Identification of novel molecular targets for Weissella species-specific real-time PCR based on pangenome analysis

A Lateral Flow-Recombinase Polymerase Amplification Method for Colletotrichum gloeosporioides Detection

The greater yam (Dioscorea alata), a widely cultivated and nutritious food crop, suffers from widespread yield reduction due to anthracnose caused by Colletotrichum gloeosporioides. Latent infection often occurs before anthracnose phenotypes can be detected, making early prevention difficult and causing significant harm to agricultural production. Through comparative genomic analysis of 60 genomes of 38 species from the Colletotrichum genus, this study identified 17 orthologous gene groups (orthogroups) that were shared by all investigated C. gloeosporioides strains but absent from all other Colletotrichum species. Four of the 17 C. gloeosporioides-specific orthogroups were used as molecular markers for PCR primer designation and C. gloeosporioides detection. All of them can specifically detect C. gloeosporioides out of microbes within and beyond the Colletotrichum genus with different sensitivities. To establish a rapid, portable, and operable anthracnose diagnostic method suitable for field use, specific recombinase polymerase amplification (RPA) primer probe combinations were designed, and a lateral flow (LF)-RPA detection kit for C. gloeosporioides was developed, with the sensitivity reaching the picogram (pg) level. In conclusion, this study identified C. gloeosporioides-specific molecular markers and developed an efficient method for C. gloeosporioides detection, which can be applied to the prevention and control of yam anthracnose as well as anthracnose caused by C. gloeosporioides in other crops. The strategy adopted by this study also serves as a reference for the identification of molecular markers and diagnosis of other plant pathogens.

The Weissella and Periweissella genera: up-to-date taxonomy, ecology, safety, biotechnological, and probiotic potential

Bacteria belonging to the genera Weissella and Periweissella are lactic acid bacteria, which emerged in the last decades for their probiotic and biotechnological potential. In 2015, an article reviewing the scientific literature till that date on the taxonomy, ecology, and biotechnological potential of the Weissella genus was published. Since then, the number of studies on this genus has increased enormously, several novel species have been discovered, the taxonomy of the genus underwent changes and new insights into the safety, and biotechnological and probiotic potential of weissellas and periweissellas could be gained. Here, we provide an updated overview (from 2015 until today) of the taxonomy, ecology, safety, biotechnological, and probiotic potential of these lactic acid bacteria.

Differentiation between Weissella cibaria and Weissella confusa Using Machine-Learning-Combined MALDI-TOF MS

Although Weissella cibaria and W. confusa are essential food-fermenting bacteria, they are also opportunistic pathogens. Despite these species being commercially crucial, their taxonomy is still based on inaccurate identification methods. In this study, we present a novel approach for identifying two important Weissella species, W. cibaria and W. confusa, by combining matrix-assisted laser desorption/ionization and time-of-flight mass spectrometer (MALDI-TOF MS) data using machine-learning techniques. After on- and off-plate protein extraction, we observed that the BioTyper database misidentified or could not differentiate Weissella species. Although Weissella species exhibited very similar protein profiles, these species can be differentiated on the basis of the results of a statistical analysis. To classify W. cibaria, W. confusa, and non-target Weissella species, machine learning was used for 167 spectra, which led to the listing of potential species-specific mass-to-charge (m/z) loci. Machine-learning techniques including artificial neural networks, principal component analysis combined with the K-nearest neighbor, support vector machine (SVM), and random forest were used. The model that applied the Radial Basis Function kernel algorithm in SVM achieved classification accuracy of 1.0 for training and test sets. The combination of MALDI-TOF MS and machine learning can efficiently classify closely-related species, enabling accurate microbial identification.

Weissella and the two Janus faces of the genus

The genus Weissella belongs to the lactic acid bacteria group. It occurs naturally in foods and is a component of the human microbiome. A few Weissella species are candidate probiotics due to their potential for survival under the harsh conditions present in the gastrointestinal tract of humans and animals. Various species have also shown potential for treating and preventing periodontal disease, skin pathologies, and atopic dermatitis; some are used as starters for the fermentation of foods due to their production of exopolysaccharides; and others are used as protective cultures due to their production of weissellicin, a bacteriocin. However, a few Weissella species are opportunistic pathogens, such as W. ceti, which is the etiological agent of weissellosis, a disease in rainbow trout. Additionally, most Weissella species are intrinsically vancomycin-resistant. Thus, the Weissella genus is important from both medical and industrial points of view, and the Janus faces of this genus should be considered in any expected biotechnological applications. In this review, we present an overview of the probiotic potential and pathogenic cases of the Weissella genus reported in the literature.

Identification of Leuconostoc species based on novel marker genes identified using real-time PCR via computational pangenome analysis

Leuconostoc species are important microorganisms in food fermentation but also cause food spoilage. Although these species are commercially important, their taxonomy is still based on inaccurate identification methods. Here, we used computational pangenome analysis to develop a real-time PCR-based method for identifying and differentiating the 12 major Leuconostoc species found in food. Analysis of pan and core-genome phylogenies showed clustering of strains into 12 distinct groups according to the species. Pangenome analysis of 130 Leuconostoc genomes from these 12 species enabled the identification of each species-specific gene. In silico testing of the species-specific genes against 143 publicly available Leuconostoc and 100 other lactic acid bacterial genomes showed that all the assays had 100% inclusivity/exclusivity. We also verified the specificity for each primer pair targeting each specific gene using 23 target and 124 non-target strains and found high specificity (100%). The sensitivity of the real-time PCR method was 10² colony forming units (CFUs)/ml in pure culture and spiked food samples. All standard curves showed good linear correlations, with an R² value of ≥0.996, suggesting that screened targets have good specificity and strong anti-interference ability from food sample matrices and non-target strains. The real-time PCR method can be potentially used to determine the taxonomic status and identify the Leuconostoc species in foods.