Complement in breast milk modifies offspring gut microbiota to promote infant health.

Breastfeeding offers demonstrable benefits to newborns and infants by providing nourishment and immune protection and by shaping the gut commensal microbiota. Although it has been appreciated for decades that breast milk contains complement components, the physiological relevance of complement in breast milk remains undefined. Here, we demonstrate that weanling mice fostered by complement-deficient dams rapidly succumb when exposed to murine pathogen Citrobacter rodentium (CR), whereas pups fostered on complement-containing milk from wild-type dams can tolerate CR challenge. The complement components in breast milk were shown to directly lyse specific members of gram-positive gut commensal microbiota via a C1-dependent, antibody-independent mechanism, resulting in the deposition of the membrane attack complex and subsequent bacterial lysis. By selectively eliminating members of the commensal gut community, complement components from breast milk shape neonate and infant gut microbial composition to be protective against environmental pathogens such as CR.

Common Disease Is More Complex Than Implied by the Core Gene Omnigenic Model.

The evidence that most adult-onset common diseases have a polygenic genetic architecture fully consistent with robust biological systems supported by multiple back-up mechanisms is now overwhelming. In this context, we consider the recent "omnigenic" or "core genes" model. A key assumption of the model is that there is a relatively small number of core genes relevant to any disease. While intuitively appealing, this model may underestimate the biological complexity of common disease, and therefore, the goal to discover core genes should not guide experimental design. We consider other implications of polygenicity, concluding that a focus on patient stratification is needed to achieve the goals of precision medicine.

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19.

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte-macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).

LARP7-Mediated U6 snRNA Modification Ensures Splicing Fidelity and Spermatogenesis in Mice.

U6 snRNA, as an essential component of the catalytic core of the pre-mRNA processing spliceosome, is heavily modified post-transcriptionally, with 2'-O-methylation being most common. The role of these modifications in pre-mRNA splicing as well as their physiological function in mammals have remained largely unclear. Here we report that the La-related protein LARP7 functions as a critical cofactor for 2'-O-methylation of U6 in mouse male germ cells. Mechanistically, LARP7 promotes U6 loading onto box C/D snoRNP, facilitating U6 2'-O-methylation by box C/D snoRNP. Importantly, ablation of LARP7 in the male germline causes defective U6 2'-O-methylation, massive alterations in pre-mRNA splicing, and spermatogenic failure in mice, which can be rescued by ectopic expression of wild-type LARP7 but not an U6-loading-deficient mutant LARP7. Our data uncover a novel role of LARP7 in regulating U6 2'-O-methylation and demonstrate the functional requirement of such modification for splicing fidelity and spermatogenesis in mice.

From genetic associations to genes: methods, applications, and challenges.

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice.

Bio-catalyzed oxidation self-charging zinc-polymer batteries.

Oxidation self-charging batteries have emerged with the demand for powering electronic devices around the clock. The low efficiency of self-charging has been the key challenge at present. Here, a more efficient autoxidation self-charging mechanism is realized by introducing hemoglobin (Hb) as a positive electrode additive in the polyaniline (PANI)-zinc battery system. The heme acts as a catalyst that reduces the energy barrier of the autoxidation reaction by regulating the charge and spin state of O2. To realize self-charging, the adsorbed O2 molecules capture electrons of the reduced (discharged state) PANI, leading to the desorption of zinc ions and the oxidation of PANI to complete self-charging. The battery can discharge for 12 min (0.5 C) after 50 self-charging/discharge cycles, while there is nearly no discharge capacity in the absence of Hb. This biology-inspired electronic regulation strategy may inspire new ideas to boost the performance of self-charging batteries.

Superior sodiophilicity and molecule crowding of crown ether boost the electrochemical performance of all-climate sodium-ion batteries.

Sodium-ion batteries (SIBs) as one of the promising alternatives to lithium-ion batteries have achieved remarkable progress in the past. However, the all-climate performance is still very challenging for SIBs. Herein, 15-Crown-5 (15-C-5) is screened as an electrolyte additive from a number of ether molecules theoretically. The good sodiophilicity, high molecule rigidity, and bulky size enable it to reshape the solvation sheath and promote the anion engagement in the solvated structures by molecule crowding. This change also enhances Na-ion transfer, inhibits side reactions, and leads to a thin and robust solid-electrolyte interphase. Furthermore, the electrochemical stability and operating temperature windows of the electrolyte are extended. These profits improve the electrochemical performance of SIBs in all climates, much better than the case without 15-C-5. This improvement is also adopted to µ-Sn, µ-Bi, hard carbon, and MoS2. This work opens a door to prioritize the potential molecules in theory for advanced electrolytes.

mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data.

Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of χ2 statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects, e.g., when the product of two SNP effects and the linkage disequilibrium (LD) correlation is negative. Here, we introduce "mBAT-combo," a set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is common in complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizophrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. The framework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures.

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.

Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key factor in comprehending the mechanisms underlying gene regulation. Non-coding variants, constituting over 90% of all variants, have garnered increasing attention in recent years. The exploration of gene variant impacts and regulatory mechanisms has spurred the development of various deep learning approaches, providing new insights into the global regulatory landscape through the analysis of extensive genetic data. Here, we provide a comprehensive overview of the development of the non-coding variants models based on bulk and single-cell sequencing data and their model-based interpretation and downstream tasks. This review delineates the popular sequencing technologies for epigenetic profiling and deep learning approaches for discerning the effects of non-coding variants. Additionally, we summarize the limitations of current approaches in variant effect prediction research and outline opportunities for improvement. We anticipate that our study will offer a practical and useful guide for the bioinformatic community to further advance the unraveling of genetic variant effects.

Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework.

High-throughput DNA sequencing technologies decode tremendous amounts of microbial protein-coding gene sequences. However, accurately assigning protein functions to novel gene sequences remain a challenge. To this end, we developed FunGeneTyper, an extensible framework with two new deep learning models (i.e., FunTrans and FunRep), structured databases, and supporting resources for achieving highly accurate (Accuracy > 0.99, F1-score > 0.97) and fine-grained classification of antibiotic resistance genes (ARGs) and virulence factor genes. Using an experimentally confirmed dataset of ARGs comprising remote homologous sequences as the test set, our framework achieves by-far-the-best performance in the discovery of new ARGs from human gut (F1-score: 0.6948), wastewater (0.6072), and soil (0.5445) microbiomes, beating the state-of-the-art bioinformatics tools and sequence alignment-based (F1-score: 0.0556-0.5065) and domain-based (F1-score: 0.2630-0.5224) annotation approaches. Furthermore, our framework is implemented as a lightweight, privacy-preserving, and plug-and-play neural network module, facilitating its versatility and accessibility to developers and users worldwide. We anticipate widespread utilization of FunGeneTyper (https://github.com/emblab-westlake/FunGeneTyper) for precise classification of protein-coding gene functions and the discovery of numerous valuable enzymes. This advancement will have a significant impact on various fields, including microbiome research, biotechnology, metagenomics, and bioinformatics.

FBXO3 stabilizes USP4 and Twist1 to promote PI3K-mediated breast cancer metastasis.

Tumor metastasis is the major cause of breast cancer morbidity and mortality. It has been reported that the F-box protein FBXO3 functions as an E3 ubiquitin ligase in regulating various biological processes, including host autoimmune, antiviral innate immunity, and inflammatory response. However, the role of FBXO3 in tumor metastasis remains elusive. We have previously shown that ΔNp63α is a common inhibitory target in oncogene-induced cell motility and tumor metastasis. In this study, we show that FBXO3 plays a vital role in PI3K-mediated breast cancer metastasis independent of its E3 ligase activity and ΔNp63α in breast cancer cells and in mouse. FBXO3 can bind to and stabilize USP4, leading to Twist1 protein stabilization and increased breast cancer cell migration and tumor metastasis. Mechanistically, FBXO3 disrupts the interaction between USP4 and aspartyl aminopeptidase (DNPEP), thereby protecting USP4 from DNPEP-mediated degradation. Furthermore, p110αH1047R facilitates the phosphorylation and stabilization of FBXO3 in an ERK1-dependent manner. Knockdown of either FBXO3 or USP4 leads to significant inhibition of PI3K-induced breast cancer metastasis. Clinically, elevated expression of p110α/FBXO3/USP4/Twist1 is associated with poor overall survival (OS) and recurrence-free survival (RFS) of breast cancer patients. Taken together, this study reveals that the FBXO3-USP4-Twist1 axis is pivotal in PI3K-mediated breast tumor metastasis and that FBXO3/USP4 may be potential therapeutic targets for breast cancer treatment.

