Genome oligopaint via local denaturation fluorescence in situ hybridization.

Cas9 in complex with a programmable guide RNA targets specific double-stranded DNA for cleavage. By harnessing Cas9 as a programmable loader of superhelicase to genomic DNA, we report a physiological-temperature DNA fluorescence in situ hybridization (FISH) method termed genome oligopaint via local denaturation (GOLD) FISH. Instead of global denaturation as in conventional DNA FISH, loading a superhelicase at a Cas9-generated nick allows for local DNA denaturation, reducing nonspecific binding of probes and avoiding harsh treatments such as heat denaturation. GOLD FISH relies on Cas9 cleaving target DNA sequences and avoids the high nuclear background associated with other genome labeling methods that rely on Cas9 binding. The excellent signal brightness and specificity enable us to image nonrepetitive genomic DNA loci and analyze the conformational differences between active and inactive X chromosomes. Finally, GOLD FISH could be used for rapid identification of HER2 gene amplification in patient tissue.

Co-evolution-based prediction of metal-binding sites in proteomes by machine learning.

Metal ions have various important biological roles in proteins, including structural maintenance, molecular recognition and catalysis. Previous methods of predicting metal-binding sites in proteomes were based on either sequence or structural motifs. Here we developed a co-evolution-based pipeline named 'MetalNet' to systematically predict metal-binding sites in proteomes. We applied MetalNet to proteomes of four representative prokaryotic species and predicted 4,849 potential metalloproteins, which substantially expands the currently annotated metalloproteomes. We biochemically and structurally validated previously unannotated metal-binding sites in several proteins, including apo-citrate lyase phosphoribosyl-dephospho-CoA transferase citX, an Escherichia coli enzyme lacking structural or sequence homology to any known metalloprotein (Protein Data Bank (PDB) codes: 7DCM and 7DCN ). MetalNet also successfully recapitulated all known zinc-binding sites from the human spliceosome complex. The pipeline of MetalNet provides a unique and enabling tool for interrogating the hidden metalloproteome and studying metal biology.

C5AR1-induced TLR1/2 pathway activation drives proliferation and metastasis in anaplastic thyroid cancer.

This study aimed to elucidate the role and mechanisms of Complement C5a receptor 1 (C5AR1) in driving the malignant progression of anaplastic thyroid carcinoma (ATC). C5AR1 expression was assessed in ATC tissues and cell lines. Functional assays evaluated the effects of C5AR1 knockdown on the malignant features of ATC cells. The interaction between C5AR1 and miR-335-5p was confirmed using a luciferase reporter assay and Fluorescence in situ hybridization, and the impact of C5AR1 knockdown on the Toll-like receptor (TLR) 1/2 signaling pathway was examined. In vivo studies evaluated the effects of C5AR1 modulation on tumor growth and metastasis. C5AR1 levels were elevated in ATC tumor samples and associated with poor survival in ATC patients. C5AR1 knockdown impeded ATC cell proliferation, migration, and invasion in vitro. MiR-335-5p was identified as an upstream regulator of C5AR1, which negatively modulates C5AR1 expression. C5AR1 knockdown diminished TLR1, TLR2, and myeloid differentiation primary response 88 (MyD88) levels, while C5AR1 overexpression activated this pathway. Blocking TLR1/2 signaling abrogated the oncogenic effects of C5AR1 overexpression. C5AR1 silencing inhibited tumor growth and lung metastasis of ATC cells in nude mice. C5AR1 contributes to ATC tumorigenesis and metastasis by activating the TLR1/2 pathway, and is negatively regulated by miR-335-5p. Targeting the miR-335-5p/C5AR1/TLR1/2 axis represents a potential therapeutic strategy for ATC.

Risk factors for anxiety in patients with epilepsy: A meta-analysis.

