Exploration of the Catalytic Cycle Dynamics of Vigna Radiata H+-Translocating Pyrophosphatases Through Hydrogen-Deuterium Exchange Mass Spectrometry.

Vigna radiata H+-translocating pyrophosphatases (VrH+-PPases, EC 3.6.1.1) are present in various endomembranes of plants, bacteria, archaea, and certain protozoa. They transport H+ into the lumen by hydrolyzing pyrophosphate, which is a by-product of many essential anabolic reactions. Although the crystal structure of H+-PPases has been elucidated, the H+ translocation mechanism of H+-PPases in the solution state remains unclear. In this study, we used hydrogen-deuterium exchange (HDX) coupled with mass spectrometry (MS) to investigate the dynamics of H+-PPases between the previously proposed R state (resting state, Apo form), I state (intermediate state, bound to a substrate analog), and T state (transient state, bound to inorganic phosphate). When hydrogen was replaced by proteins in deuterium oxide solution, the backbone hydrogen atoms, which were exchanged with deuterium, were identified through MS. Accordingly, we used deuterium uptake to examine the structural dynamics and conformational changes of H+-PPases in solution. In the highly conserved substrate binding and proton exit regions, HDX-MS revealed the existence of a compact conformation with deuterium exchange when H+-PPases were bound with a substrate analog and product. Thus, a novel working model was developed to elucidate the in situ catalytic mechanism of pyrophosphate hydrolysis and proton transport. In this model, a proton is released in the I state, and the TM5 inner wall serves as a proton piston.

One Pandemic, Two Solutions: Comparing the U.S.-China Response and Health Priorities to COVID-19 from the Perspective of "Two Types of Control".

After three years of global rampage, the COVID-19 epidemic, the most serious infectious disease to occur worldwide since the 1918 influenza pandemic, is nearing its end. From the global experience, medical control and social control are the two main dimensions in the prevention and control of COVID-19. From the perspective of "two types of control", namely medical control and social control, this paper finds that the political system, economic structure, and cultural values of the United States greatly limit the government's ability to impose social control, forcing it to adopt medical control to fight the virus in a single dimension. In contrast, China's political system, economic structure, and cultural values allow its government to adopt stringent, extensive, and frequent social control, as well as medical control to fight the virus. This approach departs from the traditional pathway of fighting the epidemic, i.e., "infection-treatment-immunization", thereby outpacing the evolution of the virus and controlling its spread more rapidly. This finding helps explain why the Chinese government adopted a strict "zeroing" and "dynamic zeroing" policy during the first three years, at the cost of enormous economic, social, and even political legitimacy. It was not until late 2022, when the Omicron variant with the waning virulence became prevalent, that China chose to "coexist" with the virus, thus avoiding a massive epidemic-related death. While the United States adopted a pulsed-style strategy at the beginning of the epidemic, i.e., "relaxation-suppression-relaxation-suppression", and began to "coexist" with the virus in just one year, resulting in a large number of excess deaths associated with the epidemic. The study contributes to explaining the difference in the interplay between public health priorities and COVID-19 response strategies in China and the United States, based on the specific public health context and the perspective of "medical control" and "social control".

Transmission Power Control in Wireless Sensor Networks Using Fuzzy Adaptive Data Rate.

As the technology of Internet of Things (IoT) becomes popular, the number of sensor nodes also increases. The network coverage, extensibility, and reliability are also the key points of technical development. To address the challenge of environmental restriction and deployment cost, most sensor nodes are powered by batteries. Therefore, the low-power consumption becomes an important issue because of the finite value of battery capacity. In addition, significant interference occurs in the environment, thereby complicating reliable wireless communication. This study proposes a fuzzy-based adaptive data rate for the transmission power control in wireless sensor networks to balance the communication quality and power consumption. The error count and error interval perform the inputs of a fuzzy system and the corresponding fuzzy system output is guard that is utilized for limiting the upper bounds of data rate and transmission power. The long-term experimental results are introduced to demonstrate that the control algorithm can overcome environmental interference and obtain low-power performance. The sensor nodes have reliable communication under an ultra-low-power consumption. The experimental results show that the total power consumption of the proposed approach has been improved 73% compared with the system without executing the algorithm and also indicate the Packet Error Rate (PER) is close to 1%. Therefore, the proposed method is suitable for the battery supply IoT system.

