Two telomere-to-telomere gapless genomes reveal insights into Capsicum evolution and capsaicinoid biosynthesis.

Chili pepper (Capsicum) is known for its unique fruit pungency due to the presence of capsaicinoids. The evolutionary history of capsaicinoid biosynthesis and the mechanism of their tissue specificity remain obscure due to the lack of high-quality Capsicum genomes. Here, we report two telomere-to-telomere (T2T) gap-free genomes of C. annuum and its wild nonpungent relative C. rhomboideum to investigate the evolution of fruit pungency in chili peppers. We precisely delineate Capsicum centromeres, which lack high-copy tandem repeats but are extensively invaded by CRM retrotransposons. Through phylogenomic analyses, we estimate the evolutionary timing of capsaicinoid biosynthesis. We reveal disrupted coding and regulatory regions of key biosynthesis genes in nonpungent species. We also find conserved placenta-specific accessible chromatin regions, which likely allow for tissue-specific biosynthetic gene coregulation and capsaicinoid accumulation. These T2T genomic resources will accelerate chili pepper genetic improvement and help to understand Capsicum genome evolution.

GBDT-IL: Incremental Learning of Gradient Boosting Decision Trees to Detect Botnets in Internet of Things.

The rapid development of the Internet of Things (IoT) has brought many conveniences to our daily life. However, it has also introduced various security risks that need to be addressed. The proliferation of IoT botnets is one of these risks. Most of researchers have had some success in IoT botnet detection using artificial intelligence (AI). However, they have not considered the impact of dynamic network data streams on the models in real-world environments. Over time, existing detection models struggle to cope with evolving botnets. To address this challenge, we propose an incremental learning approach based on Gradient Boosting Decision Trees (GBDT), called GBDT-IL, for detecting botnet traffic in IoT environments. It improves the robustness of the framework by adapting to dynamic IoT data using incremental learning. Additionally, it incorporates an enhanced Fisher Score feature selection algorithm, which enables the model to achieve a high accuracy even with a smaller set of optimal features, thereby reducing the system resources required for model training. To evaluate the effectiveness of our approach, we conducted experiments on the BoT-IoT, N-BaIoT, MedBIoT, and MQTTSet datasets. We compared our method with similar feature selection algorithms and existing concept drift detection algorithms. The experimental results demonstrated that our method achieved an average accuracy of 99.81% using only 25 features, outperforming similar feature selection algorithms. Furthermore, our method achieved an average accuracy of 96.88% in the presence of different types of drifting data, which is 2.98% higher than the best available concept drift detection algorithms, while maintaining a low average false positive rate of 3.02%.

One-pot selective biosynthesis of Tyrian purple in Escherichia coli.

Tyrian purple (6,6'-Dibromoindigo) is an ancient precious dye, which possesses remarkable properties as a biocompatible semiconductor material. Recently, biosynthesis has emerged as an alternative for the sustainable production of Tyrian purple from a natural substrate. However, the selectivity issue in enzymatic tryptophan (Trp) and bromotryptophan (6-Br-Trp) degradation was an obstacle for obtaining high-purity Tyrian purple in a single cell biosynthesis. In this study, we present a simplified one-pot process for the production of Tyrian purple from Trp in Escherichia coli (E. coli) using Trp 6-halogenase from Streptomyces toxytricini (SttH), tryptophanase from E. coli (TnaA) and a two-component indole oxygenase from Providencia Rettgeri GS-2 (GS-C and GS-D). To enhance the in vivo solubility and activity of SttH and flavin reductase (Fre) fusion enzyme (Fre-L3-SttH), a chaperone system of GroEL/GroES (pGro7) was introduced in addition to the implementation of a set of optimization strategies, including fine-tuning the expression vector, medium, concentration of bromide salt and inducer. To overcome the selectivity issue and achieve a higher conversion yield of Tyrian purple with minimal indigo formation, we applied the λpL/pR-cI857 thermoinducible system to temporally control the bifunctional fusion enzyme of TnaA and monooxygenase GS-C (TnaA-L3-GS-C). Through optimization of the fermentation process, we were able to achieve a Tyrian purple titer of 44.5 mg L-1 with minimal indigo byproduct from 500 µM Trp. To the best of our knowledge, this is the first report of the selective production of Tyrian purple in E. colivia a one-pot process.

Mechanism of sinuosity effect on self-purification capacity of rivers.