BACKGROUND: Epilepsy is a very common neurological disease, and it is important to focus on both controlling seizures and alleviating the psychological problems associated with this disease.Anxiety is an important risk factor for epilepsy and seriously affects the quality of life of patients with epilepsy (PWE). However, several risk factors for anxiety in PWE are relatively controversial and understudied. This meta-analysis was performed to identify potential risk factors for anxiety in PWE with the aim of reducing the incidence of anxiety and improving the quality of life among the individuals. METHOD: The PubMed, Embase and Cochrane Library databases were systematically searched up to July 2023 to find eligible original English studies. All the search results were reviewed based on our inclusion and exclusion criteria. We calculated the combined odds ratios (ORs), standard mean differences (SMDs) and their corresponding 95% confidence intervals (CIs) to evaluate the effect of the included risk factors on anxiety in PWE. RESULTS: Twenty-four studies involving 5,403 PWE were ultimately included. The pooled results of our meta-analysis showed that female sex (OR = 1.67; 95 % CI: 1.30,2.15; p < 0.001), unmarried/divorced/widowed (OR = 0.83; 95 % CI: 0.72,0.96; p = 0.011), low socioeconomic status (OR = 0.47; 95 % CI: 0.33,0.67; p < 0.001), education levels below high school (OR = 1.74; 95 % CI: 1.36,2.23; p < 0.001), a history of trauma (OR = 2.53; 95 % CI: 1.69,3.78; p < 0.001), monotherapy (OR = 0.49; 95 % CI: 0.39,0.62; p < 0.001), AED-induced psychiatric side effects (OR = 2.45; 95 % CI: 1.20,4.98); p = 0.014), depression (OR = 5.45 95 % CI: 2.49,11.94; p < 0.001), a history of suicide (OR = 3.56; 95 % CI: 1.72,7.38; p = 0.001), and illness-related shame (OR = 2.76; 95 % CI: 2.17,3.52; p < 0.001) were risk factors for anxiety. CONCLUSION: This meta-analysis showed that female, unmarried, low socioeconomic status, education level below senior high school, a history of trauma, monotherapy, AED-induced psychiatric side effects, depression, a history of suicide, and shame were risk factors for anxiety in PWE. However, further research is needed to determine the effect of other potential risk factors on anxiety in PWE. In addition, most of the studies included in this meta-analysis were not uniform in scale, and the risk factors were not comprehensive; therefore, larger prospective studies in different countries are needed to further investigate these risk factors.

Experimental Study of Autologous Platelet-rich Plasma Combined With Sodium Hyaluronate on Tendon-bone Healing After Rotator Cuff Injury Repair in Rabbits.

OBJECTIVE: To investigate the therapeutic effects of autologous platelet-rich plasma (PRP) combined with sodium hyaluronate on tendon healing following rotator cuff injury repair in rabbits. METHODS: New Zealand white rabbits were randomly assigned to five groups: sham operation group, control group, PRP group, sodium hyaluronate group, and combined group, each comprising 12 rabbits. A rotator cuff injury model was established in all groups except the sham operation group. At 8 weeks post-surgery, 12 lateral rotator cuff specimens were taken from each group. Four specimens were randomly selected from each group for biomechanical testing, and analyses were conducted on the expression of vascular endothelial growth factor (VEGF), the fiber area ratio of COL-I and COL-III, and tissue morphology. RESULTS: The combined group exhibited the highest biomechanical strength in the cuff tissue of white rabbits (P < 0.05). There was no significant difference in VEGF levels among the five groups (F = 0.814, P = 0.523). However, a significant difference was observed in the ratio of fiber area between COL-I and COL-III groups (F = 11.600, P < 0.001), with the combined group scoring the highest (3.82 ± 0.47 minutes). The inflammatory infiltration in tendon-bone tissue was minimal, and histological morphology was optimal. CONCLUSION: The combination of PRP and sodium hyaluronate effectively promotes the repair of rotator cuff injuries and accelerates tendon-bone healing.

Real-time observation of Cas9 postcatalytic domain motions.

CRISPR-Cas9 from Streptococcus pyogenes is an RNA-guided DNA endonuclease, which has become the most popular genome editing tool. Coordinated domain motions of Cas9 prior to DNA cleavage have been extensively characterized but our understanding of Cas9 conformations postcatalysis is limited. Because Cas9 can remain stably bound to the cleaved DNA for hours, its postcatalytic conformation may influence genome editing mechanisms. Here, we use single-molecule fluorescence resonance energy transfer to characterize the HNH domain motions of Cas9 that are coupled with cleavage activity of the target strand (TS) or nontarget strand (NTS) of DNA substrate. We reveal an NTS-cleavage-competent conformation following the HNH domain conformational activation. The 3' flap generated by NTS cleavage can be rapidly digested by a 3' to 5' single-stranded DNA-specific exonuclease, indicating Cas9 exposes the 3' flap for potential interaction with the DNA repair machinery. We find evidence that the HNH domain is highly flexible post-TS cleavage, explaining a recent observation that the HNH domain was not visible in a postcatalytic cryo-EM structure. Our results illuminate previously unappreciated regulatory roles of DNA cleavage activity on Cas9's conformation and suggest possible biotechnological applications.

