Inhibitory Effects of ab initio Antiviral Peptides Efficiently Designed Based on APD3 Database

Natural antimicrobial peptides (AMPs) aid in many organisms innate immune defense against pathogens. Engineering new therapeutics from natural AMP templates may provide an effective treatment to emerging microbial infections such as SARS-CoV-2, Ebola viruses, and drug-resistant bacteria. One way to design antimicrobial peptides is the database filtering technology (DFT). The DFT is an ab initio design that selects the most probable parameters for an AMP by statistical analysis in the antimicrobial peptide database (https://aps.unmc.edu). To our knowledge, the DFT design has never been used to develop an antiviral peptide. We present here the improved DFT that enables a faster and more efficient design. Indeed, the peptide designed in this manner inhibits both SARS-CoV-2 and Ebola viruses. We also validated that deviations from the most probable length or amino acids led to a decrease in peptide activity. Further refinement of the peptide by introducing a disulfide bond improved peptide stability to proteases such as chymotrypsin and trypsin. Our database designed and improved peptide 1 (DDIP1) has the potential as a novel antiviral agent.https://digitalcommons.unmc.edu/surp2021/1033/thumbnail.jp

Improved Database Filtering Technology Enables More Efficient Ab Initio Design of Potent Peptides against Ebola Viruses

The rapid mutations of viruses such as SARS-CoV-2 require vaccine updates and the development of novel antiviral drugs. This article presents an improved database filtering technology for a more effective design of novel antiviral agents. Different from the previous approach, where the most probable parameters were obtained stepwise from the antimicrobial peptide database, we found it possible to accelerate the design process by deriving multiple parameters in a single step during the peptide amino acid analysis. The resulting peptide DFTavP1 displays the ability to inhibit Ebola virus. A deviation from the most probable peptide parameters reduces antiviral activity. The designed peptides appear to block viral entry. In addition, the amino acid signature provides a clue to peptide engineering to gain cell selectivity. Like human cathelicidin LL-37, our engineered peptide DDIP1 inhibits both Ebola and SARS-CoV-2 viruses. These peptides, with broad antiviral activity, may selectively disrupt viral envelopes and offer the lasting efficacy required to treat various RNA viruses, including their emerging mutants

Rhizobacterial community structure in response to nitrogen addition varied between two Mollisols differing in soil organic carbon

Excessive nitrogen (N) fertilizer input to agroecosystem fundamentally alters soil microbial properties and subsequent their ecofunctions such as carbon (C) sequestration and nutrient cycling in soil. However, between soils, the rhizobacterial community diversity and structure in response to N addition is not well understood, which is important to make proper N fertilization strategies to alleviate the negative impact of N addition on soil organic C and soil quality and maintain plant health in soils. Thus, a rhizo-box experiment was conducted with soybean grown in two soils, i.e. soil organic C (SOC)-poor and SOC-rich soil, supplied with three N rates in a range from 0 to 100 mg N kg−1. The rhizospheric soil was collected 50 days after sowing and MiSeq sequencing was deployed to analyze the rhizobacterial community structure. The results showed that increasing N addition significantly decreased the number of phylotype of rhizobacteria by 12.3%, and decreased Shannon index from 5.98 to 5.36 irrespective of soils. Compared to the SOC-rich soil, the increases in abundances of Aquincola affiliated to Proteobacteria, and Streptomyces affiliated to Actinobacteria were greater in the SOC-poor soil in response to N addition. An opposite trend was observed for Ramlibacter belong to Proteobacteria. These results suggest that N addition reduced the rhizobacterial diversity and its influence on rhizobacterial community structure was soil-specific

An analytical approach to evaluate point cloud registration error utilizing targets

Point cloud registration is essential for processing terrestrial laser scanning (TLS) point cloud datasets. The registration precision directly influences and determines the practical usefulness of TLS surveys. However, in terms of target based registration, analytical point cloud registration error models employed by scanner manufactures are only suitable to evaluate target registration error, rather than point cloud registration error. This paper proposes an new analytical approach called the registration error (RE) model to directly evaluate point cloud registration error. We verify the proposed model by comparing RE and root mean square error (RMSE) for all points in three point clouds that are approximately equivalent

Neutrophils are Mediators of Metastatic Prostate Cancer Progression in Bone

Bone metastatic prostate cancer (BM-PCa) significantly reduces overall patient survival and is currently incurable. Current standard immunotherapy showed promising results for PCa patients with metastatic, but less advanced, disease (i.e., fewer than 20 bone lesions) suggesting that PCa growth in bone contributes to response to immunotherapy. We found that: (1) PCa stimulates recruitment of neutrophils, the most abundant immune cell in bone, and (2) that neutrophils heavily infiltrate regions of prostate tumor in bone of BM-PCa patients. Based on these findings, we examined the impact of direct neutrophil-prostate cancer interactions on prostate cancer growth. Bone marrow neutrophils directly induced apoptosis of PCa in vitro and in vivo, such that neutrophil depletion in bone metastasis models enhanced BM-PCa growth. Neutrophil-mediated PCa killing was found to be mediated by suppression of STAT5, a transcription factor shown to promote PCa progression. However, as the tumor progressed in bone over time, neutrophils from late-stage bone tumors failed to elicit cytotoxic effector responses to PCa. These findings are the first to demonstrate that bone-resident neutrophils inhibit PCa and that BM-PCa are able to progress via evasion of neutrophil-mediated killing. Enhancing neutrophil cytotoxicity in bone may present a novel therapeutic option for bone metastatic prostate cancer

