ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery.

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823 391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support.

ADMETlab 3.0 is the second updated version of the web server that provides a comprehensive and efficient platform for evaluating ADMET-related parameters as well as physicochemical properties and medicinal chemistry characteristics involved in the drug discovery process. This new release addresses the limitations of the previous version and offers broader coverage, improved performance, API functionality, and decision support. For supporting data and endpoints, this version includes 119 features, an increase of 31 compared to the previous version. The updated number of entries is 1.5 times larger than the previous version with over 400 000 entries. ADMETlab 3.0 incorporates a multi-task DMPNN architecture coupled with molecular descriptors, a method that not only guaranteed calculation speed for each endpoint simultaneously, but also achieved a superior performance in terms of accuracy and robustness. In addition, an API has been introduced to meet the growing demand for programmatic access to large amounts of data in ADMETlab 3.0. Moreover, this version includes uncertainty estimates in the prediction results, aiding in the confident selection of candidate compounds for further studies and experiments. ADMETlab 3.0 is publicly for access without the need for registration at: https://admetlab3.scbdd.com.

Accurate and interpretable gene expression imputation on scRNA-seq data using IGSimpute.

Single-cell ribonucleic acid sequencing (scRNA-seq) enables the quantification of gene expression at the transcriptomic level with single-cell resolution, enhancing our understanding of cellular heterogeneity. However, the excessive missing values present in scRNA-seq data hinder downstream analysis. While numerous imputation methods have been proposed to recover scRNA-seq data, high imputation performance often comes with low or no interpretability. Here, we present IGSimpute, an accurate and interpretable imputation method for recovering missing values in scRNA-seq data with an interpretable instance-wise gene selection layer (GSL). IGSimpute outperforms 12 other state-of-the-art imputation methods on 13 out of 17 datasets from different scRNA-seq technologies with the lowest mean squared error as the chosen benchmark metric. We demonstrate that IGSimpute can give unbiased estimates of the missing values compared to other methods, regardless of whether the average gene expression values are small or large. Clustering results of imputed profiles show that IGSimpute offers statistically significant improvement over other imputation methods. By taking the heart-and-aorta and the limb muscle tissues as examples, we show that IGSimpute can also denoise gene expression profiles by removing outlier entries with unexpectedly high expression values via the instance-wise GSL. We also show that genes selected by the instance-wise GSL could indicate the age of B cells from bladder fat tissue of the Tabula Muris Senis atlas. IGSimpute can impute one million cells using 64 min, and thus applicable to large datasets.

Benchmarking multi-platform sequencing technologies for human genome assembly.

Genome assembly is a computational technique that involves piecing together deoxyribonucleic acid (DNA) fragments generated by sequencing technologies to create a comprehensive and precise representation of the entire genome. Generating a high-quality human reference genome is a crucial prerequisite for comprehending human biology, and it is also vital for downstream genomic variation analysis. Many efforts have been made over the past few decades to create a complete and gapless reference genome for humans by using a diverse range of advanced sequencing technologies. Several available tools are aimed at enhancing the quality of haploid and diploid human genome assemblies, which include contig assembly, polishing of contig errors, scaffolding and variant phasing. Selecting the appropriate tools and technologies remains a daunting task despite several studies have investigated the pros and cons of different assembly strategies. The goal of this paper was to benchmark various strategies for human genome assembly by combining sequencing technologies and tools on two publicly available samples (NA12878 and NA24385) from Genome in a Bottle. We then compared their performances in terms of continuity, accuracy, completeness, variant calling and phasing. We observed that PacBio HiFi long-reads are the optimal choice for generating an assembly with low base errors. On the other hand, we were able to produce the most continuous contigs with Oxford Nanopore long-reads, but they may require further polishing to improve on quality. We recommend using short-reads rather than long-reads themselves to improve the base accuracy of contigs from Oxford Nanopore long-reads. Hi-C is the best choice for chromosome-level scaffolding because it can capture the longest-range DNA connectedness compared to 10× linked-reads and Bionano optical maps. However, a combination of multiple technologies can be used to further improve the quality and completeness of genome assembly. For diploid assembly, hifiasm is the best tool for human diploid genome assembly using PacBio HiFi and Hi-C data. Looking to the future, we expect that further advancements in human diploid assemblers will leverage the power of PacBio HiFi reads and other technologies with long-range DNA connectedness to enable the generation of high-quality, chromosome-level and haplotype-resolved human genome assemblies.

dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data.

