Abstract
In this study, the tail muscle microbiota of pacific white shrimp (Litopenaeus vannamei) sourced from five countries across Central and South America and Southeast Asia were determined and compared. The genomic DNA was sequenced at around 10 × coverage for each geographical location and was assembled de novo for comparative analysis. The assembled sequences for all the lines were classified based on their similarity to the sequences in the public database. We found that there is high correlation among the microbiota of shrimp from disparate regions, as well as the presence of some DNA from bacteria known to cause food poisoning in humans. Sequencing data has been deposited at NCBI-SRA database and can be found under the BioProject ID PRJNA282154.
Keywords: Shrimp, Litopenaeus vannamei, Microbiome, Next-generation sequencing, Geographical diversity
| Specifications | |
|---|---|
| Organism/cell line/tissue | Litopenaeus vannamei, muscle tissue, genomic DNA |
| Sex | N/A |
| Sequencer or array type | Illumina HiSeq 2000 |
| Data format | Raw |
| Experimental factors | Frozen packaged shrimp imported from Indonesia, Vietnam, Thailand, Venezuela, and Honduras were acquired from US supermarkets. From each package, 6–10 shrimp are used to isolate genomic DNA and subsequent sequencing. |
| Experimental features | Reads were assembled into contigs after pooling all the isolates. The presence/absence of contigs in each isolate followed by diversity analyses was performed to characterize the microbiota. |
| Consent | N/A |
| Sample source location | N/A |
1. Direct link to deposited data
2. Experimental design, materials and methods
2.1. Sample preparation
Frozen L. vannamei samples were purchased at the local grocery. Each bag had been packaged and imported from different countries: Indonesia, Vietnam, Thailand, Venezuela, and Honduras. Genomic DNA was isolated from 6 to 10 shrimp of each location and was sequenced separately. Tissue was sampled after peeling the shell and dissecting the tail muscle from the shrimp. DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) was used for genomic DNA isolation. Single-end libraries were constructed using the Illumina TruSeq DNA Sample Preparation Kit (Illumina, Inc., San Diego, CA, USA), as per instructions. Each library was ligated to a different index-tag adapter and sequencing was performed using the TruSeq Unique and Universal Adaptors on a HiSeq 2000 sequencer, which produced 100 bp single-end reads [1].
2.2. Bioinformatics analyses
Sequence quality was assessed using FastQC (v 0.10.1) [2]. Digital normalization was performed on the raw reads using Khmer (v 1.01) [3] before assembling the reads. Khmer was run with the default settings except for the cutoff value (c), which was set for 10. About 50–60% of the total reads retained after normalization (see Table 1) were pooled and used for assembling the genome. The Ray assembler (v 2.3.1) [4] was used to generate the initial assembly using the default k-mer size (33). Obtained scaffolds were classified as prokaryotic or non-prokaryotic based on NCBI-BLAST (v 2.2.30 +) [5] searches against the NR database (see Table 2). All scaffolds of prokaryotic origin were used for the diversity analyses. Bowtie2 (v 2.2.0) [6] was used to map the reads back to the indexed draft assembly and classify them as present or absent in an isolate (Table 3).
Table 1.
Total number of reads used for the assembly. The raw number indicates the actual number of reads obtained from the sequencing machine, whereas normalized read count indicates the number of reads retained after performing digital normalization. Only normalized reads were used for the assembly.
