CEACAM1 Negatively Regulates IL-1β Production in LPS Activated Neutrophils by Recruiting SHP-1 to a SYK-TLR4-CEACAM1 Complex

LPS-activated neutrophils secrete IL-1β by activation of TLR-4. Based on studies in macrophages, it is likely that ROS and lysosomal destabilization regulated by Syk activation may also be involved. Since neutrophils have abundant expression of the ITIM-containing co-receptor CEACAM1 and Gram-negative bacteria such as Neisseria utilize CEACAM1 as a receptor that inhibits inflammation, we hypothesized that the overall production of IL-1β in LPS treated neutrophils may be negatively regulated by CEACAM1. We found that LPS treated neutrophils induced phosphorylation of Syk resulting in the formation of a complex including TLR4, p-Syk, and p-CEACAM1, which in turn, recruited the inhibitory phosphatase SHP-1. LPS treatment leads to ROS production, lysosomal damage, caspase-1 activation and IL-1β secretion in neutrophils. The absence of this regulation in Ceacam1−/− neutrophils led to hyper production of IL-1β in response to LPS. The hyper production of IL-1β was abrogated by in vivo reconstitution of wild type but not ITIM-mutated CEACAM1 bone marrow stem cells. Blocking Syk activation by kinase inhibitors or RNAi reduced Syk phosphorylation, lysosomal destabilization, ROS production, and caspase-1 activation in Ceacam1−/− neutrophils. We conclude that LPS treatment of neutrophils triggers formation of a complex of TLR4 with pSyk and pCEACAM1, which upon recruitment of SHP-1 to the ITIMs of pCEACAM1, inhibits IL-1β production by the inflammasome. Thus, CEACAM1 fine-tunes IL-1β production in LPS treated neutrophils, explaining why the additional utilization of CEACAM1 as a pathogen receptor would further inhibit inflammation

Fault Feature Analysis of a Cracked Gear Coupled Rotor System

Considering the misalignment of gear root circle and base circle and accurate transition curve, an improved mesh stiffness model for healthy gear is proposed, and it is validated by comparison with the finite element method. On the basis of the improved method, a mesh stiffness model for a cracked gear pair is built. Then a finite element model of a cracked gear coupled rotor system in a one-stage reduction gear box is established. The effects of crack depth, width, initial position, and crack propagation direction on gear mesh stiffness, fault features in time domain and frequency domain, and statistical indicators are investigated. Moreover, fault features are also validated by experiment. The results show that the improved mesh stiffness model is more accurate than the traditional mesh stiffness model. When the tooth root crack appears, distinct impulses are found in time domain vibration responses, and sidebands appear in frequency domain. Amplitudes of all the statistical indicators ascend gradually with the growth of crack depth and width, decrease with the increasing crack initial position angle, and firstly increase and then decrease with the growth of propagation direction angle

Study on the Relationship between Combustion Parameters and Cylinder Head Vibration Signal in Time Domain

Combustion-related characteristic parameters, such as the start of combustion (SoC) and the timing of the peak pressure increase rate (PIR), can be used as the feedback signals for the closed-loop control of combustion. A dynamic Finite Element Method (FEM) model was firstly developed to confirm the closely related time period between combustion pressure and vibration. On this basis, a fast processing method was developed to estimate the timings of SoC and the peak PIR in the closely related time period. This method was verified on a twelve-cylinder heavy-duty diesel engine at various engine speed and load. Results showed that the maximum deviation of the two parameters were within 2 °CA and 1.5 °CA, respectively, which suggested that the proposed method had an adequate accuracy

LGL1 binds to Integrin β1 and inhibits downstream signaling to promote epithelial branching in the mammary gland.

Branching morphogenesis is a fundamental process by which organs in invertebrates and vertebrates form branches to expand their surface areas. The current dogma holds that directional cell migration determines where a new branch forms and thus patterns branching. Here, we asked whether mouse Lgl1, a homolog of the Drosophila tumor suppressor Lgl, regulates epithelial polarity in the mammary gland. Surprisingly, mammary glands lacking Lgl1 have normal epithelial polarity, but they form fewer branches. Moreover, we find that Lgl1 null epithelium is unable to directionally migrate, suggesting that migration is not essential for mammary epithelial branching as expected. We show that LGL1 binds to Integrin β1 and inhibits its downstream signaling, and Integrin β1 overexpression blocks epithelial migration, thus recapitulating the Lgl1 null phenotype. Altogether, we demonstrate that Lgl1 modulation of Integrin β1 signaling is essential for directional migration and that epithelial branching in invertebrates and the mammary gland is fundamentally distinct