Online Domain Adaptation for Rolling Bearings Fault Diagnosis with Imbalanced Cross-Domain Data.

Traditional machine learning methods rely on the training data and target data having the same feature space and data distribution. The performance may be unacceptable if there is a difference in data distribution between the training and target data, which is called cross-domain learning problem. In recent years, many domain adaptation methods have been proposed to solve this kind of problems and make much progress. However, existing domain adaptation approaches have a common assumption that the number of the data in source domain (labeled data) and target domain (unlabeled data) is matched. In this paper, the scenarios in real manufacturing site are considered, that the target domain data is much less than source domain data at the beginning, but the number of target domain data will increase as time goes by. A novel method is proposed for fault diagnosis of rolling bearing with online imbalanced cross-domain data. Finally, the proposed method which is tested on bearing dataset (CWRU) has achieved prediction accuracy of 95.89% with only 40 target samples. The results have been compared with other traditional methods. The comparisons show that the proposed online domain adaptation fault diagnosis method has achieved significant improvements. In addition, the deep transfer learning model by adaptive- network-based fuzzy inference system (ANFIS) is introduced to interpretation the results.

Glaucoma Detection Using Support Vector Machine Method Based on Spectralis OCT.

Spectralis optical coherence tomography (OCT) provided more detailed parameters in the peripapillary and macular areas among the OCT machines, but it is not easy to understand the enormous information (114 features) generated from Spectralis OCT in glaucoma assessment. Machine learning methodology has been well-applied in glaucoma detection in recent years and has the ability to process a large amount of information at once. Here we aimed to analyze the diagnostic capability of Spectralis OCT parameters on glaucoma detection using Support Vector Machine (SVM) classification method in our population. Our results showed that applying all OCT features with the SVM method had good capability in the detection of glaucomatous eyes (area under curve (AUC) = 0.82), as well as discriminating normal eyes from early, moderate, or severe glaucomatous eyes (AUC = 0.78, 0.89, and 0.93, respectively). Apart from using all OCT features, the minimum rim width (MRW) may be good feature groups to discriminate early glaucomatous from normal eyes (AUC = 0.78). The combination of peripapillary and macular parameters, including MRW_temporal inferior (TI), MRW_global (G), ganglion cell layer (GCL)_outer temporal (T2), GCL_inner inferior (I1), peripapillary nerve fiber layer thickness (ppNFLT)_temporal superior (TS), and GCL_inner temporal (T1), provided better results (AUC = 0.84). This study showed promise in glaucoma management in the Taiwanese population. However, further validation study is needed to test the performance of our proposed model in the real world.

Effects of Different Finger Grips and Arm Positions on the Performance of Manipulating the Chinese Brush in Chinese Adolescents.

This study aimed to investigate the effects of finger grip and wrist position on Chinese calligraphy handwriting (CCH). Thirty participants were recruited in the study and asked to manipulate the Chinese brush using two finger grip methods (three-finger grip and five-finger grip) and two wrist positions (suspended wrist and raised wrist). Three experimental writing tasks were applied to investigate writing stability, agility, and hand-eye coordination, and to evaluate the completion time (s), area of error (cm2), and error times. Subjective responses (arm aching level, ease of grip, exertion level, and comfort) regarding the four combinations of Chinese brush manipulation were measured. The results indicated significantly better performance with the three-finger grip for the stability and agility tests, and with the five-finger grip for the hand-eye coordination task. Using the suspended wrist position for CCH allowed better agility and hand-eye coordination than the raised wrist position. In consideration of the results of the four operational combinations, the three-finger grip with a suspended wrist position demonstrated the best performance in both objective and subjective measurements. It is recommended for application in the early learning stage. These findings can be considered when teaching Chinese brushes for beginners of CCH in schools.

Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion.