As one of the important characteristics of river morphology, river sinuosity has a direct impact on the river water quality and self-purification capacity. In the present study, 4 physical river channel simulation models using circulating water with a sinuosity of 2.2, 1.8, 1.4, and 1.0, respectively, were established in our laboratory. Related hydraulic tests and detection were performed, including the detection of microbial communities in overlying water, monitoring of the river flow velocity and depth, and observation of the river flow line and bank scouring. The results show that the TN reduction rate at a sinuosity of 2.2 was 1.09, 1.20, and 1.75 times that at a sinuosity of 1.8, 1.4, and 1.0, respectively. And the total plate count for the set of tests with a sinuosity of 2.2 was 3.32 times that for the set of tests with a straight channel. The sinuous rivers have more complex flow regimes, more suitable hydraulic conditions, larger hyporheic zone areas, better microbial environments, and longer river flow paths, giving them a higher purification capacity against pollution. These findings can provide a theoretical basis for the optimization of water system layout and the restoration of river environments in the process of urbanization in China.

Single cell transcriptomic analyses implicate an immunosuppressive tumor microenvironment in pancreatic cancer liver metastasis.

Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic disease refractory to all targeted and immune therapies. However, our understanding of PDAC microenvironment especially the metastatic microenvironment is very limited partly due to the inaccessibility to metastatic tumor tissues. Here, we present the single-cell transcriptomic landscape of synchronously resected PDAC primary tumors and matched liver metastases. We perform comparative analysis on both cellular composition and functional phenotype between primary and metastatic tumors. Tumor cells exhibit distinct transcriptomic profile in liver metastasis with clearly defined evolutionary routes from cancer cells in primary tumor. We also identify specific subtypes of stromal and immune cells critical to the formation of the pro-tumor microenvironment in metastatic lesions, including RGS5+ cancer-associated fibroblasts, CCL18+ lipid-associated macrophages, S100A8+ neutrophils and FOXP3+ regulatory T cells. Cellular interactome analysis further reveals that the lack of tumor-immune cell interaction in metastatic tissues contributes to the formation of the immunosuppressive microenvironment. Our study provides a comprehensive characterization of the transcriptional landscape of PDAC liver metastasis.

scPlant: A versatile framework for single-cell transcriptomic data analysis in plants.

Single-cell transcriptomics has been fully embraced in plant biological research and is revolutionizing our understanding of plant growth, development, and responses to external stimuli. However, single-cell transcriptomic data analysis in plants is not trivial, given that there is currently no end-to-end solution and that integration of various bioinformatics tools involves a large number of required dependencies. Here, we present scPlant, a versatile framework for exploring plant single-cell atlases with minimum input data provided by users. The scPlant pipeline is implemented with numerous functions for diverse analytical tasks, ranging from basic data processing to advanced demands such as cell-type annotation and deconvolution, trajectory inference, cross-species data integration, and cell-type-specific gene regulatory network construction. In addition, a variety of visualization tools are bundled in a built-in Shiny application, enabling exploration of single-cell transcriptomic data on the fly.

Identification of nitrate sources of groundwater and rivers in complex urban environments based on isotopic and hydro-chemical evidence.

Groundwater and rivers in Chinese cities suffer from severe nitrate pollution. The accurate identification of nitrate sources throughout aquatic systems is key to the water nitrate pollution management. This study investigated nitrogen components of groundwater for twelve years and analyzed the sources of nitrate in the aquatic system based on dual isotopes (δ15N-NO3- and δ18O-NO3-) in the city of Nanjing, a core city of the Yangtze River Delta region, China. Our results showed that the ratio of nitrate to the sum of ammonia and nitrate in groundwater show an increasing trend during 2010-2021. The nitrate concentration was positively correlated with the proportion of cultivated land and negatively correlated with the proportion of forest land in the buffer zone. The relationship between Cl- and NO3-/ Cl- showed that agriculture and sewage sources increased during 2010-2015, sewage sources increased during 2016-2018, agriculture sources increased during 2019-2021. Manure and sewage were the primary sources of groundwater nitrate (72 %). There was no significant difference between the developed land (78 %), cultivated land (69 %), and aquaculture area (72 %). This indicates that dense population and intensive aquaculture in the suburbs have a significant impact on nitrate pollution. The contributions of manure and sewage to the fluvial nitrate sources in the lower reaches of the Qinhuai River Basin were 61 %. The non-point sources, including groundwater N (39 %) and soil N (35 %), were 74 % over the upper reaches. This study highlights the necessity of developing different N pollution management strategies for different parts of highly urbanized watersheds and considers groundwater restoration and soil nitrogen management as momentous, long-term tasks.

