Metatranscriptome Sequencing Data Analysis

What is Metatranscriptome Sequencing Data Analysis?

Metatranscriptome sequencing is a new sequencing technology that performs high-throughput sequencing on the RNA of all microorganisms in a sample in a specific period and a specific environment, and directly obtains the information of all microorganisms in that environment. Compared with metagenome, metatranscriptome contains not only microbial species information, but also microbial gene expression information. Thus, the complex microbial community changes can be studied from the transcription level, and potential new genes can be better tapped.

Information mining of metatranscriptome data is carried out by bioinformatics, and the microbial species, genes, pathways and other information are analyzed from the G-based high-throughput sequencing data. It is possible to study the transcription of the entire genome of a group of microorganisms and the regulation of transcription regulation in a specific environment and a certain period of time, which can help to mine the functional genes of microorganisms and explore the mechanism of the relationship between microorganisms and the environment, diseases, animals and plants.

Fig 1. Metabolic circuit map constructed from the cryptophyte subset of the metatranscriptome. (Qiu D, et al. 2016)

Application of Metatranscriptome Sequencing Data Analysis

Metatranscriptome sequencing data analysis can be used for but not limited to the following microorganisms. For other type of microorganisms, Please contact us.

Advantages of CD Genomics

A complete and highly accurate analysis pipeline

An experienced analytical team.

Strict data quality control.

Reliable analysis results.

Fast analysis cycle.

CD Genomics Data Analysis Pipeline

Sample Submission Guidelines of Sequencing

Bioinformatics Analysis Content

CD Genomics has a complete, efficient, sensitive and highly accurate metatranscriptome analysis pipeline. In addition to gene expression analysis and differential gene expression analysis, we can also perform species annotation and classification analysis, comparative analysis between multiple samples, etc.

For macro transcriptome sequencing data analysis and other personalized analysis needs, please contact us by using the online inquiry and we will provide you with a reasonable analysis plan.

How It Works

CD Genomics is a high-tech company specializing in multiomic data analysis. We provide services such as project design, data analysis, and database construction. With a focus on developing breakthrough products and services, we are a pioneer in the biotechnology industry, serving researchers and partners worldwide.

CD Genomics has successfully conducted metatranscriptome sequencing data analysis on a variety of microorganisms. If you are interested in our services, please contact us for more detailed information, and a CD Genomics representative is ready to answer your questions and get a complete understanding of your needs.

Reference

1. Qiu D, et al. Cryptophyte farming by symbiotic ciliate host detected in situ[J]. Proceedings of the National Academy of Sciences, 2016:201612483.

What Does Data Analysis of Metatranscriptome Sequencing Show?

Qiime2 Species Annotation

Fig 2. The taxonomy distribution of all samples in Phylum classification level.

Species Abundance Heatmap

Fig 3. Species abundance Heatmap. Phylum.

Statistical Data of Alpha Diversity

Table 1. Statistics of Alpha diversity indices

Rarefaction Curve

Fig 4. Rarefaction curve of the sequenced reads for all samples.

Beta Diversity Analysis

Fig 5. Boxplot analysis based on Bray Curtis

PCoA Analysis

Fig 6. PCoA analysis based on Bray Curtis.

UPGMA Analysis

Fig 7. UPGMA clustering tree based on unweighted unifrac.

Gene Expression Distribution

Fig 8. Boxplot of TPM for each sample.

Title: Metatranscriptome analysis unveils the mechanisms of zero-valent iron enhancing reactivation of starvation hydrolysis acidification sludge by inducing high-level gene expression

Publication: Sci Total Environ

Main Methods: Metatranscriptome Analysis

Abstract: The study explores the use of zero-valent iron (ZVI) to accelerate sludge reactivation in hydrolysis acidification (HA) processes for wastewater treatment. ZVI addition improved active biomass, enzyme activity, and electron transfer efficiency, leading to enhanced sludge reactivation within 35 days. Metatranscriptome analysis revealed upregulated genes involved in carbohydrate degradation, electron transfer, and cofactor biosynthesis. The findings suggest that ZVI enhances bacterial growth and metabolism, facilitating effective starch conversion and volatile fatty acid (VFA) generation, and addressing sludge starvation issues in HA systems.

Main Research Results:

HA performance during reactivation process:

The authors compare sludge reactivation with and without zero-valent iron (ZVI) after organic starvation. ZVI addition improved sludge recovery, enzyme activity, and biomass, achieving stable hydrolysis and acidification within 35 days, while the control group did not stabilize within 50 days. This demonstrates ZVI's efficacy in enhancing sludge reactivation.

Fig 9. System performance: (a) SCOD/TCOD, (b) VFAs productivity, (c) pH values, and (d) MLSS and MLVSS/MLSS ratio values of sludge systems during the reactivation process.

Variation of enzyme activity and electron transfer system (ETS) activity:

The impact of zero-valent iron (ZVI) on microbial metabolic activity during sludge reactivation. ZVI addition significantly increased enzyme activity (by 11.4% to 26.7%) and ETS activity (by 566%), resulting in improved hydrolysis and acidification performance. This suggests ZVI's role in shortening the sludge reactivation period.

