The addition of glucose to soil has long been used to study the metabolic activity of microbes in soil; however, the response of the microbial ecophysiology remains poorly characterized. To address this, we sequenced the metagenomes and metatranscriptomes of glucose-amended soil microbial communities in a laboratory incubation.
ABSTRACT
The addition of glucose to soil has long been used to study the metabolic activity of microbes in soil; however, the response of the microbial ecophysiology remains poorly characterized. To address this, we sequenced the metagenomes and metatranscriptomes of glucose-amended soil microbial communities in a laboratory incubation.
ANNOUNCEMENT
Soil microbial communities are considered to be limited by carbon and energy (1, 2). The addition of glucose to soil has been used to study the maintenance energy demands of soil microbes (3), soil organic matter priming (4), and taxon-specific growth rates of soil bacteria (5, 6). However, a detailed description of the response of microbial community metabolism to a glucose addition is lacking. Here, we present metatranscriptomes and metagenomes of glucose-amended agricultural soil in a short-term laboratory incubation.
Soils from a long-term crop rotation experiment (7) at the West Virginia University Certified Organic Farm (Morgantown, WV, USA) (39.647502°N, 79.93691°W; 243.8 to 475.2 m above sea level) were sampled at 0- to 10-cm depths and shipped to Northern Arizona University (Flagstaff, AZ, USA). There, soils from separate cores were homogenized and separated into Mason jars to contain 30 g of soil each. Samples were then preincubated for 2 weeks at room temperature. After preincubation, each sample was amended with 1.6 ml of a 0.13 M glucose solution, which added 0.7 mg of glucose C per g of dry soil. Before and 8, 24, and 48 h after glucose addition, 4 replicates collected from different Mason jars were frozen using liquid N2 and stored at −80°C. RNA was extracted using the RNeasy PowerSoil total RNA kit (Qiagen) and treated with RNase-free DNase (Qiagen). DNA was extracted using the RNeasy PowerSoil DNA elution kit (Qiagen). A Qubit fluorometer (Invitrogen, Carlsbad, CA, USA) was used to determine the concentrations of extracted nucleic acids, and a NanoDrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA) was used to assess purity.
RNA and DNA were shipped to the Joint Genome Institute (JGI) for sequencing. An Illumina TruSeq stranded RNA LT kit was used to generate cDNA libraries. Prior to sequencing, heavily degraded samples were discarded, resulting in the elimination of 3 DNA samples. Plate-based DNA library preparation for Illumina sequencing was performed on the PerkinElmer Sciclone next-generation sequencing (NGS) robotic liquid-handling system using a Kapa Biosystems library preparation kit. Two hundred nanograms of sample DNA was sheared to 300 bp using a Covaris LE220 focused ultrasonicator. Sheared DNA fragments were size selected by double solid-phase reversible immobilization (SPRI), and selected fragments were end repaired, A tailed, and ligated with Illumina-compatible sequencing adaptors (IDT) containing a unique molecular index barcode for each sample library. The prepared libraries were quantified using the Kapa Biosystems NGS library quantitative PCR kit and run on a Roche LightCycler 480 real-time PCR instrument. Quantified libraries were multiplexed with other libraries, and the pool of libraries was then prepared for sequencing on the Illumina NovaSeq sequencer using NovaSeq XP v1 reagent kits and an S4 flow cell, following a 2 × 150-bp indexed run protocol. Metatranscriptome reads were filtered using BBTools v38 (8) to remove duplicate, ribosomal, low-quality, and human reads. Filtered reads were assembled using MEGAHIT v1.1.2. (9) using a custom k-mer size list (--k-list 23,43,63,83,103,123). Reads for metagenomic samples were filtered for contaminants and adaptors and trimmed for quality using BBTools v38 (8), corrected using BFC vr181 (10) (with the options -s 10g -k 21), and assembled using SPAdes v3.13.0 (11) (with the options --only-assembler, --meta, and -k33,55,77,99,127). Reads were mapped against the assembled read set using BBMap v38 (8) with the option ambiguous=random. Assembled contigs were annotated using the IMG Annotation Pipeline v5.0.1 (12, 13). In total, 13 metagenomes (minimum of 3 per time point) and 16 metatranscriptomes (4 per time point) were sequenced, assembled, and annotated (Table 1). Detailed information on the bioinformatic processing of each library is available via the JGI Genome Portal (https://genome.jgi.doe.gov/portal/Strinailability/Strinailability.info.html).
TABLE 1.
