Soil microbial ecology through the lens of metatranscriptomics

Highlights

● Metatranscriptomics uncovers the dynamic expression of functional genes in soil environments, providing insights into the intricate metabolic activities within microbial communities.

● mRNA enrichment from soil samples remains a formidable challenge due to the presence of inhibitory compounds, low RNA yields, and sample heterogeneity.

● Soil metatranscriptomics unravels the expression levels of genes involved in the real-time molecular dialogues between plants and rhizobionts, uncovering the dynamics of nutrient exchange, symbiotic interactions, and plant-microbe communication.

● Metatranscriptomics unlocks the active expression of the soil resistome, elucidating the mechanisms of resistance dissemination under anthropogenic activities.

● Metatranscriptomics provides comprehensive data regarding the identification, quantification, and evolutionary history of RNA viruses.

Abstract

Metatranscriptomics is a cutting-edge technology for exploring the gene expression by, and functional activities of, the microbial community across diverse ecosystems at a given time, thereby shedding light on their metabolic responses to the prevailing environmental conditions. The double-RNA approach involves the simultaneous analysis of rRNA and mRNA, also termed structural and functional metatranscriptomics. By contrast, mRNA-centered metatranscriptomics is fully focused on elucidating community-wide gene expression profiles, but requires either deep sequencing or effective rRNA depletion. In this review, we critically assess the challenges associated with various experimental and bioinformatic strategies that can be applied in soil microbial ecology through the lens of functional metatranscriptomics. In particular, we demonstrate how recent methodological advancements in soil metatranscriptomics catalyze the development and expansion of emerging research fields, such as rhizobiomes, antibiotic resistomes, methanomes, and viromes. Our review provides a framework that will help to design advanced metatranscriptomic research in elucidating the functional roles and activities of microbiomes in soil ecosystems.

Graphical abstract

Keywords

metatranscriptomics / mRNA / MAGs / rhizobiont / resistome / virome

References

[1] Alcock, B.P., Raphenya, A.R., Lau, T.T., Tsang, K.K., Bouchard, M., Edalatmand, A., Huynh, W., Nguyen, A.L.V., Cheng, A.A., Liu, S., 2020. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Research48, D517–D525.

[2] Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J., 1990. Basic local alignment search tool. Journal of Molecular Biology215, 403–410.

[3] Angle, J.C., Morin, T.H., Solden, L.M., Narrowe, A.B., Smith, G.J., Borton, M.A., Rey-Sanchez, C., Daly, R.A., Mirfenderesgi, G., Hoyt, D.W., Riley, W.J., Miller, C.S., Bohrer, G., Wrighton, K.C., 2017. Methanogenesis in oxygenated soils is a substantial fraction of wetland methane emissions. Nature Communications8, 1567.

[4] Bei, Q., Moser, G., Wu, X., Müller, C., Liesack, W., 2019. Metatranscriptomics reveals climate change effects on the rhizosphere microbiomes in European grassland. Soil Biology & Biochemistry138, 107604.

[5] Bei, Q., Reitz, T., Schnabel, B., Eisenhauer, N., Schädler, M., Buscot, F., Heintz-Buschart, A., 2023. Extreme summers impact cropland and grassland soil microbiomes. ISME Journal17, 1589–1600.

[6] Berg, G., Rybakova, D., Fischer, D., Cernava, T., Vergès, M.C.C., Charles, T., Chen, X., Cocolin, L., Eversole, K., Corral, G.H., 2020. Microbiome definition re-visited: old concepts and new challenges. Microbiome8, 1–22.

[7] Blanco-Míguez, A., Beghini, F., Cumbo, F., McIver, L.J., Thompson, K.N., Zolfo, M., Manghi, P., Dubois, L., Huang, K.D., Thomas, A.M., Nickols, W.A., Piccinno, G., Piperni, E., Punčochář, M., Valles-Colomer, M., Tett, A., Giordano, F., Davies, R., Wolf, J., Berry, S.E., Spector, T.D., Franzosa, E.A., Pasolli, E., Asnicar, F., Huttenhower, C., Segata, N., 2023. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nature Biotechnology41, 1–12.

[8] Blazewicz, S.J., Barnard, R.L., Daly, R.A., Firestone, M.K., 2013. Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. ISME Journal7, 2061–2068.

[9] Bolger, A.M., Lohse, M., Usadel, B., 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics (Oxford, England)30, 2114–2120.

[10] Buchfink, B., Xie, C., Huson, D.H., 2015. Fast and sensitive protein alignment using DIAMOND. Nature Methods12, 59–60.

[11] Burstein, D., Harrington, L.B., Strutt, S.C., Probst, A.J., Anantharaman, K., Thomas, B.C., Doudna, J.A., Banfield, J.F., 2017. New CRISPR–Cas systems from uncultivated microbes. Nature542, 237–241.

[12] Bushmanova, E., Antipov, D., Lapidus, A., Prjibelski, A.D., 2019. rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data. GigaScience8, giz100.

[13] Cai, Y., Zheng, Y., Bodelier, P.L., Conrad, R., Jia, Z., 2016. Conventional methanotrophs are responsible for atmospheric methane oxidation in paddy soils. Nature Communications7, 11728.

[14] Callanan, J., Stockdale, S.R., Shkoporov, A., Draper, L.A., Ross, R.P., Hill, C., 2020. Expansion of known ssRNA phage genomes: from tens to over a thousand. Science Advances6, eaay5981.

