Chimeric contribution of human extended pluripotent stem cells to monkey embryos ex vivo.

Interspecies chimera formation with human pluripotent stem cells (hPSCs) represents a necessary alternative to evaluate hPSC pluripotency in vivo and might constitute a promising strategy for various regenerative medicine applications, including the generation of organs and tissues for transplantation. Studies using mouse and pig embryos suggest that hPSCs do not robustly contribute to chimera formation in species evolutionarily distant to humans. We studied the chimeric competency of human extended pluripotent stem cells (hEPSCs) in cynomolgus monkey (Macaca fascicularis) embryos cultured ex vivo. We demonstrate that hEPSCs survived, proliferated, and generated several peri- and early post-implantation cell lineages inside monkey embryos. We also uncovered signaling events underlying interspecific crosstalk that may help shape the unique developmental trajectories of human and monkey cells within chimeric embryos. These results may help to better understand early human development and primate evolution and develop strategies to improve human chimerism in evolutionarily distant species.

Modeling Rett Syndrome Using TALEN-Edited MECP2 Mutant Cynomolgus Monkeys.

Gene-editing technologies have made it feasible to create nonhuman primate models for human genetic disorders. Here, we report detailed genotypes and phenotypes of TALEN-edited MECP2 mutant cynomolgus monkeys serving as a model for a neurodevelopmental disorder, Rett syndrome (RTT), which is caused by loss-of-function mutations in the human MECP2 gene. Male mutant monkeys were embryonic lethal, reiterating that RTT is a disease of females. Through a battery of behavioral analyses, including primate-unique eye-tracking tests, in combination with brain imaging via MRI, we found a series of physiological, behavioral, and structural abnormalities resembling clinical manifestations of RTT. Moreover, blood transcriptome profiling revealed that mutant monkeys resembled RTT patients in immune gene dysregulation. Taken together, the stark similarity in phenotype and/or endophenotype between monkeys and patients suggested that gene-edited RTT founder monkeys would be of value for disease mechanistic studies as well as development of potential therapeutic interventions for RTT.

Generation of gene-modified cynomolgus monkey via Cas9/RNA-mediated gene targeting in one-cell embryos.

Monkeys serve as important model species for studying human diseases and developing therapeutic strategies, yet the application of monkeys in biomedical researches has been significantly hindered by the difficulties in producing animals genetically modified at the desired target sites. Here, we first applied the CRISPR/Cas9 system, a versatile tool for editing the genes of different organisms, to target monkey genomes. By coinjection of Cas9 mRNA and sgRNAs into one-cell-stage embryos, we successfully achieve precise gene targeting in cynomolgus monkeys. We also show that this system enables simultaneous disruption of two target genes (Ppar-γ and Rag1) in one step, and no off-target mutagenesis was detected by comprehensive analysis. Thus, coinjection of one-cell-stage embryos with Cas9 mRNA and sgRNAs is an efficient and reliable approach for gene-modified cynomolgus monkey generation.

AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences.

The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen-antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen-antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody-antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.

Sodium-Glucose Cotransporter-2 Inhibitors and Major Adverse Cardiovascular Outcomes: A SMART-C Collaborative Meta-Analysis.

