N6-methyladenosine-driven miR-143/145-KLF4 circuit orchestrates the phenotypic switch of pulmonary artery smooth muscle cells.

Pulmonary hypertension (PH) is characterized by vascular remodeling predominantly driven by a phenotypic switching in pulmonary artery smooth muscle cells (PASMCs). However, the underlying mechanisms for this phenotypic alteration remain incompletely understood. Here, we identified that RNA methyltransferase METTL3 is significantly elevated in the lungs of hypoxic PH (HPH) mice and rats, as well as in the pulmonary arteries (PAs) of HPH rats. Targeted deletion of Mettl3 in smooth muscle cells exacerbated hemodynamic consequences of hypoxia-induced PH and accelerated pulmonary vascular remodeling in vivo. Additionally, the absence of METTL3 markedly induced phenotypic switching in PASMCs in vitro. Mechanistically, METTL3 depletion attenuated m6A modification and hindered the processing of pri-miR-143/145, leading to a downregulation of miR-143-3p and miR-145-5p. Inhibition of hnRNPA2B1, an m6A mediator involved in miRNA maturation, similarly resulted in a significant reduction of miR-143-3p and miR-145-5p. We demonstrated that miR-145-5p targets Krüppel-like factor 4 (KLF4) and miR-143-3p targets fascin actin-bundling protein 1 (FSCN1) in PASMCs. The decrease of miR-145-5p subsequently induced an upregulation of KLF4, which in turn suppressed miR-143/145 transcription, establishing a positive feedback circuit between KLF4 and miR-143/145. This regulatory circuit facilitates the persistent suppression of contractile marker genes, thereby sustaining PASMC phenotypic switch. Collectively, hypoxia-induced upregulation of METTL3, along with m6A mediated regulation of miR-143/145, might serve as a protective mechanism against phenotypic switch of PASMCs. Our results highlight a potential therapeutic strategy targeting m6A modified miR-143/145-KLF4 loop in the treatment of PH.

The characteristics of brain network in patient with post-stroke depression under cognitive task condition.

Objectives: Post-stroke depression (PSD) may be associated with the altered brain network property. This study aimed at exploring the brain network characteristics of PSD under the classic cognitive task, i.e., the oddball task, in order to promote our understanding of the pathogenesis and the diagnosis of PSD. Methods: Nineteen stroke survivors with PSD and 18 stroke survivors with no PSD (non-PSD) were recruited. The functional near-infrared spectroscopy (fNIRS) covering the dorsolateral prefrontal cortex was recorded during the oddball task state and the resting state. The brain network characteristics were extracted using the graph theory and compared between the PSD and the non-PSD subjects. In addition, the classification performance between the PSD and non-PSD subjects was evaluated using features in the resting and the task state, respectively. Results: Compared with the resting state, more brain network characteristics in the task state showed significant differences between the PSD and non-PSD groups, resulting in better classification performance. In the task state, the assortativity, clustering coefficient, characteristic path length, and local efficiency of the PSD subjects was larger compared with the non-PSD subjects while the global efficiency of the PSD subjects was smaller than that of the non-PSD subjects. Conclusion: The altered brain network properties associated with PSD in the cognitive task state were more distinct compared with the resting state, and the ability of the brain network to resist attack and transmit information was reduced in PSD patients in the task state. Significance: This study demonstrated the feasibility and superiority of investigating brain network properties in the task state for the exploration of the pathogenesis and new diagnosis methods for PSD.

Modular Synthesis of Phthalaldehyde Derivatives Enabling Access to Photoacid Generator-Bound Self-Immolative Polymer Resists with Next-Generation Photolithographic Properties.

