The long-read amplicon provides a species-level solution for the community. With the improvement of nanopore flowcells, the accuracy of Oxford Nanopore Technologies (ONT) R10.4.1 has been substantially enhanced, with an average of approximately 99%. To evaluate its effectiveness on amplicons, three types of microbiomes were analyzed by 16S ribosomal RNA (hereinafter referred to as “16S”) amplicon sequencing using Novaseq, Pacbio sequel II, and Nanopore PromethION platforms (R9.4.1 and R10.4.1) in the current study. We showed the error rate, recall, precision, and bias index in the mock sample. The error rate of ONT R10.4.1 was greatly reduced, with a better recall in the case of the synthetic community. Meanwhile, in different types of environmental samples, ONT R10.4.1 analysis resulted in a composition similar to Pacbio data. We found that classification tools and databases influence ONT data. Based on these results, we conclude that the ONT R10.4.1 16S amplicon can also be used for application in environmental samples. IMPORTANCE: The long-read amplicon supplies the community with a species-level solution. Due to the high error rate of nanopore sequencing early on, it has not been frequently used in 16S studies. Oxford Nanopore Technologies (ONT) introduced the R10.4.1 flowcell with Q20+ reagent to achieve more than 99% accuracy as sequencing technology advanced. However, there has been no published study on the performance of commercial PromethION sequencers with R10.4.1 flowcells on 16S sequencing or on the impact of accuracy improvement on taxonomy (R9.4.1 to R10.4.1) using 16S ONT data. In this study, three types of microbiomes were investigated by 16S ribosomal RNA (rRNA) amplicon sequencing using Novaseq, Pacbio sequel II, and Nanopore PromethION platforms (R9.4.1 and R10.4.1). In the mock sample, we displayed the error rate, recall, precision, and bias index. We observed that the error rate in ONT R10.4.1 is significantly lower, especially when deletions are involved. First and foremost, R10.4.1 and Pacific Bioscience platforms reveal a similar microbiome in environmental samples. This study shows that the R10.4.1 full-length 16S rRNA sequences allow for species identification of environmental microbiota.

Sesame is a source of a healthy vegetable oil, attracting a growing interest worldwide. Abiotic stresses have devastating effects on sesame yield; hence, studies have been performed to understand sesame molecular responses to abiotic stresses, but the core abiotic stress-responsive genes (CARG) that the plant reuses in response to an array of environmental stresses are unknown. We performed a meta-analysis of 72 RNA-Seq datasets from drought, waterlogging, salt and osmotic stresses and identified 543 genes constantly and differentially expressed in response to all stresses, representing the sesame CARG. Weighted gene co-expression network analysis of the CARG revealed three functional modules controlled by key transcription factors. Except for salt stress, the modules were positively correlated with the abiotic stresses. Network topology of the modules showed several hub genes predicted to play prominent functions. As proof of concept, we generated over-expressing Arabidopsis lines with hub and non-hub genes. Transgenic plants performed better under drought, waterlogging, and osmotic stresses than the wild-type plants but did not tolerate the salt treatment. As expected, the hub gene was significantly more potent than the non-hub gene. Overall, we discovered several novel candidate genes, which will fuel investigations on plant responses to multiple abiotic stresses.

Artemisinin, which has been isolated from Artemisia annua L., is the most effective antimalarial drug and has saved millions of lives. In addition, artemisinin and its derivatives have anti-tumor, anti-parasitic, anti-fibrosis, and anti-arrhythmic properties, which enhances the demand for these compounds. Improving the content of artemisinin in A. annua is therefore becoming an increasing research interest, as the chemical synthesis of this metabolite is not viable. Ultraviolet B and C irradiation have been reported to improve the artemisinin content in A. annua, but they are harmful to plant growth and development. Therefore, we screened other light sources to examine if they could promote artemisinin content without affecting plant growth and development. We found that red and blue light could enhance artemisinin accumulation by promoting the expression of the genes that were involved in artemisinin biosynthesis, such as amorpha-4,11-diene synthase (ADS) and cytochrome P450 monooxygenase (CYP71AV1) genes. Thus, in addition to being the main light sources for photosynthesis, red and blue light play a key role in plant secondary metabolism, and optimizing the combination of these light might allow for the productionof artemisinin-rich A. annua.

Perilla frutescen is used as traditional food and medicine in East Asia. Its seeds contain high levels of α-linolenic acid (ALA), which is important for health, but is scarce in our daily meals. Previous reports on RNA-seq of perilla seed had identified fatty acid (FA) and triacylglycerol (TAG) synthesis genes, but the underlying mechanism of ALA biosynthesis and its regulation still need to be further explored. So we conducted Illumina RNA-sequencing in seven temporal developmental stages of perilla seeds. Sequencing generated a total of 127 million clean reads, containing 15.88 Gb of valid data. The de novo assembly of sequence reads yielded 64,156 unigenes with an average length of 777 bp. A total of 39,760 unigenes were annotated and 11,693 unigenes were found to be differentially expressed in all samples. According to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, 486 unigenes were annotated in the “lipid metabolism” pathway. Of these, 150 unigenes were found to be involved in fatty acid (FA) biosynthesis and triacylglycerol (TAG) assembly in perilla seeds. A coexpression analysis showed that a total of 104 genes were highly coexpressed (r > 0.95). The coexpression network could be divided into two main subnetworks showing over expression in the medium or earlier and late phases, respectively. In order to identify the putative regulatory genes, a transcription factor (TF) analysis was performed. This led to the identification of 45 gene families, mainly including the AP2-EREBP, bHLH, MYB, and NAC families, etc. After coexpression analysis of TFs with highly expression of FAD2 and FAD3 genes, 162 TFs were found to be significantly associated with two FAD genes (r > 0.95). Those TFs were predicted to be the key regulatory factors in ALA biosynthesis in perilla seed. The qRT-PCR analysis also verified the relevance of expression pattern between two FAD genes and partial candidate TFs. Although it has been reported that some TFs are involved in seed development, more direct evidence is still needed to verify their function. However, these findings can provide clues to reveal the possible molecular mechanisms of ALA biosynthesis and its regulation in perilla seed.

In recent years, the field of shotgun metagenomics has witnessed remarkable advancements, primarily driven by the development and refinement of next-generation sequencing technologies, particularly long-read sequencing platforms such as Nanopore and PacBio. These platforms have significantly improved the ability to analyze microbial communities directly from environmental samples, providing valuable information on their composition, function, and dynamics without the need for pure cultivation. These technologies enhance metagenomic data assembly, annotation, and analysis by addressing longer reads, higher error rates, and complex data. In this review, we provide a comprehensive overview of the historical development of long-read metagenomics, highlighting significant landmarks and advancements. We also explore the diverse applications of long-read metagenomics, emphasizing its impact across various fields. Additionally, we summarize the essential computational tools and resources, including software, databases, and packages, developed to enhance the efficiency and accuracy of metagenomic analysis. Finally, we provide a practical guide for the installation and use of notable software available on GitHub (https://github.com/zhangtianyuan666/LongMetagenome). Overall, this review assists the metagenomics community in exploring microbial life in unprecedented depth by providing a roadmap for successful resource utilization and emphasizing possibilities for innovation.