| Origin | File names | Raw | Normalized | Retained (%) |
|---|---|---|---|---|
| Honduras | H12_TAGGCATG_L006_R1_001.fastq | 2,330,575 | 1,381,635 | 59.28% |
| H1_TAAGGCGA_L006_R1_001.fastq | 14,712,575 | 8,115,248 | 55.16% | |
| H3_CGTACTAG_L006_R1_001.fastq | 1,453,462 | 859,454 | 59.13% | |
| H5_AGGCAGAA_L006_R1_001.fastq | 2,221,334 | 1,367,269 | 61.55% | |
| H7_TCCTGAGC_L006_R1_001.fastq | 5,865,487 | 3,773,662 | 64.34% | |
| H8_GGACTCCT_L006_R1_001.fastq | 6,583,439 | 3,665,752 | 55.68% | |
| Indonesia | IO2_AGGCAGAA_L007_R1_001.fastq | 9,177,514 | 5,357,801 | 58.38% |
| IO3_TCCTGAGC_L007_R1_001.fastq | 3,197,185 | 2,168,825 | 67.84% | |
| IO4_GGACTCCT_L007_R1_001.fastq | 14,167,640 | 8,210,733 | 57.95% | |
| IO5_TAGGCATG_L007_R1_001.fastq | 11,396,545 | 6,795,882 | 59.63% | |
| IO6_CTCTCTAC_L007_R1_001.fastq | 10,435,103 | 6,227,204 | 59.68% | |
| IO7_CAGAGAGG_L007_R1_001.fastq | 7,342,116 | 4,524,518 | 61.62% | |
| IO8_GCTACGCT_L007_R1_001.fastq | 4,276,929 | 2,587,323 | 60.49% | |
| IO9_CGAGGCTG_L007_R1_001.fastq | 4,233,317 | 2,611,674 | 61.69% | |
| Thailand | T10_CTCTCTAC_L008_R1_001.fastq | 11,028,257 | 6,402,355 | 58.05% |
| T12_CAGAGAGG_L008_R1_001.fastq | 13,584,934 | 8,121,686 | 59.78% | |
| T1_TAAGGCGA_L008_R1_001.fastq | 15,171,966 | 8,789,103 | 57.93% | |
| T3_CGTACTAG_L008_R1_001.fastq | 2,504,357 | 1,720,428 | 68.70% | |
| T4_AGGCAGAA_L008_R1_001.fastq | 7,341,971 | 4,321,487 | 58.86% | |
| T5_TCCTGAGC_L008_R1_001.fastq | 5,802,738 | 3,669,116 | 63.23% | |
| T7_GGACTCCT_L008_R1_001.fastq | 5,869,011 | 3,614,750 | 61.59% | |
| T9_TAGGCATG_L008_R1_001.fastq | 23,687,158 | 13,906,904 | 58.71% | |
| Venezuela | V10_CAGAGAGG_L005_R1_001.fastq | 6,584,510 | 4,175,631 | 63.42% |
| V11_GCTACGCT_L005_R1_001.fastq | 14,586,243 | 7,767,055 | 53.25% | |
| V12_CGAGGCTG_L005_R1_001.fastq | 15,782,536 | 8,900,226 | 56.39% | |
| V1_TAAGGCGA_L005_R1_001.fastq | 21,065,170 | 11,223,993 | 53.28% | |
| V2_CGTACTAG_L005_R1_001.fastq | 11,940,498 | 7,142,047 | 59.81% | |
| V3_AGGCAGAA_L005_R1_001.fastq | 17,026,545 | 9,454,844 | 55.53% | |
| V4_TCCTGAGC_L005_R1_001.fastq | 10,803,314 | 6,567,535 | 60.79% | |
| V5_GGACTCCT_L005_R1_001.fastq | 7,554,075 | 4,433,658 | 58.69% | |
| V8_TAGGCATG_L005_R1_001.fastq | 10,224,862 | 6,104,682 | 59.70% | |
| V9_CTCTCTAC_L005_R1_001.fastq | 5,872,950 | 3,047,919 | 51.90% | |
| Vietnam | VN12_GTAGAGGA_L008_R1_001.fastq | 9,564,954 | 4,872,018 | 50.93% |
| VN1_GCTACGCT_L008_R1_001.fastq | 7,092,963 | 3,912,680 | 55.16% | |
| VN2_CGAGGCTG_L008_R1_001.fastq | 14,190,209 | 8,738,927 | 61.58% | |
| VN3_AAGAGGCA_L008_R1_001.fastq | 3,430,308 | 2,069,722 | 60.34% | |
| VN4_CGAGGCTG_L006_R1_001.fastq | 35,679,513 | 20,000,119 | 56.05% | |
| VN5_AAGAGGCA_L006_R1_001.fastq | 11,274,952 | 6,484,876 | 57.52% | |
| VN8_AAGAGGCA_L007_R1_001.fastq | 5,376,761 | 2,709,392 | 50.39% | |
| VN9_GTAGAGGA_L007_R1_001.fastq | 6,887,031 | 3,555,985 | 51.63% |
Table 2.