Sample numbers and summaries for metagenomes and metatranscriptomes of laboratory-incubated agricultural soil amended with glucose
| IMG identification no. | Sample name | Time (h) | JGI analysis project type | NCBI BioProject accession no. | NCBI BioSample accession no. | SRA accession no. | No. of reads | Assembled genome size (bp) | No. of genes | No. of scaffolds | N 50 (bp) | GC content (%) | % of genes with predicted function using: |
||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| COG database | Pfam database | KEGG database | |||||||||||||
| 3300032003 | C0D1 | 0 | Metagenome | PRJNA539715 | SAMN11528526 | SRR9032300 | 122,905,108 | 1,355,893,536 | 3,466,757 | 2,855,912 | 851,672 | 63.9 | 53.7 | 51.5 | 24.9 |
| 3300031892 | C0D2 | 0 | Metagenome | PRJNA539712 | SAMN11533409 | SRR9032202 | 111,310,488 | 1,152,269,571 | 2,964,388 | 2,455,617 | 739,392 | 64.1 | 53.9 | 51.6 | 25.0 |
| 3300032000 | C0D3 | 0 | Metagenome | PRJNA539720 | SAMN11533240 | SRR9032615 | 126,283,392 | 1,651,468,217 | 4,265,445 | 3,536,653 | 1,067,790 | 64.4 | 53.9 | 51.7 | 25.3 |
| 3300032122 | C0D4 | 0 | Metagenome | PRJNA539713 | SAMN11532952 | SRR9032258 | 128,790,659 | 1,467,471,286 | 3,759,875 | 3,098,631 | 927,590 | 64.1 | 53.8 | 51.6 | 25.0 |
| 3300034668 | C0R1 | 0 | Metatranscriptome | PRJNA570403 | SAMN12814391 | SRR10849656 | 99,039,727 | 302,858,706 | 718,971 | 631,914 | 217,331 | 60.4 | 45.8 | 43.2 | 17.8 |
| 3300034480 | C0R2 | 0 | Metatranscriptome | PRJNA570401 | SAMN12814786 | SRR10904363 | 70,227,109 | 64,495,153 | 148,170 | 137,255 | 47,055 | 57.4 | 37.6 | 35.3 | 14.8 |
| 3300034674 | C0R3 | 0 | Metatranscriptome | PRJNA570409 | SAMN12812743 | SRR10849749 | 61,722,077 | 92,886,127 | 211,898 | 192,180 | 64,296 | 58.7 | 41.0 | 38.6 | 15.9 |
| 3300036405 | C0R4 | 0 | Metatranscriptome | PRJNA647753 | SAMN15601796 | SRR12334071 | 94,316,601 | 394,204,764 | 925,493 | 806,549 | 272,244 | 59.2 | 46.7 | 43.6 | 18.0 |
| 3300031908 | C24D1 | 24 | Metagenome | PRJNA539717 | SAMN11533408 | SRR9032509 | 205,837,446 | 3,525,197,514 | 8,695,255 | 7,014,884 | 1,984,116 | 64.0 | 52.6 | 51.5 | 24.4 |
| 3300031940 | C24D2 | 24 | Metagenome | PRJNA539718 | SAMN11532657 | SRR9032510 | 111,794,949 | 1,202,955,554 | 3,148,390 | 2,627,975 | 810,700 | 64.3 | 54.4 | 51.8 | 25.6 |
| 3300032012 | C24D3 | 24 | Metagenome | PRJNA539719 | SAMN11532357 | SRR9032617 | 170,867,131 | 2,561,938,476 | 6,450,661 | 5,267,248 | 1,531,865 | 63.9 | 52.9 | 51.4 | 24.7 |
| 3300034671 | C24R1 | 24 | Metatranscriptome | PRJNA570406 | SAMN12813766 | SRR10849744 | 70,179,661 | 321,462,175 | 764,600 | 659,038 | 224,121 | 60.5 | 54.5 | 51.4 | 26.6 |
| 3300034672 | C24R2 | 24 | Metatranscriptome | PRJNA570407 | SAMN12815093 | SRR10849745 | 77,871,015 | 277,611,432 | 656,390 | 568,464 | 191,550 | 59.2 | 53.5 | 50.7 | 25.8 |
| 3300034673 | C24R3 | 24 | Metatranscriptome | PRJNA570408 | SAMN12812652 | SRR10849807 | 58,429,105 | 296,367,375 | 697,412 | 602,090 | 202,562 | 59.6 | 53.7 | 50.9 | 25.6 |