The resolution, line edge roughness, and sensitivity (RLS) trade-off has fundamentally limited the lithographic performance of chemically amplified resists. Production of next-generation transistors using extreme ultraviolet (EUV) lithography depends on a solution to this problem. A resist that simultaneously increases the effective reaction radius of its photogenerated acids while limiting their diffusion radius should provide an elegant solution to the RLS barrier. Here, we describe a generalized synthetic approach to phthalaldehyde derivatives using sulfur(VI) fluoride exchange click chemistry that dramatically expands usable chemical space by enabling virtually any non-ionic photoacid generator (PAG) to be tethered to phthalaldehyde. The resulting polymers represent the first ever PAG-tethered self-immolative resists in an architecture that simultaneously displays high contrast, extraordinary sensitivity, and low roughness under EUV exposure. We believe this class of resists will ultimately enable researchers to overcome the RLS trade-off.

Synthesis of End-Cap Enabled Self-Immolative Photoresists For Extreme Ultraviolet Lithography.

Conventional chemically amplified resists (CARs) rely on the usage of photoacid generators to serve as the source of chemical amplification. However, acid diffusion inevitably accompanies CARs and has led to the resolution, line edge roughness, and sensitivity (RLS) trade-off, which is the most challenging technical problem for modern photoresists. Herein, we take advantage of the self-immolative property of polyphthalaldehyde (PPA) derivatives to create end-cap enabled chain scissionable resists for extreme ultraviolet (EUV) lithography. The feasibility of this strategy was demonstrated under UV photodegradation experiments. The dose-to-clear (DTC) under EUV radiation was 90 mJ/cm2 for the most promising resist, representing more than a 100-fold improvement over previous PPA resists. Density functional theory (DFT) calculations were conducted to understand the structural origin of end-cap EUV sensitivity.

QKI 6 ameliorates CIRI through promoting synthesis of triglyceride in neuron and inhibiting neuronal apoptosis associated with SIRT1-PPARγ-PGC-1α axis.

BACKGROUND: The stroke induced by ischemia of brain remains high incidence and death rate. The study wanted to confirm the effects of Quaking 6 (QKI 6) on the protection role in neurons of rat model of cerebral ischemia/reperfusion injury (CIRI). MATERIAL AND METHODS: The rat model with CIRI induced by middle cerebral artery occlusion was well established and rat neurons were isolated to characterize the effects of QKI 6 mediated by sirtuin 1 (SIRT1) on synthesis of triglyceride in neuron and neuronal apoptosis via activation of SIRT1-peroxisome proliferater-activated receptor (PPAR)γ- peroxisome proliferator-activated receptor coactivator (PGC)-1α signaling pathway. RESULTS: The expression levels of SIRT1 or QKI 6, and acetylation level of QKI 6 were decreased in neurons of rat model with CIRI. QKI 6 deacetylated and mediated by SIRT1 that contributed to suppressing the progression of neuronal apoptosis in rat through promoting synthesis of triglyceride in vivo and in vitro via SIRT1-PPARγ-PGC-1α signaling pathway, then inhibiting CIRI. CONCLUSIONS: Our results demonstrated SIRT1 deacetylates QKI 6, the RNA-binding protein, that affects significantly the synthesis of triglyceride in neurons of CIRI rat model. Moreover, it activated transcription factor peroxisome proliferator-activated receptorγ coactivator-1α (PGC-1α) through post-transcriptional regulation of the expression of PPARγ, and further enhanced synthesis of triglyceride, thereby restrained the progression of neural apoptosis and CIRI.

Aluminum porphyrins with quaternary ammonium halides as catalysts for copolymerization of cyclohexene oxide and CO2: metal-ligand cooperative catalysis.