Number of scaffolds with matches to NR database and its classification (prokaryotes/eukaryotes).
| Classification | ≥ 5000 K nt | 1000–5000 K nt | ≥ 1000 nt |
|---|---|---|---|
| Eukaryotes | 163 (24.33%) | 4661 (5.04%) | 4824 (5.14%) |
| Non-eukaryotes | 378 (56.42%) | 5534 (5.98%) | 5912 (6.30%) |
| Scaffolds with hits (NR) | 541 (80.75%) | 10,195 (11.02%) | 10,736 (11.43%) |
| Scaffolds without hits | 129 (19.25%) | 82,360 (88.98%) | 82,489 (87.85%) |
| Total scaffolds | 670 | 92,555 | 93,895 |
Table 3.
Mapping percent for various isolates to the draft assembly.
| Origin | Isolate | Normalized | Aligned | Percent |
|---|---|---|---|---|
| Honduras | H12_TAGGCATG_L006_R1_001.fastq | 2,330,575 | 135,668 | 5.82% |
| H1_TAAGGCGA_L006_R1_001.fastq | 14,712,575 | 816,820 | 5.55% | |
| H3_CGTACTAG_L006_R1_001.fastq | 1,453,462 | 72,543 | 4.99% | |
| H5_AGGCAGAA_L006_R1_001.fastq | 2,221,334 | 125,013 | 5.63% | |
| H7_TCCTGAGC_L006_R1_001.fastq | 5,865,487 | 406,069 | 6.92% | |
| H8_GGACTCCT_L006_R1_001.fastq | 6,583,439 | 352,841 | 5.36% | |
| Indonesia | IO2_AGGCAGAA_L007_R1_001.fastq | 9,177,514 | 584,184 | 6.37% |
| IO3_TCCTGAGC_L007_R1_001.fastq | 3,197,185 | 199,306 | 6.23% | |
| IO4_GGACTCCT_L007_R1_001.fastq | 14,167,640 | 970,809 | 6.85% | |
| IO5_TAGGCATG_L007_R1_001.fastq | 11,396,545 | 791,905 | 6.95% | |
| IO6_CTCTCTAC_L007_R1_001.fastq | 10,435,103 | 717,542 | 6.88% | |
| IO7_CAGAGAGG_L007_R1_001.fastq | 7,342,116 | 517,977 | 7.05% | |
| IO8_GCTACGCT_L007_R1_001.fastq | 4,276,929 | 289,595 | 6.77% | |
| IO9_CGAGGCTG_L007_R1_001.fastq | 4,233,317 | 303,002 | 7.16% | |
| Thailand | T10_CTCTCTAC_L008_R1_001.fastq | 11,028,257 | 677,696 | 6.15% |
| T12_CAGAGAGG_L008_R1_001.fastq | 13,584,934 | 915,014 | 6.74% | |
| T1_TAAGGCGA_L008_R1_001.fastq | 15,171,966 | 877,129 | 5.78% | |
| T3_CGTACTAG_L008_R1_001.fastq | 2,504,357 | 177,428 | 7.08% | |
| T4_AGGCAGAA_L008_R1_001.fastq | 7,341,971 | 452,370 | 6.16% | |
| T5_TCCTGAGC_L008_R1_001.fastq | 5,802,738 | 389,395 | 6.71% | |
| T7_GGACTCCT_L008_R1_001.fastq | 5,869,011 | 380,675 | 6.49% | |
| T9_TAGGCATG_L008_R1_001.fastq | 23,687,158 | 1,531,907 | 6.47% | |
| Venezuela | V10_CAGAGAGG_L005_R1_001.fastq | 6,584,510 | 460,195 | 6.99% |
| V11_GCTACGCT_L005_R1_001.fastq | 14,586,243 | 810,484 | 5.56% | |
| V12_CGAGGCTG_L005_R1_001.fastq | 15,782,536 | 964,607 | 6.11% | |
| V1_TAAGGCGA_L005_R1_001.fastq | 21,065,170 | 1,127,598 | 5.35% | |