| 3300036404 | C24R4 | 24 | Metatranscriptome | PRJNA647752 | SAMN15601797 | SRR12334066 | 86,292,361 | 280,662,482 | 666,005 | 577,618 | 197,189 | 60.7 | 52.0 | 48.5 | 23.5 |
| 3300031854 | C48D1 | 48 | Metagenome | PRJNA539721 | SAMN11532519 | SRR9032715 | 174,461,389 | 2,640,998,970 | 6,618,271 | 5,384,216 | 1,554,238 | 64.2 | 53.0 | 51.5 | 24.7 |
| 3300032013 | C48D3 | 48 | Metagenome | PRJNA539722 | SAMN11532950 | SRR9032716 | 171,678,685 | 2,642,150,213 | 6,610,764 | 5,368,173 | 1,553,058 | 65.1 | 53.5 | 51.9 | 24.9 |
| 3300031847 | C48D4 | 48 | Metagenome | PRJNA539723 | SAMN11532599 | SRR9032694 | 134,609,818 | 1,664,342,186 | 4,245,763 | 3,497,047 | 1,045,780 | 64.0 | 54.0 | 51.8 | 25.3 |
| 3300034675 | C48R1 | 48 | Metatranscriptome | PRJNA570410 | SAMN12814942 | SRR10849803 | 65,457,552 | 150,659,653 | 355,892 | 318,387 | 110,009 | 58.2 | 46.6 | 44.2 | 20.8 |
| 3300034676 | C48R2 | 48 | Metatranscriptome | PRJNA570411 | SAMN12812625 | SRR10849941 | 81,394,607 | 36,2044,275 | 860,612 | 746,114 | 253,443 | 59.8 | 50.4 | 47.7 | 22.7 |
| 3300034677 | C48R3 | 48 | Metatranscriptome | PRJNA570412 | SAMN12812728 | SRR10849938 | 68,921,951 | 88,305,274 | 207,453 | 187,559 | 64,424 | 59.1 | 45.4 | 42.5 | 20.2 |
| 3300034678 | C48R4 | 48 | Metatranscriptome | PRJNA570413 | SAMN12814182 | SRR10849936 | 67,586,882 | 252,944,636 | 602,314 | 526,545 | 180,208 | 60.7 | 50.3 | 47.3 | 22.6 |
| 3300032211 | C8D1 | 8 | Metagenome | PRJNA539714 | SAMN11532583 | SRR9032259 | 131,787,411 | 1,841,805,081 | 475,6397 | 3,945,810 | 1,187,662 | 64.1 | 52.6 | 50.8 | 24.4 |
| 3300031858 | C8D2 | 8 | Metagenome | PRJNA539711 | SAMN11533337 | SRR9032199 | 172,412,632 | 2,503,797,881 | 6,199,898 | 5,009,710 | 1,429,077 | 64.6 | 53.4 | 51.9 | 24.7 |
| 3300032017 | C8D4 | 8 | Metagenome | PRJNA539716 | SAMN11532664 | SRR9032267 | 125,460,262 | 1,413,722,004 | 3,629,789 | 2,995,887 | 896,872 | 63.7 | 53.2 | 51.2 | 24.7 |
| 3300034667 | C8R1 | 8 | Metatranscriptome | PRJNA570402 | SAMN12815108 | SRR10849655 | 82,347,388 | 440,040,760 | 1,041,764 | 882,412 | 293,847 | 60.1 | 56.8 | 53.5 | 30.4 |
| 3300034666 | C8R2 | 8 | Metatranscriptome | PRJNA570400 | SAMN12813333 | SRR10849401 | 91,524,568 | 366,955,569 | 892,052 | 759,185 | 260,726 | 61.0 | 58.0 | 53.8 | 30.9 |
| 3300034669 | C8R3 | 8 | Metatranscriptome | PRJNA570404 | SAMN12814692 | SRR10849649 | 59,135,875 | 307,696,835 | 741,250 | 629,624 | 215,105 | 62.3 | 58.1 | 54.1 | 31.2 |
| 3300034670 | C8R4 | 8 | Metatranscriptome | PRJNA570405 | SAMN12814921 | SRR10849743 | 73,470,419 | 259,146,587 | 615,760 | 533,046 | 180,822 | 60.1 | 54.5 | 51.4 | 28.8 |
Data availability.
Metadata curation and public repository registration for the metagenomes and metatranscriptomes were managed by the Genomes OnLine Database (GOLD) (14) under study number Gs0135756 (https://gold.jgi.doe.gov/study?id=Gs0135756). Annotations are located in the IMG database. IMG identification numbers, SRA accession numbers, and sample information can be found in Table 1.