Bifunctional AlIII porphyrins with quaternary ammonium halides, 2-Cl and 2-Br, worked as excellent catalysts for the copolymerization of cyclohexene oxide (CHO) and CO2 at 120 °C. Turnover frequency (TOF) and turnover number (TON) reached 10 000 h-1 and 55 000, respectively, and poly(cyclohexene carbonate) (PCHC) with molecular weight of up to 281 000 was obtained with a catalyst loading of 0.001 mol%. In contrast, bifunctional MgII and ZnII counterparts, 3-Cl and 4-Cl, as well as a binary catalyst system, 1-Cl with bis(triphenylphosphine)iminium chloride (PPNCl), showed poor catalytic performances. Kinetic studies revealed that the reaction rate was first-order in [CHO] and [2-Br] and zero-order in [CO2], and the activation parameters were determined: ΔH ‡ = 12.4 kcal mol-1, ΔS ‡ = -26.1 cal mol-1 K-1, and ΔG ‡ = 21.6 kcal mol-1 at 80 °C. Comparative DFT calculations on two model catalysts, AlIII complex 2' and MgII complex 3', allowed us to extract key factors in the catalytic behavior of the bifunctional AlIII catalyst. The high polymerization activity and carbonate-linkage selectivity originate from the cooperative actions of the metal center and the quaternary ammonium cation, both of which facilitate the epoxide-ring opening by the carbonate anion to form the carbonate linkage in the key transition state such as TS3b (ΔH ‡ = 13.3 kcal mol-1, ΔS ‡ = -3.1 cal mol-1 K-1, and ΔG ‡ = 14.4 kcal mol-1 at 80 °C).

Expert consensus on protocol of rehabilitation for COVID-19 patients using framework and approaches of WHO International Family Classifications.

Coronavirus disease 2019 (COVID-19) has widely spread all over the world and the numbers of patients and deaths are increasing. According to the epidemiology, virology, and clinical practice, there are varying degrees of changes in patients, involving the human body structure and function and the activity and participation. Based on the World Health Organization (WHO) International Classification of Functioning, Disability and Health (ICF) and its biopsychosocial model of functioning, we use the WHO Family of International Classifications (WHO-FICs) framework to form an expert consensus on the COVID-19 rehabilitation program, focusing on the diagnosis and evaluation of disease and functioning, and service delivery of rehabilitation, and to establish a standard rehabilitation framework, terminology system, and evaluation and intervention systems based the WHO-FICs.

Alternating Copolymerization of CO2 and Cyclohexene Oxide Catalyzed by Cobalt-Lanthanide Mixed Multinuclear Complexes.

Heteromultimetallic complexes consisting of three Co(II) ions and one lanthanide ion were synthesized and applied to the alternating copolymerization of CO2 and cyclohexene oxide. Unlike the conventional cobalt(III) salen complexes, the high thermal stability of the present catalyst allowed us to reach a turnover number of 13000, one of the highest values ever reported for multimetallic systems. The chain propagation was first-order to the catalyst, suggesting a cooperative behavior of the metal centers.

In utero gene expression in the Slc39a8(neo/neo) knockdown mouse.

Slc39a8 encodes ZIP8, a divalent cation/bicarbonate symporter expressed in pluripotent mouse embryonic stem cells, and therefore ubiquitous in adult tissues; ZIP8 influxes Zn2+, Mn2+ and Fe2+. Slc39a8(neo/neo) knockdown mice exhibit 10-15% of wild-type ZIP8 mRNA and protein levels, and show pleiotropic phenotype of stunted growth, neonatal lethality, multi-organ dysmorphogenesis, and dysregulated hematopoiesis manifested as severe anemia. Herein we performed RNA-seq analysis of gestational day (GD)13.5 yolk sac and placenta, and GD16.5 liver, kidney, lung, heart and cerebellum, comparing Slc39a8(neo/neo) with Slc39a8(+/+) wild-type. Meta-data analysis of differentially-expressed genes revealed 29 unique genes from all tissues - having enriched GO categories associated with hematopoiesis and hypoxia and KEGG categories of complement, response to infection, and coagulation cascade - consistent with dysregulated hematopoietic stem cell fate. Based on transcription factor (TF) profiles in the JASPAR database, and searching for TF-binding sites enriched by Pscan, we identified numerous genes encoding zinc-finger and other TFs associated with hematopoietic stem cell functions. We conclude that, in this mouse model, deficient ZIP8-mediated divalent cation transport affects zinc-finger (e.g. GATA proteins) and other TFs interacting with GATA proteins (e.g. TAL1), predominantly in yolk sac. These data strongly support the phenotype of dysmorphogenesis and anemia seen in Slc39a8(neo/neo) mice in utero.