This paper proposes an estimation approach for tool wear and surface roughness using deep learning and sensor fusion. The one-dimensional convolutional neural network (1D-CNN) is utilized as the estimation model with X- and Y-coordinate vibration signals and sound signal fusion using sensor influence analysis. First, machining experiments with computer numerical control (CNC) parameters are designed using a uniform experimental design (UED) method to guarantee the variety of collected data. The vibration, sound, and spindle current signals are collected and labeled according to the machining parameters. To speed up the degree of tool wear, an accelerated experiment is designed, and the corresponding tool wear and surface roughness are measured. An influential sensor selection analysis is proposed to preserve the estimation accuracy and to minimize the number of sensors. After sensor selection analysis, the sensor signals with better estimation capability are selected and combined using the sensor fusion method. The proposed estimation system combined with sensor selection analysis performs well in terms of accuracy and computational effort. Finally, the proposed approach is applied for on-line monitoring of tool wear with an alarm, which demonstrates the effectiveness of our approach.

Deep Learning Approach for Vibration Signals Applications.

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

End-to-End Deep Learning by MCU Implementation: An Intelligent Gripper for Shape Identification.

This paper introduces a real-time processing and classification of raw sensor data using a convolutional neural network (CNN). The established system is a microcontroller-unit (MCU) implementation of an intelligent gripper for shape identification of grasped objects. The pneumatic gripper has two embedded accelerometers to sense acceleration (in the form of vibration signals) on the jaws for identification. The raw data is firstly transferred into images by short-time Fourier transform (STFT), and then the CNN algorithm is adopted to extract features for classifying objects. In addition, the hyperparameters of the CNN are optimized to ensure hardware implementation. Finally, the proposed artificial intelligent model is implemented on a MCU (Renesas RX65N) from raw data to classification. Experimental results and discussions are introduced to show the performance and effectiveness of our proposed approach.

Nanoemulsion adjuvantation strategy of tumor-associated antigen therapy rephrases mucosal and immunotherapeutic signatures following intranasal vaccination.

BACKGROUND: Emulsion adjuvants are a potent tool for effective vaccination; however, the size matters on mucosal signatures and the mechanism of action following intranasal vaccination remains unclear. Here, we launch a mechanistic study to address how mucosal membrane interacts with nanoemulsion of a well-defined size at cellular level and to elucidate the impact of size on tumor-associated antigen therapy. METHODS: The squalene-based emulsified particles at the submicron/nanoscale could be elaborated by homogenization/extrusion. The mucosal signatures following intranasal delivery in mice were evaluated by combining whole-mouse genome microarray and immunohistochemical analysis. The immunological signatures were tested by assessing their ability to influence the transportation of a model antigen ovalbumin (OVA) across nasal mucosal membranes and drive cellular immunity in vivo. Finally, the cancer immunotherapeutic efficacy is monitored by assessing tumor-associated antigen models consisting of OVA protein and tumor cells expressing OVA epitope. RESULTS: Uniform structures with ~200 nm in size induce the emergence of membranous epithelial cells and natural killer cells in nasal mucosal tissues, facilitate the delivery of protein antigen across the nasal mucosal membrane and drive broad-spectrum antigen-specific T-cell immunity in nasal mucosal tissues as well as in the spleen. Further, intranasal vaccination of the nanoemulsion could assist the antigen to generate potent antigen-specific CD8+ cytotoxic T-lymphocyte response. When combined with immunotherapeutic models, such an effective antigen-specific cytotoxic activity allowed the tumor-bearing mice to reach up to 50% survival 40 days after tumor inoculation; moreover, the optimal formulation significantly attenuated lung metastasis. CONCLUSIONS: In the absence of any immunostimulator, only 0.1% content of squalene-based nanoemulsion could rephrase the mucosal signatures following intranasal vaccination and induce broad-spectrum antigen-specific cellular immunity, thereby improving the efficacy of tumor-associated antigen therapy against in situ and metastatic tumors. These results provide critical mechanistic insights into the adjuvant activity of nanoemulsion and give directions for the design and optimization of mucosal delivery for vaccine and immunotherapy.

An Evaluation of Posture Recognition Based on Intelligent Rapid Entire Body Assessment System for Determining Musculoskeletal Disorders.