Dynamic and Label-Free Sensing of Cardiomyocyte Responses to Nanosized Vesicles for Cardiac Oxidative Stress Injury Therapy.

Cardiac oxidative stress is a significant phenotype of myocardial infarction disease, a leading cause of global health threat. There is an urgent need to develop innovative therapies. Nanosized extracellular vesicle (nEV)-based therapy shows promise, yet real-time monitoring of cardiomyocyte responses to nEVs remains a challenge. In this study, a dynamic and label-free cardiomyocyte biosensing system using microelectrode arrays (MEAs) was constructed. Cardiomyocytes were cultured on MEA devices for electrophysiological signal detection and treated with nEVs from E. coli, gardenia, HEK293 cells, and mesenchymal stem cells (MSC), respectively. E. coli-nEVs and gardenia-nEVs induced severe paroxysmal fibrillation, revealing distinct biochemical communication compared to MSC-nEVs. Principal component analysis identified variations and correlations between nEV types. MSC-nEVs enhanced recovery without inducing arrhythmias in a H2O2-induced oxidative stress injury model. This study establishes a fundamental platform for assessing biochemical communication between nEVs and cardiomyocytes, offering new avenues for understanding nEVs' functions in the cardiovascular system.

Understanding the Role of Titanium Metal-Organic Framework Nanosheets in Modulating Anode Chemistry for Aqueous Zinc-Ion Batteries.

Aqueous zinc-ion batteries have attracted a continually increasing level of interest for large-scale energy storage because they are highly safe and have high energy density and abundant reserves. However, Zn anodes face significant challenges such as severe dendrite growth and hydrogen evolution reaction (HER). We here propose an efficient Zn2+ sieve strategy for modulating the anode chemistry using two-dimensional NH2-MIL-125 (Ti) metal-organic framework (MOF) nanosheets. Theoretical investigations reveal the crucial role of the Ti MOF in regulating Zn2+ solvation structures for fast diffusion and uniform deposition and decreasing HER reactivity. The structure of the nanosheets enables abundant accessible desolvation sites and shortened ionic pathways. As a result, the MOF nanosheet-protected Zn anode exhibited greatly improved cycling stability in both symmetric cells and full cells. Operando optical monitoring and postmortem analysis revealed effective suppression of dendrite growth and HER by Ti MOF nanosheets. This anti-HER MOF-enabled Zn2+ sieve strategy provides a viable Zn anode and provides new insights for optimizing aqueous batteries.

Machine Learning Model for Screening Thyroid Stimulating Hormone Receptor Agonists Based on Updated Datasets and Improved Applicability Domain Metrics.

Machine learning (ML) models for screening endocrine-disrupting chemicals (EDCs), such as thyroid stimulating hormone receptor (TSHR) agonists, are essential for sound management of chemicals. Previous models for screening TSHR agonists were built on imbalanced datasets and lacked applicability domain (AD) characterization essential for regulatory application. Herein, an updated TSHR agonist dataset was built, for which the ratio of active to inactive compounds greatly increased to 1:2.6, and chemical spaces of structure-activity landscapes (SALs) were enhanced. Resulting models based on 7 molecular representations and 4 ML algorithms were proven to outperform previous ones. Weighted similarity density (ρs) and weighted inconsistency of activities (IA) were proposed to characterize the SALs, and a state-of-the-art AD characterization methodology ADSAL{ρs, IA} was established. An optimal classifier developed with PubChem fingerprints and the random forest algorithm, coupled with ADSAL{ρs ≥ 0.15, IA ≤ 0.65}, exhibited good performance on the validation set with the area under the receiver operating characteristic curve being 0.984 and balanced accuracy being 0.941 and identified 90 TSHR agonist classes that could not be found previously. The classifier together with the ADSAL{ρs, IA} may serve as efficient tools for screening EDCs, and the AD characterization methodology may be applied to other ML models.

Applicability Domains Based on Molecular Graph Contrastive Learning Enable Graph Attention Network Models to Accurately Predict 15 Environmental End Points.