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.

Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST .

Up-regulation of micro-RNA765 in human failing hearts is associated with post-transcriptional regulation of protein phosphatase inhibitor-1 and depressed contractility.

AIMS: Impaired sarcoplasmic reticulum (SR) Ca(2+) cycling and depressed contractility, a hallmark of human and experimental heart failure, has been partially attributed to increased protein phosphatase 1 (PP-1) activity, associated with down-regulation of its endogenous inhibitor-1. The levels and activity of inhibitor-1 are reduced in failing hearts, contributing to dephosphorylation and inactivation of key calcium cycling proteins. Therefore, we investigated the mechanisms that mediate decreases in inhibitor-1 by post-transcriptional modification. METHODS AND RESULTS: Bioinformatics revealed that 17 human microRNAs may serve as modulators of inhibitor-1. However, real-time PCR analysis identified only one of these microRNAs, miR-765, as being increased in human failing hearts concomitant with decreased inhibitor-1 levels. Expression of miR-765 in HEK293 cells or mouse ventricular myocytes confirmed suppression of inhibitor-1 levels through binding of this miR-765 to the 3'-untranslated region of inhibitor-1 mRNA. To determine the functional significance of miR-765 in Ca(2+) cycling, pri-miR-765 as well as a non-translated nucleotide sequence (miR-Ctrl) were expressed in adult mouse ventricular myocytes. The inhibitor-1 expression levels were decreased, accompanied by enhanced PP-1 activity in the miR-765 cardiomyocytes, and these reflected depressed contractile mechanics and Ca(2+) transients, compared with the miR-Ctrl group. The depressive effects were associated with decreases in the phosphorylation of phospholamban and SR Ca(2+) load. These miR-765 negative inotropic effects were abrogated in inhibitor-1-deficient cardiomyocytes, suggesting its apparent specificity for inhibitor-1. CONCLUSIONS: miR-765 levels are increased in human failing hearts. Such increases may contribute to depressed cardiac function through reduced inhibitor-1 expression and enhanced PP-1 activity, associated with reduced SR Ca(2+) load.

Network-Based Analysis on Orthogonal Separation of Human Plasma Uncovers Distinct High Density Lipoprotein Complexes.

High density lipoprotein (HDL) particles are blood-borne complexes whose plasma levels have been associated with protection from cardiovascular disease (CVD). Recent studies have demonstrated the existence of distinct HDL subspecies; however, these have been difficult to isolate and characterize biochemically. Here, we present the first report that employs a network-based approach to systematically infer HDL subspecies. Healthy human plasma was separated into 58 fractions using our previously published three orthogonal chromatography techniques. Similar local migration patterns among HDL proteins were captured with a novel similarity score, and individual comigration networks were constructed for each fraction. By employing a graph mining algorithm, we identified 183 overlapped cliques, among which 38 were further selected as candidate HDL subparticles. Each of these 38 subparticles had at least two literature supports. In addition, GO function enrichment analysis showed that they were enriched with fundamental biological and CVD protective functions. Furthermore, gene knockout experiments in mouse model supported the validity of these subparticles related to three apolipoproteins. Finally, analysis of an apoA-I deficient human patient's plasma provided additional support for apoA-I related complexes. Further biochemical characterization of these putative subspecies may facilitate the mechanistic research of CVD and guide targeted therapeutics aimed at its mitigation.

A novel essential domain perspective for exploring gene essentiality.

