18S rRNA metatranscriptomic research focuses on transcriptomic information centered around the 18S rRNA gene transcripts of eukaryotic microorganisms. By utilizing high-throughput sequencing technology, it captures all 18S rRNA and related transcripts from eukaryotic microorganisms in specific environmental or biological samples, revealing the composition of active microorganisms, their functional expression, and mechanisms of environmental adaptation. This article delves into 18S rRNA metatranscriptomics, dissecting its core technologies, principles, and the synergistic effects of 18S and metatranscriptomics. It details experimental essentials, data analysis workflows, illustrates biological significance through typical case studies, and explores technical challenges, solutions, and future directions, providing a technical guide for eukaryotic microorganism research.

What is 18S rRNA Metatranscriptomics?

As an emerging and highly promising technique, 18S rRNA metatranscriptomics is gradually making its mark in the field of microbial research. It cleverly combines the strengths of 18S rRNA sequencing and metatranscriptomics, offering a fresh perspective for delving deeper into the mysteries of eukaryotic microorganisms.

Principles of 18S rRNA Metatranscriptomics

18S rRNA sequencing targets the ribosomal RNA genes of eukaryotic microorganisms. Through sequencing analysis of this specific region, it can accurately achieve species classification and abundance assessment. This technology is akin to creating an "identity map" for eukaryotic microorganisms, allowing us to identify which types of eukaryotic microorganisms are present in a sample and their relative quantities. Metatranscriptomics, on the other hand, focuses on all RNAs in an active transcriptional state within environmental samples. It directly reflects the current actual metabolic activities of microorganisms, acting like a "real-time camera" that captures the physiological states and functional activities of microorganisms at specific moments. For instance, when studying marine microbial communities, 18S rRNA sequencing can inform us about the presence of algae and fungi in the sample, while metatranscriptomics can reveal the photosynthesis, substance synthesis, and other metabolic processes these microorganisms are undergoing.

Advantages of Technological Integration

The integration of 18S rRNA and metatranscriptomics achieves a "1+12" effect. It not only answers the fundamental question of "who are the eukaryotic microorganisms," but also further addresses the critical question of "what are they doing." This dual-answer capability is particularly prominent in the study of complex environmental samples. Take soil samples as an example, which contain a rich diversity of fungi and algae with complex interactions. Through 18S rRNA metatranscriptomics, we can comprehensively understand the species composition of these microorganisms and delve into their specific roles and interrelationships in processes such as material cycling and energy flow. Relevant studies have shown that in forest soils, certain fungi and algae participate in carbon fixation through symbiotic relationships, a discovery made possible by the comprehensive application of this technology.

Data Analysis Workflow for 18S rRNA Metatranscriptomics

Data analysis serves as the crucial bridge that transforms raw sequencing data into valuable biological insights, requiring a suite of specialized tools and methodologies.

Toolchain Integration

• Quality Control: At the outset of data analysis, quality control takes precedence. Utilizing FastQC software for a comprehensive examination of raw sequencing data enables swift identification of low-quality sequences, adapter sequences, and potential contaminants. Subsequently, Trimmomatic software is employed to eliminate these low-quality sequences, ensuring the integrity of the data for subsequent analysis. For instance, in a study of lake microbial samples, quality control measures removed approximately 15% of low-quality sequences, significantly enhancing data accuracy and reliability.
• 18S Analysis: For 18S sequencing data, the DADA2 plugin within QIIME2 is utilized for Amplicon Sequence Variant (ASV) identification. ASV identification offers superior precision in distinguishing microbial sequences, elevating species classification resolution. Compared to traditional Operational Taxonomic Unit (OTU) methods, ASV identification uncovers more nuanced species differences, providing detailed insights for in-depth microbial diversity research. Studies confirm that in soil fungal diversity research, ASV identification detects over 20% more species than OTU methods.
• Transcriptome Analysis: During transcriptome analysis, the initial step involves aligning sequencing data to a reference genome using HISAT2 software to pinpoint the source gene for each read. Subsequently, StringTie software assembles transcripts, constructing a comprehensive transcriptome map. This process accurately identifies expressed genes and their transcription levels in the sample, laying the groundwork for subsequent functional analysis. For example, in a transcriptomic study of plant endophytic fungi, this analysis workflow successfully identified several key genes implicated in plant-fungal interactions.
• Functional Annotation: Combining eggNOG-mapper with the KEGG database for functional annotation of transcripts is a pivotal step in elucidating microbial metabolic pathways. eggNOG-mapper swiftly maps gene sequences to known functional categories, while the KEGG database offers a wealth of metabolic pathway information. By integrating these resources, we gain a holistic understanding of microbial gene functions and their roles within metabolic networks. For instance, in a study of intestinal fungi, functional annotation unveiled critical metabolic pathways linked to host nutrient absorption and immune regulation.

Data analysis tool for 18S rRNA metatranscriptomics

Innovative Analytical Approaches

• Correlation Network Construction: Employing the SparCC algorithm to construct correlation networks between microbial taxa and functional gene expression enables an in-depth exploration of interactions within microbial communities. This method identifies synergistic or antagonistic relationships among different microbial species as well as between microbial functional genes, offering fresh perspectives on understanding the structure and function of microbial communities. For instance, in a study of soil microbial communities, correlation network construction unveiled close symbiotic relationships between certain fungi and bacteria, which jointly participated in the decomposition of soil organic matter.
• Time-Series Analysis: Time-series analysis tracks the dynamic responses of eukaryotic microbial communities to environmental changes. By analyzing samples collected at different time points, we can understand the patterns of change and adaptive mechanisms of microbial communities when faced with environmental disturbances. For example, in a study of microbial communities during the eutrophication process of water bodies, time-series analysis revealed the relative abundance changes of different microbial groups at various stages, as well as their dynamic relationships with environmental factors.