[15] Camargo, A.P., Nayfach, S., Chen, I.M.A., Palaniappan, K., Ratner, A., Chu, K., Ritter, S.J., Reddy, T., Mukherjee, S., Schulz, F., Call, L., Neches, R.Y., Woyke, T., Ivanova, N.N., Eloe-Fadrosh, E.A., Kyrpides, N.C., Roux, S., 2023. IMG/VR v4: an expanded database of uncultivated virus genomes within a framework of extensive functional, taxonomic, and ecological metadata. Nucleic Acids Research51, D733–D743.

[16] Carrión, V.J., Perez-Jaramillo, J., Cordovez, V., Tracanna, V., De Hollander, M., Ruiz-Buck, D., Mendes, L.W., van Ijcken, W.F., Gomez-Exposito, R., Elsayed, S.S., Mohanraju, P., Arifah, A., van der Oost, J., Paulson, J.N., Mendes, R., van Wezel, G.P., Medema, M.H., Raaijmakers, J.M., 2019. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science366, 606–612.

[17] Celaj, A., Markle, J., Danska, J., Parkinson, J., 2014. Comparison of assembly algorithms for improving rate of metatranscriptomic functional annotation. Microbiome2, 1–13.

[18] Chakoory, O., Comtet-Marre, S., Peyret, P., 2022. RiboTaxa: combined approaches for rRNA genes taxonomic resolution down to the species level from metagenomics data revealing novelties. NAR Genomics and Bioinformatics4, lqac070.

[19] Chappell, L., 2012. Finding a needle in a haystack. Nature Reviews Microbiology10, 446–446.

[20] Chen, J., Quiles-Puchalt, N., Chiang, Y.N., Bacigalupe, R., Fillol-Salom, A., Chee, M.S.J., Fitzgerald, J.R., Penadés, J.R., 2018. Genome hypermobility by lateral transduction. Science362, 207–212.

[21] Chen, Y.M., Sadiq, S., Tian, J.H., Chen, X., Lin, X.D., Shen, J.J., Chen, H., Hao, Z.Y., Wille, M., Zhou, Z.C., Wu, J., Li, F., Wang, H.W., Yang, W.D., Xu, Q.Y., Wang, W., Gao, W.H., Holmes, E.C., Zhang, Y.Z., 2022. RNA viromes from terrestrial sites across China expand environmental viral diversity. Nature Microbiology7, 1312–1323.

[22] Chevallereau, A., Pons, B.J., van Houte, S., Westra, E.R., 2022. Interactions between bacterial and phage communities in natural environments. Nature Reviews Microbiology20, 49–62.

[23] Ciuffreda, L., Rodríguez-Pérez, H., Flores, C., 2021. Nanopore sequencing and its application to the study of microbial communities. Computational and Structural Biotechnology Journal19, 1497–1511.

[24] Culviner, P.H., Guegler, C.K., Laub, M.T., 2020. A simple, cost-effective, and robust method for rRNA depletion in RNA-sequencing studies. mBio11, e00010–e00020.

[25] Damon, C., Lehembre, F., Oger-Desfeux, C., Luis, P., Ranger, J., Fraissinet-Tachet, L., Marmeisse, R., 2012. Metatranscriptomics reveals the diversity of genes expressed by eukaryotes in forest soils. PLoS One7, e28967.

[26] de Menezes, A., Clipson, N., Doyle, E., 2012. Comparative metatranscriptomics reveals widespread community responses during phenanthrene degradation in soil. Environmental Microbiology14, 2577–2588.

[27] Despotovic, M., de Nies, L., Busi, S.B., Wilmes, P., 2023. Reservoirs of antimicrobial resistance in the context of One Health. Current Opinion in Microbiology73, 102291.

[28] Eddy, S.R., 2001. Non-coding RNA genes and the modern RNA world. Nature Reviews Genetics2, 919–929.

[29] Emerson, J.B., Roux, S., Brum, J.R., Bolduc, B., Woodcroft, B.J., Jang, H.B., Singleton, C.M., Solden, L.M., Naas, A.E., Boyd, J.A., Hodgkins, S.B., Wilson, R.M., Trubl, G., Li, C., Frolking, S., Pope, P.B., Wrighton, K.C., Crill, P.M., Chanton, J.P., Saleska, S.R., Tyson, G.W., Rich, V.I., Sullivan, M.B., 2018. Host-linked soil viral ecology along a permafrost thaw gradient. Nature Microbiology3, 870–880.

[30] Esser, S.P., Rahlff, J., Zhao, W., Predl, M., Plewka, J., Sures, K., Wimmer, F., Lee, J., Adam, P.S., McGonigle, J., Turzynski, V., Banas, I., Schwank, K., Krupovic, M., Bornemann, T.L.V., Figueroa-Gonzalez, P.A., Jarett, J., Rattei, T., Amano, Y., Blaby, I.K., Cheng, J.F., Brazelton, W.J., Beisel, C.L., Woyke, T., Zhang, Y., Probst, A.J., 2023. A predicted CRISPR-mediated symbiosis between uncultivated archaea. Nature Microbiology8, 1619–1633.