MOTIVATION: Genes with indispensable functions are identified as essential; however, the traditional gene-level studies of essentiality have several limitations. In this study, we characterized gene essentiality from a new perspective of protein domains, the independent structural or functional units of a polypeptide chain. RESULTS: To identify such essential domains, we have developed an Expectation-Maximization (EM) algorithm-based Essential Domain Prediction (EDP) Model. With simulated datasets, the model provided convergent results given different initial values and offered accurate predictions even with noise. We then applied the EDP model to six microbial species and predicted 1879 domains to be essential in at least one species, ranging 10-23% in each species. The predicted essential domains were more conserved than either non-essential domains or essential genes. Comparing essential domains in prokaryotes and eukaryotes revealed an evolutionary distance consistent with that inferred from ribosomal RNA. When utilizing these essential domains to reproduce the annotation of essential genes, we received accurate results that suggest protein domains are more basic units for the essentiality of genes. Furthermore, we presented several examples to illustrate how the combination of essential and non-essential domains can lead to genes with divergent essentiality. In summary, we have described the first systematic analysis on gene essentiality on the level of domains. CONTACT: huilu.bioinfo@gmail.com or Long.Lu@cchmc.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An integrated machine-learning model to predict prokaryotic essential genes.

Essential genes are indispensable for the target organism's survival. Large-scale identification and characterization of essential genes has shown to be beneficial in both fundamental biology and medicine fields. Current existing genome-scale experimental screenings of essential genes are time consuming and costly, also sometimes confer erroneous essential gene annotations. To circumvent these difficulties, many research groups turn to computational approaches as the alternative to identify essential genes. Here, we developed an integrative machine-learning based statistical framework to accurately predict essential genes in microorganisms. First we extracted a variety of relevant features derived from different aspects of an organism's genomic sequences. Then we selected a subset of features have high predictive power of gene essentiality through a carefully designed feature selection system. Using the selected features as input, we constructed an ensemble classifier and trained the model on a well-studied microorganism. After fine-tuning the model parameters in cross-validation, we tested the model on the other microorganism. We found that the tenfold cross-validation results within the same organism achieves a high predictive accuracy (AUC ~0.9), and cross-organism predictions between distant related organisms yield the AUC scores from 0.69 to 0.89, which significantly outperformed homology mapping.

A statistical framework for improving genomic annotations of transposon mutagenesis (TM) assigned essential genes.

Whole-genome transposon mutagenesis (TM) experiment followed by sequence-based identification of insertion sites is the most popular genome-wise experiment to identify essential genes in Prokaryota. However, due to the limitation of high-throughput technique, this approach yields substantial systematic biases resulting in the incorrect assignments of many essential genes. To obtain unbiased and accurate annotations of essential genes from TM experiments, we developed a novel Poisson model based statistical framework to refine these TM assignments. In the model, first we identified and incorporated several potential factors such as gene length and TM insertion information which may cause the TM assignment biases into the basic Poisson model. Then we calculated the conditional probability of an essential gene given the observed TM insertion number. By factorizing this probability through introducing a latent variable the real insertion number, we formalized the statistical framework. Through iteratively updating and optimizing model parameters to maximize the goodness-of-fit of the model to the observed TM insertion data, we finalized the model. Using this model, we are able to assign the probability score of essentiality to each individual gene given its TM assignment, which subsequently correct the experimental biases. To enable our model widely useable, we established a user-friendly Web-server that is accessible to the public: http://research.cchmc.org/essentialgene/.

Discovering essential domains in essential genes.

Genes with indispensable functions are identified as essential; however, the traditional gene-level perspective of essentiality has several limitations. We hypothesized that protein domains, the independent structural or functional units of a polypeptide chain, are responsible for gene essentiality. If the essentiality of domains is known, the essential genes could be identified. To find such essential domains, we have developed an EM algorithm-based Essential Domain Prediction (EDP) Model. With simulated datasets, the model provided convergent results given different initial values and offered accurate predictions even with noise. We then applied the EDP model to six microbes and predicted 3,450 domains to be essential in at least one species, ranging 8-24 % in each species.