Substrate-induced dimerization of elaiophylin glycosyltransferase reveals a novel self-activating form of glycosyltransferase for symmetric glycosylation.

Elaiophylin (Ela), a unique 16-membered symmetric macrodiolide antibiotic, displays broad biological activity. Two rare 2-deoxy-L-fucose moieties at the ends of Ela are critical for its activity. Previously, elaiophylin glycosyltransferase (ElaGT) was identified as the enzyme that is responsible for the symmetric glycosylation of Ela, acting as a potential enzymatic tool for enhancing the diversity and activity of Ela. However, a symmetric catalytic mechanism has never been reported for a glycosyltransferase (GT). To explore the catalytic mechanism, the structure of ElaGT was determined in four forms: the apo form and Ela-bound, thymidine diphosphate-bound and uridine diphosphate-bound forms. In the Ela-bound structure, two ElaGTs form a `face-to-face' C2-symmetric homodimer with a continuous acceptor-binding pocket, allowing a molecule of Ela to shuffle through. Interestingly, this dimer interface resembles that of the activator-dependent GT EryCIII with its activator EryCII. Sequence analysis also indicates that ElaGT belongs to the activator-dependent GT family, but no putative activator has been identified in the Ela gene cluster. It was then found that the ElaGT homodimer may utilize this `face-to-face' arrangement to stabilize the Ela-binding loops on the interface and to simultaneously allosterically regulate the catalytic center. Therefore, these structures present a novel self-activating model for symmetric sugar transfer in the GT family and a new potential regulation site for substrate specificity.

Predicting Mucosal Healing in Crohn's Disease: A Nomogram Model Developed from a Retrospective Cohort.

Purpose: Mucosal healing (MH) has become a therapeutic end point for Crohn's disease (CD). The purpose of this study was to identify potential risk factors responsible for a lower probability of mucosal healing in CD. It also aimed to create and validate a noninvasive tool for predicting mucosal healing in CD to aid clinical decision-making. Patients and Methods: We established a derivation cohort diagnosed with CD, in which endoscopic examination was performed before and after treatment at the First Affiliated Hospital of Nanjing Medical University between January 2010 and June 2021. Patient data including demographic and clinical characteristics and treatment details were collected. The achievement of mucosal healing (without ulceration on endoscopic examination) after treatment was the endpoint observed during follow-up. We performed logistic regression analysis to identify factors associated with mucosal healing. These factors were used to develop a model (CD mucosal healing prediction nomogram) to predict mucosal healing in CD. External validation was performed using a new cohort of 60 patients from the Second Affiliated Hospital of Soochow University between January 2012 and June 2021. Results: A total of 331 patients were included in the derivation cohort. We found the following factors to be independently associated with mucosal healing after treatment: disease course <11 months, ulcer size <0.5 cm, Harvey-Bradshaw Index score <9, infliximab treatment, and non-exclusive use of 5-aminosalicylic acid. The model incorporating these factors achieved good discrimination, calibration, and clinical decision curve analysis results on internal validation (C-index: 0.788, 95% confidence interval [CI]: 0.74-0.84). The external validation cohort also demonstrated good discrimination (C-index: 0.785, 95% CI: 0.68-0.90) and calibration. Conclusion: The CD mucosal healing prediction nomogram model demonstrated good reliability and validated. It can potentially be developed into a simple and clinically useful tool for predicting mucosal healing in CD.

Combination of serological biomarkers and clinical features to predict mucosal healing in Crohn's disease: a multicenter cohort study.