BACKGROUND: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) consistently improve heart failure and kidney-related outcomes; however, effects on major adverse cardiovascular events (MACE) across different patient populations are less clear. METHODS: This was a collaborative trial-level meta-analysis from the SGLT2i Meta-analysis Cardio-Renal Trialists Consortium, which includes all phase 3, placebo-controlled, outcomes trials of SGLT2i across 3 patient populations (patients with diabetes at high risk for atherosclerotic cardiovascular disease, heart failure [HF], or chronic kidney disease). The outcomes of interest were MACE (composite of cardiovascular death, myocardial infarction , or stroke), individual components of MACE (inclusive of fatal and nonfatal events), all-cause mortality, and death subtypes. Effect estimates for SGLT2i versus placebo were meta-analyzed across trials and examined across key subgroups (established atherosclerotic cardiovascular disease, previous myocardial infarction, diabetes, previous HF, albuminuria, chronic kidney disease stages, and risk groups). RESULTS: A total of 78 607 patients across 11 trials were included: 42 568 (54.2%), 20 725 (26.4%), and 15 314 (19.5%) were included from trials of patients with diabetes at high risk for atherosclerotic cardiovascular disease, HF, or chronic kidney disease, respectively. SGLT2i reduced the rate of MACE by 9% (hazard ration [HR], 0.91 [95% CI, 0.87-0.96], P<0.0001) with a consistent effect across all 3 patient populations (I2=0%) and across all key subgroups. This effect was primarily driven by a reduction in cardiovascular death (HR, 0.86 [95% CI, 0.81-0.92], P<0.0001), with no significant effect for myocardial infarction in the overall population (HR, 0.95 [95% CI, 0.87-1.04], P=0.29), and no effect on stroke (HR, 0.99 [95% CI, 0.91-1.07], P=0.77). The benefit for cardiovascular death was driven primarily by reductions in HF death and sudden cardiac death (HR, 0.68 [95% CI, 0.46-1.02] and HR, 0.86 [95% CI, 0.78-0.95], respectively) and was generally consistent across subgroups, with the possible exception of being more apparent in those with albuminuria (Pinteraction=0.02). CONCLUSIONS: SGLT2i reduce the risk of MACE across a broad range of patients irrespective of atherosclerotic cardiovascular disease, diabetes, kidney function, or other major clinical characteristics at baseline. This effect is driven primarily by a reduction of cardiovascular death, particularly HF death and sudden cardiac death, without a significant effect on myocardial infarction in the overall population, and no effect on stroke. These data may help inform selection for SGLT2i therapies across the spectrum of cardiovascular-kidney-metabolic disease.

ML-PLIC: a web platform for characterizing protein-ligand interactions and developing machine learning-based scoring functions.

Cracking the entangling code of protein-ligand interaction (PLI) is of great importance to structure-based drug design and discovery. Different physical and biochemical representations can be used to describe PLI such as energy terms and interaction fingerprints, which can be analyzed by machine learning (ML) algorithms to create ML-based scoring functions (MLSFs). Here, we propose the ML-based PLI capturer (ML-PLIC), a web platform that automatically characterizes PLI and generates MLSFs to identify the potential binders of a specific protein target through virtual screening (VS). ML-PLIC comprises five modules, including Docking for ligand docking, Descriptors for PLI generation, Modeling for MLSF training, Screening for VS and Pipeline for the integration of the aforementioned functions. We validated the MLSFs constructed by ML-PLIC in three benchmark datasets (Directory of Useful Decoys-Enhanced, Active as Decoys and TocoDecoy), demonstrating accuracy outperforming traditional docking tools and competitive performance to the deep learning-based SF, and provided a case study of the Serine/threonine-protein kinase WEE1 in which MLSFs were developed by using the ML-based VS pipeline in ML-PLIC. Underpinning the latest version of ML-PLIC is a powerful platform that incorporates physical and biological knowledge about PLI, leveraging PLI characterization and MLSF generation into the design of structure-based VS pipeline. The ML-PLIC web platform is now freely available at http://cadd.zju.edu.cn/plic/.

Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?

Binding affinity prediction largely determines the discovery efficiency of lead compounds in drug discovery. Recently, machine learning (ML)-based approaches have attracted much attention in hopes of enhancing the predictive performance of traditional physics-based approaches. In this study, we evaluated the impact of structural dynamic information on the binding affinity prediction by comparing the models trained on different dimensional descriptors, using three targets (i.e. JAK1, TAF1-BD2 and DDR1) and their corresponding ligands as the examples. Here, 2D descriptors are traditional ECFP4 fingerprints, 3D descriptors are the energy terms of the Smina and NNscore scoring functions and 4D descriptors contain the structural dynamic information derived from the trajectories based on molecular dynamics (MD) simulations. We systematically investigate the MD-refined binding affinity prediction performance of three classical ML algorithms (i.e. RF, SVR and XGB) as well as two common virtual screening methods, namely Glide docking and MM/PBSA. The outcomes of the ML models built using various dimensional descriptors and their combinations reveal that the MD refinement with the optimized protocol can improve the predictive performance on the TAF1-BD2 target with considerable structural flexibility, but not for the less flexible JAK1 and DDR1 targets, when taking docking poses as the initial structure instead of the crystal structures. The results highlight the importance of the initial structures to the final performance of the model through conformational analysis on the three targets with different flexibility.

Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency.

Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening.

Molecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.

An integrative systems genetic analysis of mammalian lipid metabolism.

Dysregulation of lipid homeostasis is a precipitating event in the pathogenesis and progression of hepatosteatosis and metabolic syndrome. These conditions are highly prevalent in developed societies and currently have limited options for diagnostic and therapeutic intervention. Here, using a proteomic and lipidomic-wide systems genetic approach, we interrogated lipid regulatory networks in 107 genetically distinct mouse strains to reveal key insights into the control and network structure of mammalian lipid metabolism. These include the identification of plasma lipid signatures that predict pathological lipid abundance in the liver of mice and humans, defining subcellular localization and functionality of lipid-related proteins, and revealing functional protein and genetic variants that are predicted to modulate lipid abundance. Trans-omic analyses using these datasets facilitated the identification and validation of PSMD9 as a previously unknown lipid regulatory protein. Collectively, our study serves as a rich resource for probing mammalian lipid metabolism and provides opportunities for the discovery of therapeutic agents and biomarkers in the setting of hepatic lipotoxicity.

Aggregation-Enabled Electrochemistry in Confined Nanopore Capable of Complementary Faradaic and Non-Faradaic Detection.

Electrochemistry that empowers innovative nanoscopic analysis has long been pursued. Here, the concept of aggregation-enabled electrochemistry (AEE) in a confined nanopore is proposed and devised by reactive oxygen species (ROS)-responsive aggregation of CdS quantum dots (QDs) within a functional nanopipette. Complementary Faradaic and non-Faradaic operations of the CdS QDs aggregate could be conducted to simultaneously induce the signal-on of the photocurrents and the signal-off of the ionic signals. Such a rationale permits the cross-checking of the mutually corroborated signals and thus delivers more reliable results for single-cell ROS analysis. Combined with the rich biomatter-light interplay, the concept of AEE can be extended to other stimuli-responsive aggregations for electrochemical innovations.

Genome-Wide Identification of Gene Loss Events Suggests Loss Relics as a Potential Source of Functional lncRNAs in Humans.

Gene loss is a prevalent source of genetic variation in genome evolution. Calling loss events effectively and efficiently is a critical step for systematically characterizing their functional and phylogenetic profiles genome wide. Here, we developed a novel pipeline integrating orthologous inference and genome alignment. Interestingly, we identified 33 gene loss events that give rise to evolutionarily novel long noncoding RNAs (lncRNAs) that show distinct expression features and could be associated with various functions related to growth, development, immunity, and reproduction, suggesting loss relics as a potential source of functional lncRNAs in humans. Our data also demonstrated that the rates of protein gene loss are variable among different lineages with distinct functional biases.

A methodology for gene level omics-WAS integration identifies genes influencing traits associated with cardiovascular risks: the Long Life Family Study.

The Long Life Family Study (LLFS) enrolled 4953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8 × 10-7), 29 of which replicated in the Framingham Heart Study (FHS) cohort. Notably, 20 of the 29 replicated genes do not have a previously known trait-associated variant in the GWAS Catalog within 50 kb. Thirteen modules in Protein-Protein Interaction (PPI) networks are significantly enriched in genes with low meta-analysis p-values for at least one trait, three of which are replicated in the FHS cohort. The functional annotation of genes in these modules showed a significant over-representation of trait-related biological processes including sterol transport, protein-lipid complex remodeling, and immune response regulation. Among major findings, our results suggest a role of triglyceride-associated and mast-cell functional genes FCER1A, MS4A2, GATA2, HDC, and HRH4 in atherosclerosis risks. Our findings also suggest that lower expression of ATG2A, a gene we found to be associated with BMI, may be both a cause and consequence of obesity. Finally, our results suggest that ENPP3 may play an intermediary role in triglyceride-induced inflammation. Our pipeline is freely available and implemented in the Nextflow workflow language, making it easily runnable on any compute platform ( https://nf-co.re/omicsgenetraitassociation ).

Genome-wide characterization of LEA gene family reveals a positive role of BnaA.LEA6.a in freezing tolerance in rapeseed (Brassica napus L.).