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely used to explore dynamic changes in gene expression of transcription factors (TFs) and their target genes. This information is useful to reconstruct cell-type-specific gene regulatory networks (GRNs). However, the existing tools are commonly designed to analyze either time-course bulk gene expression data or static scRNA-seq data via pseudo-time cell ordering. A few methods successfully utilize the information from multiple time points while also considering the characteristics of scRNA-seq data. We proposed dynDeepDRIM, a novel deep learning model to reconstruct GRNs using time-course scRNA-seq data. It represents the joint expression of a gene pair as an image and utilizes the image of the target TF-gene pair and the ones of the potential neighbors to reconstruct GRNs from time-course scRNA-seq data. dynDeepDRIM can effectively remove the transitive TF-gene interactions by considering neighborhood context and model the gene expression dynamics using high-dimensional tensors. We compared dynDeepDRIM with six GRN reconstruction methods on both simulation and four real time-course scRNA-seq data. dynDeepDRIM achieved substantially better performance than the other methods in inferring TF-gene interactions and eliminated the false positives effectively. We also applied dynDeepDRIM to annotate gene functions and found it achieved evidently better performance than the other tools due to considering the neighbor genes.

DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data.

Single-cell RNA sequencing has enabled to capture the gene activities at single-cell resolution, thus allowing reconstruction of cell-type-specific gene regulatory networks (GRNs). The available algorithms for reconstructing GRNs are commonly designed for bulk RNA-seq data, and few of them are applicable to analyze scRNA-seq data by dealing with the dropout events and cellular heterogeneity. In this paper, we represent the joint gene expression distribution of a gene pair as an image and propose a novel supervised deep neural network called DeepDRIM which utilizes the image of the target TF-gene pair and the ones of the potential neighbors to reconstruct GRN from scRNA-seq data. Due to the consideration of TF-gene pair's neighborhood context, DeepDRIM can effectively eliminate the false positives caused by transitive gene-gene interactions. We compared DeepDRIM with nine GRN reconstruction algorithms designed for either bulk or single-cell RNA-seq data. It achieves evidently better performance for the scRNA-seq data collected from eight cell lines. The simulated data show that DeepDRIM is robust to the dropout rate, the cell number and the size of the training data. We further applied DeepDRIM to the scRNA-seq gene expression of B cells from the bronchoalveolar lavage fluid of the patients with mild and severe coronavirus disease 2019. We focused on the cell-type-specific GRN alteration and observed targets of TFs that were differentially expressed between the two statuses to be enriched in lysosome, apoptosis, response to decreased oxygen level and microtubule, which had been proved to be associated with coronavirus infection.

Prevalence and risk factors of long COVID 6-12 months after infection with the Omicron variant among nonhospitalized patients in Hong Kong.

Long COVID has been reported among patients with COVID-19, but little is known about the prevalence and risk factors associated with long COVID 6-12 months after infection with the Omicron variant. This is a large-scale retrospective study. A total of 6242 out of 12 950 nonhospitalized subjects of all ages with SARS-CoV-2 infection (confirmed by polymerase chain reaction/rapid antigen test) during the Omicron dominant outbreak (December 31, 2021-May 6, 2022) in Hong Kong were included. Prevalence of long COVID, frequencies of symptoms, and risk factors were analyzed. Three thousand four hundred and thirty (55.0%) subjects reported at least one long COVID symptom. The most reported symptom was fatigue (1241, 36.2%). Female gender, middle age, obesity, comorbidities, vaccination after infection, having more symptoms, and presenting fatigue/chest tightness/headache/diarrhea in the acute stage of illness were identified as associated risk factors for long COVID. Patients who had received three or more doses of vaccine were not associated with a lower risk of long COVID (adjusted odds ratio 1.105, 95% confidence interval 0.985-1.239, p = 0.088). Among patients with at least three doses of vaccine, there was no significant difference in the risk of long COVID between the CoronaVac vaccine and BNT162b2 vaccine (p > 0.05). Omicron infection can lead to long COVID in a significant proportion of nonhospitalized patients 6-12 months after infection. Further investigation is needed to uncover the mechanisms underlying the development of long COVID and determine the impact of various risk factors such as vaccines.

Weekly symptom profiles of nonhospitalized individuals infected with SARS-CoV-2 during the Omicron outbreak in Hong Kong: A retrospective observational study from a telemedicine center.