Applications of 18S rRNA Metatranscriptomics

The 18S rRNA metatranscriptomics technology has demonstrated significant application value across multiple biological fields, as illustrated by the following typical case studies.

Case Study 1: 18S Metatranscriptomics for Eukaryotic Community Expression Profiling

Terrón-Camero and colleagues harnessed this technology to devise an 18S analysis pipeline centered around Kraken2 + Bracken, rigorously testing it using both simulated and real datasets comprising eukaryotic microorganisms like fungi and protists. Their workflow involved initial filtering with SortMeRNA to eliminate host and low-quality reads, followed by k-mer classification of 18S reads using Kraken2. Finally, Bracken refined species abundance estimates through a Bayesian algorithm.

The results were remarkable, showcasing a significantly higher classification accuracy at the species level (Pearson r = 0.82) compared to QIIME2 (r = 0.67), along with a staggering 344-fold acceleration in computational speed. Moreover, this approach outperformed traditional read-by-read alignment methods in identifying low-abundance eukaryotic species with greater sensitivity. For researchers aiming to capture the "active" eukaryotic microbial community and its gene expression profile, 18S rRNA metatranscriptomics, coupled with the Kraken2/Bracken pipeline, stands out as the preferred choice, ensuring efficient and reproducible species quantification along with functional annotation.

Utilizing 18S rRNA metatranscriptomics to identify eukaryotic microbial communities and profile their gene expression (Terrón-Camero et al., 2022)

Case Study 2: 18S RNA Metatranscriptomics for Microbial Species Identification

Xie and colleagues focused their study on raw metagenomic/metatranscriptomic reads derived from complex microbial communities, such as those found in activated sludge, the gut, and soil. They employed an 18S rRNA metatranscriptomics strategy with a unique approach: instead of relying on PCR amplification, they directly extracted 18S rRNA reads from the V4-V7 region within shotgun data. This was achieved using a 23-nt conserved recognition sequence (RS) and a 33-nt variable tag (TS), enabling them to capture activity information from eukaryotic microorganisms.

Their results were impressive. The RiboTagger tool they utilized could process 40 million reads in just 1.5 hours, boasting both sensitivity and specificity exceeding 95%. From metatranscriptomic samples taken from a Singaporean wastewater treatment plant, they successfully recovered 4,867 distinct 18S V4 tags. This allowed for precise differentiation among eukaryotic groups, including fungi and protists. In essence, this research provides an efficient, unbiased method for rapidly analyzing active eukaryotic microorganisms within complex ecosystems, offering a valuable tool for industry professionals in environmental monitoring, biotechnology, and pharmaceutical research.

Identifying diverse microbial species through 18S rRNA metatranscriptomics (Xie et al., 2016)

Technical Challenges and Solutions

Although 18S rRNA metatranscriptomics holds immense potential, its practical application still faces certain hurdles.

Current Bottlenecks

At present, publicly available reference genomes for eukaryotic microorganisms are limited, covering less than 30%. This poses significant challenges for species classification and functional annotation. Due to the scarcity of reference sequences, much sequencing data cannot be accurately matched to known genomes, resulting in a large number of unknown sequences. For instance, in research on rare fungi, the absence of corresponding reference genomes in databases makes it impossible to determine their species classification and functional characteristics, hindering in-depth studies.

Additionally, RNA in environmental samples is highly susceptible to rapid degradation by RNase, making RNA degradation during sample collection, preservation, and transportation a major issue. RNA degradation leads to a decline in sequencing data quality, affecting subsequent analysis results. For example, if water samples are not promptly stored at low temperatures and processed quickly after collection, noticeable RNA degradation occurs within a short time, resulting in the loss of valuable information in sequencing data.

Solutions

To address the database scarcity problem, hybrid capture technology has emerged. By designing specific probes, this technology can enrich the RNA of target eukaryotic microorganisms, increasing the proportion of target sequences in sequencing data. This approach can partially compensate for the database's deficiencies and help researchers discover new microbial species and functional genes. For example, when studying fungi in extreme environmental samples, hybrid capture technology successfully enriched the RNA of some rare fungi, providing possibilities for in-depth research on their biological characteristics.

Moreover, the advent of AI prediction tools like AlphaFold3 offers a new way to tackle the issue of unknown functional genes. These tools can predict the protein structures encoded by gene sequences, thereby inferring protein functions. By predicting the protein structures of unknown functional genes and combining this with existing biological knowledge, researchers can gain preliminary insights into the metabolic pathways and biological processes these genes may be involved in. For instance, in genomic research on soil fungi, AlphaFold3 was used to predict the protein structures of some unknown functional genes, revealing that some of these proteins may be related to antibiotic synthesis, providing clues for further exploration of fungal bioactive substances.

Conclusion

As an emerging interdisciplinary technology, 18S rRNA metatranscriptomics brings both fresh opportunities and challenges to eukaryotic microorganism research. By adeptly combining the strengths of 18S rRNA sequencing and metatranscriptomics, this technology enables a comprehensive and in-depth exploration of the species composition and metabolic activities of eukaryotic microbes.

Looking ahead, with continuous technological integration and intelligent advancements, 18S rRNA metatranscriptomics is poised to play an increasingly vital role across diverse fields, including marine ecology, agricultural soils, and human health. It will offer robust support for tackling major biological questions and driving scientific progress.

References:

1. Terrón-Camero LC, Gordillo-González F, Salas-Espejo E, et al. "Comparison of Metagenomics and Metatranscriptomics Tools: A Guide to Making the Right Choice." Genes. 2022;13(12):2280. https://doi.org/10.3390/genes13122280.