[31] Franzosa, E.A., McIver, L.J., Rahnavard, G., Thompson, L.R., Schirmer, M., Weingart, G., Lipson, K.S., Knight, R., Caporaso, J.G., Segata, N., Huttenhower, C., 2018. Species-level functional profiling of metagenomes and metatranscriptomes. Nature Methods15, 962–968.

[32] Frias-Lopez, J., Shi, Y., Tyson, G.W., Coleman, M.L., Schuster, S.C., Chisholm, S.W., DeLong, E.F., 2008. Microbial community gene expression in ocean surface waters. Proceedings of the National Academy of Sciences of the United States of America105, 3805–3810.

[33] Fu, L., Niu, B., Zhu, Z., Wu, S., Li, W., 2012. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics (Oxford, England)28, 3150–3152.

[34] Geisen, S., Tveit, A.T., Clark, I.M., Richter, A., Svenning, M.M., Bonkowski, M., Urich, T., 2015. Metatranscriptomic census of active protists in soils. ISME Journal9, 2178–2190.

[35] Gelsinger, D.R., Uritskiy, G., Reddy, R., Munn, A., Farney, K., DiRuggiero, J., 2020. Regulatory noncoding small RNAs are diverse and abundant in an extremophilic microbial community. mSystems5, e00584–e00519.

[36] Gifford, S.M., Sharma, S., Rinta-Kanto, J.M., Moran, M.A., 2011. Quantitative analysis of a deeply sequenced marine microbial metatranscriptome. ISME Journal5, 461–472.

[37] Gilbert, J.A., Field, D., Huang, Y., Edwards, R., Li, W., Gilna, P., Joint, I., 2008. Detection of large numbers of novel sequences in the metatranscriptomes of complex marine microbial communities. PLoS One3, e3042.

[38] Gilbert, J.A., Thomas, S., Cooley, N.A., Kulakova, A., Field, D., Booth, T., McGrath, J.W., Quinn, J.P., Joint, I., 2009. Potential for phosphonoacetate utilization by marine bacteria in temperate coastal waters. Environmental Microbiology11, 111–125.

[39] Grabherr, M.G., Haas, B.J., Yassour, M., Levin, J.Z., Thompson, D.A., Amit, I., Adiconis, X., Fan, L., Raychowdhury, R., Zeng, Q., Chen, Z., Mauceli, E., Hacohen, N., Gnirke, A., Rhind, N., di Palma, F., Birren, B.W., Nusbaum, C., Lindblad-Toh, K., Friedman, N., Regev, A., 2011. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology29, 644–652.

[40] Grant, S., Grant, W.D., Cowan, D.A., Jones, B.E., Ma, Y., Ventosa, A., Heaphy, S., 2006. Identification of eukaryotic open reading frames in metagenomic cDNA libraries made from environmental samples. Applied and Environmental Microbiology72, 135–143.

[41] Gu, Y., Banerjee, S., Dini-Andreote, F., Xu, Y., Shen, Q., Jousset, A., Wei, Z., 2022. Small changes in rhizosphere microbiome composition predict disease outcomes earlier than pathogen density variations. ISME Journal16, 2448–2456.

[42] Hayden, H.L., Savin, K.W., Wadeson, J., Gupta, V.V., Mele, P.M., 2018. Comparative metatranscriptomics of wheat rhizosphere microbiomes in disease suppressive and non-suppressive soils for Rhizoctonia solani AG8. Frontiers in Microbiology9, 859.

[43] He, S., Wurtzel, O., Singh, K., Froula, J.L., Yilmaz, S., Tringe, S.G., Wang, Z., Chen, F., Lindquist, E.A., Sorek, R., Hugenholtz, P., 2010. Validation of two ribosomal RNA removal methods for microbial metatranscriptomics. Nature Methods7, 807–812.

[44] Hempel, C.A., Wright, N., Harvie, J., Hleap, J.S., Adamowicz, S.J., Steinke, D., 2022. Metagenomics versus total RNA sequencing: most accurate data-processing tools, microbial identification accuracy and perspectives for ecological assessments. Nucleic Acids Research50, 9279–9293.

[45] Hillary, L.S., Adriaenssens, E.M., Jones, D.L., McDonald, J.E., 2022. RNA-viromics reveals diverse communities of soil RNA viruses with the potential to affect grassland ecosystems across multiple trophic levels. ISME Communications2, 1–10.

[46] Huang, L., Zhang, H., Wu, P., Entwistle, S., Li, X., Yohe, T., Yi, H., Yang, Z., Yin, Y., 2018. dbCAN-seq: a database of carbohydrate-active enzyme (CAZyme) sequence and annotation. Nucleic Acids Research46, D516–D521.

[47] Huang, Y., Sheth, R.U., Kaufman, A., Wang, H.H., 2020. Scalable and cost-effective ribonuclease-based rRNA depletion for transcriptomics. Nucleic Acids Research48, e20.

[48] Huson, D.H., Auch, A.F., Qi, J., Schuster, S.C., 2007. MEGAN analysis of metagenomic data. Genome Research17, 377–386.

[49] Ivanova, A.A., Wegner, C.E., Kim, Y., Liesack, W., Dedysh, S.N., 2016. Identification of microbial populations driving biopolymer degradation in acidic peatlands by metatranscriptomic analysis. Molecular Ecology25, 4818–4835.