SERPINB3/B4 contributes to early inflammation and barrier dysfunction in an experimental murine model of atopic dermatitis.

Serine proteases are critical for epidermal barrier homeostasis, and their aberrant expression and/or activity is associated with chronic skin diseases. Elevated levels of the serine protease inhibitors SERPINB3 and SERPINB4 are seen in patients with atopic dermatitis and psoriasis. However, their mechanistic role in the skin is unknown. To evaluate the contribution of Serpinb3a (mouse homolog of SERPINB3 and SERPINB4) in atopic dermatitis, we examined the effect of topical Aspergillus fumigatus extract exposure in wild-type and Serpinb3a-null mice on transepidermal water loss (TEWL), sensitization, and inflammation. Allergen exposure induced Serpinb3a expression in the skin, along with increased TEWL, epidermal thickness, and skin inflammation, all of which were attenuated in the absence of Serpinb3a. Attenuated TEWL correlated with decreased expression of the pro-inflammatory marker S100A8. Silencing of SERPINB3/B4 in human keratinocytes decreased S100A8 expression, supporting a role for SERPINB3/B4 in the initiation of the acute inflammatory response. RNA-seq analysis following allergen exposure identified a network of pro-inflammatory genes induced in wild-type mice that was absent in Serpinb3a-null mice. In conclusion, Serpinb3a deficiency attenuates barrier dysfunction and the early inflammatory response following cutaneous allergen exposure, supporting a role for Serpinb3a (mice) and SERPINB3/B4 (humans) early in atopic dermatitis.

BMP4 enhances foam cell formation by BMPR-2/Smad1/5/8 signaling.

Atherosclerosis and its complications are characterized by lipid-laden foam cell formation. Recently, an obvious up-regulation of BMP4 was observed in atherosclerotic plaque, however, its function and the underlying mechanism remains unknown. In our study, BMP4 pretreatment induced macrophage foam cell formation. Furthermore, a dramatic increase in the ratio of cholesteryl ester (CE) to total cholesterol (TC) was observed in BMP4-treated macrophages, accompanied by the reduction of cholesterol outflow. Importantly, BMP4 stimulation inhibited the expression levels of the two most important cellular cholesterol transporters ABCA1 and ABCG1, indicating that BMP4 may induce formation of foam cells by attenuating transporters expression. Further mechanism analysis showed that BMPR-2, one of the BMP4 receptors, was significantly increased in BMP4 treated macrophage foam cells. That blocking its expression using specific siRNA significantly increased ABCA1 and ABCG1 levels. Additionally, BMP4 treatment triggered the activation of Smad1/5/8 pathway by BMPR-2 signaling. After blocking the Smad1/5/8 with its inhibitor, ABCA1 and ABCG1 expression levels were up-regulated significantly, suggesting that BMP4 inhibited the expression of ABCA1 and ABCG1 through the BMPR-2/Smad1/2/8 signaling pathway. Therefore, our results will provide a new insight about how BMP4 accelerate the progressio of atherosclerosis, and it may become a potential target against atherosclerosis and its complications.

Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus.

