Development of a novel hybrid antimicrobial peptide for enhancing antimicrobial spectrum and potency against food-borne pathogens.

AIMS: To address the increasingly serious challenge of the transmission of foodbrone pathogens in the food chain. METHODS AND RESULTS: In this study, we employed rational design strategies, including truncation, amino acid substitution, and heterozygosity, to generate seven engineered peptides with α-helical structure, cationic property, and amphipathic characteristics based on the original Abhisin template. Among them, as the hybird antimicrobial peptide (AMP), AM exhibits exceptional stability, minimal toxicity, as well as broad-spectrum and potent antimicrobial activity against foodborne pathogens. Besides, it was observed that the electrostatic incorporation demonstrates by AM results in its primary targeting and disruption of the cell wall and membrane of Escherichia coli O157: H7 (EHEC) and methicillin-resistant Staphylococcus aureus (MRSA), resulting in membrane perforation and enhanced permeability. Additionally, AM effectively counteracts the deleterious effects of lipopolysaccharide, eradicating biofilms and ultimately inducing the demise of both food spoilage and pathogenic microorganisms. CONCLUSIONS: The findings highlight the significant potential of AM as a highly promising candidate for a novel food preservative and its great importance in the design and optimization of AMP-related agents.

The inhibitory action of lactocin 63 on deterioration of seabass (Lateolabrax japonicus) during chilled storage.

BACKGROUND: The bacteriocins, particularly derived from lactic acid bacteria, currently exhibit potential as a promising food preservative owing to their low toxicity and potent antimicrobial activity. This study aimed to evaluate the efficacy of lactocin 63, produced by Lactobacillus coryniformis, in inhibiting the deterioration of Lateolabrax japonicas during chilled storage, while also investigating its underlying inhibitory mechanism. The measurement of total viable count, biogenic amines, and volatile organic compounds were conducted, along with high-throughput sequencing and sensory evaluation. RESULTS: The findings demonstrated that treatment with lactocin 63 resulted in a notable retardation of bacterial growth in L. japonicas fish fillet during refrigerated storage compared with the water-treated and nisin-treated groups. Moreover, lactocin 63 effectively maintained the microbial flora balance in the fish fillet and inhibited the proliferation and metabolic activity of specific spoilage microorganisms, particularly Shewanella, Pseudomonas, and Acinetobacter. Furthermore, the production of unacceptable volatile organic compounds (e.g. 1-octen-3-ol, hexanal, nonanal), as well as the biogenic amines derived from the bacterial metabolism, could be hindered, thus preventing the degradation in the quality of fish fillets and sustaining relatively high sensory quality. CONCLUSION: The results of this study provide valuable theoretical support for the development and application of lactocin 63, or other bacteriocins derived from lactic acid bacteria, as potential bio-preservatives in aquatic food. © 2024 Society of Chemical Industry.

Rational Design and Antimicrobial Potency Assessment of Abaecin Analogues.

The widespread and escalating emergence of multidrug resistance is now recognized as one of the most severe global threats to human health. To address the urgent issue of drug-resistant bacteria and the limitation of effective clinical treatments, antimicrobial peptides (AMPs) have been developed as promising substituents of conventional antibiotics. In this study, rational design strategies were employed to acquire seven cationic and α-helical engineered peptides based on the original template of Abaecin. After investigation, we found that AC7 (LLRRWKKLFKKIIRWPRPLPNPGH) demonstrated potent and broad-spectrum antimicrobial activity. Additionally, it demonstrated low cytotoxicity and hemolysis while maintaining good stability. Notably, AC7 displays the antibacterial mechanism with superior abilities in cell membrane disruption and potential DNA binding in vitro, as well as effectively disrupting biofilms. Moreover, the murine skin wound model infected with drug-resistant Pseudomonas aeruginosa was employed to evaluate the anti-infective efficacy and therapeutic potential of AC7. It was observed that AC7 displays a remarkable capacity to inhibit wound colonization, reduce levels of inflammatory cytokines (TNF-α) and inflammatory cells (white blood cells (WBC), monocytes (MONO), lymphocytes (LYMPH), neutrophils (GRAN)), promote the levels of IL-10 and VEGF, and enhance wound healing. Overall, these findings demonstrate the potential of AC7 as a viable alternative to traditional antibiotics.

Rational design of Abhisin-like peptides enables generation of potent antimicrobial activity against pathogens.