Determining the potential risks of musculoskeletal disorders through working postures in a workplace is expensive and time-consuming. A novel intelligent rapid entire body assessment (REBA) system based on convolutional pose machines (CPM), entitled the Quick Capture system, was applied to determine the risk levels. The aim of the study was to validate the feasibility and reliability of the CPM-based REBA system through a simulation experiment. The reliability was calculated from the differences of motion angles between the CPM-based REBA and a motion capture system. Results show the data collected by the Quick Capture system were consistent with those of the motion capture system; the average of root mean squared error (RMSE) was 4.77 and the average of Spearman's rho (ρ) correlation coefficient in the different 12 postures was 0.915. For feasibility evaluation, the linear weighted Cohen's kappa between the REBA score obtained by the Quick Capture system and those from the three experts were used. The result shows good agreement, with an average proportion agreement index (P0) of 0.952 and kappa of 0.738. The Quick Capture system does not only accurately analyze working posture, but also accurately determines risk level of musculoskeletal disorders. This study suggested that the Quick Capture system could be applied for a rapid and real-time on-site assessment.

Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function.

This study aimed to propose a prognostic method based on a one-dimensional convolutional neural network (1-D CNN) with clustering loss by classification training. The 1-D CNN was trained by collecting the vibration signals of normal and malfunction data in hybrid loss function (i.e., classification loss in output and clustering loss in feature space). Subsequently, the obtained feature was adopted to estimate the status for prognosis. The open bearing dataset and established gear platform were utilized to validate the functionality and feasibility of the proposed model. Moreover, the experimental platform was used to simulate the gear mechanism of the semiconductor robot to conduct a practical experiment to verify the accuracy of the model estimation. The experimental results demonstrate the performance and effectiveness of the proposed method.

Influence of China's 2009 healthcare reform on the utilisation of continuum of care for maternal health services: evidence from two cross-sectional household surveys in Shaanxi Province.

BACKGROUND: Continuum of care for maternal health services (CMHS) is a proven approach to improve health and safety for mothers and newborns. This study aims to explore the influence of China's 2009 healthcare reform on improving the CMHS utilisation. METHODS: This population-based cross-sectional quantitative study included 2332 women drawn from the fourth and fifth National Health Service Surveys of Shaanxi Province, conducted in 2008 and 2013 respectively, before and after China's 2009 healthcare reform. A generalised linear mixed model (GLMM) was applied to analyse the influence of this healthcare reform on utilisation of CMHS. Concentration curves, concentration indexes and its decomposition method were used to analyse the equity of changes in utilisation. RESULTS: This study showed post-reform CMHS utilisation was higher in both rural and urban women than the CMHS utilisation pre-reform (according to China's policy defining CMHS). The rate of CMHS utilisation increased from 24.66 to 41.55% for urban women and from 18.31 to 50.49% for rural women (urban: χ2 = 20.64, P < 0.001; rural: χ2 = 131.38, P < 0.001). This finding is consistent when the WHO's definition of CMHS is applied for rural women after reform (12.13% vs 19.26%; χ2 = 10.99, P = 0.001); for urban women, CMHS utilisation increased from 15.70 to 20.56% (χ2 = 2.57, P = 0.109). The GLMM showed that the rate of CMHS utilisation for urban women post-reform was five times higher than pre-reform rates (OR = 5.02, 95%CL: 1.90, 13.31); it was close to 15 times higher for rural women (OR = 14.70, 95%CL: 5.43, 39.76). The concentration index for urban women decreased from 0.130 pre-reform (95%CI: - 0.026, 0.411) to - 0.041 post-reform (95%CI: - 0.096, 0.007); it decreased from 0.104 (95%CI: - 0.012, 0.222) to 0.019 (95%CI: - 0.014, 0.060) for rural women. The horizontal inequity index for both groups of women also decreased (0.136 to - 0.047 urban and 0.111 to 0.019 for rural). CONCLUSIONS: China's 2009 healthcare reform has positively influenced utilisation rates and equity of CMHS's utilisation among both urban and rural women in Shaanxi Province. Addressing economic and educational attainment gaps between the rich and the poor may be effective ways to improve the persistent health inequities for rural women.

Potassium Stimulation of IAA Transport Mediated by the Arabidopsis Importer AUX1 Investigated in a Heterologous Yeast System.