Omicron BA.2.2 is the dominant variant in the Hong Kong outbreak since December 31, 2021. There is no study reporting the weekly symptom profile after infection. In this retrospective study, participants who tested positive for SARS-CoV-2 after December 31, 2021, and registered in the telemedicine system between March 14 and May 6, 2022, were analyzed. Among registered 12 950 self-quarantined COVID-19-positive patients, 11 776 symptomatic patients were included for weekly symptom profile analysis. A total of 4718 (40.1%) patients reported symptoms in the first week after a positive test, 2501 (21.2%) in the second week, 1498 (12.7%) in the third week, 1048 (8.9%) in the fourth week, and 2011 (17.1%) in over 4 weeks. Cough was the most common symptom in all participants. Patients in the first week had higher odds of reporting fever (0.206, 95% confidence interval [CI]: 0.161-0.263, p < 0.001) and sore throat (0.228, 95% CI: 0.208-0.252, p < 0.001). Patients in over 4 weeks had higher odds of reporting fatigue (1.263, 95% CI: 1.139-1.402, p < 0.001). Further, having at least two vaccine doses linked to lower odds of having fever (0.675, 95% CI: 0.562-0.811, p < 0.001), but not associated with the presence of cough and fatigue. Diabetic patients had higher odds of reporting diarrhea (1.637, 95% CI: 1.351-1.982, p < 0.001). Symptoms from Omicron infection may last for more than 4 weeks and symptom profiles vary from week to week. Vaccination and comorbidity affect the symptom profiles.

Reporting quality of randomized controlled trials of angina pectoris with integrated traditional Chinese and western medicine interventions: a cross-sectional study.

BACKGROUND AND OBJECTIVE: Integrated traditional Chinese and western medicine (ITCWM), as a representative type of complex intervention, is commonly used for the treatment of angina pectoris (AP) in clinical practice. However, it is unclear whether the details of ITCWM interventions, such as rationale for selection and design, implementation and potential interactions for different therapies, were adequately reported. Therefore, this study aimed to describe the reporting characteristics and quality in randomized controlled trials (RCTs) of AP with ITCWM interventions. METHODS: Through a search of 7 electronic databases, we identified RCTs of AP with ITCWM interventions published in both English and Chinese from 1st Jan 2017 to 6th Aug 2022. The general characteristics of included studies were summarized, further, the quality of reporting was assessed based on three Checklists, including the CONSORT with 36 items (except for one item 1b about abstract), the CONSORT for abstracts (17 items), and a self-designed ITCWM-related checklist (21 items covering rationale and details of interventions, outcome assessment and analysis). The quality of RCTs published in English and Chinese, as well as journals and dissertations were also compared. RESULTS: A total of 451 eligible RCTs were included. For the reporting compliance, the mean score (95% Confidence Interval) of the CONSORT (72 scores in total), CONSORT for abstract (34 scores in total), and ITCWM-related (42 scores in total) checklists was 27.82 (27.44-28.19), 14.17 (13.98-14.37) and 21.06 (20.69-21.43), respectively. More than half items were evaluated as poor quality (reporting rate < 50%) among each Checklist. Moreover, the reporting quality of publications in English journals was higher than that in Chinese journals in terms of the CONSORT items. The reporting of published dissertations was better than that in journal publications regarding both the CONSORT and ITCWM-specific items. CONCLUSION: Although the CONSORT appears to have enhanced the reporting of RCTs in AP, the quality of ITCWM specifics is variable and in need of improvement. Reporting guideline of the ITCWM recommendations should be developed thus to improve their quality.

Natural Compounds Targeting the Autophagy Pathway in the Treatment of Colorectal Cancer.

Autophagy is a highly conserved intracellular degradation pathway by which misfolded proteins or damaged organelles are delivered in a double-membrane vacuolar vesicle and finally degraded by lysosomes. The risk of colorectal cancer (CRC) is high, and there is growing evidence that autophagy plays a critical role in regulating the initiation and metastasis of CRC; however, whether autophagy promotes or suppresses tumor progression is still controversial. Many natural compounds have been reported to exert anticancer effects or enhance current clinical therapies by modulating autophagy. Here, we discuss recent advancements in the molecular mechanisms of autophagy in regulating CRC. We also highlight the research on natural compounds that are particularly promising autophagy modulators for CRC treatment with clinical evidence. Overall, this review illustrates the importance of autophagy in CRC and provides perspectives for these natural autophagy regulators as new therapeutic candidates for CRC drug development.

Exploring the Potential of Aptamers in Targeting Neuroinflammation and Neurodegenerative Disorders: Opportunities and Challenges.

