilumab targets immune cells called Th2 cells, which play a role in inflammation and allergies. While dupilumab has shown promise in relieving DDEB itching, the way it works in this condition is unclear. This study, carried out by a group of researchers in Taiwan, looked at gene expression in DDEB-affected and unaffected skin, and compared it to gene expression in healthy skin samples. We found heightened activity in Th2 immune cells and abnormal gene signals related to itching, similar to atopic dermatitis. These findings support using dupilumab and other anti-inflammatory drugs to alleviate itching in DDEB. Clinical trials will be crucial to evaluate the effectiveness of these drugs for managing DDEB symptoms. This research opens doors for enhanced treatment options and improving the quality of life of people living with DDEB.

Metabolic alterations in urine among the patients with severe fever with thrombocytopenia syndrome.

BACKGROUND: The pathogenesis of severe fever with thrombocytopenia syndrome (SFTS) remained unclear. We aimed to profile the metabolic alterations in urine of SFTS patients and provide new evidence for its pathogenesis. METHODS: A case-control study was conducted in the 154th hospital in China. Totally 88 cases and 22 controls aged ≥ 18 years were enrolled. The cases were selected from laboratory-confirmed SFTS patients. The controls were selected among SFTSV-negative population. Those with diabetes, cancer, hepatitis and other sexually transmitted diseases were excluded in both groups. Fatal cases and survival cases were 1:1 matched. Inter-group differential metabolites and pathways were obtained, and the inter-group discrimination ability was evaluated. RESULTS: Tryptophan metabolism and phenylalanine metabolism were the top one important metabolism pathway in differentiating the control and case groups, and the survival and fatal groups, respectively. The significant increase of differential metabolites in tryptophan metabolism, including 5-hydroxyindoleacetate (5-HIAA), L-kynurenine (KYN), 5-hydroxy-L-tryptophan (5-HTP), 3-hydroxyanthranilic acid (3-HAA), and the increase of phenylpyruvic acid and decrease of hippuric acid in phenylalanine metabolism indicated the potential metabolic alterations in SFTSV infection. The increase of 5-HIAA, KYN, 5-HTP, phenylpyruvic acid and hippuric acid were involved in the fatal progress of SFTS patients. CONCLUSIONS: Tryptophan metabolism and phenylalanine metabolism might be involved in the pathogenesis of SFTSV infection. These findings provided new evidence for the pathogenesis and treatment of SFTS.

Melatonin antagonizes oxidative stress-induced apoptosis in retinal ganglion cells through activating the thioredoxin-1 pathway.

This study aimed to explore the role of melatonin in oxidative stress-induced injury on retinal ganglion cells and the underlying mechanisms. The immortalized RGC-5 cells were treated with H2O2 to induce oxidative injury. Cell viability was measured by Cell Counting Kit-8, and apoptosis was determined by flow cytometry and western blot assays. Reactive oxygen species (ROS), lactate dehydrogenase (LDH), and malondialdehyde (MDA) levels were examined to evaluate oxidative stress levels. In addition, Thioredoxin-1 (Trx1) was silenced in RGC-5 cells using small interfering RNA followed by signaling pathway examination to explore the underlying mechanisms of melatonin in alleviating oxidative injury. Melatonin pre-treatment significantly alleviated H2O2-induced apoptosis in RGC-5 cells. Melatonin also markedly reversed the upregulation of cleaved-caspase 3, cleaved-caspase 9, and Bax expression and downregulation of Bcl-2 expression induced by H2O2. Further analyses presented that melatonin significantly attenuated the increase of ROS, LDH, and MDA levels in RGC-5 cells after H2O2 treatment. Melatonin also abolished the downregulated expression of Superoxide dismutase type 1, Trx1, and Thioredoxin reductase 1, and the reduced activity of thioredoxin reductase in RGC-5 cells after H2O2 treatment. Notably, Trx1 knockdown significantly mitigated the protective effect of melatonin in alleviating H2O2-induced apoptosis and oxidative stress, while administration of compound C, a common inhibitor of c-Jun N-terminal kinase (JNK) signaling, partially reversed the effect of Trx1 silencing, thereby ameliorating the apoptosis and oxidative injury induced by H2O2 in RGC-5 cells. Melatonin could significantly alleviate oxidative stress-induced injury of retinal ganglion cells via modulating Trx1-mediated JNK signaling pathway.

Factors associated with treatment responses to pioglitazone in patients with steatotic liver disease: A 3-year prospective cohort study.

AIM: The response rate to pioglitazone and the predictive factors for its effects on improving liver biochemistry in patients with steatotic liver disease (SLD) remain elusive, so we aimed to investigate these issues. METHODS: A 3-year prospective cohort study of 126 Taiwanese patients with SLD treated with pioglitazone (15-30 mg/day) was conducted. Phospholipase domain-containing protein 3 I148M rs738409, methylenetetrahydrofolate reductase rs1801133, aldehyde dehydrogenase 2 (ALDH2) rs671 and lipoprotein lipase rs10099160 single nucleotide polymorphisms were assessed in the patients. RESULTS: Of 126 patients, 78 (61.9%) were men, and the mean and median ages were 54.3 and 56.5 years, respectively. Pioglitazone responders were defined as those with decreased alanine aminotransferase (ALT) levels at 6 months post-treatment, and 105 (83.3%) patients were responders. Compared with non-responders, responders were more frequently women and had higher baseline ALT levels. The proportion of patients with the ALDH2 rs671 GG genotype was lower among responders (38.6% vs. 66.6%, p = .028). Female sex [odds ratio (OR): 4.514, p = .023] and baseline ALT level (OR: 1.015, p = .046; cut-off level: ≥82 U/L) were associated with pioglitazone response. Among responders, the liver biochemistry and homeostasis model assessment of insulin resistance improved from 6 to 24 months post-treatment. The total cholesterol levels decreased within 6 months, while increases in high-density lipoprotein cholesterol levels and decreases in triglyceride levels and fibrosis-4 scores were noted only at 24 months post-treatment. The 2-year cumulative incidences of cardiovascular events, cancers and hepatic events were similar between responders and non-responders. CONCLUSIONS: Regarding liver biochemistry, over 80% of Taiwanese patients with SLD had a pioglitazone response, which was positively associated with female sex and baseline ALT levels. Insulin resistance improved as early as 6 months post-treatment, while liver fibrosis improvement was not observed until 24 months post-treatment. The link between the pioglitazone response and the ALDH2 genotype warrants further investigation.

Stereodivergent Synthesis of Alkaloid (±)-223A and (±)-6-epi-223A via Rh-Catalyzed Hydroformylation Double Cyclization.

A stereodivergent approach toward total syntheses of Dendrobatid alkaloids 223A and 6-epi-223A is described. The approach features a concise construction of an indolizidine skeleton by Rh-catalyzed domino hydroformylation double cyclization and sequential stereocontrolled transformations such as reductive alkylation or anti-selective α-alkylation of the 5-oxoindolizidine. These stereoselective reactions afford the desired stereochemistry in the targets.