Auxin regulates diverse processes involved in plant growth and development. AUX1 is the first identified and most widely investigated auxin importer, and plays an important role in root gravitropism and the development of lateral root and root hair. However, the regulation of auxin transport by AUX1 is still not well understood. In this study, we examined the effect of metal ions on AUX1 transport function and found that the activity could be specifically stimulated four times by K+. Further experiments revealed the preference of KF on the enhancement of transport activity of AUX1 over KCl, KBr, and KI. In addition, the interaction between K+ and AUX1 confers AUX1 more resistant to thermal stress but more vulnerable to proteolysis. Conventional chemical modification indicated that the extracellular acidic amino acids of AUX1 play a key role in the K+ stimulation. Site-specific mutagenesis showed that the replacement of Asp166, Asp293, and Asp312 of AUX1 to alanine deteriorated the K+-stimulated auxin transport. By contrast, when these residues were mutated to glutamate, lysine, or asparagine, only the D312E variant restored the IAA transport activity to the wild-type level. It is thus convinced that D312 is presumably the most promising residue for the K+ stimulation on AUX1.

Regulation of H+-pyrophosphatase by 14-3-3 Proteins from Arabidopsis thaliana.

Plant vacuolar H+-transporting inorganic pyrophosphatase (V-PPase; EC 3.6.1.1) is a crucial enzyme that exists on the tonoplast to maintain pH homeostasis across the vacuolar membrane. This enzyme generates proton gradient between cytosol and vacuolar lumen by hydrolysis of a metabolic byproduct, pyrophosphate (PP i ). The regulation of V-PPase at protein level has drawn attentions of many workers for decades, but its mechanism is still unclear. In this work, we show that AVP1, the V-PPase from Arabidopsis thaliana, is a target protein for regulatory 14-3-3 proteins at the vacuolar membrane, and all twelve 14-3-3 isoforms were analyzed for their association with AVP1. In the presence of 14-3-3ν, -µ, -ο, and -ι, both enzymatic activities and its associated proton pumping of AVP1 were increased. Among these 14-3-3 proteins, 14-3-3 µ shows the highest stimulation on coupling efficiency. Furthermore, 14-3-3ν, -µ, -ο, and -ι exerted protection of AVP1 against the inhibition of suicidal substrate PP i at high concentration. Moreover, the thermal profile revealed the presence of 14-3-3ο improves the structural stability of AVP1 against high temperature deterioration. Additionally, the 14-3-3 proteins mitigate the inhibition of Na+ to AVP1. Besides, the binding sites/motifs of AVP1 were identified for each 14-3-3 protein. Taken together, a working model was proposed to elucidate the association of 14-3-3 proteins with AVP1 for stimulation of its enzymatic activity.

Functional and fluorescence analyses of tryptophan residues in H+-pyrophosphatase of Clostridium tetani.

Homodimeric proton-translocating pyrophosphatase (H+-PPase; EC 3.6.1.1) maintains the cytoplasmic pH homeostasis of many bacteria and higher plants by coupling pyrophosphate (PPi) hydrolysis and proton translocation. H+-PPase accommodates several essential motifs involved in the catalytic mechanism, including the PPi binding motif and Acidic I and II motifs. In this study, 3 intrinsic tryptophan residues, Trp-75, Trp-365, and Trp-602, in H+-PPase from Clostridium tetani were used as internal probes to monitor the local conformational state of the periplasm domain, transmembrane region, and cytoplasmic domain, respectively. Upon binding of the substrate analog Mg-imidodiphosphate (Mg-IDP), local structural changes prevented the modification of tryptophan residues by N-bromosuccinimide (NBS), especially at Trp-602. Following Mg-Pi binding, Trp-75 and Trp-365, but not Trp-602, were slightly protected from structural modifications by NBS. These results reveal the conformation of H+-PPase is distinct in the presence of different ligands. Moreover, analyses of the Stern-Volmer relationship and steady-state fluorescence anisotropy also indicate that the local structure around Trp-602 is more exposed to solvent and varied under different environments. In addition, Trp-602 was identified to be a crucial residue in the H+-PPase that may potentially be involved in stabilizing the structure of the catalytic region by site-directed mutagenesis analysis.

An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchronization.