Effects of IL8 and Immune Cells on the Regulation of Luteal Progesterone Secretion

Recent studies suggest that chemokines may mediate the luteolytic action of PGF2α (PGF). Our objective was to identify chemokines induced by PGF in vivo and to determine the effects of IL8 on specific luteal cell types in vitro. Midcycle cows were injected with saline or PGF, ovaries were removed after 0.5 - 4 h and chemokine expression was analyzed by qPCR. In vitro expression of IL8 was analyzed after PGF administration and with cell signaling inhibitors to determine the mechanism of PGF-induced chemokine expression. Purified neutrophils were analyzed for migration and activation in response to IL8 and PGF. Purified luteal cell types (steroidogenic, endothelial and fibroblast cells) were used to identify which cells respond to chemokines. Neutrophils and peripheral blood mononuclear cells (PBMCs) were co-cultured with steroidogenic cells to determine their effect on progesterone production. IL8, CXCL2, CCL2, and CCL8 transcripts were rapidly increased following PGF treatment in vivo and. The stimulatory action of PGF on IL8 mRNA expression in vitro was prevented by inhibition of p38 and JNK signaling. IL8, but not PGF, TNF, or TGFB1, stimulated neutrophil migration. IL8 had no apparent action in purified luteal steroidogenic, endothelial, or fibroblast cells, but IL8 stimulated ERK phosphorylation in neutrophils. In co-culture experiments neither IL8 nor activated neutrophils altered basal or LH-stimulated luteal cell progesterone synthesis. In contrast, activated PBMCs inhibited LH-stimulated progesterone synthesis from cultured luteal cells. These data implicate a complex cascade of events during luteolysis involving chemokine signaling, neutrophil recruitment, and immune cell action within the corpus luteum

The Cartesian Path Planning of Free-Floating Space Robot using Particle Swarm Optimization

The Cartesian path planning of free-floating space robot is much more complex than that of fixed-based manipulators, since the end-effector pose (position and orientation) is path dependent, and the position-level kinematic equations can not be used to determine the joint angles. In this paper, a method based on particle swarm optimization (PSO) is proposed to solve this problem. Firstly, we parameterize the joint trajectory using polynomial functions, and then normalize the parameterized trajectory. Secondly, the Cartesian path planning is transformed to an optimization problem by integrating the differential kinematic equations. The object function is defined according to the accuracy requirement, and it is the function of the parameters to be defined. Finally, we use the Particle Swarm Optimization (PSO) algorithm to search the unknown parameters. The approach has the following traits: 1) The limits on joint angles, rates and accelerations are included in the planning algorithm; 2) There exist not any kinematic and dynamic singularities, since only the direct kinematic equations are used; 3) The attitude singularities do not exist, for the orientation is represented by quaternion; 4) The optimization algorithm is not affected by the initial parameters. Simulation results verify the proposed method

The Cartesian Path Planning of Free- Floating Space Robot using Particle Swarm Optimization

The Cartesian path planning of free-floating space robot is much more complex than that of fixed-based manipulators, since the end-effector pose (position and orientation) is path dependent, and the position-level kinematic equations can not be used to determine the joint angles. In this paper, a method based on particle swarm optimization (PSO) is proposed to solve this problem. Firstly, we parameterize the joint trajectory using polynomial functions, and then normalize the parameterized trajectory. Secondly, the Cartesian path planning is transformed to an optimization problem by integrating the differential kinematic equations. The object function is defined according to the accuracy requirement, and it is the function of the parameters to be defined. Finally, we use the Particle Swarm Optimization (PSO) algorithm to search the unknown parameters. The approach has the following traits: 1) The limits on joint angles, rates and accelerations are included in the planning algorithm; 2) There exist not any kinematic and dynamic singularities, since only the direct kinematic equations are used; 3) The attitude singularities do not exist, for the orientation is represented by quaternion; 4) The optimization algorithm is not affected by the initial parameters. Simulation results verify the proposed method

Variable Dimensional Scaling Method: A Novel Method for Path Planning and Inverse Kinematics

Traditional methods for solving the inverse kinematics of a hyper-redundant manipulator (HRM) can only plan the path of the end-effector with a complicated solving process, where obstacle avoidance is also not considered. To solve the above problems, a novel method for solving inverse kinematics of HRM is proposed in this paper: the variable dimension scaling method (VDSM), which can solve complex inverse kinematics while avoiding obstacles. Through this method, the path of the end-effector is scaled under a certain proportion and is adjusted depending on the position of the obstacle, which has good universality. The number of link angles changed is as small as possible in the process of achieving the end-effector moving along the desired path. With the redundancy of HRM, obstacle avoidance can be implemented in any environment by the proposed method. Through simulation and experiments in different environments, the above advantages of VDSM are verified