Neuroinflammation is the precursor for several neurodegenerative diseases (NDDs), such as Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS). Targeting neuroinflammation has emerged as a promising strategy to address a wide range of CNS pathologies. These NDDs still present significant challenges in terms of limited and ineffective diagnosis and treatment options, driving the need to explore innovative and novel therapeutic alternatives. Aptamers are single-stranded nucleic acids that offer the potential for addressing these challenges through diagnostic and therapeutic applications. In this review, we summarize diagnostic and therapeutic aptamers for inflammatory biomolecules, as well as the inflammatory cells in NDDs. We also discussed the potential of short nucleotides for Aptamer-Based Targeted Brain Delivery through their unique features and modifications, as well as their ability to penetrate the blood-brain barrier. Moreover, the unprecedented opportunities and substantial challenges of using aptamers as therapeutic agents, such as drug efficacy, safety considerations, and pharmacokinetics, are also discussed. Taken together, this review assesses the potential of aptamers as a pioneering approach for target delivery to the CNS and the treatment of neuroinflammation and NDDs.

Cell death mechanisms induced by synergistic effects of halofuginone and artemisinin in colorectal cancer cells.

Our previous study found that the combination of halofuginone (HF) and artemisinin (ATS) synergistically arrest colorectal cancer (CRC) cells at the G1/G0 phase of the cell cycle; however, it remains unclear whether HF-ATS induces cell death. Here we report that HF-ATS synergistically induced caspase-dependent apoptosis in CRC cells. Specifically, both in vitro and in vivo experiments showed that HF or HF-ATS induces apoptosis via activation of caspase-9 and caspase-8 while only caspase-9 is involved in ATS-induced apoptosis. Furthermore, we found HF or HF-ATS induces autophagy; ATS can't induce autophagy until caspase-9 is blocked. Further analyzing the crosstalk between autophagic and caspase activation in CRC cells, we found autophagy is essential for activation of caspase-8, and ATS switches to activate capase-8 via induction of autophagy when caspase-9 is inhibited. When apoptosis is totally blocked, HF-ATS switches to induce autophagic cell death. This scenario was then confirmed in studies of chemoresistance CRC cells with defective apoptosis. Our results indicate that HF-ATS induces cell death via interaction between apoptosis and autophagy in CRC cells. These results highlight the value of continued investigation into the potential use of this combination in cancer therapy.

Hot and Cold Theory: A Personalized Medicine Approach.

The hot and cold theory is an important part of Traditional Medicines (TMs) which can be used in health care, disease prevention, diagnosis, and treatment purposes. However, little has been said about the material basis of the theory and how the hot and cold theory can be integrated with the conventional medicine. This article will summarize how the Hot and Cold Theory may help health care providers to personalize their treatment, as well as the material basis of the theory and its future prospects.

WHO Trial Registration Data Set (TRDS) extension for traditional Chinese medicine 2020: recommendations, explanation, and elaboration.

BACKGROUND: Although the WHO Trial Registration Data Set (TRDS) has been published for many years, the quality of clinical trial registrations with traditional Chinese medicine (TCM) is still not satisfactory, especially about the inadequate reporting on TCM interventions. The development of the WHO TRDS for TCM Extension 2020 (WHO TRDS-TCM 2020) aims to address this inadequacy. METHODS: A group of clinical experts, methodologists, epidemiologists, and editors has developed this WHO TRDS-TCM 2020 through a comprehensive process, including the baseline survey, draft of the initial items, three-round of Delphi survey, solicitation of comments, revision, and finalization. RESULTS: The WHO TRDS-TCM 2020 statement extends the latest version (V.1.3.1) of TRDS published in November 2017. The checklist includes 11 extended items (including subitems), namely Source(s) of Monetary or Material Support (Item 4), Scientific Title (Item 10a and 10b), Countries of Recruitment (Item 11), Health Condition(s) or Problem(s) Studied (Item 12), Intervention(s) (Item 13a, 13b and 13c), Key Inclusion and Exclusion Criteria (Item 14), Primary and Key Secondary Outcomes (Item 19 to 20), and Lay Summary (Item B1). For Item 13 (Interventions), three common TCM interventions--i.e., Chinese herbal medicine formulas, acupuncture and moxibustion-are elaborated. CONCLUSIONS: The group hopes that the WHO TRDS-TCM 2020 can improve the reporting quality and transparency of TCM trial registrations, assist registries in assessing the registration quality of TCM trials, and help readers understand TCM trial design.