PURPOSE: Mucosal healing (MH) has become the treatment goal of patients with Crohn's disease (CD). This study aims to develop a noninvasive and reliable clinical tool for individual evaluation of mucosal healing in patients with Crohn's disease. METHODS: A multicenter retrospective cohort was established. Clinical and serological variables were collected. Separate risk factors were incorporated into a binary logistic regression model. A primary model and a simple model were established, respectively. The model performance was evaluated with C-index, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. Internal validation was performed in patients with small intestinal lesions. RESULTS: A total of 348 consecutive patients diagnosed with CD who underwent endoscopic examination and review after treatment from January 2010 to June 2021 were composed in the derivation cohort, and 112 patients with small intestinal lesions were included in the validation cohort. The following variables were independently associated with the MH and were subsequently included into the primary prediction model: PLR (platelet to lymphocyte ratio), CAR (C-reactive protein to albumin ratio), ESR (erythrocyte sedimentation rate), HBI (Harvey-Bradshaw Index) score and infliximab treatment. The simple model only included factors of PLR, CAR and ESR. The primary model performed better than the simple one in C-index (87.5% vs. 83.0%, p = 0.004). There was no statistical significance between these two models in sensitivity (70.43% vs. 62.61%, p = 0.467), specificity (87.12% vs. 80.69%, p = 0.448), PPV (72.97% vs. 61.54%, p = 0.292), NPV (85.65% vs. 81.39%, p = 0.614), and accuracy (81.61% vs. 74.71%, p = 0.303). The primary model had good calibration and high levels of explained variation and discrimination in validation cohort. CONCLUSIONS: This model can be used to predict MH in post-treatment patients with CD. It can also be used as an indication of endoscopic surveillance to evaluate mucosal healing in patients with CD after treatment.

Whole-Cell Biotransformation of Penicillin G by a Three-Enzyme Co-expression System with Engineered Deacetoxycephalosporin†C Synthase.

Deacetoxycephalosporin C synthase (DAOCS) catalyzes the transformation of penicillin G to phenylacetyl-7-aminodeacetoxycephalosporanic acid (G-7-ADCA) for which it depends on 2-oxoglutarate (2OG) as co-substrate. However, the low activity of DAOCS and the expense of 2OG restricts its practical applications in the production of G-7-ADCA. Herein, a rational design campaign was performed on a DAOCS from Streptomyces clavuligerus (scDAOCS) in the quest to construct novel expandases. The resulting mutants showed 25∼58 % increase in activity compared to the template. The dominant DAOCS variants were then embedded into a three-enzyme co-expression system, consisting of a catalase and an L-glutamic oxidase for the generation of 2OG, to convert penicillin G to G-7-ADCA in E. coli. The engineered whole-cell enzyme cascade was applied to an up-scaled reaction, exhibiting a yield of G-7-ADCA up to 39.21 mM (14.6 g ⋅ L-1 ) with a conversion of 78.42 mol %. This work highlights the potential of the integrated whole-cell system that may inspire further research on green and efficient production of 7-ADCA.

Cell cycle kinase CHEK2 in macrophages alleviates the inflammatory response to Staphylococcus aureus-induced pneumonia.

BACKGROUND: Excessive macrophage-mediated inflammation participates in the development of Staphylococcus aureus (S. aureus)-induced pneumonia. Checkpoint kinase 2 (Chek2) was screened out as macrophage-related infantile pneumonia gene after the differentially expressed analysis of RNAseq data derived from pam3CSK4 stimulated bone marrow-derived macrophages (BMDMs). METHODS: RAW264.7 macrophage cells were transfected with Chek2-specific gRNA, which were further overexpressed with wide-type Chek2 or Chek2 kinase activity mutant (Chek2 KD, D368N). At the same time, the relative protein and mRNA expression of inflammatory cytokines were determined. C57BL/6J WT mice were intranasally infected with S. aureus to induce S. aureus-induced pneumonia, which was treated with BML-277, an inhibitor of Chek2. The symptoms of pneumonia mice and inflammatory cytokines associated with the nuclear factor kappa B (NF-κB) signaling pathways were further examined. RESULTS: In vivo, BML-277 significantly promoted pneumonia symptoms, including mortality, lung infiltration of immune cells, and the abundance of lung pro-inflammatory cytokines. Mechanically, BML-277 did not affect BMDMs survival but up-regulated the mRNA expression of tumor necrosis factor (Tnf), nitric oxide synthase 2 (Nos2), interleukin (Il)23a, and the secretion of Tnf-α and Il-23a. At the same time, genetic complementation experiment testified that Chek2 KD did not inhibit NF-κB and relevant inflammatory cytokines expression. CONCLUSION: Chek2 functions through the kinase mechanism to down-regulate the NF-κB pathway in macrophages to alleviate S. aureus-induced pneumonia in mice.

P4HA2-induced prolyl hydroxylation suppresses YAP1-mediated prostate cancer cell migration, invasion, and metastasis.

