Antimicrobial peptides as promising antibiotic adjuvants to combat drug-resistant pathogens.

The widespread antimicrobial resistance (AMR) calls for the development of new antimicrobial strategies. Antibiotic adjuvant rescues antibiotic activity and increases the life span of the antibiotics, representing a more productive, timely, and cost-effective strategy in fighting drug-resistant pathogens. Antimicrobial peptides (AMPs) from synthetic and natural sources are considered new-generation antibacterial agents. Besides their direct antimicrobial activity, growing evidence shows that some AMPs effectively enhance the activity of conventional antibiotics. The combinations of AMPs and antibiotics display an improved therapeutic effect on antibiotic-resistant bacterial infections and minimize the emergence of resistance. In this review, we discuss the value of AMPs in the age of resistance, including modes of action, limiting evolutionary resistance, and their designing strategies. We summarise the recent advances in combining AMPs and antibiotics against antibiotic-resistant pathogens, as well as their synergistic mechanisms. Lastly, we highlight the challenges and opportunities associated with the use of AMPs as potential antibiotic adjuvants. This will shed new light on the deployment of synergistic combinations to address the AMR crisis.

Evaluation of the Antibacterial Potential of Two Short Linear Peptides YI12 and FK13 against Multidrug-Resistant Bacteria.

The accelerating spread of antibiotic resistance has significantly weakened the clinical efficacy of existing antibiotics, posing a severe threat to public health. There is an urgent need to develop novel antimicrobial alternatives that can bypass the mechanisms of antibiotic resistance and effectively kill multidrug-resistant (MDR) pathogens. Antimicrobial peptides (AMPs) are one of the most promising candidates to treat MDR pathogenic infections since they display broad-spectrum antimicrobial activities and are less prone to achieve drug resistance. In this study, we investigated the antibacterial capability and mechanisms of two machine learning-driven linear peptide compounds termed YI12 and FK13. We reveal that YI12 and FK13 exhibit broad-spectrum antibacterial properties against clinically significant bacterial pathogens, inducing no or minimal hemolysis in mammalian red blood cells. We further ascertain that YI12 and FK13 are resilient to heat and acid-base conditions, and exhibit susceptibility to hydrolytic enzymes and divalent cations under physiological conditions. Initial mechanistic investigations reveal that YI12 and FK13 compromise bacterial membrane integrity, leading to membrane potential dissipation and excessive reactive oxygen species (ROS) generation. Collectively, our findings highlight the prospective utility of these two cationic amphiphilic peptides as broad-spectrum antibacterial agents.

Density functional theory-based study on the structural, electronic and spectral properties of gas-phase PbMg n - (n = 2-12) clusters.

Gas-phase PbMg n- (n = 2-12) cluster structures were globally searched on their potential energy surfaces by means of the CALYPSO prediction software. Structural optimization and calculations of properties such as relative energy and electronic structure were then carried out by density functional theory for each size of low energy isomer. The structural, relative stability, natural charge population, natural electronic configuration and distribution of the strongest peaks of the infrared and Raman spectra of the low energy isomers of PbMg n- (n = 2-12) clusters were systematically investigated in the present work. It was shown that the PbMg7- cluster ground state isomer exhibits the highest stability, for which special electronic excitation and chemical bonding analyses were performed. It is reasonable to believe that this work enriches the structural, spectroscopic and other data of magnesium-based clusters and provides some theoretical basis for possible future experimental syntheses.

Mutations in the COPII vesicle component gene SEC24B are associated with human neural tube defects.

Neural tube defects (NTDs) are severe birth malformations that affect one in 1,000 live births. Recently, mutations in the planar cell polarity (PCP) pathway genes had been implicated in the pathogenesis of NTDs in both the mouse model and in human cohorts. Mouse models indicate that the homozygous disruption of Sec24b, which mediates the ER-to-Golgi transportation of the core PCP gene Vangl2 as a component of the COPII vesicle, will result in craniorachischisis. In this study, we found four rare missense heterozygous SEC24B mutations (p.Phe227Ser, p.Phe682Leu, p.Arg1248Gln, and p.Ala1251Gly) in NTDs cases that were absent in all controls. Among them, p.Phe227Ser and p.Phe682Leu affected its protein stability and physical interaction with VANGL2. Three variants (p.Phe227Ser, p.Arg1248Gln, and p.Ala1251Gly) were demonstrated to affect VANGL2 subcellular localization in cultured cells. Further functional analysis in the zebrafish including overexpression and dosage-dependent rescue study suggested that these four mutations all displayed loss-of-function effects compared with wild-type SEC24B. Our study demonstrated that functional mutations in SEC24B might contribute to the etiology of a subset of human NTDs and further expanded our knowledge of the role of PCP pathway-related genes in the pathogenesis of human NTDs.

Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability Identification Strategy of Wavelet Singular Spectrum.

The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and active control of the aeroengine. In this paper, an aerodynamic system modeling method combination with the wavelet transform and gray wolf algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion technology based on the feature correlation of chaotic data. Because of the chaotic characteristic represented by the sequence, the correlation-correlation (C-C) algorithm is adopted to reconstruct the phase space of the spatial modal. On the premise of finding out the local law of the dynamic system variety, the machine learning method is applied to model the reconstructed low-frequency components and high-frequency components, respectively. As the key part, the parameters of the SVR model are optimized by the gray wolf optimization algorithm (GWO) from the biological view inspired by the predatory behavior of gray wolves. In the definition of the hunting behaviors of gray wolves by mathematical equations, it is superior to algorithms such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy of the model, the multi-resolution and equivalent frequency distribution of the wavelet transform (WT) are used to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better prediction accuracy and reliability with the wavelet reconstruction coefficients as the inputs. In order to effectively identify the sign of the instability in the modeling system, a wavelet singular information entropy algorithm is proposed to detect the stall inception. By using the three sigma criteria as the identification strategy, the instability early warning can be given about 102r in advance, which is helpful for the active control.

Pericyte loss leads to circulatory failure and pleiotrophin depletion causing neuron loss.

Pericytes are positioned between brain capillary endothelial cells, astrocytes and neurons. They degenerate in multiple neurological disorders. However, their role in the pathogenesis of these disorders remains debatable. Here we generate an inducible pericyte-specific Cre line and cross pericyte-specific Cre mice with iDTR mice carrying Cre-dependent human diphtheria toxin receptor. After pericyte ablation with diphtheria toxin, mice showed acute blood-brain barrier breakdown, severe loss of blood flow, and a rapid neuron loss that was associated with loss of pericyte-derived pleiotrophin (PTN), a neurotrophic growth factor. Intracerebroventricular PTN infusions prevented neuron loss in pericyte-ablated mice despite persistent circulatory changes. Silencing of pericyte-derived Ptn rendered neurons vulnerable to ischemic and excitotoxic injury. Our data demonstrate a rapid neurodegeneration cascade that links pericyte loss to acute circulatory collapse and loss of PTN neurotrophic support. These findings may have implications for the pathogenesis and treatment of neurological disorders that are associated with pericyte loss and/or neurovascular dysfunction.

Central role for PICALM in amyloid-ß blood-brain barrier transcytosis and clearance.

PICALM is a highly validated genetic risk factor for Alzheimer's disease (AD). We found that reduced expression of PICALM in AD and murine brain endothelium correlated with amyloid-ß (Aß) pathology and cognitive impairment. Moreover, Picalm deficiency diminished Aß clearance across the murine blood-brain barrier (BBB) and accelerated Aß pathology in a manner that was reversible by endothelial PICALM re-expression. Using human brain endothelial monolayers, we found that PICALM regulated PICALM/clathrin-dependent internalization of Aß bound to the low density lipoprotein receptor related protein-1, a key Aß clearance receptor, and guided Aß trafficking to Rab5 and Rab11, leading to Aß endothelial transcytosis and clearance. PICALM levels and Aß clearance were reduced in AD-derived endothelial monolayers, which was reversible by adenoviral-mediated PICALM transfer. Inducible pluripotent stem cell-derived human endothelial cells carrying the rs3851179 protective allele exhibited higher PICALM levels and enhanced Aß clearance. Thus, PICALM regulates Aß BBB transcytosis and clearance, which has implications for Aß brain homeostasis and clearance therapy.

GLUT1 reductions exacerbate Alzheimer's disease vasculo-neuronal dysfunction and degeneration.

The glucose transporter GLUT1 at the blood-brain barrier (BBB) mediates glucose transport into the brain. Alzheimer's disease is characterized by early reductions in glucose transport associated with diminished GLUT1 expression at the BBB. Whether GLUT1 reduction influences disease pathogenesis remains, however, elusive. Here we show that GLUT1 deficiency in mice overexpressing amyloid ß-peptide (Aß) precursor protein leads to early cerebral microvascular degeneration, blood flow reductions and dysregulation and BBB breakdown, and to accelerated amyloid ß-peptide (Aß) pathology, reduced Aß clearance, diminished neuronal activity, behavioral deficits, and progressive neuronal loss and neurodegeneration that develop after initial cerebrovascular degenerative changes. We also show that GLUT1 deficiency in endothelium, but not in astrocytes, initiates the vascular phenotype as shown by BBB breakdown. Thus, reduced BBB GLUT1 expression worsens Alzheimer's disease cerebrovascular degeneration, neuropathology and cognitive function, suggesting that GLUT1 may represent a therapeutic target for Alzheimer's disease vasculo-neuronal dysfunction and degeneration.