ACKNOWLEDGMENTS
This work was supported by funding from the USDA National Institute of Food and Agriculture Foundational Program (award 2017-67019-26396); additional support for P.D. was provided by the U.S. Department of Energy (DOE), Office of Biological and Environmental Research, Genomic Science Program at Lawrence Livermore National Laboratory, Microbes Persist Soil Microbiome Scientific Focus Area (award SCW1632). The work conducted by the U.S. DOE JGI, a DOE Office of Science User Facility, is supported under contract DE-AC02-05CH11231.
REFERENCES
- 1. Hobbie JE, Hobbie EA. 2013. Microbes in nature are limited by carbon and energy: the starving-survival lifestyle in soil and consequences for estimating microbial rates. Front Microbiol 4:324. doi: 10.3389/fmicb.2013.00324. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Schimel JP, Weintraub MN. 2003. The implications of exoenzyme activity on microbial carbon and nitrogen limitation in soil: a theoretical model. Soil Biol Biochem 35:549–563. doi: 10.1016/S0038-0717(03)00015-4. [DOI] [Google Scholar]
- 3. Anderson TH, Domsch KH. 1985. Maintenance carbon requirements of actively-metabolizing microbial populations under in situ conditions. Soil Biol Biochem 17:197–203. doi: 10.1016/0038-0717(85)90115-4. [DOI] [Google Scholar]
- 4. Hill PW, Farrar JF, Jones DL. 2008. Decoupling of microbial glucose uptake and mineralization in soil. Soil Biol Biochem 40:616–624. doi: 10.1016/j.soilbio.2007.09.008. [DOI] [Google Scholar]
- 5. Papp K, Hungate BA, Schwartz E. 2020. Glucose triggers strong taxon‐specific responses in microbial growth and activity: insights from DNA and RNA qSIP. Ecology 101:e02887. doi: 10.1002/ecy.2887. [DOI] [PubMed] [Google Scholar]
- 6. Reischke S, Rousk J, Bååth E. 2014. The effects of glucose loading rates on bacterial and fungal growth in soil. Soil Biol Biochem 70:88–95. doi: 10.1016/j.soilbio.2013.12.011. [DOI] [Google Scholar]
- 7. Pena-Yewtukhiw EM, Romano EL, Waterland NL, Grove JH. 2017. Soil health indicators during transition from row crops to grass-legume sod. Soil Sci Soc Am J 81:1486–1495. doi: 10.2136/sssaj2016.12.0439. [DOI] [Google Scholar]
- 8. Bushnell B. 2014. BBTools software package. https://sourceforge.net/projects/bbmap.
- 9. Li D, Liu CM, Luo R, Sadakane K, Lam TW. 2015. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31:1674–1676. doi: 10.1093/bioinformatics/btv033. [DOI] [PubMed] [Google Scholar]
- 10. Li H. 2015. BFC: correcting Illumina sequencing errors. Bioinformatics 31:2885–2887. doi: 10.1093/bioinformatics/btv290. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA. 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455–477. doi: 10.1089/cmb.2012.0021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Chen IMA, Chu K, Palaniappan K, Pillay M, Ratner A, Huang J, Huntemann M, Varghese N, White JR, Seshadri R, Smirnova T, Kirton E, Jungbluth SP, Woyke T, Eloe-Fadrosh EA, Ivanova NN, Kyrpides NC. 2019. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res 47:D666–D677. doi: 10.1093/nar/gky901. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Huntemann M, Ivanova NN, Mavromatis K, Tripp HJ, Paez-Espino D, Tennessen K, Palaniappan K, Szeto E, Pillay M, Chen IMA, Pati A, Nielsen T, Markowitz VM, Kyrpides NC. 2016. The standard operating procedure of the DOE-JGI Metagenome Annotation Pipeline (MAP v.4). Stand Genomic Sci 11:17. doi: 10.1186/s40793-016-0138-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Katta HY, Mojica A, Chen I-MA, Kyrpides NC, Reddy TBK. 2019. Genomes OnLine database (GOLD) v.7: updates and new features. Nucleic Acids Res 47:D649–D659. doi: 10.1093/nar/gky977. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Metadata curation and public repository registration for the metagenomes and metatranscriptomes were managed by the Genomes OnLine Database (GOLD) (14) under study number Gs0135756 (https://gold.jgi.doe.gov/study?id=Gs0135756). Annotations are located in the IMG database. IMG identification numbers, SRA accession numbers, and sample information can be found in Table 1.