Yes-associated protein 1 (YAP1), a key player in the Hippo pathway, has been shown to play a critical role in tumor progression. However, the role of YAP1 in prostate cancer cell invasion, migration, and metastasis is not well defined. Through functional, transcriptomic, epigenomic, and proteomic analyses, we showed that prolyl hydroxylation of YAP1 plays a critical role in the suppression of cell migration, invasion, and metastasis in prostate cancer. Knockdown (KD) or knockout (KO) of YAP1 led to an increase in cell migration, invasion, and metastasis in prostate cancer cells. Microarray analysis showed that the EMT pathway was activated in Yap1-KD cells. ChIP-seq analysis showed that YAP1 target genes are enriched in pathways regulating cell migration. Mass spectrometry analysis identified P4H prolyl hydroxylase in the YAP1 complex and YAP1 was hydroxylated at multiple proline residues. Proline-to-alanine mutations of YAP1 isoform 3 identified proline 174 as a critical residue, and its hydroxylation suppressed cell migration, invasion, and metastasis. KO of P4ha2 led to an increase in cell migration and invasion, which was reversed upon Yap1 KD. Our study identified a novel regulatory mechanism of YAP1 by which P4HA2-dependent prolyl hydroxylation of YAP1 determines its transcriptional activities and its function in prostate cancer metastasis.

Movement signaling in ventral pallidum and dopaminergic midbrain is gated by behavioral state in singing birds.

Movement-related neuronal discharge in ventral tegmental area (VTA) and ventral pallidum (VP) is inconsistently observed across studies. One possibility is that some neurons are movement related and others are not. Another possibility is that the precise behavioral conditions matter-that a single neuron can be movement related under certain behavioral states but not others. We recorded single VTA and VP neurons in birds transitioning between singing and nonsinging states while monitoring body movement with microdrive-mounted accelerometers. Many VP and VTA neurons exhibited body movement-locked activity exclusively when the bird was not singing. During singing, VP and VTA neurons could switch off their tuning to body movement and become instead precisely time-locked to specific song syllables. These changes in neuronal tuning occurred rapidly at state boundaries. Our findings show that movement-related activity in limbic circuits can be gated by behavioral context.NEW & NOTEWORTHY Neural signals in the limbic system have long been known to represent body movements as well as reward. Here, we show that single neurons dramatically change their tuning from movement to song timing when a bird starts to sing.

A simple quantitative PCR assay to determine TRAMP transgene zygosity.

BACKGROUND: The TRansgenic Adenocarcinoma of the Mouse Prostate (TRAMP) model remains one of the most widely used transgenic mouse models of prostate cancer. This is due to its ability to recapitulate with ~100% penetrance multiple aspects of the human disease such as prostatic intraepithelial neoplasia lesions, invasive carcinoma, progression to castration-resistant prostate cancer including aggressive neuroendocrine prostate cancer and metastasis. Despite its popularity, the use of TRAMP mice is limited/slowed by the inability to distinguish the zygosity of the TRAMP transgene. This is especially true for breeding strategies implementing multiple crosses and alleles and when the rapid generation of large animal cohorts with the desired genotype is needed. METHODS: We developed a quantitative PCR (qPCR) approach to determine the relative TRAMP transgene copy number of mice. RESULTS: This method was validated by three independent laboratories across two institutions, which successfully identified the genotype of the mice 98.2% of the time (165/168) in the first attempt. The genotypes of the uncertain mice were correctly identified in the repeated experiments. CONCLUSIONS: We develop the first straightforward, qPCR approach to reliably determine the TRAMP transgene zygosity. The development of this qPCR-based genotyping method enables researchers to streamline breeding strategies when creating complex genetic mouse models involving TRAMP mice; thus, ultimately reducing the required animal numbers, cost, and investigator time.

Transcription factor NF-κB promotes acute lung injury via microRNA-99b-mediated PRDM1 down-regulation.

