GAGE is a method for identification of plant species based on whole genome analysis and genome editing

Analysis of Whole-Genome for Alternaria Species Identification

The genus Alternaria, functioning as a saprobe, endophyte, and plant pathogen, is widely distributed across various natural and human-impacted environments. Leaf spot and black spot diseases, caused by Alternaria species, are the most prevalent plant diseases within this genus, leading to significant reductions in crop yields and substantial economic losses. To facilitate the timely detection of Alternaria species during the early stages of infection, enable targeted treatments, and mitigate associated damages, we employed a species identification method based on Analysis of whole-GEnome (AGE). We downloaded 148 genomes, including 31 Alternaria species, from the NCBI GenBank database. Through bioinformatics analysis, we constructed a specific-target sequence library and selected a representative sequence per species. The specific target sequences of the seven exemplary Alternaria species were subsequently used for validation and rapid detection, utilizing Sanger sequencing and CRISPR-Cas12a technology, respectively. The results demonstrated that our method accurately identified the target species. Additionally, by combining Enzymatic Recombinase Amplification (ERA) with CRISPR-Cas12a, we achieved rapid and precise identification of genomic DNA samples, with a detection limit as low as 0.01 ng/µL within 30 min. Therefore, AGE proves to be a highly robust and efficient method for the detection of Alternaria species, offering broad potential for various applications.

From Species to Varieties: How Modern Sequencing Technologies Are Shaping Medicinal Plant Identification

BACKGROUND/OBJECTIVES

Modern sequencing technologies have transformed the identification of medicinal plant species and varieties, overcoming the limitations of traditional morphological and chemical approaches. This review explores the key DNA-based techniques, including molecular markers, DNA barcoding, and high-throughput sequencing, and their contributions to enhancing the accuracy and reliability of plant identification. Additionally, the integration of multi-omics approaches is examined to provide a comprehensive understanding of medicinal plant identity.

METHODS

The literature search for this review was conducted across databases such as Google Scholar, Web of Science, and PubMed, using keywords related to plant taxonomy, genomics, and biotechnology. Inclusion criteria focused on peer-reviewed studies closely related to plant identification methods and techniques that contribute significantly to the field.

RESULTS

The review highlights that while sequencing technologies offer substantial improvements, challenges such as high costs, technical expertise, and the lack of standardized protocols remain barriers to widespread adoption. Potential solutions, including AI-driven data analysis and portable sequencers, are discussed.

CONCLUSIONS

This review provides a comprehensive overview of molecular techniques, their transformative impact, and future perspectives for more accurate and efficient medicinal plant identification.

Analysis of Whole-Genome for Identification of Seven Penicillium Species with Significant Economic Value

The Penicillium genus exhibits a broad global distribution and holds substantial economic value in sectors including agriculture, industry, and medicine. Particularly in agriculture, Penicillium species significantly impact plants, causing diseases and contamination that adversely affect crop yields and quality. Timely detection of Penicillium species is crucial for controlling disease and preventing mycotoxins from entering the food chain. To tackle this issue, we implement a novel species identification approach called Analysis of whole GEnome (AGE). Here, we initially applied bioinformatics analysis to construct specific target sequence libraries from the whole genomes of seven Penicillium species with significant economic impact: P. canescens, P. citrinum, P. oxalicum, P. polonicum, P. paneum, P. rubens, and P. roqueforti. We successfully identified seven Penicillium species using the target we screened combined with Sanger sequencing and CRISPR-Cas12a technologies. Notably, based on CRISPR-Cas12a technology, AGE can achieve rapid and accurate identification of genomic DNA samples at a concentration as low as 0.01 ng/µL within 30 min. This method features high sensitivity and portability, making it suitable for on-site detection. This robust molecular approach provides precise fungal species identification with broad implications for agricultural control, industrial production, clinical diagnostics, and food safety.

Analysis of Whole-Genome as a Novel Strategy for Animal Species Identification

Survival crises stalk many animals, especially endangered and rare animals. Accurate species identification plays a pivotal role in animal resource conservation. In this study, we developed an animal species identification method called Analysis of whole-GEnome (AGE), which identifies species by finding species-specific sequences through bioinformatics analysis of the whole genome and subsequently recognizing these sequences using experimental technologies. To clearly demonstrate the AGE method, Cervus nippon, a well-known endangered species, and a closely related species, Cervus elaphus, were set as model species, without and with published genomes, respectively. By analyzing the whole genomes of C. nippon and C. elaphus, which were obtained through next-generation sequencing and online databases, we built specific sequence databases containing 7,670,140 and 570,981 sequences, respectively. Then, the species specificities of the sequences were confirmed experimentally using Sanger sequencing and the CRISPR-Cas12a system. Moreover, for 11 fresh animal samples and 35 commercially available products, our results were in complete agreement with those of other authoritative identification methods, demonstrating AGE's precision and potential application. Notably, AGE found a mixture in the 35 commercially available products and successfully identified it. This study broadens the horizons of species identification using the whole genome and sheds light on the potential of AGE for conserving animal resources.

Identification of closely related species in Aspergillus through Analysis of Whole-Genome

The challenge of discriminating closely related species persists, notably within clinical diagnostic laboratories for invasive aspergillosis (IA)-related species and food contamination microorganisms with toxin-producing potential. We employed Analysis of the whole-GEnome (AGE) to address the challenges of closely related species within the genus Aspergillus and developed a rapid detection method. First, reliable whole genome data for 77 Aspergillus species were downloaded from the database, and through bioinformatic analysis, specific targets for each species were identified. Subsequently, sequencing was employed to validate these specific targets. Additionally, we developed an on-site detection method targeting a specific target using a genome editing system. Our results indicate that AGE has successfully achieved reliable identification of all IA-related species (Aspergillus fumigatus, Aspergillus niger, Aspergillus nidulans, Aspergillus flavus, and Aspergillus terreus) and three well-known species (A. flavus, Aspergillus parasiticus, and Aspergillus oryzae) within the Aspergillus section. Flavi and AGE have provided species-level-specific targets for 77 species within the genus Aspergillus. Based on these reference targets, the sequencing results targeting specific targets substantiate the efficacy of distinguishing the focal species from its closely related species. Notably, the amalgamation of room-temperature amplification and genome editing techniques demonstrates the capacity for rapid and accurate identification of genomic DNA samples at a concentration as low as 0.1 ng/μl within a concise 30-min timeframe. Importantly, this methodology circumvents the reliance on large specialized instrumentation by presenting a singular tube operational modality and allowing for visualized result assessment. These advancements aptly meet the exigencies of on-site detection requirements for the specified species, facilitating prompt diagnosis and food quality monitoring. Moreover, as an identification method based on species-specific genomic sequences, AGE shows promising potential as an effective tool for epidemiological research and species classification.