In silico models for predicting physicochemical properties and environmental fate parameters are necessary for the sound management of chemicals. This study employed graph attention network (GAT) algorithms to construct such models on 15 end points. The results showed that the GAT models outperformed the previous state-of-the-art models, and their performance was not influenced by the presence or absence of compounds with certain structures. Molecular similarity density (ρs) was found to be a key metrics characterizing data set modelability, in addition to the proportion of compounds at activity cliffs. By introducing molecular graph (MG) contrastive learning, MG-based ρs and molecular inconsistency in activities (IA) were calculated and employed for characterizing the structure-activity landscape (SAL)-based applicability domain ADSAL{ρs, IA}. The GAT models coupled with ADSAL{ρs, IA} significantly improved the prediction coefficient of determination (R2) on all the end points by an average of 14.4% and enabled all the end points to have R2 > 0.9, which could hardly be achieved previously. The models were employed to screen persistent, mobile, and/or bioaccumulative chemicals from inventories consisting of about 106 chemicals. Given the current state-of-the-art model performance and coverage of the various environmental end points, the constructed models with ADSAL{ρs, IA} may serve as benchmarks for future efforts to improve modeling efficacy.

Analysis of Mechanical Properties and Fatigue Life of Microturbine Angular Contact Ball Bearings under Eccentric Load Conditions.

Angular contact ball bearings are common basic components in rotating machinery. During the operation of the bearing, the rolling slips, resulting in contact sliding friction between it and the raceway, which in turn causes wear in the rolling element and increase in the radial clearance of the bearing. The increase in clearance also affects the stiffness of the bearing, which in turn affects the natural frequency and fatigue life of the bearing. At present, there are few studies on the influence of bearing wear (variation of clearance) on life. In this paper, the finite element model based on the theory of contact mechanics is established for the angular contact ball bearing with medium- and high-speed rotation, and the mechanical properties and fatigue life influenced by the internal action of the bearing are analyzed. The effects of radial load and deflection angle on the mechanical properties and fatigue life of the bearing are also studied. Based on the analysis results of bearing contact mechanical properties and clearance changes, the calculation method of bearing life under rolling element wear is established. The influence of the variation of clearance and preload clearance on bearing life is analyzed, and the optimal preload is obtained. The research results of this paper can provide a theoretical basis for optimizing the installation of angular contact ball bearings, reasonably determining the service conditions, and prolonging the service life of bearings, which is necessary for engineering practice.

Approach to the Quantitative Diagnosis of Rolling Bearings Based on Optimized VMD and Lempel-Ziv Complexity under Varying Conditions.

The quantitative diagnosis of rolling bearings is essential to automating maintenance decisions. Over recent years, Lempel-Ziv complexity (LZC) has been widely used for the quantitative assessment of mechanical failures as one of the most valuable indicators for detecting dynamic changes in nonlinear signals. However, LZC focuses on the binary conversion of 0-1 code, which can easily lose some effective information about the time series and cannot fully mine the fault characteristics. Additionally, the immunity of LZC to noise cannot be insured, and it is difficult to quantitatively characterize the fault signal under strong background noise. To overcome these limitations, a quantitative bearing fault diagnosis method based on the optimized Variational Modal Decomposition Lempel-Ziv complexity (VMD-LZC) was developed to fully extract the vibration characteristics and to quantitatively characterize the bearing faults under variable operating conditions. First, to compensate for the deficiency that the main parameters of the variational modal decomposition (VMD) have to be selected by human experience, a genetic algorithm (GA) is used to optimize the parameters of the VMD and adaptively determine the optimal parameters [k, α] of the bearing fault signal. Furthermore, the IMF components that contain the maximum fault information are selected for signal reconstruction based on the Kurtosis theory. The Lempel-Ziv index of the reconstructed signal is calculated and then weighted and summed to obtain the Lempel-Ziv composite index. The experimental results show that the proposed method is of high application value for the quantitative assessment and classification of bearing faults in turbine rolling bearings under various operating conditions such as mild and severe crack faults and variable loads.

Solid Base Assisted Dual-Promoted Heterogeneous Conversion of PVC to Metal-Free Porous Carbon Catalyst.

Polyvinyl chloride (PVC) is widely used in daily life, but its waste has become a serious environmental problem. A solid base assisted low-temperature solvothermal dehalogenation was developed in this work to sustainably and efficiently transform PVC into high-value dimethylamine hydrochloride (DMACl) chemical and N,O co-doped carbon monolith with hierarchically porous structure. The synergistic promotion of solid-base catalyst and solvent decomposition with the removal of HCl can shift forward the chemical equilibrium to promote the dechlorination of PVC and increase the carbon yield. Meanwhile, the solid-base catalyst can also act as a pore-forming additive to fabricate the carbon monolith with hierarchical pores. Induced by the high specific surface area, hierarchical pores and N,O co-doped structure, the generated carbon monolith exhibits superior electrocatalytic performance towards H2 evolution. These discoveries shed light on the design of synergistically coupled solvent and solid catalyst to promote the heterogeneous conversion of waste chlorinated plastics into high-value chemicals for a sustainable future.

