Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides.

INTRODUCTION: Antimicrobial peptides (AMPs) are valuable alternatives to traditional antibiotics, possess a variety of potent biological activities and exhibit immunomodulatory effects that alleviate difficult-to-treat infections. Clarifying the structure-activity relationships of AMPs can direct the synthesis of desirable peptide therapeutics. OBJECTIVES: In this study, the lipopolysaccharide-binding domain (LBD) was identified through machine learning-guided directed evolution, which acts as a functional domain of the anti-lipopolysaccharide factor family of AMPs identified from Marsupenaeus japonicus. METHODS: LBDA-D was identified as an output of this algorithm, in which the original LBDMj sequence was the input, and the three-dimensional solution structure of LBDB was determined using nuclear magnetic resonance. Furthermore, our study involved a comprehensive series of experiments, including morphological studies and in vitro and in vivo antibacterial tests. RESULTS: The NMR solution structure showed that LBDB possesses a circular extended structure with a disulfide crosslink at the terminus and two 310-helices and exhibits a broad antimicrobial spectrum. In addition, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) showed that LBDB induced the formation of a cluster of bacteria wrapped in a flexible coating that ruptured and consequently killed the bacteria. Finally, coinjection of LBDB, Vibrio alginolyticus and Staphylococcus aureus in vivo improved the survival of M. japonicus, demonstrating the promising therapeutic role of LBDB for treating infectious disease. CONCLUSIONS: The findings of this study pave the way for the rational drug design of activity-enhanced peptide antibiotics.

Identification and functional analysis of a serine protease inhibitor using machine learning strategy.

In the intricate realm of animal biology, a multitude of vital processes heavily rely on precisely orchestrated proteinase cascades, but the potential for havoc makes proteinase inhibitors indispensable, with serine proteinase inhibitors (serpins) at the forefront, serving as custodians of homeostasis and participating in various critical biological processes. Importantly, there are still many unexplored facets of serpin functionality. In this study, we focused on the serpin family proteins from Marsupenaeus japonicus, utilizing a fine-tuned pretrained protein language model. This approach led to the identification and evolutionary validation of 28 serpins, one of which, referred to as Mjserpin-1, was both computationally and experimentally demonstrated to show potential as an antiviral and apoptosis inhibitor. Our research unveils exciting prospects for the fusion of state-of-the-art artificial intelligence and rich bioinformatics, holding the promise of significant discoveries that could pave the way for future therapeutic advancements.

Metagenomic exploration of the rhizosphere soil microbial community and their significance in facilitating the development of wild-simulated ginseng.

Panax ginseng, a prized medicinal herb, has faced increasingly challenging field production due to soil degradation and fungal diseases in Northeast China. Wild-simulated cultivation has prevailed because of its sustainable soil management and low disease incidence. Despite the recognized benefits of rhizosphere microorganisms in ginseng cultivation, their genomic and functional diversity remain largely unexplored. In this work, we utilized shotgun metagenomic analysis to reveal that Pseudomonadota, Actinomycetota, and Acidobacteriota were dominant in the ginseng rhizobiome and recovered 14 reliable metagenome-assembled genomes. Functional analysis indicated an enrichment of denitrification-associated genes, potentially contributing to the observed decline in soil fertility, while genes associated with aromatic carbon degradation may be linked to allelochemical degradation. Further analysis demonstrated enrichment of Actinomycetota in 9-year-old wild-simulated ginseng (WSG), suggesting the need for targeted isolation of Actinomycetota bacteria. Among these, at least three different actinomycete strains were found to play a crucial role in fungal disease resistance, with Streptomyces spp. WY144 standing out for its production of actinomycin natural products active against the pathogenic fungus Ilyonectria robusta. These findings not only enhance our understanding of the rhizobiome of WSG but also present promising avenues for combating detrimental fungal pathogens, underscoring the importance of ginseng in both medicinal and agricultural contexts.IMPORTANCEWild-simulated ginseng, growing naturally without human interference, is influenced by its soil microbiome. Using shotgun metagenomics, we analyzed the rhizospheric soil microbiome of 7- and 9-year-old wild-simulated ginseng. The study aimed to reveal its composition and functions, exploring the microbiome's key roles in ginseng growth. Enrichment analysis identified Streptomycetes in ginseng soil, with three strains inhibiting plant pathogenic fungi. Notably, one strain produced actinomycins, suppressing the ginseng pathogenic fungus Ilyonectria robusta. This research accelerates microbiome application in wild-simulated ginseng cultivation, offering insights into pathogen protection and supporting microbiome utilization in agriculture.

A Streptomyces species from the ginseng rhizosphere exhibits biocontrol potential.

Plants and their associated microbes live in complicated, changeable, and unpredictable environments. They usually interact with each other in many ways through multidimensional, multiscale, and multilevel coupling manners, leading to challenges in the coexistence of randomness and determinism or continuity and discreteness. Gaining a deeper understanding of these diverse interaction mechanisms can facilitate the development of data-mining theories and methods for complex systems, coupled modeling for systems with different spatiotemporal scales and functional properties, or even a universal theory of information and information interactions. In this study, we use a "closed-loop" model to present a plant-microbe interaction system and describe the probable functions of microbial natural products. Specifically, we report a rhizosphere species, Streptomyces ginsengnesis G7, which produces polyketide lydicamycins and other active metabolites. Interestingly, these distinct molecules have the potential to function both as antibiotics and as herbicides for crop protection. Detailed laboratory experiments conducted in Arabidopsis (Arabidopsis thaliana), combined with a comprehensive bioinformatics analysis, allow us to rationalize a model for this specific plant-microbe interaction process. Our work reveals the benefits of exploring otherwise neglected resources for the identification of potential functional molecules and provides a reference to better understand the system biology of complex ecosystems.

Comparative transcriptome analysis revealed that auxin and cell wall biosynthesis play important roles in the formation of hollow hearts in cucumber.

BACKGROUND: Hollow heart is a kind of physiological defect that seriously affects the yield, quality, and economic value of cucumber. However, the formation of hollow hearts may relate to multiple factors in cucumber, and it is necessary to conduct analysis. RESULTS: In this study, hollow and non-hollow fruits of cucumber K07 were used for comparative transcriptome sequencing and analysis. 253 differentially expressed genes and 139 transcription factors were identified as being associated with the formation of hollow hearts. Hormone (auxin) signaling and cell wall biosynthesis were mainly enriched in GO and KEGG pathways. Expression levels of key genes involved in indole-3-acetic acid biosynthesis in carpel were lower in the hollow fruits than non-hollow fruits, while there was no difference in the flesh. The concentration of indole-3-acetic also showed lower in the carpel than flesh. The biosynthetic pathway and content analysis of the main components of the cell wall found that lignin biosynthesis had obvious regularity with hollow heart, followed by hemicellulose and cellulose. Correlation analysis showed that there may be an interaction between auxin and cell wall biosynthesis, and they collectively participate in the formation of hollow hearts in cucumber. Among the differentially expressed transcription factors, MYB members were the most abundant, followed by NAC, ERF, and bHLH. CONCLUSIONS: The results and analyses showed that the low content of auxin in the carpel affected the activity of enzymes related to cell wall biosynthesis at the early stage of fruit development, resulting in incomplete development of carpel cells, thus forming a hollow heart in cucumber. Some transcription factors may play regulatory roles in this progress. The results may enrich the theory of the formation of hollow hearts and provide a basis for future research.