BACKGROUND: Freezing stress is one of the major abiotic stresses that causes extensive damage to plants. LEA (Late embryogenesis abundant) proteins play a crucial role in plant growth, development, and abiotic stress. However, there is limited research on the function of LEA genes in low-temperature stress in Brassica napus (rapeseed). RESULTS: Total 306 potential LEA genes were identified in B. rapa (79), B. oleracea (79) and B. napus (148) and divided into eight subgroups. LEA genes of the same subgroup had similar gene structures and predicted subcellular locations. Cis-regulatory elements analysis showed that the promoters of BnaLEA genes rich in cis-regulatory elements related to various abiotic stresses. Additionally, RNA-seq and real-time PCR results indicated that the majority of BnaLEA family members were highly expressed in senescent tissues of rapeseed, especially during late stages of seed maturation, and most BnaLEA genes can be induced by salt and osmotic stress. Interestingly, the BnaA.LEA6.a and BnaC.LEA6.a genes were highly expressed across different vegetative and reproductive organs during different development stages, and showed strong responses to salt, osmotic, and cold stress, particularly freezing stress. Further analysis showed that overexpression of BnaA.LEA6.a increased the freezing tolerance in rapeseed, as evidenced by lower relative electrical leakage and higher survival rates compared to the wild-type (WT) under freezing treatment. CONCLUSION: This study is of great significance for understanding the functions of BnaLEA genes in freezing tolerance in rapeseed and offers an ideal candidate gene (BnaA.LEA6.a) for molecular breeding of freezing-tolerant rapeseed cultivars.

EHD1 impaired decidualization of endometrial stromal cells in recurrent implantation failure: role of SENP1 in modulating progesterone receptor signalling†.

Recurrent implantation failure (RIF) patients exhibit poor endometrial receptivity and abnormal decidualization with reduced effectiveness and exposure to progesterone, which is an intractable clinical problem. However, the associated molecular mechanisms remain elusive. We found that EH domain containing 1 (EHD1) expression was abnormally elevated in RIF and linked to aberrant endometrial decidualization. Here we show that EHD1 overexpressed in human endometrial stromal cells significantly inhibited progesterone receptor (PGR) transcriptional activity and the responsiveness to progesterone. No significant changes were observed in PGR mRNA levels, while a significant decrease in progesterone receptor B (PRB) protein level. Indeed, EHD1 binds to the PRB protein, with the K388 site crucial for this interaction. Overexpression of EHD1 promotes the SUMOylation and ubiquitination of PRB, leading to the degradation of the PRB protein. Supplementation with the de-SUMOylated protease SENP1 ameliorated EHD1-repressed PRB transcriptional activity. To establish a functional link between EHD1 and the PGR signalling pathway, sg-EHD1 were utilized to suppress EHD1 expression in HESCs from RIF patients. A significant increase in the expression of prolactin and insulin-like growth factor-binding protein 1 was detected by interfering with the EHD1. In conclusion, we demonstrated that abnormally high expression of EHD1 in endometrial stromal cells attenuated the activity of PRB associated with progesterone resistance in a subset of women with RIF.

fastDRH: a webserver to predict and analyze protein-ligand complexes based on molecular docking and MM/PB(GB)SA computation.

Predicting the native or near-native binding pose of a small molecule within a protein binding pocket is an extremely important task in structure-based drug design, especially in the hit-to-lead and lead optimization phases. In this study, fastDRH, a free and open accessed web server, was developed to predict and analyze protein-ligand complex structures. In fastDRH server, AutoDock Vina and AutoDock-GPU docking engines, structure-truncated MM/PB(GB)SA free energy calculation procedures and multiple poses based per-residue energy decomposition analysis were well integrated into a user-friendly and multifunctional online platform. Benefit from the modular architecture, users can flexibly use one or more of three features, including molecular docking, docking pose rescoring and hotspot residue prediction, to obtain the key information clearly based on a result analysis panel supported by 3Dmol.js and Apache ECharts. In terms of protein-ligand binding mode prediction, the integrated structure-truncated MM/PB(GB)SA rescoring procedures exhibit a success rate of >80% in benchmark, which is much better than the AutoDock Vina (~70%). For hotspot residue identification, our multiple poses based per-residue energy decomposition analysis strategy is a more reliable solution than the one using only a single pose, and the performance of our solution has been experimentally validated in several drug discovery projects. To summarize, the fastDRH server is a useful tool for predicting the ligand binding mode and the hotspot residue of protein for ligand binding. The fastDRH server is accessible free of charge at http://cadd.zju.edu.cn/fastdrh/.