Elevated extracellular matrix protein 1 in circulating extracellular vesicles supports breast cancer progression under obesity conditions.

The cargo content in small extracellular vesicles (sEVs) changes under pathological conditions. Our data shows that in obesity, extracellular matrix protein 1 (ECM1) protein levels are significantly increased in circulating sEVs, which is dependent on integrin-ß2. Knockdown of integrin-ß2 does not affect cellular ECM1 protein levels but significantly reduces ECM1 protein levels in the sEVs released by these cells. In breast cancer (BC), overexpressing ECM1 increases matrix metalloproteinase 3 (MMP3) and S100A/B protein levels. Interestingly, sEVs purified from high-fat diet-induced obesity mice (D-sEVs) deliver more ECM1 protein to BC cells compared to sEVs from control diet-fed mice. Consequently, BC cells secrete more ECM1 protein, which promotes cancer cell invasion and migration. D-sEVs treatment also significantly enhances ECM1-mediated BC metastasis and growth in mouse models, as evidenced by the elevated tumor levels of MMP3 and S100A/B. Our study reveals a mechanism and suggests sEV-based strategies for treating obesity-associated BC.

Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity.

Although long-read sequencing enables the generation of complete genomes for unculturable microbes, its high cost limits the widespread adoption of long-read sequencing in large-scale metagenomic studies. An alternative method is to assemble short-reads with long-range connectivity, which can be a cost-effective way to generate high-quality microbial genomes. Here, we develop Pangaea, a bioinformatic approach designed to enhance metagenome assembly using short-reads with long-range connectivity. Pangaea leverages connectivity derived from physical barcodes of linked-reads or virtual barcodes by aligning short-reads to long-reads. Pangaea utilizes a deep learning-based read binning algorithm to assemble co-barcoded reads exhibiting similar sequence contexts and abundances, thereby improving the assembly of high- and medium-abundance microbial genomes. Pangaea also leverages a multi-thresholding algorithm strategy to refine assembly for low-abundance microbes. We benchmark Pangaea on linked-reads and a combination of short- and long-reads from simulation data, mock communities and human gut metagenomes. Pangaea achieves significantly higher contig continuity as well as more near-complete metagenome-assembled genomes (NCMAGs) than the existing assemblers. Pangaea also generates three complete and circular NCMAGs on the human gut microbiomes.

Reporting characteristics and quality of randomized controlled trial protocols in traditional Chinese medicine: a cross-sectional study.

Objectives: The impact of the Standard Protocol Items: Recommendations for Interventional Trials of Traditional Chinese Medicine (SPIRIT-TCM) Extension 2018 statement on the reporting quality of randomized controlled trial (RCT) protocols in traditional Chinese medicine (TCM) is not clear. This review aimed to assess the reporting characteristics and quality of RCT protocols involving interventions such as Chinese herbal medicine formulas (CHMF), acupuncture, and moxibustion published in the last 3 years. Methods: We conducted an extensive search among multiple databases, including All EBM Reviews, Allied and Complementary Medicine (AMED), Embase, Ovid MEDLINE(R), PubMed, Web of Science, Google Scholar, and ClinicalTrials.gov for publications in English from 1 January 2020 to 10 August 2023. Two reviewers independently assessed the eligibility of the publications, extracted predetermined information, and evaluated the reporting based on the SPIRIT-TCM Extension 2018 checklist. Results: Of the 420 eligible protocols (comprising 163 studies on CHMF, 239 on acupuncture, and 18 on moxibustion), the average reporting compliance rate was only 35.4%. Approximately half of the assessed items fell into the category of poorly reported, demonstrating a compliance rate below 65%. Notably, reporting compliance in acupuncture and moxibustion interventional studies exhibited higher scores than compliance in CHMF studies. Conclusion: Continued, concerted, and coordinated efforts are required by journals, editors, reviewers, and investigators to improve the application and promotion of the SPIRIT-TCM Extension 2018 reporting guideline.

Evaluation of compliance of CONSORT-CHM formula 2017 in randomized controlled trials of Chinese herbal medicine formulas: protocol of a five-year review.