Cell viability:

CLSM assesses cell viability in sludge during reactivation with zero-valent iron (ZVI). Results showed higher live cell counts in ZVI-amended sludge, correlating with improved hydrolysis acidification performance and enzyme activity. The released Fe2+ enhanced microbial growth, supporting ZVI's positive effect on sludge reactivation.

Fig 10. Microbial activity.

EPS characteristics:

They examined extracellular polymeric substances (EPS) in starvation and reactivation sludge, with and without zero-valent iron (ZVI). Results showed that ZVI improved microbial activity and EPS composition, increasing protein and polysaccharide content. The presence of ZVI promoted the generation of tryptophan-like substances, enhancing sludge reactivation and microbial metabolism.

Fig 11. EPS characteristics: PN and PS content in (a) TB-EPS and (b) LB-EPS; (c) FRI analysis of LB-EPS and TB-EPS samples from starvation sludge and reactivation sludges.

Response of microbial community structure:

Zero-valent iron (ZVI) improved microbial community richness and decreased diversity during sludge reactivation. Key hydrolysis acidification phyla, Firmicutes, Proteobacteria, and Bacteroidetes, were enriched, especially Proteobacteria in ZVI-amended systems, enhancing acetate production and organic compound degradation, thus promoting successful sludge reactivation.

Microbial genera in hydrolysis acidification (HA) processes. In reactivated sludge, Geotrichum, Clostridium, and Zoogloea were enriched, with ZVI-amended systems showing significant genus recovery. Zoogloea, dominant in ZVI-treated sludge, correlated with higher volatile fatty acids (VFAs) and acetate production, enhancing the HA process.

Fig 12. Microbial community characteristics: (a) α diversity indexes; (b-c) microbial community structure at phylum level and genus level.

Met-transcriptomic identification of key enzymes during electron transfer:

Met-transcriptomic analysis showed that zero-valent iron (ZVI) enhanced sludge reactivation by upregulating genes involved in electron transfer. Genes related to riboflavin, ubiquinone, ferritin, and heme synthesis were more active under ZVI stimulation. Additionally, iron uptake by microbes increased, boosting hydrolysis acidification performance and microbial metabolism.

Met-transcriptomic analysis of cofactors during bacterial growth and metabolism:

Zero-valent iron (ZVI) enhanced sludge reactivation by upregulating genes involved in folic acid and vitamin B12 biosynthesis. This upregulation promoted cell viability, active biomass, and EPS characteristics, suggesting ZVI's role in improving microbial growth and metabolism by enhancing cofactor synthesis during sludge reactivation.

Conclusion:

Zero-valent iron (ZVI) was used to accelerate the reactivation of starved hydrolysis acidification (HA) sludge, achieving stable performance within 35 days. Enhanced active biomass, enzyme, and ETS activity, cell viability, and EPS characteristics were observed. Metatranscriptome analysis revealed that ZVI stimulated the biosynthesis of key enzymes and cofactors essential for carbon metabolism and electron transport. These results highlight ZVI's effectiveness in promoting sludge reactivation and provide insights into microbial growth and metabolism under ZVI influence.

1. What is the process of metatranscriptomics sequencing data analysis?

The workflow for metatranscriptomics sequencing data analysis includes RNA extraction, library preparation, sequencing, quality control, read alignment, functional annotation, and data interpretation.

2. What is the difference between metatranscriptomics, metaproteomics, metabolomics, and metagenomics?

Metatranscriptomics, metaproteomics, metabolomics, and metagenomics are four -omics approaches used to study different aspects of biological systems, particularly within complex microbial communities. Metatranscriptomics focuses on RNA transcripts to provide insight into gene expression and community function, utilizing RNA sequencing techniques. Metaproteomics analyzes the protein complement of a community to understand functional activities and physiological states, employing mass spectrometry and liquid chromatography.

Metabolomics investigates small molecule metabolites to offer a snapshot of metabolic states and biochemical activities, using techniques like mass spectrometry and nuclear magnetic resonance spectroscopy.

Metagenomics examines the collective genome (DNA) of a community to assess genetic potential and diversity through whole-genome shotgun sequencing and 16S rRNA gene sequencing.

Each approach provides unique insights, with metatranscriptomics focusing on RNA, metaproteomics on proteins, metabolomics on metabolites, and metagenomics on DNA.

3. What is the difference between metatranscriptomics and transcriptomics?

Metatranscriptomics analyzes RNA transcription in complex microbial communities from environments like the rhizosphere, while transcriptomics focuses on RNA from single-cultured organisms, often requiring RNA amplification for detailed analysis.

4. How much data is generally recommended for metatranscriptomics?

For fecal/gut samples, 5 GB of data is recommended; for environmental samples like soil/water, 10 GB; and host-dominated tissue samples, at least 10 GB, based on research objectives.

5. Is species annotation and differential gene analysis part of metatranscriptomic bioinformatics analysis?

Yes. Our standard analysis workflow covers species annotation, differential gene analysis, and functional annotation at both species and gene levels.