Graph Attention Network Model with Defined Applicability Domains for Screening PBT Chemicals.

In silico models for screening environmentally persistent, bio-accumulative, and toxic (PBT) substances are necessary for sound management of chemicals. Due to the complex structure-activity landscapes (SALs) on the PBT attributes, previous models for screening PBT chemicals lack either applicability domain (AD) characterizations or interpretability, restricting their applications. Herein, graph attention networks (GATs), a novel neural network architecture, were introduced to construct models for screening PBT chemicals. Results show that the GAT model not only outperformed those in previous studies but also exhibited interpretability since it optimizes attention weight parameters (PAW) that indicate contributions of each atom to the PBT attributes. An AD characterization termed ADFP-AC, which considers both molecular fingerprint (FP) similarities and compounds at activity cliffs (ACs) of SALs, was proposed to describe the ADs, which further assured the performance of the GAT model. Eight previously unidentified classes of compounds were identified as PBT chemicals from the Inventory of Existing Chemical Substances in China. The GAT model together with the ADFP-AC characterization may serve as efficient tools for screening PBT chemicals, and the modeling methodology can be applied to other physicochemical, environmental, behavioral, and toxicological parameters of chemicals that are necessary for their risk assessment and management.

Single molecule analysis of effects of non-canonical guide RNAs and specificity-enhancing mutations on Cas9-induced DNA unwinding.

Cas9 has made a wide range of genomic manipulation possible. However, its specificity continues to be a challenge. Non-canonical gRNAs and new engineered variants of Cas9 have been developed to improve specificity, but at the cost of the on-target activity. DNA unwinding is a checkpoint before cleavage by Cas9, and was shown to be made more sensitive to sequence mismatches by specificity-enhancing mutations in engineered Cas9s. Here we performed single-molecule FRET-based DNA unwinding experiments using various combinations of non-canonical gRNAs and different Cas9s. All engineered Cas9s were less promiscuous than wild type when canonical gRNA was used, but HypaCas9 had much-reduced on-target unwinding. Cas9-HF1 and eCas9 showed the best balance between low promiscuity and high on-target activity with canonical gRNA. When extended gRNAs with one or two non-matching guanines added to the 5' end were used, Sniper1-Cas9 showed the lowest promiscuity while maintaining high on-target activity. Truncated gRNA generally reduced unwinding and adding a non-matching guanine to the 5' end of gRNA influenced unwinding in a sequence-context dependent manner. Our results are consistent with cell-based cleavage data and provide a mechanistic understanding of how various Cas9/gRNA combinations perform in genome engineering.

A Novel Method about the Representation and Discrimination of Traffic State.

The representation and discrimination of various traffic states play an essential role in solving traffic accidents and congestion as the foundation of traffic state prediction. However, the existing representation of the traffic state usually only considers the road congestion layer and divides the traffic state into congested and unblocked. Representation only at the congestion layer is difficult to reflect the road traffic state comprehensively. Therefore, we select three indicators from the layers of road congestion, road safety, and road stability, respectively, then utilizing K-means to cluster the traffic state. The clustering results can be regarded as a new type for the representation of a traffic state. As a result, the traffic states are divided into four classes, which comprehensively reflects the level of road congestion, safety, and stability. Using the four traffic states obtained from the clustering results as class labels, we applied a multi-layer perceptron (MLP) to classify the different traffic states, and the receiver operating characteristic (ROC) curve is assessed to verify the superiority of the classification results. Finally, a visual display of the real-time traffic state in a city's central area was given.

Sequence-Based Prediction of Cysteine Reactivity Using Machine Learning.