[50] Jansson, J.K., Wu, R., 2022. Soil viral diversity, ecology and climate change. Nature Reviews Microbiology21, 296–311.

[51] Ju, F., Beck, K., Yin, X., Maccagnan, A., McArdell, C.S., Singer, H.P., Johnson, D.R., Zhang, T., Bürgmann, H., 2019. Wastewater treatment plant resistomes are shaped by bacterial composition, genetic exchange, and upregulated expression in the effluent microbiomes. ISME Journal13, 346–360.

[52] Kopylova, E., Noé, L., Touzet, H., 2012. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics (Oxford, England)28, 3211–3217.

[53] Krinos, A.I., Cohen, N.R., Follows, M.J., Alexander, H., 2023. Reverse engineering environmental metatranscriptomes clarifies best practices for eukaryotic assembly. BMC Bioinformatics24, 1–36.

[54] Lackner, M., Drew, D., Bychkova, V., Mustakhimov, I., 2022. Value-Added Products from Natural Gas Using Fermentation Processes: Fermentation of Natural Gas as Valorization Route, Part 1. In: Ravanchi, M.T., ed. Natural Gas—New Perspectives and Future Developments. IntechOpen Limited, London. pp. 23–46

[55] Langmead, B., Salzberg, S.L., 2012. Fast gapped-read alignment with Bowtie 2. Nature Methods9, 357–359.

[56] Law, S.R., Serrano, A.R., Daguerre, Y., Sundh, J., Schneider, A.N., Stangl, Z.R., Castro, D., Grabherr, M., Näsholm, T., Street, N.R., Hurry, V., 2022. Metatranscriptomics captures dynamic shifts in mycorrhizal coordination in boreal forests. Proceedings of the National Academy of Sciences of the United States of America119, e2118852119.

[57] Lawther, K., Santos, F.G., Oyama, L.B., Rubino, F., Morrison, S., Creevey, C.J., McGrath, J.W., Huws, S.A., 2022. Resistome analysis of global livestock and soil microbiomes. Frontiers in Microbiology13, 897905.

[58] Leininger, S., Urich, T., Schloter, M., Schwark, L., Qi, J., Nicol, G.W., Prosser, J.I., Schuster, S., Schleper, C., 2006. Archaea predominate among ammonia-oxidizing prokaryotes in soils. Nature442, 806–809.

[59] Leung, H.C., Yiu, S.M., Parkinson, J., Chin, F.Y., 2013. IDBA-MT: de novo assembler for metatranscriptomic data generated from next-generation sequencing technology. Journal of Computational Biology20, 540–550.

[60] Levy-Booth, D.J., Hashimi, A., Roccor, R., Liu, L.Y., Renneckar, S., Eltis, L.D., Mohn, W.W., 2021. Genomics and metatranscriptomics of biogeochemical cycling and degradation of lignin-derived aromatic compounds in thermal swamp sediment. ISME Journal15, 879–893.

[61] Li, D., Liu, C.M., Luo, R., Sadakane, K., Lam, T.W., 2015. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics (Oxford, England)31, 1674–1676.

[62] Li, H., Durbin, R., 2009. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics (Oxford, England)25, 1754–1760.

[63] Liao, H., Li, H., Duan, C.S., Zhou, X.Y., Luo, Q.P., An, X.L., Zhu, Y.G., Su, J.Q., 2022. Response of soil viral communities to land use changes. Nature Communications13, 6027.

[64] Liao, H., Liu, C., Ai, C., Gao, T., Yang, Q.E., Yu, Z., Gao, S., Zhou, S., Friman, V.P., 2023. Mesophilic and thermophilic viruses are associated with nutrient cycling during hyperthermophilic composting. ISME Journal17, 916–930.

[65] Liu, B., Zheng, D., Jin, Q., Chen, L., Yang, J., 2019. VFDB 2019: a comparative pathogenomic platform with an interactive web interface. Nucleic Acids Research47, D687–D692.

[66] Lott, S.C., Voigt, K., Lambrecht, S.J., Hess, W.R., Steglich, C., 2020. A framework for the computational prediction and analysis of non-coding RNAs in microbial environmental populations and their experimental validation. ISME Journal14, 1955–1965.

[67] Martin, M., 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal17, 10–12.

[68] McGrath, K.C., Thomas-Hall, S.R., Cheng, C.T., Leo, L., Alexa, A., Schmidt, S., Schenk, P.M., 2008. Isolation and analysis of mRNA from environmental microbial communities. Journal of Microbiological Methods75, 172–176.

[69] McIlroy, S.J., Leu, A.O., Zhang, X., Newell, R., Woodcroft, B.J., Yuan, Z., Hu, S., Tyson, G.W., 2023. Anaerobic methanotroph ‘Candidatus Methanoperedens nitroreducens’ has a pleomorphic life cycle. Nature Microbiology8, 321–331.

[70] Mettel, C., Kim, Y., Shrestha, P.M., Liesack, W., 2010. Extraction of mRNA from soil. Applied and Environmental Microbiology76, 5995–6000.

[71] Middleton, H., Yergeau, É., Monard, C., Combier, J.P., El Amrani, A., 2021. Rhizospheric plant–microbe interactions: miRNAs as a key mediator. Trends in Plant Science26, 132–141.

[72] Miller, C.S., Baker, B.J., Thomas, B.C., Singer, S.W., Banfield, J.F., 2011. EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome Biology12, 1–14.