| V2_CGTACTAG_L005_R1_001.fastq | 11,940,498 | 789,086 | 6.61% | |
| V3_AGGCAGAA_L005_R1_001.fastq | 17,026,545 | 1,025,844 | 6.02% | |
| V4_TCCTGAGC_L005_R1_001.fastq | 10,803,314 | 722,363 | 6.69% | |
| V5_GGACTCCT_L005_R1_001.fastq | 7,554,075 | 452,362 | 5.99% | |
| V8_TAGGCATG_L005_R1_001.fastq | 10,224,862 | 653,237 | 6.39% | |
| V9_CTCTCTAC_L005_R1_001.fastq | 5,872,950 | 278,656 | 4.74% | |
| Vietnam | VN12_GTAGAGGA_L008_R1_001.fastq | 9,564,954 | 177,669 | 1.86% |
| VN1_GCTACGCT_L008_R1_001.fastq | 7,092,963 | 149,412 | 2.11% | |
| VN2_CGAGGCTG_L008_R1_001.fastq | 14,190,209 | 332,937 | 2.35% | |
| VN3_AAGAGGCA_L008_R1_001.fastq | 3,430,308 | 69,846 | 2.04% | |
| VN4_CGAGGCTG_L006_R1_001.fastq | 35,679,513 | 896,728 | 2.51% | |
| VN5_AAGAGGCA_L006_R1_001.fastq | 11,274,952 | 294,998 | 2.62% | |
| VN8_AAGAGGCA_L007_R1_001.fastq | 5,376,761 | 85,758 | 1.59% | |
| VN9_GTAGAGGA_L007_R1_001.fastq | 6,887,031 | 132,293 | 1.92% |
Conflict of interest
The authors declare that there are no conflicts of interest.
Acknowledgments
This project was supported by funding from the USDA NIFA, the NRSP8 Aquaculture Genome Coordination Program and the College of Agriculture and Life Sciences, the State of Iowa and Hatch funds. The advice and assistance provided by Dr. James Dickson is appreciated.
References
- 1.Kawaler E., Seetharam A.S., Du Z.-Q., Severin A.J., Rothschild M.F. 2015. A comparison of the microbiomes of Litopenaeus vannamei from disparate geographical regions. (submitted for publication) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Available from:
- 3.Brown C.T., Howe A., Zhang Q., Pyrkosz A.B., Brom T.H. 2012. A reference-free algorithm for computational normalization of shotgun sequencing data. (eprint arXiv:1203.4802) [Google Scholar]
- 4.Boisvert S., Laviolette F., Corbeil J. Ray: simultaneous assembly of reads from a mix of high-throughput sequencing technologies. J. Comput. Biol. 2010;17(11):1519–1533. doi: 10.1089/cmb.2009.0238. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Altschul S.F., Gish W., Miller W., Myers E.W., Lipman D.J. Basic local alignment search tool. J. Mol. Biol. 1990;215(3):403–410. doi: 10.1016/S0022-2836(05)80360-2. [DOI] [PubMed] [Google Scholar]
- 6.Langmead B., Salzberg S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods. 2012;9(4):357–359. doi: 10.1038/nmeth.1923. [DOI] [PMC free article] [PubMed] [Google Scholar]