As one of the most intrinsically reactive amino acids, cysteine carries a variety of important biochemical functions, including catalysis and redox regulation. Discovery and characterization of cysteines with heightened reactivity will help annotate protein functions. Chemical proteomic methods have been used to quantitatively profile cysteine reactivity in native proteomes, showing a strong correlation between the chemical reactivity of a cysteine and its functionality; however, the relationship between the cysteine reactivity and its local sequence has not yet been systematically explored. Herein, we report a machine learning method, sbPCR (sequence-based prediction of cysteine reactivity), which combines the basic local alignment search tool, truncated composition of k-spaced amino acid pair analysis, and support vector machine to predict cysteines with hyper-reactivity based on only local sequence features. Using a benchmark set compiled from hyper-reactive cysteines in human proteomes, our method can achieve a prediction accuracy of 98%, a precision of 95%, and a recall ratio of 89%. We utilized these governing features of local sequence motifs to expand the prediction to potential hyper-reactive cysteines in other proteomes deposited in the UniProt database. We validated our predictions in Escherichia coli by activity-based protein profiling and discovered a hyper-reactive cysteine from a functionally uncharacterized protein, YecH. Biochemical analysis suggests that the hyper-reactive cysteine might be involved in metal binding. Our computational method provides a large inventory of potential hyper-reactive cysteines in proteomes and is highly complementary to other experimental approaches to guide systematic annotation of protein functions in the postgenome era.

Quantitative Profiling of Protein Carbonylations in Ferroptosis by an Aniline-Derived Probe.

Ferroptosis is a regulated form of necrotic cell death implicated in carcinogenesis and neurodegeneration that is driven by phospholipid peroxidation. Lipid-derived electrophiles (LDEs) generated during this process can covalently modify proteins ("carbonylation") and affect their functions. Here we report the development of a quantitative chemoproteomic method to profile carbonylations in ferroptosis by an aniline-derived probe. Using the method, we established a global portrait of protein carbonylations in ferroptosis with >400 endogenously modified proteins and for the first time, identified >20 residue sites with endogenous LDE modifications in ferroptotic cells. Specifically, we discovered and validated a novel cysteine site of modification on voltage-dependent anion-selective channel protein 2 (VDAC2) that might play an important role in sensitizing LDE signals and mediating ferroptosis. Our results will contribute to the understanding of ferroptotic signaling and pathogenesis and provide potential biomarkers for ferroptosis detection.

Flower-like MoSe2 /C Composite with Expanded (0 0 2) Planes of Few-layer MoSe2 as the Anode for High-Performance Sodium-Ion Batteries.

Sodium-ion batteries (SIBs) have caught considerable attention in last few years owing to the abundance of sodium in comparison to lithium. The commercial graphite anode is demonstrated unsuitable as an anode material for SIBs due to the larger radius of Na+ ions, whereas the transition metal dichalcogenides (TMDs) show great potential as anodes for SIBs because of their high achievable capacity. The sluggish kinetics, large volume expansion, and aggregation of those materials however results in severe decay of the electrochemical performance. In this work, a flower-like MoSe2 /C composite is synthesized with ethylenediamine and cassava starch (denoted as MoSe2 /Ccas ) and designed based on these principles: 1) expand the d-spacing of (0 0 2) planes of MoSe2 to enhance the kinetics for the intercalation-deintercalation of Na+ ions and 2) embed MoSe2 into the carbon matrix to enhance the conductivity and restrict the volume expansion and aggregation of MoSe2 . As a result, MoSe2 /Ccas exhibits superior cycle performance and rate capability for sodium storage. It shows durable long-life cycle capability with a reversible capacity of 360 mAh g-1 after 350 cycles at 0.5 Ag-1 . At the current density of 4 Ag-1 , the reversible capacity is still maintained at 266 mAh g-1 .

Conformation switching of AIM2 PYD domain revealed by NMR relaxation and MD simulation.

Protein absent in melanoma 2 (AIM2) is a double-strand DNA (ds DNA) sensor mainly located in cytoplasm of cell. It includes one N terminal PYD domain and one C terminal HIN domain. When the ds DNA such as DNA viruses and bacteria entered cytoplasm, the HIN domain of AIM2 will recognize and bind to DNA, and the PYD domain will bind to ASC protein which will result in the formation of AIM2 inflammasome. Three AIM2 PYD domain structures have been solved, but every structure yields a unique conformation around the α3 helix region. To understand why different AIM2 PYD structures show different conformations in this region, we use NMR relaxation techniques to study the backbone dynamics of mouse AIM2 PYD domain and perform molecular dynamics (MD) simulations on both mouse and human AIM2 PYD structures. Our results indicate that this region is highly flexible in both mouse and human AIM2 PYD domains, and the PYD domain may exist as a conformation ensemble in solution. Different environment makes the population vary among pre-existing conformational substrates of the ensemble, which may be the reason why different AIM2 PYD structures were observed under different conditions. Further docking analysis reveals that the conformation switching may be important for the autoinhibition of the AIM2 protein.