[73] Moran, M.A., Satinsky, B., Gifford, S.M., Luo, H., Rivers, A., Chan, L.K., Meng, J., Durham, B.P., Shen, C., Varaljay, V.A., Smith, C.B., Yager, P.L., Hopkinson, B.M., 2013. Sizing up metatranscriptomics. ISME Journal7, 237–243.

[74] Muscatt, G., Hilton, S., Raguideau, S., Teakle, G., Lidbury, I.D., Wellington, E.M., Quince, C., Millard, A., Bending, G.D., Jameson, E., 2022. Crop management shapes the diversity and activity of DNA and RNA viruses in the rhizosphere. Microbiome10, 1–16.

[75] Neri, U., Wolf, Y.I., Roux, S., Camargo, A.P., Lee, B., Kazlauskas, D., Chen, I.M., Ivanova, N., Allen, L.Z., Paez-Espino, D., 2022. Expansion of the global RNA virome reveals diverse clades of bacteriophages. Cell185, 4023–4037.e18.

[76] Nuccio, E.E., Nguyen, N.H., Nunes da Rocha, U., Mayali, X., Bougoure, J., Weber, P.K., Brodie, E., Firestone, M., Pett-Ridge, J., 2021. Community RNA-Seq: multi-kingdom responses to living versus decaying roots in soil. ISME Communications1, 1–10.

[77] Nuccio, E.E., Starr, E., Karaoz, U., Brodie, E.L., Zhou, J., Tringe, S.G., Malmstrom, R.R., Woyke, T., Banfield, J.F., Firestone, M.K., Pett-Ridge, J., 2020. Niche differentiation is spatially and temporally regulated in the rhizosphere. ISME Journal14, 999–1014.

[78] Ojala, T., Häkkinen, A.E., Kankuri, E., Kankainen, M., 2023. Current concepts, advances, and challenges in deciphering the human microbiota with metatranscriptomics. Trends in Genetics39, 686–702.

[79] Ortiz, R., Gera, P., Rivera, C., Santos, J.C., 2021. Pincho: a modular approach to high quality de novo transcriptomics. Genes12, 953.

[80] Parro, V., Moreno-Paz, M., González-Toril, E., 2007. Analysis of environmental transcriptomes by DNA microarrays. Environmental Microbiology9, 453–464.

[81] Passmore, L.A., Coller, J., 2022. Roles of mRNA poly (A) tails in regulation of eukaryotic gene expression. Nature Reviews Molecular Cell Biology23, 93–106.

[82] Peng, J., Wegner, C.E., Bei, Q., Liu, P., Liesack, W., 2018. Metatranscriptomics reveals a differential temperature effect on the structural and functional organization of the anaerobic food web in rice field soil. Microbiome6, 1–16.

[83] Perez-Coronel, E., Michael Beman, J., 2022. Multiple sources of aerobic methane production in aquatic ecosystems include bacterial photosynthesis. Nature Communications13, 6454.

[84] Poretsky, R.S., Bano, N., Buchan, A., LeCleir, G., Kleikemper, J., Pickering, M., Pate, W.M., Moran, M.A., Hollibaugh, J.T., 2005. Analysis of microbial gene transcripts in environmental samples. Applied and Environmental Microbiology71, 4121–4126.

[85] Prosser, J.I., 2015. Dispersing misconceptions and identifying opportunities for the use of ʻomics’ in soil microbial ecology. Nature Reviews Microbiology13, 439–446.

[86] Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., Glöckner, F.O., 2012. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research41, D590–D596.

[87] Roux, S., Adriaenssens, E.M., Dutilh, B.E., Koonin, E.V., Kropinski, A.M., Krupovic, M., Kuhn, J.H., Lavigne, R., Brister, J.R., Varsani, A., Amid, C., Aziz, R.K., Bordenstein, S.R., Bork, P., Breitbart, M., Cochrane, G.R., Daly, R.A., Desnues, C., Duhaime, M.B., Emerson, J.B., Enault, F., Fuhrman, J.A., Hingamp, P., Hugenholtz, P., Hurwitz, B.L., Ivanova, N.N., Labonté, J.M., Lee, K.B., Malmstrom, R.R., Martinez-Garcia, M., Mizrachi, I.K., Ogata, H., Páez-Espino, D., Petit, M.A., Putonti, C., Rattei, T., Reyes, A., Rodriguez-Valera, F., Rosario, K., Schriml, L., Schulz, F., Steward, G.F., Sullivan, M.B., Sunagawa, S., Suttle, C.A., Temperton, B., Tringe, S.G., Thurber, R.V., Webster, N.S., Whiteson, K.L., Wilhelm, S.W., Wommack, K.E., Woyke, T., Wrighton, K.C., Yilmaz, P., Yoshida, T., Young, M.J., Yutin, N., Allen, L.Z., Kyrpides, N.C., Eloe-Fadrosh, E.A., 2019. Minimum information about an uncultivated virus genome (MIUViG). Nature Biotechnology37, 29–37.

[88] Sabino, Y.N.V., Santana, M.F., Oyama, L.B., Santos, F.G., Moreira, A.J.S., Huws, S.A., Mantovani, H.C., 2019. Characterization of antibiotic resistance genes in the species of the rumen microbiota. Nature Communications10, 5252.