Background: The CONSORT Extension for Chinese Herbal Medicine Formula 2017 (CONSORT-CHM Formula 2017) has established a reporting standard for randomized controlled trials (RCTs) of Chinese Herbal Medicine Formula (CHMF) interventions; however, its adherence and implications for the design and execution of study design remain ambiguous. It is necessary to evaluate the level of compliance with the CONSORT-CHM Formula 2017 in RCTs conducted over the past 5 years, and to determine the reporting quality of clinical trials in this field. Methods: First, a systematic search is conducted for RCTs on CHMF in EBM Reviews, Allied and Complementary Medicine (AMED), Embase, Ovid-MEDLINE(R), Wanfang data, China National Knowledge Infrastructure (CNKI), VIP Chinese Medical Journal Database (VIP) and Chinese Biomedical Literature (CBM) database, that encompassed CHMF interventional RCTs published from 1 January 2018 to 8 June 2022, with language restriction to English or Chinese. Second, a descriptive analysis will be performed regarding the study design and general characteristics of the included trials. Third, for the quality assessment, we have subdivided the CONSORT-CHM Formula 2017 checklist (consisting of 22 extended items) into a total of 42 sub-questions to facilitate scoring, with a specific focus on the description, quality control, and safety assessment of CHMF interventions. Professional training and a pilot test on 100 randomly selected articles will be provided for all reviewers. Throughout this process, a standard operating procedure (SOP) for quality assessment will be developed to ensure consistency. Each item will be assessed by two reviewers in a paired back-to-back manner, and the compliance rate will be calculated to assess inter-rater agreement. Discussion: This review will identify the current reporting characteristics and quality of CHMF interventional studies and further evaluate the impact of CONSORT-CHM Formula 2017. The results may provide suggestions for future application or promotion of the guideline. Registration: The study has been registered on Open Science Framework (https://osf.io/xpn7f).

Artesunate treats obesity in male mice and non-human primates through GDF15/GFRAL signalling axis.

Obesity, a global health challenge, is a major risk factor for multiple life-threatening diseases, including diabetes, fatty liver, and cancer. There is an ongoing need to identify safe and tolerable therapeutics for obesity management. Herein, we show that treatment with artesunate, an artemisinin derivative approved by the FDA for the treatment of severe malaria, effectively reduces body weight and improves metabolic profiles in preclinical models of obesity, including male mice with overnutrition-induced obesity and male cynomolgus macaques with spontaneous obesity, without inducing nausea and malaise. Artesunate promotes weight loss and reduces food intake in obese mice and cynomolgus macaques by increasing circulating levels of Growth Differentiation Factor 15 (GDF15), an appetite-regulating hormone with a brainstem-restricted receptor, the GDNF family receptor α-like (GFRAL). Mechanistically, artesunate induces the expression of GDF15 in multiple organs, especially the liver, in mice through a C/EBP homologous protein (CHOP)-directed integrated stress response. Inhibition of GDF15/GFRAL signalling by genetic ablation of GFRAL or tissue-specific knockdown of GDF15 abrogates the anti-obesity effect of artesunate in mice with diet-induced obesity, suggesting that artesunate controls bodyweight and appetite in a GDF15/GFRAL signalling-dependent manner. These data highlight the therapeutic benefits of artesunate in the treatment of obesity and related comorbidities.

The molecular insights of bile acid homeostasis in host diseases.

Bile acids (BAs) function as detergents promoting nutrient absorption and as hormones regulating nutrient metabolism. Most BAs are key regulatory factors of physiological activities, which are involved in the regulation of glucose, lipid, and drug metabolisms. Hepatic and intestinal diseases have close connections with the systemic cycling disorders of BAs. The abnormal in BA absorption came up with overmuch BAs could be involved in the pathophysiology of liver and bowel and metabolic disorders such as fatty liver diseases and inflammatory bowel diseases. The primary BAs (PBAs), which are synthesized in the liver, can be transformed into the secondary BAs (SBAs) by gut microbiota. The transformation processes are tightly associated with the gut microbiome and the host endogenous metabolism. The BA biosynthesis gene cluster bile-acid-inducible operon is essential for modulating BA pool, gut microbiome composition, and the onset of intestinal inflammation. This forms a bidirectional interaction between the host and its gut symbiotic ecosystem. The subtle changes in the composition and abundance of BAs perturb the host physiological and metabolic activity. Therefore, maintaining the homeostasis of BAs pool contributes to the balance of the body's physiological and metabolic system. Our review aims to dissect the molecular mechanisms underlying the BAs homeostasis, assess the key factors sustaining the homeostasis and the role of BA acting on host diseases. By linking the BAs metabolic disorders and their associated diseases, we illustrate the effects of BAs homeostasis on health and potential clinical interventions can be taken under the latest research findings.