This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.

Functional investigation of transmembrane helix 3 in H⁺-translocating pyrophosphatase.

H⁺-translocating pyrophosphatase (H⁺-PPase, EC 3.6.1.1) plays an important role in acidifying vacuoles by transporting protons across membranes at the expense of pyrophosphate (PP(i)) hydrolysis. Vigna radiata H⁺-PPase (VrH⁺-PPase) contains 16 transmembrane helices (TMs). The hydrophobicity of TM3 is relatively lower than that of most other TMs, and the amino acids in this TM are highly conserved in plants. Furthermore, TM5 and -6, which are the core TMs involving in H⁺-PPase functions, are near TM3. It is thus proposed that TM3 is associated with H⁺-PPase activity. To address this possibility, site-directed mutagenesis was applied in this investigation to determine the role of TM3 in VrH⁺-PPase. Upon alanine/serine substitution, T138 and S142, whose side chains face toward the center TMs, were found to be involved in efficient proton transport. G149/S153 and G160/A164 pairs at the crucial termini of the two GxxxG-like motifs are indispensable in maintaining enzymatic activities and conformational stability. Moreover, stability in the vicinity surrounding G149 is pivotal for efficient expression. S153, M161 and A164 are critical for the K⁺-mediated stimulation of H⁺-PPase. Taken together, our results demonstrate that TM3 plays essential roles in PP(i) hydrolysis, proton transport, expression, and K⁺ stimulation of H⁺-PPase.

Squeezing at entrance of proton transport pathway in proton-translocating pyrophosphatase upon substrate binding.

Homodimeric proton-translocating pyrophosphatase (H(+)-PPase; EC 3.6.1.1) is indispensable for many organisms in maintaining organellar pH homeostasis. This unique proton pump couples the hydrolysis of PPi to proton translocation across the membrane. H(+)-PPase consists of 14-16 relatively hydrophobic transmembrane domains presumably for proton translocation and hydrophilic loops primarily embedding a catalytic site. Several highly conserved polar residues located at or near the entrance of the transport pathway in H(+)-PPase are essential for proton pumping activity. In this investigation single molecule FRET was employed to dissect the action at the pathway entrance in homodimeric Clostridium tetani H(+)-PPase upon ligand binding. The presence of the substrate analog, imidodiphosphate mediated two sites at the pathway entrance moving toward each other. Moreover, single molecule FRET analyses after the mutation at the first proton-carrying residue (Arg-169) demonstrated that conformational changes at the entrance are conceivably essential for the initial step of H(+)-PPase proton translocation. A working model is accordingly proposed to illustrate the squeeze at the entrance of the transport pathway in H(+)-PPase upon substrate binding.

Essential calcium-binding cluster of Leptospira LipL32 protein for inflammatory responses through the Toll-like receptor 2 pathway.

Leptospirosis is the most widespread zoonosis caused by the pathogenic Leptospira worldwide. LipL32, a 32-kDa lipoprotein, is the most abundant protein on the outer membrane of Leptospira and has an atypical poly(Asp) motif ((161)DDDDDGDD(168)). The x-ray crystallographic structure of LipL32 revealed that the calcium-binding cluster of LipL32 includes several essential residues Asp(132), Thr(133), Asp(164), Asp(165), and Tyr(178). The goals of this study were to determine possible roles of the Ca(2+)-binding cluster for the interaction of LipL32 and Toll-like receptor 2 (TLR2) in induced inflammatory responses of human kidney cells. Site-directed mutagenesis was employed to individually mutate Ca(2+)-binding residues of LipL32 to Ala, and their effects subsequently were observed. These mutations abolished primarily the structural integrity of the calcium-binding cluster in LipL32. The binding assay and atomic force microscopy analysis further demonstrated the decreased binding capability of LipL32 mutants to TLR2. Inflammatory responses induced by LipL32 variants, as determined by TLR2 pathway intermediates hCXCL8/IL-8, hCCL2/MCP-1, hMMP7, and hTNF-α, were also lessened. In conclusion, the calcium-binding cluster of LipL32 plays essential roles in presumably sustaining LipL32 conformation for its proper association with TLR2 to elicit inflammatory responses in human renal cells.