[89] Schoelmerich, M.C., Ouboter, H.T., Sachdeva, R., Penev, P.I., Amano, Y., West-Roberts, J., Welte, C.U., Banfield, J.F., 2022. A widespread group of large plasmids in methanotrophic Methanoperedens archaea. Nature Communications13, 7085.

[90] Semmouri, I., De Schamphelaere, K.A., Mees, J., Janssen, C.R., Asselman, J., 2020. Evaluating the potential of direct RNA nanopore sequencing: Metatranscriptomics highlights possible seasonal differences in a marine pelagic crustacean zooplankton community. Marine Environmental Research153, 104836.

[91] Shakya, M., Lo, C.C., Chain, P.S., 2019. Advances and challenges in metatranscriptomic analysis. Frontiers in Genetics10, 904.

[92] Shi, M., Lin, X.D., Chen, X., Tian, J.H., Chen, L.J., Li, K., Wang, W., Eden, J.S., Shen, J.J., Liu, L., Holmes, E.C., Zhang, Y.Z., 2018. The evolutionary history of vertebrate RNA viruses. Nature556, 197–202.

[93] Shi, M., Lin, X.D., Tian, J.H., Chen, L.J., Chen, X., Li, C.X., Qin, X.C., Li, J., Cao, J.P., Eden, J.S., Buchmann, J., Wang, W., Xu, J., Holmes, E.C., Zhang, Y.Z., 2016. Redefining the invertebrate RNA virosphere. Nature540, 539–543.

[94] Shi, Y., Tyson, G.W., DeLong, E.F., 2009. Metatranscriptomics reveals unique microbial small RNAs in the ocean’s water column. Nature459, 266–269.

[95] Shrestha, P.M., Kube, M., Reinhardt, R., Liesack, W., 2009. Transcriptional activity of paddy soil bacterial communities. Environmental Microbiology11, 960–970.

[96] Söllinger, A., Séneca, J., Borg Dahl, M., Motleleng, L.L., Prommer, J., Verbruggen, E., Sigurdsson, B.D., Janssens, I., Peñuelas, J., Urich, T., Richter, A., Tveit, A.T., 2022. Down-regulation of the bacterial protein biosynthesis machinery in response to weeks, years, and decades of soil warming. Science Advances8, eabm3230.

[97] Starr, E.P., Nuccio, E.E., Pett-Ridge, J., Banfield, J.F., Firestone, M.K., 2019. Metatranscriptomic reconstruction reveals RNA viruses with the potential to shape carbon cycling in soil. Proceedings of the National Academy of Sciences of the United States of America116, 25900–25908.

[98] Stewart, F.J., Ottesen, E.A., DeLong, E.F., 2010. Development and quantitative analyses of a universal rRNA-subtraction protocol for microbial metatranscriptomics. ISME Journal4, 896–907.

[99] Tan, S., Liu, J., Fang, Y., Hedlund, B.P., Lian, Z.H., Huang, L.Y., Li, J.T., Huang, L.N., Li, W.J., Jiang, H.C., Dong, H.L., Shu, W.S., 2019. Insights into ecological role of a new deltaproteobacterial order Candidatus Acidulodesulfobacterales by metagenomics and metatranscriptomics. ISME Journal13, 2044–2057.

[100] Täumer, J., Marhan, S., Groß, V., Jensen, C., Kuss, A.W., Kolb, S., Urich, T., 2022. Linking transcriptional dynamics of CH₄-cycling grassland soil microbiomes to seasonal gas fluxes. ISME Journal16, 1788–1797.

[101] Tong, D., Wang, Y., Yu, H., Shen, H., Dahlgren, R.A., Xu, J., 2023. Viral lysing can alleviate microbial nutrient limitations and accumulate recalcitrant dissolved organic matter components in soil. ISME Journal17, 1247–1256.

[102] Toseland, A., Moxon, S., Mock, T., Moulton, V., 2014. Metatranscriptomes from diverse microbial communities: assessment of data reduction techniques for rigorous annotation. BMC Genomics15, 1–7.

[103] Turner, T.R., Ramakrishnan, K., Walshaw, J., Heavens, D., Alston, M., Swarbreck, D., Osbourn, A., Grant, A., Poole, P.S., 2013. Comparative metatranscriptomics reveals kingdom level changes in the rhizosphere microbiome of plants. ISME Journal7, 2248–2258.

[104] Urich, T., Lanzén, A., Qi, J., Huson, D.H., Schleper, C., Schuster, S.C., 2008. Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS One3, e2527.

[105] Van Goethem, M.W., Osborn, A.R., Bowen, B.P., Andeer, P.F., Swenson, T.L., Clum, A., Riley, R., He, G., Koriabine, M., Sandor, L., Yan, M., Daum, C.G., Yoshinaga, Y., Makhalanyane, T.P., Garcia-Pichel, F., Visel, A., Pennacchio, L.A., O’Malley, R.C., Northen, T.R., 2021. Long-read metagenomics of soil communities reveals phylum-specific secondary metabolite dynamics. Communications Biology4, 1–10.

[106] Wahl, A., Huptas, C., Neuhaus, K., 2022. Comparison of rRNA depletion methods for efficient bacterial mRNA sequencing. Scientific Reports12, 1–11.