Robust Spontaneous Raman Flow Cytometry for Single‐Cell Metabolic Phenome Profiling via pDEP‐DLD‐RFC

Abstract A full‐spectrum spontaneous single‐cell Raman spectrum (fs‐SCRS) captures the metabolic phenome for a given cellular state of the cell in a label‐free, landscape‐like manner. Herein a positive dielectrophoresis induced deterministic lateral displacement‐based Raman flow cytometry (pDEP‐DLD‐RFC) is established. This robust flow cytometry platform utilizes a periodical positive dielectrophoresis induced deterministic lateral displacement (pDEP‐DLD) force that is exerted to focus and trap fast‐moving single cells in a wide channel, which enables efficient fs‐SCRS acquisition and extended stable running time. It automatically produces deeply sampled, heterogeneity‐resolved, and highly reproducible ramanomes for isogenic cell populations of yeast, microalgae, bacteria, and human cancers, which support biosynthetic process dissection, antimicrobial susceptibility profiling, and cell‐type classification. Moreover, when coupled with intra‐ramanome correlation analysis, it reveals state‐ and cell‐type‐specific metabolic heterogeneity and metabolite‐conversion networks. The throughput of ≈30–2700 events min−1 for profiling both nonresonance and resonance marker bands in a fs‐SCRS, plus the >5 h stable running time, represent the highest performance among reported spontaneous Raman flow cytometry (RFC) systems. Therefore, pDEP‐DLD‐RFC is a valuable new tool for label‐free, noninvasive, and high‐throughput profiling of single‐cell metabolic phenomes

Culture-free identification of fast-growing cyanobacteria cells by Raman-activated gravity-driven encapsulation and sequencing

By directly converting solar energy and carbon dioxide into biobased products, cyanobacteria are promising chassis for photosynthetic biosynthesis. To make cyanobacterial photosynthetic biosynthesis technology economically feasible on industrial scales, exploring and engineering cyanobacterial chassis and cell factories with fast growth rates and carbon fixation activities facing environmental stresses are of great significance. To simplify and accelerate the screening for fast-growing cyanobacteria strains, a method called Individual Cyanobacteria Vitality Tests and Screening (iCyanVS) was established. We show that the 13C incorporation ratio of carotenoids can be used to measure differences in cell growth and carbon fixation rates in individual cyanobacterial cells of distinct genotypes that differ in growth rates in bulk cultivations, thus greatly accelerating the process screening for fastest-growing cells. The feasibility of this approach is further demonstrated by phenotypically and then genotypically identifying individual cyanobacterial cells with higher salt tolerance from an artificial mutant library via Raman-activated gravity-driven encapsulation and sequencing. Therefore, this method should find broad applications in growth rate or carbon intake rate based screening of cyanobacteria and other photosynthetic cell factories

Artificial intelligence‐assisted automatic and index‐based microbial single‐cell sorting system for One‐Cell‐One‐Tube

Abstract Identification, sorting, and sequencing of individual cells directly from in situ samples have great potential for in‐depth analysis of the structure and function of microbiomes. In this work, based on an artificial intelligence (AI)‐assisted object detection model for cell phenotype screening and a cross‐interface contact method for single‐cell exporting, we developed an automatic and index‐based system called EasySort AUTO, where individual microbial cells are sorted and then packaged in a microdroplet and automatically exported in a precisely indexed, “One‐Cell‐One‐Tube” manner. The target cell is automatically identified based on an AI‐assisted object detection model and then mobilized via an optical tweezer for sorting. Then, a cross‐interface contact microfluidic printing method that we developed enables the automated transfer of cells from the chip to the tube, which leads to coupling with subsequent single‐cell culture or sequencing. The efficiency of the system for single‐cell printing is >93%. The throughput of the system for single‐cell printing is ~120 cells/h. Moreover, >80% of single cells of both yeast and Escherichia coli are culturable, suggesting the superior preservation of cell viability during sorting. Finally, AI‐assisted object detection supports automated sorting of target cells with high accuracy from mixed yeast samples, which was validated by downstream single‐cell proliferation assays. The automation, index maintenance, and vitality preservation of EasySort AUTO suggest its excellent application potential for single‐cell sorting