Quantitative model suggests both intrinsic and contextual features contribute to the transcript coding ability determination in cells.

Gene transcription and protein translation are two key steps of the 'central dogma.' It is still a major challenge to quantitatively deconvolute factors contributing to the coding ability of transcripts in mammals. Here, we propose ribosome calculator (RiboCalc) for quantitatively modeling the coding ability of RNAs in human genome. In addition to effectively predicting the experimentally confirmed coding abundance via sequence and transcription features with high accuracy, RiboCalc provides interpretable parameters with biological information. Large-scale analysis further revealed a number of transcripts with a variety of coding ability for distinct types of cells (i.e. context-dependent coding transcripts), suggesting that, contrary to conventional wisdom, a transcript's coding ability should be modeled as a continuous spectrum with a context-dependent nature.

White adipose tissue-derived small extracellular vesicles: A new potential therapeutic reagent for accelerating diabetic wound healing.

Small extracellular vesicles (sEVs) from adipose-derived stem cells (ADSCs) have gained great attention and have been widely used in cell-free therapies for treating diabetic non-healing wounds in recent years. However, further clinical application of ADSC-sEVs have been limited due to their unsolvable defects, including cumbersome extraction procedure, high cost, low yield, etc. Thus, we urgently need to find one therapeutic reagent that could not only accelerate diabetic wound healing as ADSC-sEVs but also overcome these shortcomings. As the extraction process of adipose tissue-derived sEVs (AT-sEVs) is quite simple and labor saving, we put our focus on the efficiencies of white adipose tissue-derived sEVs (WAT-sEVs) and brown adipose tissue-derived sEVs (BAT-sEVs) in diabetic wound repair. After successfully isolating WAT-sEVs and BAT-sEVs by ultracentrifugation, we thoroughly characterized them and compared their diabetic wound healing capabilities both in vitro and in vivo. According to our study, AT-sEVs possess similar competence in diabetic wound healing as compared with ADSC-sEVs. While the effect of BAT-sEVs is not as stable as WAT-sEVs and ADSC-sEVs, the repair efficiency is also slightly lower than the other two sEVs in some cases. In summary, we are the first to discover that WAT-sEVs show great potential in diabetic wound repair. With advantages that are specific to tissue-derived sEVs (Ti-sEVs) such as time- and cost-saving, high-yield, and simple isolation procedure, we believe WAT-sEVs could serve as a novel reliable cell-free therapy for clinical diabetic wound treatment.

Faecalibacterium prausnitzii Attenuates CKD via Butyrate-Renal GPR43 Axis.

BACKGROUND: Despite available clinical management strategies, chronic kidney disease (CKD) is associated with severe morbidity and mortality worldwide, which beckons new solutions. Host-microbial interactions with a depletion of Faecalibacterium prausnitzii in CKD are reported. However, the mechanisms about if and how F prausnitzii can be used as a probiotic to treat CKD remains unknown. METHODS: We evaluated the microbial compositions in 2 independent CKD populations for any potential probiotic. Next, we investigated if supplementation of such probiotic in a mouse CKD model can restore gut-renal homeostasis as monitored by its effects on suppression on renal inflammation, improvement in gut permeability and renal function. Last, we investigated the molecular mechanisms underlying the probiotic-induced beneficial outcomes. RESULTS: We observed significant depletion of Faecalibacterium in the patients with CKD in both Western (n=283) and Eastern populations (n=75). Supplementation of F prausnitzii to CKD mice reduced renal dysfunction, renal inflammation, and lowered the serum levels of various uremic toxins. These are coupled with improved gut microbial ecology and intestinal integrity. Moreover, we demonstrated that the beneficial effects in kidney induced by F prausnitzii-derived butyrate were through the GPR (G protein-coupled receptor)-43. CONCLUSIONS: Using a mouse CKD model, we uncovered a novel beneficial role of F prausnitzii in the restoration of renal function in CKD, which is, at least in part, attributed to the butyrate-mediated GPR-43 signaling in the kidney. Our study provides the necessary foundation to harness the therapeutic potential of F prausnitzii for ameliorating CKD.