Deficiency of Transcription Factor Sp1 Contributes to Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is the most prevalent monogenic heart disorder. However, the pathogenesis of HCM, especially its nongenetic mechanisms, remains largely unclear. Transcription factors are known to be involved in various biological processes including cell growth. We hypothesized that SP1 (specificity protein 1), the first purified TF in mammals, plays a role in the cardiomyocyte growth and cardiac hypertrophy of HCM. METHODS: Cardiac-specific conditional knockout of Sp1 mice were constructed to investigate the role of SP1 in the heart. The echocardiography, histochemical experiment, and transmission electron microscope were performed to analyze the cardiac phenotypes of cardiac-specific conditional knockout of Sp1 mice. RNA sequencing, chromatin immunoprecipitation sequencing, and adeno-associated virus experiments in vivo were performed to explore the downstream molecules of SP1. To examine the therapeutic effect of SP1 on HCM, an SP1 overexpression vector was constructed and injected into the mutant allele of Myh6 R404Q/+ (Myh6 c. 1211C>T) HCM mice. The human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from a patient with HCM were used to detect the potential therapeutic effects of SP1 in human HCM. RESULTS: The cardiac-specific conditional knockout of Sp1 mice developed a typical HCM phenotype, displaying overt myocardial hypertrophy, interstitial fibrosis, and disordered myofilament. In addition, Sp1 knockdown dramatically increased the cell area of hiPSC-CMs and caused intracellular myofibrillar disorganization, which was similar to the hypertrophic cardiomyocytes of HCM. Mechanistically, Tuft1 was identified as the key target gene of SP1. The hypertrophic phenotypes induced by Sp1 knockdown in both hiPSC-CMs and mice could be rescued by TUFT1 (tuftelin 1) overexpression. Furthermore, SP1 overexpression suppressed the development of HCM in the mutant allele of Myh6 R404Q/+ mice and also reversed the hypertrophic phenotype of HCM hiPSC-CMs. CONCLUSIONS: Our study demonstrates that SP1 deficiency leads to HCM. SP1 overexpression exhibits significant therapeutic effects on both HCM mice and HCM hiPSC-CMs, suggesting that SP1 could be a potential intervention target for HCM.

Larger cerebral cortex is genetically correlated with greater frontal area and dorsal thickness.

Human cortical expansion has occurred non-uniformly across the brain. We assessed the genetic architecture of cortical global expansion and regionalization by comparing two sets of genome-wide association studies of 24 cortical regions with and without adjustment for global measures (i.e., total surface area, mean cortical thickness) using a genetically informed parcellation in 32,488 adults. We found 393 and 756 significant loci with and without adjusting for globals, respectively, where 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recovered loci associated with global measures. Genetic factors that contribute to total surface area of the cortex particularly expand anterior/frontal regions, whereas those contributing to thicker cortex predominantly increase dorsal/frontal-parietal thickness. Interactome-based analyses revealed significant genetic overlap of global and dorsolateral prefrontal modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.

A Prelimbic Cortex-Thalamus Circuit Bidirectionally Regulates Innate and Stress-Induced Anxiety-Like Behavior.

Anxiety-related disorders respond to cognitive behavioral therapies, which involved the medial prefrontal cortex (mPFC). Previous studies have suggested that subregions of the mPFC have different and even opposite roles in regulating innate anxiety. However, the specific causal targets of their descending projections in modulating innate anxiety and stress-induced anxiety have yet to be fully elucidated. Here, we found that among the various downstream pathways of the prelimbic cortex (PL), a subregion of the mPFC, PL-mediodorsal thalamic nucleus (MD) projection, and PL-ventral tegmental area (VTA) projection exhibited antagonistic effects on anxiety-like behavior, while the PL-MD projection but not PL-VTA projection was necessary for the animal to guide anxiety-related behavior. In addition, MD-projecting PL neurons bidirectionally regulated remote but not recent fear memory retrieval. Notably, restraint stress induced high-anxiety state accompanied by strengthening the excitatory inputs onto MD-projecting PL neurons, and inhibiting PL-MD pathway rescued the stress-induced anxiety. Our findings reveal that the activity of PL-MD pathway may be an essential factor to maintain certain level of anxiety, and stress increased the excitability of this pathway, leading to inappropriate emotional expression, and suggests that targeting specific PL circuits may aid the development of therapies for the treatment of stress-related disorders.

Transcriptome analysis with different leaf blades identifies the phloem-specific phosphate transporter OsPHO1;3 required for phosphate homeostasis in rice.

Phosphate (Pi) is essential for plant growth and development. One strategy to improve Pi use efficiency is to enhance Pi remobilization among leaves. Using transcriptome analysis with first (top) and fourth (down) leaf blades from rice (Oryza sativa) in Pi-sufficient and deficient conditions, we identified 1384 genes differentially expressed among these leaf blades. These genes were involved in physiological processes, metabolism, transport, and photosynthesis. Moreover, we identified the Pi efflux transporter gene, OsPHO1;3, responding to Pi-supplied conditions among these leaf blades. OsPHO1;3 is highly expressed in companion cells of phloem, but not xylem, in leaf blades and induced by Pi starvation. Mutation of OsPHO1;3 led to Pi accumulation in second to fourth leaves under Pi-sufficient conditions, but enhanced Pi levels in first leaves under Pi-deficient conditions. These Pi accumulations in leaves of Ospho1;3 mutants resulted from induction of OsPHT1;2 and OsPHT1;8 in root and reduction of Pi remobilization in leaf blades, revealed by the decreased Pi in phloem of leaves. Importantly, lack of OsPHO1;3 caused growth defects under a range of Pi-supplied conditions. These results demonstrate that Pi remobilization is essential for Pi homeostasis and plant growth irrespective of Pi-supplied conditions, and OsPHO1;3 plays an essential role in Pi remobilization for normal plant growth.

Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM.

With the emergence of high-throughput technologies, computational screening based on gene expression profiles has become one of the most effective methods for drug discovery. More importantly, profile-based approaches remarkably enhance novel drug-disease pair discovery without relying on drug- or disease-specific prior knowledge, which has been widely used in modern medicine. However, profile-based systematic screening of active ingredients of traditional Chinese medicine (TCM) has been scarcely performed due to inadequate pharmacotranscriptomic data. Here, we develop the largest-to-date online TCM active ingredients-based pharmacotranscriptomic platform integrated traditional Chinese medicine (ITCM) for the effective screening of active ingredients. First, we performed unified high-throughput experiments and constructed the largest data repository of 496 representative active ingredients, which was five times larger than the previous one built by our team. The transcriptome-based multi-scale analysis was also performed to elucidate their mechanism. Then, we developed six state-of-art signature search methods to screen active ingredients and determine the optimal signature size for all methods. Moreover, we integrated them into a screening strategy, TCM-Query, to identify the potential active ingredients for the special disease. In addition, we also comprehensively collected the TCM-related resource by literature mining. Finally, we applied ITCM to an active ingredient bavachinin, and two diseases, including prostate cancer and COVID-19, to demonstrate the power of drug discovery. ITCM was aimed to comprehensively explore the active ingredients of TCM and boost studies of pharmacological action and drug discovery. ITCM is available at http://itcm.biotcm.net.

A deep learning method to predict bacterial ADP-ribosyltransferase toxins.

MOTIVATION: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members. RESULTS: We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity in training ARTNet. Subsequently, we employed a data optimization strategy by utilizing ART-related domain subsequences instead of the primary full sequences, thereby significantly enhancing the performance of ARTNet. ARTNet achieved a Matthew's correlation coefficient (MCC) of 0.9351 and an F1-score (macro) of 0.9666 on repeated independent test datasets, outperforming three other deep learning models and six traditional machine learning models in terms of time efficiency and accuracy. Furthermore, we empirically demonstrated the ability of ARTNet to predict novel bARTTs across domain superfamilies without sequence similarity. We anticipate that ARTNet will greatly facilitate the screening and identification of novel bARTTs from bacterial genomes. AVAILABILITY AND IMPLEMENTATION: ARTNet is publicly accessible at http://www.mgc.ac.cn/ARTNet/. The source code of ARTNet is freely available at https://github.com/zhengdd0422/ARTNet/.

Single-cell immune profiling of mouse liver aging reveals Cxcl2+ macrophages recruit neutrophils to aggravate liver injury.

BACKGROUND AND AIMS: Immune cells play a crucial role in liver aging. However, the impact of dynamic changes in the local immune microenvironment on age-related liver injury remains poorly understood. We aimed to characterize intrahepatic immune cells at different ages to investigate key mechanisms associated with liver aging. APPROACH AND RESULTS: We carried out single-cell RNA sequencing on mouse liver tissues at 4 different ages, namely, the newborn, suckling, young, and aged stages. The transcriptomic landscape, cellular classification, and intercellular communication were analyzed. We confirmed the findings by multiplex immunofluorescence staining, flow cytometry, in vitro functional experiments, and chimeric animal models. Nine subsets of 89,542 immune cells with unique properties were identified, of which Cxcl2+ macrophages within the monocyte/macrophage subset were preferentially enriched in the aged liver. Cxcl2+ macrophages presented a senescence-associated secretory phenotype and recruited neutrophils to the aged liver through the CXCL2-CXCR2 axis. Through the secretion of IL-1ß and TNF-α, Cxcl2+ macrophages stimulated neutrophil extracellular traps formation. Targeting the CXCL2-CXCR2 axis limited the neutrophils migration toward the liver and attenuated age-related liver injury. Moreover, the relationship between Cxcl2+ macrophages and neutrophils in age-related liver injury was further validated by human liver transplantation samples. CONCLUSIONS: This in-depth study illustrates that the mechanism of Cxcl2+ macrophage-driven neutrophil activation involves the CXCL2-CXCR2 axis and provides a potential therapeutic strategy for age-related liver injury.