[107] Wang, F., Fu, Y.H., Sheng, H.J., Topp, E., Jiang, X., Zhu, Y.G., Tiedje, J.M., 2021. Antibiotic resistance in the soil ecosystem: A One Health perspective. Current Opinion in Environmental Science & Health20, 100230.

[108] Wang, J., Qu, Y.N., Evans, P.N., Guo, Q., Zhou, F., Nie, M., Jin, Q., Zhang, Y., Zhai, X., Zhou, M., Yu, Z., Fu, Q.L., Xie, Y.G., Hedlund, B.P., Li, W.J., Hua, Z.S., Wang, Z., Wang, Y., 2023. Evidence for nontraditional mcr-containing archaea contributing to biological methanogenesis in geothermal springs. Science Advances9, eadg6004.

[109] Wood, D.E., Lu, J., Langmead, B., 2019. Improved metagenomic analysis with Kraken 2. Genome Biology20, 1–13.

[110] Woodcroft, B.J., Singleton, C.M., Boyd, J.A., Evans, P.N., Emerson, J.B., Zayed, A.A., Hoelzle, R.D., Lamberton, T.O., McCalley, C.K., Hodgkins, S.B., Wilson, R.M., Purvine, S.O., Nicora, C.D., Li, C., Frolking, S., Chanton, J.P., Crill, P.M., Saleska, S.R., Rich, V.I., Tyson, G.W., 2018. Genome-centric view of carbon processing in thawing permafrost. Nature560, 49–54.

[111] Xia, R., Sun, M., Balcázar, J.L., Yu, P., Hu, F., Alvarez, P.J., 2023. Benzo[a]pyrene stress impacts adaptive strategies and ecological functions of earthworm intestinal viromes. ISME Journal17, 1004–1014.

[112] Xu, L., Dong, Z., Chiniquy, D., Pierroz, G., Deng, S., Gao, C., Diamond, S., Simmons, T., Wipf, H.M.L., Caddell, D., Varoquaux, N., Madera, M.A., Hutmacher, R., Deutschbauer, A., Dahlberg, J.A., Guerinot, M.L., Purdom, E., Banfield, J.F., Taylor, J.W., Lemaux, P.G., Coleman-Derr, D., 2021. Genome-resolved metagenomics reveals role of iron metabolism in drought-induced rhizosphere microbiome dynamics. Nature Communications12, 3209.

[113] Yates, M.C., Derry, A.M., Cristescu, M.E. 2021. Environmental RNA: a revolution in ecological resolution?. Trends in Ecology & Evolution36, 601–609.

[114] Yergeau, E., Tremblay, J., Joly, S., Labrecque, M., Maynard, C., Pitre, F.E., St-Arnaud, M., Greer, C.W., 2018. Soil contamination alters the willow root and rhizosphere metatranscriptome and the root–rhizosphere interactome. ISME Journal12, 869–884.

[115] Yin, Z., Ye, L., Jing, C., 2022. Genome-resolved metagenomics and metatranscriptomics reveal that Aquificae dominates arsenate reduction in Tengchong geothermal springs. Environmental Science & Technology56, 16473–16482.

[116] Yuan, C., Lei, J., Cole, J., Sun, Y., 2015. Reconstructing 16S rRNA genes in metagenomic data. Bioinformatics (Oxford, England)31, i35–i43.

[117] Yuan, L., Wang, Y., Zhang, L., Palomo, A., Zhou, J., Smets, B.F., Bürgmann, H., Ju, F., 2021. Pathogenic and indigenous denitrifying bacteria are transcriptionally active and key multi-antibiotic-resistant players in wastewater treatment plants. Environmental Science & Technology55, 10862–10874.

[118] Zhou, Z., Zhang, C., Liu, P., Fu, L., Laso-Pérez, R., Yang, L., Bai, L., Li, J., Yang, M., Lin, J., Wang, W., Wegener, G., Li, M., Cheng, L., 2022. Non-syntrophic methanogenic hydrocarbon degradation by an archaeal species. Nature601, 257–262.

Acknowledgments

This study was financially supported by the National Natural Science Foundation of China (41977038 and 42277307) and the National Key Research and Development Program of China (2021YFD1900100).

Author contributions

J.J.P., X.Z., C.R., W.L., and Y.G.Z. wrote the manuscript. All authors read and approved the final manuscript.

Declaration of interests

No interests are declared.

Glossary

Antimicrobial resistance (AMR): the ability of microorganisms, such as bacteria, viruses, fungi, and parasites, to resist the effects of drugs.

Auxiliary metabolic genes (AMGs): while not essential for basic cellular functions, AMGs encode proteins involved in various host metabolic pathways. Originating from bacterial cells, they are frequently found in many bacteriophages.

Bacteriophage (phage): a virus that explicitly infects, and replicates within, bacteria or archaea.

Metagenome-assembled genomes (MAGs): genomes reconstructed from metagenomic data by binning or clustering contigs that are derived from the same organism or closely related organisms. MAGs represent the genomic information of individual microorganisms present in a metagenomic sample.

Metagenomics: a technology that allows for direct sequencing of genetic material from environmental or clinical samples without the need to culture or isolate individual organisms.

Metatranscriptomics: a technology that allows for the sequencing analysis of total RNA or enriched mRNA extracted from a complex biological sample. It aims to understand and interpret the transcriptome of entire microbial communities within a specific environment.