Acute lung injury (ALI), is a rapidly progressing heterogenous pulmonary disorder that possesses a high risk of mortality. Accumulating evidence has implicated the activation of the p65 subunit of NF-κB [NF-κB(p65)] activation in the pathological process of ALI. microRNAs (miRNAs), a group of small RNA molecules, have emerged as major governors due to their post-transcriptional regulation of gene expression in a wide array of pathological processes, including ALI. The dysregulation of miRNAs and NF-κB activation has been implicated in human diseases. In the current study, we set out to decipher the convergence of miR-99b and p65 NF-κB activation in ALI pathology. We measured the release of pro-inflammatory cytokines (IL-1ß, IL-6, and TNFα) in bronchoalveolar lavage fluid using ELISA. MH-S cells were cultured and their viability were detected with cell counting kit 8 (CCK8) assays. The results showed that miR-99b was up-regulated, while PRDM1 was down-regulated in a lipopolysaccharide (LPS)-induced murine model of ALI. Mechanistic investigations showed that NF-κB(p65) was enriched at the miR-99b promoter region, and further promoted its transcriptional activity. Furthermore, miR-99b targeted PRDM1 by binding to its 3'UTR, causing its down-regulation. This in-creased lung injury, as evidenced by increased wet/dry ratio of mouse lung, myeloperoxidase activity and pro-inflammatory cytokine secretion, and enhanced infiltration of inflammatory cells in lung tissues. Together, our findings indicate that NF-κB(p65) promotion of miR-99b can aggravate ALI in mice by down-regulating the expression of PRDM1.

Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images.

In recent years, increasing image data comes from various sensors, and object detection plays a vital role in image understanding. For object detection in complex scenes, more detailed information in the image should be obtained to improve the accuracy of detection task. In this paper, we propose an object detection algorithm by jointing semantic segmentation (SSOD) for images. First, we construct a feature extraction network that integrates the hourglass structure network with the attention mechanism layer to extract and fuse multi-scale features to generate high-level features with rich semantic information. Second, the semantic segmentation task is used as an auxiliary task to allow the algorithm to perform multi-task learning. Finally, multi-scale features are used to predict the location and category of the object. The experimental results show that our algorithm substantially enhances object detection performance and consistently outperforms other three comparison algorithms, and the detection speed can reach real-time, which can be used for real-time detection.

Actor-critic reinforcement learning in the songbird.

It feels rewarding to ace your opponent on match point. Here, we propose common mechanisms underlie reward and performance learning. First, when a singing bird unexpectedly hits the right note, its dopamine (DA) neurons are activated as when a thirsty monkey receives an unexpected juice reward. Second, these DA signals reinforce vocal variations much as they reinforce stimulus-response associations. Third, limbic inputs to DA neurons signal the predicted quality of song syllables much like they signal the predicted reward value of a place or a stimulus during foraging. Finally, songbirds may solve difficult problems in reinforcement learning - such as credit assignment and catastrophic forgetting - with node perturbation and consolidation of reinforced vocal patterns in motor cortical circuits. Consolidation occurs downstream of a canonical 'actor-critic' circuit motif that learns to maximize performance quality in essentially the same way it learns to maximize reward: by computing and learning from prediction errors.

Suppressed nuclear factor-kappa B alleviates lipopolysaccharide-induced acute lung injury through downregulation of CXCR4 mediated by microRNA-194.

Acute lung injury (ALI) is a highly lethal pulmonary disease that causes edema, hypoxemia and respiratory failure. Recent evidence indicates that nuclear factor-kappa B (NF-κB) plays a crucial role in ALI development. However, the regulatory mechanism of NF-κB on ALI remains enigmatic. In this study, we investigated potential molecular mechanism of NF-κB on ALI induced by lipopolysaccharide (LPS). BALB/c mice were subjected to intratracheal spraying of LPS to generate an ALI mode, with the activity of NF-κB in mice tissues being detected by enzyme linked immunosorbent assay (ELISA), and the number of inflammatory cells in bronchoalveolar lavage fluid being counted. Then, the macrophage cell line RAW264.7 exposed to LPS were treated with ammonium pyrrolidinedithiocarbamate (PDTC) (inhibitor of NF-κB), miR-194 mimic, or oe-chemokine receptor type 4 (CXCR4) separately or in combination. After that, ELISA and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were used to detect the expression level of IL-1ß, IL-6, TNF-α, miR-194 and CXCR4, respectively. In addition, the targeting relationship between miR-194 and CXCR4 was verified by dual-luciferase reporter gene assay. The dry/wet ratio of lung and the MPO activity were also measured to assess the inflammatory response in mice. Activation of NF-κB down-regulated the miR-194 expression in LPS-induced ALI. Overexpression of miR-194 alleviated LPS-induced ALI and reduced the expression of inflammatory factors IL-1ß, IL-6 and TNF-α via targeting CXCR4. In LPS-induced ALI, NF-κB mediates the CXCR4 expression by inhibiting the expression of miR-194, thus promoting the inflammatory injury of lung.