BACKGROUND: Aspergillus fumigatus (Af) is a ubiquitous and opportunistic pathogen capable of causing acute, invasive pulmonary disease in susceptible hosts. Despite current therapeutic options, mortality associated with invasive Af infections remains unacceptably high, increasing 357% since 1980. Therefore, there is an urgent need for the development of novel therapeutic strategies, including more efficacious drugs acting on new targets. Thus, as noted in a recent review, "the identification of essential genes in fungi represents a crucial step in the development of new antifungal drugs". Expanding the target space by rapidly identifying new essential genes has thus been described as "the most important task of genomics-based target validation". RESULTS: In previous research, we were the first to show that essential gene annotation can be reliably transferred between distantly related four Prokaryotic species. In this study, we extend our machine learning approach to the much more complex Eukaryotic fungal species. A compendium of essential genes is predicted in Af by transferring known essential gene annotations from another filamentous fungus Neurospora crassa. This approach predicts essential genes by integrating diverse types of intrinsic and context-dependent genomic features encoded in microbial genomes. The predicted essential datasets contained 1674 genes. We validated our results by comparing our predictions with known essential genes in Af, comparing our predictions with those predicted by homology mapping, and conducting conditional expressed alleles. We applied several layers of filters and selected a set of potential drug targets from the predicted essential genes. Finally, we have conducted wet lab knockout experiments to verify our predictions, which further validates the accuracy and wide applicability of the machine learning approach. CONCLUSIONS: The approach presented here significantly extended our ability to predict essential genes beyond orthologs and made it possible to predict an inventory of essential genes in Eukaryotic fungal species, amongst which a preferred subset of suitable drug targets may be selected. By selecting the best new targets, we believe that resultant drugs would exhibit an unparalleled clinical impact against a naive pathogen population. Additional benefits that a compendium of essential genes can provide are important information on cell function and evolutionary biology. Furthermore, mapping essential genes to pathways may also reveal critical check points in the pathogen's metabolism. Finally, this approach is highly reproducible and portable, and can be easily applied to predict essential genes in many more pathogenic microbes, especially those unculturable.

miR-21 attenuates lipopolysaccharide-induced lipid accumulation and inflammatory response: potential role in cerebrovascular disease.

BACKGROUND: Atherosclerosis constitutes the leading contributor to morbidity and mortality in cardiovascular and cerebrovascular diseases. Lipid deposition and inflammatory response are the crucial triggers for the development of atherosclerosis. Recently, microRNAs (miRNAs) have drawn more attention due to their prominent function on inflammatory process and lipid accumulation in cardiovascular and cerebrovascular disease. Here, we investigated the involvement of miR-21 in lipopolysaccharide (LPS)-induced lipid accumulation and inflammatory response in macrophages. METHODS: After stimulation with the indicated times and doses of LPS, miR-21 mRNA levels were analyzed by Quantitative real-time PCR. Following transfection with miR-21 or anti-miR-21 inhibitor, lipid deposition and foam cell formation was detected by high-performance liquid chromatography (HPLC) and Oil-red O staining. Furthermore, the inflammatory cytokines interleukin 6 (IL-6) and interleukin 10 (IL-10) were evaluated by Enzyme-linked immunosorbent assay (ELISA) assay. The underlying molecular mechanism was also investigated. RESULTS: In this study, LPS induced miR-21 expression in macrophages in a time- and dose-dependent manner. Further analysis confirmed that overexpression of miR-21 by transfection with miR-21 mimics notably attenuated lipid accumulation and lipid-laden foam cell formation in LPS-stimulated macrophages, which was reversely up-regulated when silencing miR-21 expression via anti-miR-21 inhibitor transfection, indicating a reverse regulator of miR-21 in LPS-induced foam cell formation. Further mechanism assays suggested that miR-21 regulated lipid accumulation by Toll-like receptor 4 (TLR4) and nuclear factor-κB (NF-κB) pathway as pretreatment with anti-TLR4 antibody or a specific inhibitor of NF-κB (PDTC) strikingly dampened miR-21 silence-induced lipid deposition. Additionally, overexpression of miR-21 significantly abrogated the inflammatory cytokines secretion of IL-6 and increased IL-10 levels, the corresponding changes were also observed when silencing miR-21 expression, which was impeded by preconditioning with TLR4 antibody or PDTC. CONCLUSIONS: Taken together, these results corroborated that miR-21 could negatively regulate LPS-induced lipid accumulation and inflammatory responses in macrophages by the TLR4-NF-κB pathway. Accordingly, our research will provide a prominent insight into how miR-21 reversely abrogates bacterial infection-induced pathological processes of atherosclerosis, indicating a promising therapeutic prospect for the prevention and treatment of atherosclerosis by miR-21 overexpression.