Methanome: comprises the methane-producing (methanogenic) and methane-oxidizing (methanotrophic) microorganisms, which inhabit a wide range of natural ecosystems.

Non-coding RNA (ncRNA): a broad category of RNA molecules that do not code for proteins but have diverse functional roles in the cell. These molecules include small regulatory RNAs (sRNAs), rRNAs (rRNAs), and tRNAs (tRNAs).

Resistome: the collective genetic reservoir of resistance genes in a microbial community of a given environment. These genes encode proteins that enable microorganisms to withstand the effects of antibiotics and other antimicrobial agents.

Rhizobiome: the collection of microorganisms inhabiting the rhizosphere, with the latter being defined as the soil surrounding the plant roots while influenced by them. The rhizobiome encompasses bacteria, fungi, archaea, and other microorganisms that are thought to interact with plant roots. The term “rhizobiome” is derived from the word “rhizosphere” (the soil zone influenced by plant roots) and “biome” (a complex ecological community).

RNA-dependent RNA polymerase (RdRP): an enzyme responsible for the replication and transcription of RNA in certain viruses.

Virome: the collective genetic material of viruses presenting in a particular ecosystem or within a specific organism, and the term is indeed derived from “virus” and “biome” (a complex ecological community). It encompasses all the viruses, including bacteriophages, in a given environment or host.

Text boxes

Box 1 Hidden treasure uncovered: Non-coding small RNA

Recent scientific investigations have transcended the conventional realms of “three flavors” (mRNA, rRNA, and tRNA) and expanded their view to encompass the often overlooked class of non-coding RNAs, particularly the small RNAs (sRNAs) which are essential components in the global control of transcriptional regulation (Eddy, 2001). sRNAs are a crucial type of non-coding RNA molecules that typically vary in size from 50 to 500 bases. It has been a decade since the first discovery of sRNAs in the ocean through metatranscriptomics (Shi et al., 2009). sRNAs are involved in several biological processes, including translational control, RNA-RNA mediated interactions, nutrient cycling, stress response, and quorum sensing. Metatranscriptomics has enabled a more comprehensive study of sRNAs, allowing for detection of their expression in non-protein-coding regions and the community-level analysis of sRNA abundances (Lott et al., 2020). Through a comparison of cDNAs with metagenomic data retrieved form marine samples, Shi et al. (2009) found that over 28% of the unassigned cDNA reads are represented by sRNAs, indicating the importance of this class of RNA in environmental processes like carbon metabolism and nutrient acquisition. More recently, Gelsinger et al. (2020) employed metatranscriptomics to elucidate the abundance and diversity of non-coding sRNAs within an extremophilic microbial community. Their study highlighted the regulatory role of sRNAs in gene expression and response to environmental stress by demonstrating a correlation between sRNA expression levels and their corresponding target mRNAs (Gelsinger et al., 2020).

Box 2 Soil viruses under climate change

Soil viruses, abundant and diverse biological entities, are crucial in shaping soil microbiota and essential soil functions (Liao et al., 2022). As our planet experiences the effects of global warming and extreme weather events, the pivotal role of viruses in shaping soil microbiota and their functional activities has gained significant research attention. Based on their life cycle, phages can be categorized as virulent or temperate, which differ in their infective properties (Chevallereau et al., 2022). Virulent phages operate as predators, exerting control over bacterial communities by lysing cells, aligning with the “Kill-the-Winner” model (Muscatt et al., 2022). By liberating cellular matter through host lysis, they contribute to the soil organic carbon pool, thus facilitating what we refer to as the “viral shunt.” In marine ecosystems, phages lyse 20%−40% of prokaryotes daily, thus constituting a significant proportion (6%−26%) of the marine carbon exchange and exerting a considerable impact on the marine carbon cycle (Tong et al., 2023). Experimental evidence supports the existence of a “viral shuttle” in soils, where soil phages enhance the abundance of recalcitrant dissolved organic matter components, creating a delicate balance between net carbon sink and carbon source during the lysis process (Tong et al., 2023). In contrast, temperate phages integrate their genetic material into the host's chromosomes and replicate their nucleic acids alongside the host, in what is known as the lysogenic cycle (Chen et al., 2018). Contrary to virulent phages whose dynamics are explained by the “Kill-the-Winner” model, temperate (or lysogenic) phages enable their hosts to endure harsh environments by integrating auxiliary metabolic genes (AMGs) into the host's genome, an intriguing concept known as the “Piggyback-the-Winner” model (Jansson and Wu, 2022). For instance, viruses have been found to encode glycoside hydrolases that can potentially influence complex carbon degradation (Emerson et al., 2018; Jansson and Wu, 2022). Furthermore, the changing climate, characterized by elevated temperatures, thawing permafrost, and altered precipitation patterns, significantly influences soil viral communities (Jansson and Wu, 2022). Increased soil temperatures, fostering microbial activity, may induce temperate phages to transition into the lytic life cycle, thereby triggering an escalating arms race between viruses and hosts and, in consequence, contributing to carbon cycling through the viral shunt (Chen et al., 2018; Jansson and Wu, 2022). These discoveries emphasize the substantial impact of viruses on soil services and functions in crucial terrestrial ecosystems influenced by climate change.

Electronic supplementary material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s42832-023-0217-z and is accessible for authorized users.

RIGHTS & PERMISSIONS

2023 Higher Education Press