Infections caused by pathogens can be a significant challenge in wound healing, particularly when antimicrobial resistance is a factor. This can pose a serious threat to human health and well-being. In this scenario, it is imperative to explore novel antimicrobial agents to fight against multi-drug resistant (MDR) pathogenic bacteria. This study employed rational design strategies, including truncation, amino acid replacement, and heterozygosity, to obtain seven α-helical, cationic, and engineered peptides based on the original template of Abhisin. Among the analogs of Abhisin, AB7 displayed broad-spectrum and potent antimicrobial activity, superior targeting of membranes and DNA, and the ability to disrupt biofilms and anti-endotoxins in vitro. Additionally, we evaluated the anti-infection ability of AB7 using a murine skin wound model infected with methicillin-resistant Staphylococcus aureus (MRSA) and found that AB7 displayed negligible toxicity both in vitro and in vivo. Furthermore, AB7 exhibited desirable therapeutic efficacy by reducing bacterial burden and pro-inflammatory mediators, modulating cytokines, promoting wound healing, and enhancing angiogenesis. These results highlight the potential of AB7 as a promising candidate for a new antibiotic. KEY POINTS: • A α-helical, cationic, and engineered peptide AB7 was obtained based on Abhisin. • AB7 exhibited potent antimicrobial activity and multiple bactericidal actions. • AB7 effectively treated infected skin wounds in mice.

Personalized Audio-Driven 3D Facial Animation Via Style-Content Disentanglement.

We present a learning-based approach for generating 3D facial animations with the motion style of a specific subject from arbitrary audio inputs. The subject style is learned from a video clip (1-2 minutes) either downloaded from the Internet or captured through an ordinary camera. Traditional methods often require many hours of the subject's video to learn a robust audio-driven model and are thus unsuitable for this task. Recent research efforts aim to train a model from video collections of a few subjects but ignore the discrimination between the subject style and underlying speech content within facial motions, leading to inaccurate style or articulation. To solve the problem, we propose a novel framework that disentangles subject-specific style and speech content from facial motions. The disentanglement is enabled by two novel training mechanisms. One is two-pass style swapping between two random subjects, and the other is joint training of the decomposition network and audio-to-motion network with a shared decoder. After training, the disentangled style is combined with arbitrary audio inputs to generate stylized audio-driven 3D facial animations. Compared with start-of-the-art methods, our approach achieves better results qualitatively and quantitatively, especially in difficult cases like bilabial plosive and bilabial nasal phonemes.

H-CNN: Spatial Hashing Based CNN for 3D Shape Analysis.

We present a novel spatial hashing based data structure to facilitate 3D shape analysis using convolutional neural networks (CNNs). Our method builds hierarchical hash tables for an input model under different resolutions that leverage the sparse occupancy of 3D shape boundary. Based on this data structure, we design two efficient GPU algorithms namely hash2col and col2hash so that the CNN operations like convolution and pooling can be efficiently parallelized. The perfect spatial hashing is employed as our spatial hashing scheme, which is not only free of hash collision but also nearly minimal so that our data structure is almost of the same size as the raw input. Compared with existing 3D CNN methods, our data structure significantly reduces the memory footprint during the CNN training. As the input geometry features are more compactly packed, CNN operations also run faster with our data structure. The experiment shows that, under the same network structure, our method yields comparable or better benchmark results compared with the state-of-the-art while it has only one-third memory consumption when under high resolutions (i.e., 2563).

FaceWarehouse: a 3D facial expression database for visual computing.

We present FaceWarehouse, a database of 3D facial expressions for visual computing applications. We use Kinect, an off-the-shelf RGBD camera, to capture 150 individuals aged 7-80 from various ethnic backgrounds. For each person, we captured the RGBD data of her different expressions, including the neutral expression and 19 other expressions such as mouth-opening, smile, kiss, etc. For every RGBD raw data record, a set of facial feature points on the color image such as eye corners, mouth contour, and the nose tip are automatically localized, and manually adjusted if better accuracy is required. We then deform a template facial mesh to fit the depth data as closely as possible while matching the feature points on the color image to their corresponding points on the mesh. Starting from these fitted face meshes, we construct a set of individual-specific expression blendshapes for each person. These meshes with consistent topology are assembled as a rank-3 tensor to build a bilinear face model with two attributes: identity and expression. Compared with previous 3D facial databases, for every person in our database, there is a much richer matching collection of expressions, enabling depiction of most human facial actions. We demonstrate the potential of FaceWarehouse for visual computing with four applications: facial image manipulation, face component transfer, real-time performance-based facial image animation, and facial animation retargeting from video to image.